(upbeat music) >> Hello everyone, and welcome to this CUBE conversation where we're going to go deep into system performance. We're here with an expert. Kim Leyenaar is the Principal Performance Architect at Broadcom. Kim. Great to see you. Thanks so much for coming on. >> Thanks so much too. >> So you have a deep background in performance, performance assessment, benchmarking, modeling. Tell us a little bit about your background, your role. >> Thanks. So I've been a storage performance engineer and architect for about 22 years. And I'm specifically been for abroad with Broadcom for I think next month is going to be my 14 year mark. So what I do there is initially I built and I manage their international performance team, but about six years ago I moved back into architecture, and what my roles right now are is I generate performance projections for all of our next generation products. And then I also work on marketing material and I interface with a lot of the customers and debugging customer issues, and looking at how our customers are actually using our storage. >> Great. Now we have a graphic that we want to share. It talks to how storage has evolved over the past decade. So my question is what changes have you seen in storage and how has that impacted the way you approach benchmarking. In this graphic we got sort of big four items that impact performance, memory processor, IO pathways, and the storage media itself, but walk us through this data if you would. >> Sure. So what I put together is a little bit of what we've seen over the past 15 to 20 years. So I've been doing this for about 22 years and kind of going back and focusing a little bit on the storage, we looked back at hard disk, they ruled for. And nearly they had almost 50 years of ruling. And our first hard drive that came out back in the 1950s was only capable of five megabytes in capacity. and one and a half iOS per second. It had almost a full second in terms of seat time. So we've come a long way since then. But when I first came on, we were looking at Ultra 320 SCSI. And one of the biggest memories that I have of that was my office is located close to our tech support. And I could hear the first question was always, what's your termination like? And so we had some challenges with SCSI, and then we moved on into SAS and data protocols. And we continued to move on. But right now, back in the early 2000s when I came on board, the best drives really could do maybe 400 iOS per second. Maybe two 250 megabytes per second, with millisecond response times. And so when I was benchmarking way back when it was always like, well, IOPS are IOPS. We were always faster than what the drives to do. And that was just how it was. The drives were always the bottleneck in the system. And so things started changing though by the early 2000s, mid 2000s. We started seeing different technologies come out. We started seeing that virtualization and multi-tenant infrastructures becoming really popular. And then we had cloud computing that was well on the horizon. And so at this point, we're like, well, wait a minute, we really can't make processors that much faster. And so everybody got excited to include (indistinct) and the home came out but, they had two cores per processor and four cores per processor. And so we saw a little time period where actually the processing capability kind of pulled ahead of everybody else. And memory was falling behind. We had good old DVR, 2, 6, 67. It was new with the time, but we only had maybe one or two memory channels per processor. And then in 2007 we saw disk capacity hit one terabyte. And we started seeing a little bit of an imbalance because we were seeing these drives are getting massive, but their performance per drive was not really kind of keeping up. So now we see a revolution around 2010. And my co-worker and I at the time, we have these little USB discs, if you recall, we would put them in. They were so fast. We were joking at the time. "Hey, you know what, wonder if we could make a raid array out of these little USB disks?" They were just so fast. The idea was actually kind of crazy until we started seeing it actually happen. So in 2010 SSD started revolutionizing storage. And the first SSDs that we really worked with these plaint LS-300 and they were amazing because they were so over-provisioned that they had almost the same reader, right performance. But to go from a drive that could do maybe 400 IOS per second to a drive like 40,000 plus iOS per second, really changed our thought process about how our storage controller could actually try and keep up with the rest of the system. So we started falling behind. That was a big challenge for us. And then in 2014, NVMe came around as well. So now we've got these drives, they're 30 terabytes. They can do one and a half million iOS per second, and over 6,000 megabytes per second. But they were expensive. So people start relegating SSDs more towards tiered storage or cash. And as the prices of these drives kind of came down, they became a lot more mainstream. And then the memory channels started picking up. And they started doubling every few years. And we're looking now at DVR 5 4800. And now we're looking at cores that used to go from two to four cores per processor up to 48 with some of the latest different processes that are out there. So our ability to consume the computing and the storage resources, it's astounding, you know, it's like that whole saying, 'build it and they will come.' Because I'm always amazed, I'm like, how are we going to possibly utilize all this memory bandwidth? How are we going to utilize all these cores? But we do. And the trick to this is having just a balanced infrastructure. It's really critical. Because if you have a performance mismatch between your server and your storage, you really lose a lot of productivity and it does impact your revenue. >> So that's such a key point. Pardon, begin that slide up again with the four points. And that last point that you made Kim about balance. And so here you have these, electronic speeds with memory and IO, and then you've got the spinning disc, this mechanical disc. You mentioned that SSD kind of changed the game, but it used to be, when I looked at benchmarks, it was always the D stage bandwidth of the cash out to the spinning disc was always the bottleneck. And, you go back to the days of you it's symmetrics, right? The huge backend disk bandwidth was how they dealt with that. But, and then you had things the oxymoron of the day was high spin speed disks of a high performance disk. Compared to memories. And, so the next chart that we have is show some really amazing performance increases over the years. And so you see these bars on the left-hand side, it looks at historical performance for 4k random IOPS. And on the right-hand side, it's the storage controller performance for sequential bandwidth from 2008 to 2022. That's 22 is that yellow line. It's astounding the increases. I wonder if you could tell us what we're looking at here, when did SSD come in and how did that affect your thinking? (laughs) >> So I remember back in 2007, we were kind of on the precipice of SSDs. We saw it, the writing was on the wall. We had our first three gig SAS and SATA capable HPAs that had come out. And it was a shock because we were like, wow, we're going to really quickly become the bottleneck once this becomes more mainstream. And you're so right though about people work in, building these massive hard drive based back ends in order to handle kind of that tiered architecture that we were seeing that back in the early 2010s kind of when the pricing was just so sky high. And I remember looking at our SAS controllers, our very first one, and that was when I first came in at 2007. We had just launched our first SAS controller. We're so proud of ourselves. And I started going how many IOPS can this thing, even handled? We couldn't even attach enough drives to figure it out. So what we would do is we'd do these little tricks where we would do a five 12 byte read, and we would do it on a 4k boundary, so that it was actually reading sequentially from the disc, but we were handling these discrete IOPS. So we were like, oh, we can do around 35,000. Well, that's just not going to hit it anymore. Bandwidth wise we were doing great. Really our limitation and our bottleneck on bandwidth was always either the host or the backend. So, our controllers are there basically, there were three bottlenecks for our storage controllers. The first one is the bottleneck from the host to the controller. So that is typically a PCIe connection. And then there's another bottleneck on the controller to the disc. And that's really the number of ports that we have. And then the third one is the discs themselves. So in typical storage, that's what we look at. And we say, well, how do we improve this? So some of these are just kind of evolutionary, such as PCIE generations. And we're going to talk a little bit about that, but some of them are really revolutionary, and those are some of the things that we've been doing over the last five or six years to try and make sure that we are no longer the bottleneck. And we can enable these really, really fast drives. >> So can I ask a question? I'm sorry to interrupted but on these blue bars here. So these all spinning disks, I presume, out years they're not. Like when did flash come in to these blue bars? is that..you said 27 you started looking at it, but on these benchmarks, is it all spinning disc? Is it all flash? How should we interpret that? >> No, no. Initially they were actually all hard drives. And the way that we would identify, the max iOS would be by doing very small sequential reads to these hard drives. We just didn't have SSDs at that point. And then somewhere around 2010 is where we.. it was very early in that chart, we were able to start incorporating SSD technology into our benchmarking. And so what you're looking at here is really the max that our controller is capable of. So we would throw as many drives as we could and do what we needed to do in order to just make sure our controller was the bottleneck and what can we expose. >> So the drive then when SSD came in was no longer the bottleneck. So you guys had to sort of invent and rethink sort of how, what your innovation and your technology, because, I mean, these are astounding increases in performance. I mean, I think in the left-hand side, we've built this out pad, you got 170 X increase for the 4k random IOPS, and you've got a 20 X increase for the sequential bandwidth. How were you able to achieve that level of performance over time? >> Well, in terms of the sequential bandwidth, really those come naturally by increases in the PCIe or the SAS generation. So we just make sure we stay out of the way, and we enable that bandwidth. But the IOPS that's where it got really, really tricky. So we had to start thinking about different things. So, first of all, we started optimizing all of our pathways, all of our IO management, we increased the processing capabilities on our IO controllers. We added more on-chip memory. We started putting in IO accelerators, these hardware accelerators. We put in SAS poor kind of enhancements. We even went and improved our driver to make sure that our driver was as thin as possible. So we can make sure that we can enable all the IOPS on systems. But a big thing happening a few couple of generations ago was we started introducing something called tri capable controllers, which means that you could attach NVMe. You could attach SAS or you could attach SATA. So you could have this really amazing deployment of storage infrastructure based around your customized needs and your cost requirements by using one controller. >> Yeah. So anybody who's ever been to a trade show where they were displaying a glass case with a Winchester disc drive, for example, you see it's spinning and its actuators is moving, wow, that's so fast. Well, no. That's like a tourist slower. It's like a snail compared to the system's speed. So it's, in a way life was easy back in those days, because when you did a right to a disk, you had plenty of time to do stuff, right. And now it's changed. And so I want to talk about Gen3 versus Gen4, and how all this relates to what's new in Gen4 and the impacts of PCIe here, you have a chart here that you've shared with us that talks to that. And I wonder if you could elaborate on that, Kim. >> Sure. But first, you said something that kind of hit my funny bone there. And I remember I made a visit once about 15 or 20 years ago to IBM. And this gentleman actually had one of those old ones in his office and he referred to them as disk files. And he never until the day he retired, he'd never stopped calling them disc files. And it's kind of funny to be a part of that history. >> Yeah. DASD. They used to call it. (both laughing) >> SD, DASD. I used to get all kinds of, you know, you don't know what it was like back then, but yeah. But now nowadays we've got it quite easily enabled because back then, we had, SD DASD and all that. And then, ATA and then SCSI, well now we've got PCIe. And what's fabulous about PCIe is that it just has the generations are already planned out. It's incredible. You know, we're looking at right now, Gen3 moving to Gen4, and that's a lot about what we're going to be talking about. And that's what we're trying to test out. What is Gen4 PCIe when to bias? And it really is. It's fantastic. And PCIe came around about 18 years ago and Broadcom is, and we do participate and contribute to the PCIe SIG, which is, who develops the standards for PCIe, but the host in both our host interface in our NVMe desk and utilize the standards. So this is really, really a big deal, really critical for us. But if you take a look here, you can see that in terms of the capabilities of it, it's really is buying us a lot. So most of our drives right now NVMe drives tend to be by four. And a lot of people will connect them. And what that means is four lanes of NVMe and a lot of people that will connect them either at by one or by two kind of depending on what their storage infrastructure will allow. But the majority of them you could buy, or there are so, as you can see right now, we've gone from eight gig transfers per second to 16 gig of transfers per second. What that means is for a by four, we're going from one drive being able to do 4,000 to do an almost 8,000 megabytes per second. And in terms of those 4k IOPS that really evade us, they were really really tough sometimes to squeeze out of these drives, but now we're got 1 million, all we have to 2 million, it's just, it's insane. You know, just the increase in performance. And there's a lot of other standards that are going to be sitting on top of PCIe. So it's not going away anytime soon. We've got to open standards like CXL and things like that, but we also have graphics cards. You've got all of your hosts connections, they're also sitting on PCIe. So it's fantastic. It's backwards, it's orbits compatible, and it really is going to be our future. >> So this is all well and good. And I think I really believe that a lot of times in our industry, the challenges in the plumbing are underappreciated. But let's make it real for the audience because we have all these new workloads coming out, AI, heavily data oriented. So I want to get your thoughts on what types of workloads are going to benefit from Gen4 performance increases. In other words, what does it mean for application performance? You shared a chart that lists some of the key workloads, and I wonder if we could go through those. >> Yeah, yeah. I could have a large list of different workloads that are able to consume large amounts of data, whether or not it's in small or large kind of bytes of data. But as you know right now, and I said earlier, our ability to consume these compute and storage resources is amazing. So you build it and we'll use it. And the world's data we're expected to grow 61% to 175 zettabytes by the year 2025, according to IDC. So that's just a lot of data to manage. It's a lot of data to have, and it's something that's sitting around, but to be useful, you have to actually be able to access it. And that's kind of where we come in. So who is accessing it? What kind of applications? I spend a lot of time trying to understand that. And recently I attended a virtual conference SDC and what I like to do when I attend these conferences is to try to figure out what the buzz words are. What's everybody talking about? Because every year it's a little bit different, but this year was edge, edge everything. And so I kind of put edge on there first in, even you can ask anybody what's edge computing and it's going to mean a lot of different things, but basically it's all the computing outside of the cloud. That's happening typically at the edge of the network. So it tends to encompass a lot of real time processing on those instant data. So in the data is usually coming from either users or different sensors. It's that last mile. It's where we kind of put a lot of our content caching. And, I uncovered some interesting stuff when I was attending this virtual conference and they say only about 25% of all the usable data actually even reach the data center. The rest is ephemeral and it's localized, locally and in real time. So what it does is in the goal of edge computing is to try and reduce the bandwidth costs for these kinds of IOT devices that go over a long distance. But the reality is the growth of real-time applications that require these kinds of local processing are going to drive this technology forward over the coming years. So Dave, your toaster and your dishwasher they're, IOT edge devices probably in the next year, if they're not already. So edge is a really big one and consumes a lot of the data. >> The buzzword does your now is met the metaverse, it's almost like the movie, the matrix is going to come in real time. But the fact is it's all this data, a lot of videos, some of the ones that I would call out here, you mentioned facial recognition, real-time analytics. A lot of the edge is going to be real-time inferencing, applying AI. And these are just a massive, massive data sets that you again, you and of course your customers are enabling. >> When we first came out with our very first Gen3 product, our marketing team actually asked me, "Hey, how can we show users how they can consume this?" So I actually set up a head to environment. I decided I'm going to learn how to do this. I set up this massive environment with Hadoop, and at the time they called big data, the 3V's, I don't know if you remember these big 3Vs, the volume, velocity and variety. Well Dave, did you know, there are now 10 Vs? So besides those three, we got velocity, we got valued, we got variability, validity, vulnerability, volatility, visualization. So I'm thinking we need just to add another beat of that. >> Yeah. (both laughing) Well, that's interesting. You mentioned that, and that sort of came out of the big data world, a dupe world, which was very centralized. You're seeing the cloud is expanding, the world's getting, you know, data is by its very nature decentralized. And so you've got to have the ability to do an analysis in place. A lot of the edge analytics are going to be done in real time. Yes, sure. Some of it's going to go back in the cloud for detailed modeling, but we are the next decade Kim, ain't going to be like the last I often say. (laughing) I'll give you the last word. I mean, how do you see this sort of evolving, who's going to be adopting this stuff. Give us a sort of a timeframe for this kind of rollout in your world. >> In terms of the timeframe. I mean really nobody knows, but we feel like Gen5, that it's coming out next year. It may not be a full rollout, but we're going to start seeing Gen5 devices and Gen5 infrastructure is being built out over the next year. And then follow very, very, very quickly by Gen6. And so what we're seeing though is, we're starting to see these graphics processors, These GPU's, and I'm coming out as well, that are going to be connecting, using PCIe interfaces as well. So being able to access lots and lots and lots of data locally is going to be a really, really big deal and order because worldwide, all of our companies they're using business analytics. Data is money. And the person that actually can improve their operational efficiency, bolster those sales and increase your customer satisfaction. Those are the companies that are going on to win. And those are the companies that are going to be able to effectively store, retrieve and analyze all the data that they're collecting over the years. And that requires an abundance of data. >> Data is money and it's interesting. It kind of all goes back to when Steve jobs decided to put flash inside of an iPhone and the industry exploded, consumer economics kicked in 5G now edge AI, a lot of the things you talked about, GPU's the neural processing unit. It's all going to be coming together in this decade. Very exciting. Kim, thanks so much for sharing this data and your perspectives. I'd love to have you back when you got some new perspectives, new benchmark data. Let's do that. Okay. >> I look forward to it. Thanks so much. >> You're very welcome. And thank you for watching this CUBE conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

>> Narrator: Live from Austin, Texas, it's The Cube covering KubeCon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and The Cube's ecosystem partners. >> Welcome back, everyone. Live here, The Cube's exclusive coverage in Austin, Texas. This is CloudNativeCon and KubeCon for Kubernetes Conference. I'm John Furrier, Stu Miniman. My next two guests from Red Hat, Joe Brockmeier, senior evangelist, Linux Containers, Red Hat and Kimberly Craven, Director of Portfolio Marketing at Red Hat. Welcome to The Cube, good to see you guys. >> Thank you, good to see you, too. So I was saying at re:Invent last week that Red Hat's stamp of approval has always been in the enterprise. You guys are, you know, winning the enterprise, been there for years. But now, at Cloud Native, kind of things are coming together. You've got a lot of customers that have been, I won't say quietly going with Red Hat with OpenShift, and now with Kubernetes. Huge bet a few years ago. >> Mmhmm. >> Yep. >> Only two years ago. Kind of changed the game. >> Yeah, fortunately we made a strategic decision to replatform our own platform on Kubernetes and it was the right decision to make. So we've been lucky in that we've been able to, I'd say we've been able to invest in the right open source projects. So Joe, would you agree that over the years, I mean, starting with Linux. >> Yep. >> But in other technologies as well? >> Yeah, historically, I think we, not every, not 100% of the time, but a large enough percentage of the time, picked the right horse community wise. Open Stack, now Kubernetes, Linux-Colonel, obviously. I used to work for a company called LinuxMall and we sponsored these Linux pavilions. And I remember NetBSD guys telling me how Linux was doomed because it wasn't as elegant. >> Doomed, it sure didn't turn out that way. But certainly, the community model has changed. You're starting to see, you know, Dan Cohen, in his opening slide, actually kind of laid out the circle of innovation, project, products and profit. >> Joe: Yeah. >> And so now, it's okay to have profitability objectives as an outcome of great products. And so still bringing in the culture of innovation because the business market for this is pretty large. I see the number of people coming on board. The demand is pretty strong. >> Not just innovation, but I think, one of the important things about Kubernetes is that is has been a community project where it's a community of equals contributing to the project. And it's about each company bringing the right thing for the project, not the right thing necessarily just for that company, but the right thing for the overall project, which is really important. >> Timing's everything, right? I mean, as they say in life, but remember, all that FUD about past layers and infrastructures as a service, and again, the DevOps community was still growing. No one really talks about that anymore because people just want working software. >> Joe: Right. >> Right? So it's fun not to have those kind of conversations. Instead, the conversation's about how to orchestrate great workloads, how to onboard and accelerate more application developers. This is the narrative that we wanted a couple years ago. Now it's here. What are you guys doing at Red Hat to take that to the next level? >> Kimberly: So I'm going to defer to Joe for that one. >> Joe: Okay. To take that to the next level. First, before people can get to the next level, one thing I want to point out is that while everybody here is hip deep in Kubernetes and they're ready, there are a lot of companies out there that are still digesting virtualization and still digesting cloud. >> Kimberly: Right. >> Private or public, and so one of our key roles is actually to help them consume open-source software and get from Point A to Point B. So the role that we're really playing right now is about taking customers with their workloads today that are running on bare metal, that are running on virtualization, that are pet workloads, right? And getting those into the cloud and getting in those into Kubernetes and that sort of thing. So the next level for a lot of folks is actually getting up to speed to the things that were announced today. >> Right. >> Well the question I want to ask, that I want to get this on the record, 'cause it's important to get the definition, what does Kubernetes mean to the enterprise? For us in Cloud Native, we understand what it is, we get it, but to the enterprise customer, what does Kubernetes mean to them? So I would say, based on the customer conversations that we've had, it's all about getting your workloads to the cloud and being more cloud native much more quickly. So that's the end goal for adopting containers and adopting Kubernetes. It's all about getting to be in a position where you can migrate your workloads to the cloud but also develop new on the cloud much more quickly than you could before. So it's about automating, it's about all of the processes behind that, if you will. >> Joe, comment? >> I agree with everything Kimberly said. I would also just add I think it's really about kind of an almost an end-stage of software packaging, which is something that Red Hat has been doing for 20+ years, is figuring out how do we take goodness of software, open-source software, and get it into a consumable format? First it was RPM, then it was YUM, now it's containers, now it's orchestrated containers that are, you know, able to be worked on with service mesh and all these other wonderful things, cloud native storage. It's basically about taking that software and making it scale. >> Yeah, I mean, yours is a service mesh. So let's take it to the next level of customer conversation. I love this stuff, I'm going to the cloud as soon as possible. I got some stuff in the public stuff now, I got a lot of on-premise stuff activity, I love hybrid cloud. So I got a lot of different use cases. I got some bare metal, I got some hybrid cloud and I got some public cloud. Is this where the OpenShift fits in? I mean, in that environment of a customer conversation, what's the current state of the art for Red Hat to engage that customer? >> So organizations, they're taking inventory of everything that they have today. So they're looking at what do they have on bare metal today, what do they have in virtualization, what different workloads do they have and where does it make sense to deploy them both financially and from an advancement perspective? Because some workloads don't have to be, they don't have to be advanced as quickly. You don't have to make additional updates. But there are other workloads that are moving much more quickly. And one of the things that Red Hat does and where we help our customers, especially with OpenShift, is we allow them to deploy those workloads across, whether they're going to on-premises with a bare metal if you say, or as well as virtualization, private cloud, potentially a mixture of multicloud environment where they have some workloads going to Google, some workloads going to AWS, and some going to Azure. It's being able to do that consistently, that OpenShift for guidance. >> Is that a common use case right now? Is that the number one use case, this hybrid? >> So when you say that, the hybrid cloud, it's not, it's a combination of multiple use cases. People aren't necessarily looking just yet to take the same workload and move it such that it's spanning multiple clouds, but they want to have that flexibility so that if they choose to go to a certain public cloud, and it becomes it's not cost-effective for them to do so anymore, they want to be able to take that workload and move it. And that's what we're working towards. >> Joe, I got to ask about OpenShift because, you know, we've been following you guys since the Open Stack days and now with the formation of this, seeing nice lines of sight of value proposition. What's going on with OpenShift? We're hearing a lot of good customer wins, a lot of people are using it. I heard a comment in the hallway saying that OpenShift has more customers than most of these vendors here combined. I'm not sure I believe that, that might have been just kind of chatter, but is that true or can you share the success? Because it's been on a tear. What are some of the OpenShift success points? >> Kimberly: Well-- >> So is it true there are more customers than all everyone else combined? >> I'd like to say so, I mean-- >> John: Pretty close or-- >> You were at Red Hat Summit this past year back in the May timeframe and we had many OpenShift customers that were on stage. I mean, it was-- >> John: You got lots. >> Yeah, we had to turn sessions away from customers because we didn't have enough room for them. >> So one of the things we actually haven't gotten to highlight yet at this event, Red Hat does, at a lot of these shows, ahead of the show, it's called OpenShift Commons, maybe you can give our audience a little bit of what goes into that. 'Cause all the container shows, the Cloud Native shows, you know, OpenShift has been there. >> Yeah, with OpenShift Commons, it's a great way for the community to collaborate around OpenShift specifically. It's, whether it be with our ISVs, working with our ISVs on different plugins to extend OpenShift as well as our customers to be able to provide us with feedback in terms of what they're looking for. And then we take that to the community. For example, Clayton was a top contributor. That was announced yesterday. >> Yes, Clayton got an award offered for that on stage, yeah. >> Yeah, and in essence, our customers are providing feedback to us directly in OpenShift Commons and in other forums. And that allows us to steer the community more effectively to meet their needs. >> I just want to add it's not a two-way conversation with Commons. It's also, you know, I was also there on Tuesday when we did Commons and we had Tellus, for example, telling their story to the other customers in the room. And so they're not just telling us, like, hey, this works for us, this doesn't work. They're telling each other and they're sharing successes, which is part of the wonder of open sourcing community. It's not just about, you know, you can have, I don't want to use an example, you can have a two-way conversation with any vendor that's taking your money. How many vendors are bringing you together to talk to your other customers? You have to have a lot of confidence, I think, in people being happy with your solution to build something out like that. >> Yeah, and experience, too. You guys had the experience. >> Yeah, you mentioned, we were right about that time, we'd been there a number of years. I feel the open source community is a little bit better at allowing those customers to kind of come forward. Because not only are they using it, they're usually contributing to some of these technologies. Some traditional shows, you know, getting a customer to get up on stage is pretty challenging. Any comments on that? >> Well it's funny because I think it's getting much easier, moving forward, for customers to participate in the communities, as you'll see with Netflix, for example. They were up on stage earlier and talking about the contributions that they're also making to the community. I think that it's much easier than it was even, I'd say, 5-10 years ago. With that said, there are a lot of customers that want help in terms of creating additional functionality in the community where they might have something that's, perhaps, not quite ready, not quite good enough, that we help to shepherd. >> Is there a profile of customer that's adopting Kubernetes? I mean, I've seen a lot of media coverage, obviously Netflix is on AWS. ACHB on stage today. Is it coincidental that there'd be two large big media online kind of companies, or-- >> Well, it's funny you should ask that because we're conducting a research project and we recently got some data back where we, in essence, sent out a survey to customers and non-customers to see where their adoption was. What we're finding is financial services, the media, communications organization, government, and even healthcare, to some extent, are taking a look at and adopting. I'd say that, based on the adoption curve, what's funny to note is, with government, government started looking, on average, at containers three years ago, whereas with financial services, they started to get more heavily invested, now this is in general, if you're looking at the median, two years ago. With that said, I think that financial services is actually adopting containers more quickly than government is. >> I'd love to see the data on that survey because we're always doing kind of probing, anecdotal kind of stirrup holes, friends and guests of The Cube. And it's the trend, from our standpoint, is that it seems that anywhere that there's been this transformation opportunity. >> Kimberly: Mmhmm. >> I mean, look at government. Who would've though public sector could be so fast and change? So public sector, media and entertainment, people with their modernizing seems to be where the action is. But financial services is always going to be on the IT dollar spend. But like, I mean, I'm really surprised at how fast public sector is evolving. >> And what's interesting about it, too, is also the industries that are predominantly concerned with security. Security and performance are very important to financial services and to government and to communications. And it's interesting how quickly this technology is being adopted with those considerations. >> Joe, one of the things coming into the show, I listened to some previews and they're saying, you know, we're not even going to talk about containers of the show. Of course, there's containers kind of underneath. Maybe speak a little bit about that dynamic. Red Hat, you know, so heavily involved. You know, of course Linux containers, you know, underneath there. Compare and contrast to kind of what we're kind of doing here in the Kubernetes and Cloud Native space. >> Yeah, so it really isn't about the individual container anymore than five years ago it was about the individual RPM. The container runtime and the ability to spin up a container is table stakes. And so that is no longer really where the value is. Same as like, hypervisors in cloud. Like, the real value is not in the hypervisor. It's around that, it's the ecosystem around it and the ability to do it. So yeah, I mean, we're still talking about, it's funny, when I have conversations, not here, but in other places, the parlance is still to say containers when they really mean, you know, like Kubernetes and orchestration, the whole schmear. But yeah, it's not where the value and the action is these days. >> Where's the Red Hat situation with the people now? Because we've seen, we've noticed, that you guys have really kind of continued to evolve as a company. Obviously, I mean, or in the early days of Red Hat, open source wasn't tier one. You guys made it tier one as a culture, that's well-documented. But then there's a whole new Red Hat mojo going on now. OpenShift, seeing you bringing that same principles. Talk about what's going on in the company now. We've seen a lot of smart people continuing to do the Red Hat thing. What is Red Hat now in the marketplace? The same old Red Hat? What's different, what's the same? 'Cause you guys are doing really well. >> Kimberly: Mmhmm. >> What's it like there? >> I think, I've been at Red Hat for about six years and I would say that the culture has continued to evolve since I joined. One of the things that first attracted me about it was that there are a lot of smart people that work at Red Hat and it's a very collaborative culture. It's a culture that's based on meritocracy and the best ideas truly win. So very similar to the way that OpenSource projects are run or should be run, for the good OpenSource projects, it's very much about getting people together, hearing what everyone has to say, and making sure that the right ideas are the ones that move forward. >> John: Surely they attract great people, too. >> Yeah. >> To build on that, in this industry there's so much kind of hype, boom and bust. On the outside, you look at it, I mean, from a financial standpoint, Red Hat's one of the most consistent performers out there. You know, quarter after quarter, Kim talks about the growth. So you know, I'm not asking you to talk about the financials but, you know, worth a show. Nobody here can keep up with all the changes. So you know, just, when you talk about all these projects and everything, Red Hat, can you keep up with the changes? Or is it just that you've got so many people and contribute so many places? >> We're working on it and I think, I mean, the nice thing about it is that everybody's very passionate about all of those changes that are happening. And we like change, oddly enough, we embrace it. It's interesting, but that's one of the parts of being at Red Hat. And I'd say, I mean, I would think that that's something that's inherent to us. >> Well, I mean, our corporate mission, part of our corporate mission is to be the catalyst for change and communities. And we, you know, I've worked at a couple of larger companies and this is the only one where I feel like if I don't agree with something I can send an email directly to Jim and say, "I don't agree with this and I think we should do something different." >> And he'll respond within four hours. >> And Jim will respond unless he's on a plane. >> Yeah, he'll respond and you know, even if they don't agree, which is impossible, everybody always agrees with me. (group laughs) But even if they don't agree, you know, they engage honestly and respectfully, and that's super important in this kind of industry. If you can't do that, you can't run with open source. >> Joe, Kimberly, thanks for coming on The Cube, and continued success and thanks for all the Red Hat contribution. You guys are doing a great job in the community. Continue to appreciate it. >> Thank you. >> Red Hat, here on The Cube, continuing to do the Red Hat thing. Red Hat, stamp of approval from the enterprise. Certainly well-respected and the leader inside The Cube here at the CloudNativeCon and KubeCon for KubernetesCon, not Cube. I'm John Furrier, Stu Miniman. We'll be back with more after this short break. (upbeat music)

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Welcome to the >>Special Cube program. We are here with the Amazon Web Services Public Sector Partner Awards program. It's a celebration of AWS public sectors partners and their end user customers where there's been innovation and we're pleased to have on this show here, the award winner for the most innovative AI and ML Artificial intelligence and machine learning solution. Axial three D is the partner, and the end user is Belfast Hospital. He got Roger Johnson, the CEO of actual three D, and Dr Tim Brown consulted transplant surgeon at Belfast Hospital, who has been doing amazing things not only on the as an innovative partner, but really during Covic making things happen by solving the problem of the surgical gap in the number of surgeries that you're doing really high performance saving lives. Congratulations. First of all, congratulations. Roger. Dr Kimberly. Thanks for joining me. >>Re pleasure. >>Okay, let's get into it. First of all, Dr Tim Brown, I really want to commend you on the amazing work that you're doing before we get into some of the partnership awards conversations. You have been at the front lines solving a lot of problems around the gap between the number of surgeries that could take place with Cove. It, um, tell that story real quick. I really think it's super important. Take a minute to >>explain. Yeah, thanks for the opportunity. And it's been an incredible rollercoaster for the last three months, pretty much all of the transplant programs across the world who have been affected by Coupet of shut down but with some pretty innovative on the grill leadership team Working advances with managed to open a program up again. And and Belfast, we have a bytes and 50 to 50 disease donor transplants year over the last three months, with just a 90 90 kidney transplants. Pretty much we've cleared the whole waiting list in Northern Ireland, pretty much for people waiting for a kidney transplant at this time. And it's been a remarkable few weeks, but it really is a testament to the critical care community. People that work in intensive care is the high marks, a support organ donation. Of course, our donors who have given so selflessly at such a tragic time for them. So I'd like to pay tribute to all of our donors into the amazing people who have been involved in the team. Mark belt faster this time. >>That's super amazing. Can you just I just want to pause from and just captured the number of order of magnitude. You said it was 6 to 10 year and you didn't 90 90. >>Yeah, so six weeks basically Teoh, two years work in six weeks old in the middle of the night as well. So it's been It's been hard of hard work, so you can see the sleeplessness. I'm trying to catch up with a minute, but it's been really, really satisfying. An incredible I come for patients and legacy of this of this, the program is gonna last about faster. 40 years. >>Well, I want to say congratulations. I'll give you my Cube Award for not changing the world but saving the world. One person at a time. 90 interviews and six weeks. That's amazing. That's like thinking clearing the waiting list. You really changing lives there. Congratulations. >>That's very kind of you. Thank you very much. >>Roger. Good. A great partner and customer. You have here. Talk about this award. You guys have talked about the company? What is this all about? Why you guys in this position? Why are you winning? >>Yes, So I think our motivation for our company is driven by our partners, such such as? In what they're doing transforms care And even in these horrific situation, our scenarios. We have the moment with Kobe. Think you're hearing the start of the amazing story our job is to give Surgeons liked him the best possible insight that he can have going into his surgeries For the last 20 years, surgeons of relied largely on two D imaging, so C, t and memory scans or for being able to plan their surgeries when it's murdered, technology should apply them much greater insight or they actually perform the surgery. So we've created a technology that platforms on AWS that allows us to turn those traditional hard to understand to the images into micro millimeter precise models off the patients exact anatomy. The value hopefully, two amazing colleagues like Tim is that instead of trying to interpret what a two D image CD or memory scan might mean he can actually see for the first time before he opens the patient up exactly what he's going to find when when he when he starts the surgery. So he immediately start to complete that planning before the surgery actually takes. So hopefully that analyze a number of benefits to results without the shorter operations. Find less surgical meeting we brought into the surgery. Hopefully, faster Surgeries names last risk of infection For patients being shorter Time means most >>awesome. Dr. Brian, I want to get your take on this. Can you describe the impact on your side because you know the future of work, which is everyone's been talking about in the tech industry for many years now, with code we were just talking about. The success is you're having and changing lives and saving lives. The notion of work workplace work, forces, work loads, work flows are all changing. Certainly the workplace people aren't as on site as they used to be. The workforce has to be protected. How does the AI and how does the actual three D help you and your work flows? Are you getting more done? Can you give specifics around the impact to your job? >>Yeah, it's a bit It's been a fantastic journey to date. We're still learning away. It's a journey. We're trying to work out exactly where this lies in. The fact that Kubla does not come along, which has changed, or working practices, that means that we have to look for different solutions on this, I think, is very 100 solution to amend. My practice over the last three years has been in terms of complex and real surgery on oncological surgery, where we have, for example, a tumor and kidney where we think, my goodness, we're gonna have to take this kidney I and throw it in the bin because it's very badly disease. So the index case that we were involved with that was building a child who wanted to donate his kidney to his daughter. But when we worked him up, we find a tumor in his kidney, which ordinarily would have to be discarded. But thanks to the imaging that Excel was able to produce for us, we were able to plan Well, geez, well cut well and as a result of kidney, I really plan a removal of the tumor from the kidney itself. We really repair kidney and then transplant it into his daughter. So with the technology that was available, we were able to save two lives on one particular case on, and it's really grown from there on. We've been involved in five or six different, really complex cases where the imaging has changed the outcomes for our patients who ordinarily wouldn't have been able to. Chief insight comes, I think, the AI interface on the AI solution we've developed in our partnership with the Excel. As I said, it's a journey and we're still finding our way. But to insights that I've really got our the first is that what we want to do is reduce variability, not just in our in our observers, from the way that we interpret imaging tradition is what you're saying is, look a two D images. We're now able to sit and look at this, emerging in a three dimensional space on our desk. Rather than trying to reconstruct these things in your head, we can look at them and discuss the different images with our colleagues in real time, a zealous that which I think is probably the most important thing, is that we're not able to engage our patients and a partnership. Before we had a bit of an unfair advantage that we're able to interpret these images because 20 or 30 years of getting used to doing this as professionals. But the patients are presented with some incredibly difficult decisions to make by their own health and with very little understanding that. But now I can handle the model of their own disease very easy to understand, and that gives my patient autonomy to make the decisions about their own bodies back again. And I think that's a hugely powerful, powerful tool for these guys have about potential decisions that they have to make that more effective for the rest of their lives. >>So the problem you're solving was one of the technical problem. So you're trying to figure out manually, get more insight into the the imaging and to the customer or the patient. This case customer, the patient. I can make a better decision. Those are two problems, statements that seem to be the big ones that I missed. Anything? >>Absolutely, absolutely. >>Okay, so actual three d you guys have a great solution? How >>did you >>get here? Tell us about your story. What's what's What's the big trajectory for you guys? In terms of the value proposition, it seems to be amazing and again highlights. The advantages of technology really solves the problem. But the outcome on the patient side is pretty phenomenal. >>Yes, so the chance for us is there or the development that we have made. The lately, we admit, is to be able to automatically turn these two D images into three D models. So we take each of the slices off of memory or cities. Using AWS is machine learning. We construct three D macro millimeter precise representation of For me. That's only possible. First of all, we treat the algorithms that we created on Amazon platform using over a 1,000,000 pre labeled CDs. Consume our system automatically detect. Yeah, it's a level. What is bone? What is ligament? What is on our earlier vessel? With the training that we're able to perform, we've been able to with with these 1,000,000 images we've been able to in effect, tree and our system automatically detect the parts of me with this micro service level that hasn't been previously possible. This technology, or the ability to create three D models, has existed for maybe 10 or 15 years, but it's it's needed. Experts like him who were, in effect manually code the two D image pixel level and could affect so some software and turn it into a three D image. Typically, too, it's in ours, often expert like them to do. And the problem is, Tim could only do one of the time. We estimate there about three million of these complex surgeries each year in the world that need open effort from greatly from this enhanced imaging. And we couldn't get 33 million under these, especially. And that. So we have this process no on the AWS platform, with dozens of these models in parallel, and each more will take maybe a few minutes to turn from the CD into the into the three D representation. So through the park off the Amazon Public cloud, we've been able to provide this this powerful machine learning automated solution that can actually scale toe man >>Dr Brian talk about the impact because, I mean Andy Jassy, the CEO of AWS, always talks about this. When I interviewed him, he says, you know, we're here to help do the heavy lifting this sounds like some pretty heavy lifting. What was just talked about? I mean, the manual work involved. You essentially have a collective intelligence and supercomputer power with AWS. What's your take on this as this evolves? Why isn't everyone doing this? >>Yeah, well, I don't know why. Every minute. That's that's That's the key question. It really is. From my perspective, there is no heavy lifting at all, and what I do is I push a couple buttons. I put a bit of data, and I send it off. From my perspective, it is about as easy as it gets is probably a ZTE sending email, which we do hundreds of times a day. And so, from from my perspective, I'm delighted to say there's no heavy lifting until I get a patient's data. I send data through to excel, who will then fool me and say, Listen to what is it exactly that we want to have a personal service from actual on? A couple days later, there's a delivery of a beautiful life size three D representation model, will check and then take to plan on and treat a patient with. So the heavy lifting really has all been done. A Z Roger alluded to in the past. It was hugely time consuming work that required a huge amount of training. But basically that's being replaced with a push of a button on. These supercomputers have taken all of my heavy lifting away on, and I think this is one of the true representation. Zoff technology really, really advances real world solutions and my patients are benefactors. From this >>Roger Dr Brown. Lay out the architecture because, first of all, pretend I want to take this every single friend that I have here in California and around the world. I want to just deploy this. What's the architecture and what's needed on the deployment side? Say it to Belfast as you deploy this. What's kind of involved in you? Just take us through high level. I must be cloud scales. Amazing, No doubt about it. We just talked about that. But what's involved in the architecture side of my standing? A bunch PC two's Is there sage maker involvement? What's the architecture and then deployment? What does that look like? >>Sure, So again, a slight step back. One of the challenges when, when we is the MedTech community try and introduce innovation into health and hospitals that the hospitals i t. Infrastructure network definition is often very locked on. So we're trying to bring new software and load it and install it in the hospital data system. That is a huge, often lengthy process that has to be done through lots of hoops in terms off Hey, network a compliance. Lots of different steps along the journey and that often wants from a good reasons, is a significant barrier to the timely adoption off innovative technologies in the cars. What a what a platform a selfie on AWS allies were just another website, as Tennis said, is, uh, only that, though his only existence with actual three D in terms of the interface is dragging and dropping the CT scan into our website into a portal portal exists quickly on the AWS instance. In one of our region, we are working with a little in the US. Never leave the US We use the the public client version in US East. We take advantage of many features within AWS, but a sage maker is probably a core of what we do. It's not innovation that AWS introduced know several years ago that was like juice this this machine learning trained set of algorithms that allow us to give this disruption. >>And it sounds like the more you use it, the more get smarter. Or is that as well? >>Absolutely. So our journey is, As Tim said, we're on a journey not only in terms off the technology and you're very receptive. In terms of yes, the more we train it, the more we treated on specific anatomy types or pathology types or trouble types, the better our system gets recognizing the specific characteristics of those. More importantly, this is about a journey I having made this disruption, we make the change and transformation off new standards of care pathways. That's the innovation that we just enable. It's amazing. Surgical teams like hymns. Let me transformation >>Dr Brown on your side. You're sitting there. I got a big problem trying to solve these problems. I got patients one but one better outcomes. They want to live. I don't want to throw away kitty, so I don't have to you to solve that problem that when when they bring that over, what was it like over on your side of the house is a practitioner. Deploying it. You've got you've got two jobs going. You're kind of doing I t integration on one hand and you're a surgeon on the other, trying to make things happen. You know what I see? This is not a lot of I t here. What's the deployment? Looks like. >>Yeah, deployment means I don't know. Why ever announces doing that. Such a straightforward, easy situation. It's that's remarkable. Ready? It's such a good solution, and I think part of any sort of change management program, and this again is change management. It's challenging the way we think about things. It's challenging people's comfort zones on any time we need to do change. We've got this anatomy of change. You've got innovators go early, adopters will lead the doctors, and I think what we're going to see over the next 5 to 10 years is people are recognizing that this technology is a game changer, possibly being driven by their patients who say I'm on the three D model and I want to see what this actually looks like because basically not black and white picture you're showing me doesn't make any sense to me and I think there's going to be the two drivers is that the first is that we want to have a consistency of care on the lack of variation in our care across across old old services. But as well is that patients? I think we're gonna drive this as well. So once once we get the innovators and the early adopters of this technology on board, then we'll see a tipping point. And that's that's when it becomes an acceptable normal thing for people to do. When they come in the hospital, they'll be sure print tight off their three d printed like moral off their pathology. I'm not a huge demand for their decision making for treatment processes, and that's a true collaboration between doctor or surgeon on the patient. That's that's where we need to be in the 21st century. It's it's going to be a collaborative decision making process. You talked about the pressures, journeys and this This is a really integral part. This is the roadmap of your journey to a large extent. So I think this I can see this being rolled out worldwide, being driven by patients buying a correction and variability of healthcare provision. >>That's a great example is an innovative award winner for the most innovative use of artificial intelligence and machine learning. Three D images saving lives Congratulations, Tim Rogers. Phenomenal Final question As we end this out, what's the scar tissue pun intended? You know, What did you learn? What was some of the things that you could share with folks as people look at this and say This is an example of cloud scale and the technology for good. What lessons have you learned? What can you share for folks? Take a minute to explain the split. Roger. We'll start with you. >>Yeah, sure. So I think a number off lessons for us on this journey Assistances, This is Ah, we're at the start of a journey of understanding the power off the what three d imaging can bring just to providing a consistent use variable care, but also as a stem also alluded to in terms of off the patient understanding, I think that patient understanding is one of the huge leap forwards that way. Didn't set out initially thinking we're going to be able to help educate on better inform patients. But that was one of the derive benefits suddenly part. So that was a great lesson. I think there is incredible levels of adoption that we're starting to see across the US across Europe because it's so easy to adopt. Compared to traditional methods, surgeons registered for Canadian start transacting and instead of us almost as opposed to having to have these huge I t programs. So I think we're now starting to really scratch the surface and start seeing the benefits of this isn't an administrative system. It's not me. HR system. It's not a finance system. Or maybe a healthcare was comfortable. And using public like this is core hard core clinical services, clinical diagnosis. Clinical education on the Amazon cloud is enabling that it just wouldn't be possible with this technology we started. Actually, the lessons were learning or just just >>Dr Tim Brown and take us home and the segment with your take lessons learned and advice to others. >>I think the lessons learned are the doctors and health care providers are all extremely wary off change of new innovations because they feel that already they're overburdened. Probably my colleagues in the states and across Europe perfectly like they were a bit over, burdened by all the things that we have to do, and this may potentially have been more difficult or wants to your workloads. And actually, let's make your workload along each year convincing people and getting people to understand that this really does make your life a lot easier. It actually removes all the scar tissue, removes the difficulties that have been put in place by by organizations on once. People realize that, that's what that there is no heavy lifting. And this will make a huge difference to your practices, your patients understanding of your practice, and we'll stop so people really realize that the tipping point will be achieved. I'm looking forward to that day because this this is going to be the new normal in the next 5 to 10 years. >>While the performance that you're putting up the numbers of 90 transplant successfully over six weeks dwarfs the full year, last year really kind of shows the outcome is a game changer. And again, congratulations on your success. Roger think Thank you for coming on Corrections on being the award winner. Eight of his partner for the most innovative AI and machine learning solutions. Thanks for taking the time for this 80 s partner awards program. Thank you. >>Thank you. >>Okay, I'm John Furrier. We're covering the AWS Public Sector Partner Awards program put on by the Cube and AWS Public Sector Partners. Thanks for watching. Yeah, Yeah, yeah, yeah, yeah.

>>LA Las Vegas. It's the cube hovering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Hey, welcome back everyone. This is the cubes live covers Las Vegas anus. Re-invent. I'm John furrier with Dave Alante extracting the signal from the noise sponsored by Intel and AWS. They put the stage together, two big stages. Day two, we're here day Jew, I rapid fire a devil's execs coming on. Dave McCann, cube alumni, VP of ADAS migration marketplace and control services known most for the marketplace and a lot of stuff going on. That's exciting in the marketplace. It's where all the ecosystem actions happening. Congratulations on you six. I know you're busy, you've got new stuff, but the marketplace seems to be changing the procurement and the consumption of software and solutions, whether it's SAS or images and technology, your demand on the marketplace. So great to be back, Kimberly. It's another reinvent. This is my sixth. Um, so lots going on. Marketplace has got a lot bigger in the last year. >>We're up to 260,000 customers, so not substantial growth from last year. And we're adding thousands of customers every month. Um, big headline I have to start with is marketplace has been a marketplace for software for the last seven years. And two weeks ago we launched a marketplace for data and it's a new service that we call AWS data exchange. And instead of allowing you to point, click subscribe to software, and if you're a data consumer and a bank and you're an analyst or you're a researcher and a pharma company, you actually buy data from hundreds of companies, you know, you can go into the new console, find the product and market, please go over to this console called data exchange. And you can go buy research data or you can buy healthcare data from change healthcare. You can buy news data from Thomson Reuters, you can buy consumer data from Experian. >>And we've launched 1400 products from 19 data providers and we've made it available globally. So it's a whole new class of intellectual property data sources in there as well. There's some open source public sources as well. And we're adding literally dozens of products every day. So really easy API. And the cool thing is that after you subscribe, you copy it right into your S three bucket, moves into your VPC and then you move it into your project and you can actually create a Lambda function with the next version of the data. The next day gets updated and know the data just gets updated. And the use case here is like, if I'm a retail outlet, I could buy or go and get weather data and do some things. Is that kind of the model? Exactly. Right. I mean companies all over the world by $150 billion worth of data, but it's all delivered thousands of different EPA. >>Dave, we got cube data, we put all of our advanced data out there, which might be an opportunity. But seriously, Q three 65 is our new listing on the market place. So we have a Q cloud service, little plug for the cube cube three 65 on the marketplace and we're, we're happy. But I want to ask you because one of the things that's coming up is, um, from your team in the marketplace, the industry is this notion of buying through the marketplace. The trend is increasing private offers is a hot feature that you guys have put in place. And there's some news there. Could you explain how private offers is changing the game in the marketplace? I'd love to show you, if you think about it, a lot of our customers are developers and builders and they're working on something on test and it's a pilot and you use it for a few hours or a week. >>But once a company contracts for software and if you're contracting for a lot of software, procurement, one's best price, legal one's best terms, and there's going to be in negotiation and we call that negotiation of private offer. And so that involves salespeople. And so our top software vendors like a Splunk and new Relic of trend micro, uh, Palo Alto, their sales guys, or negotiate our sales ladies and negotiating with the customer for a couple hundred thousand dollars and there's a price and terms. When are you going to pay? What clauses do you agree? How many of you buying? Where are you going to deploy? All of that's negotiated and no, we have a portal for the sailor. We've had it for a year, we've made some really good changes and the central, they arose the seller to automate that price court rate into your account and then the buyer subscribes, and this is allowing our sailors to do quotations in the hundreds of thousands, the millions and sometimes in the tens of millions on a contract rate through marketplace, you're doing millions of dollars of business with with private offers today we've seen vendors write contracts for over $10 million, Peter over three years SAS contracts. >>So we've had that program available for the last year and we'd be working on a lot of features with the help of people at Splunk and new Relic today, we've made it available for all ISBNs and marketplace. You say all the iterations get to take place in the market place, so it's all those informations. I should just speak, just make sure I get it right before private offers were invite only kind of thing. Now you're making it available to all ASVs. Correct. We've got one. As of today, we've over 1,500 ASVs in the marketplace. You're one of them. And with those 1500 vendors within our go into marketplace, there's a new button and the seller portal and it says create Piper offer and any over ISV can note create a private. So I'm going to put my little seller hat on. I have a SAS application. Look at, I don't have a big Salesforce. >>How can you guys help me? How do I, how do I get more sales? Is there a, there's the money just following my bank account. Oh, are you overstaffed to do marketing? You have to do some discussions. You know, we had a company in the UK called Matilda MAF last year on, on the cube. Medallian Staffan was 17 engineers and new salespeople and now they're like 300 people, two runs of venture and everything's through marketplace. Big booth here. Well, congratulations to those guys. We love them. And to come Mytilene again, they engage rafted with you guys. It is all the sales and go to market through AWS complete everything goes through marketplace. Okay. We've made it available to 1,500 vendors today. Okay. So changing procurement. I love that trend. You kind of modernizing the procurement process with the marketplace. What about um, resellers? What's the update there? >>So the big update there is, you know, for the first six years of marketplace we couldn't handle the resaler. We didn't conceive of the VAR or the consulting partner and we got a lot of feedback that we had to do work. And so we've taken private offers and we've designed consulting partner, private offers and no, we've saved up over a hundred top consulting partner resellers, the likes of an OCT of an Ashi, a Rackspace in Europe computer center and Softcat and they were working with all of the world's top resellers and know if you are a Splunk or trend micro, you can authorize computer center to offer private prices to their customers and you can actually authorize a wholesale price from Splunk directly to computer and get paid for. Well, they could actually set the price. Mark it up. I got to ask you, Dave, what's your vision for marketplace? >>Because you're doing a great job. It seems like you're paddling as fast as you can constantly improving the service. I know you've got a big to do list, you want to make it easy or make it faster, all that good stuff, but what's the vision? Where do you see marketplace evolving? You know, Jeff Bezos says it's only day one. We're seven years old. We've barely scratched the surface. Global software is 450 billion growing 8% data is 150 billion growing at 3% you've got a $600 billion industry. Marketplace has not touched a tiny percentage. We want all of our customers to be able to find, discover, provision, and run all of their software and their data out of marketplace and it's gonna take us another 10 years and you get a lot of teen. How big is the team? We never publish JFK K but just let's say the Andy Jassy continues to invest in the business and as we add engineers and we add business people and development people, you know we work well with our partners. >>We cool market. Yeah, we grew up well, as Andy always says, you know, and you always say this, the customer needs come first. That's kind of a vetting process. Then working backwards documents, we know all about that history. What is the number one customer need that you're hearing, that you're addressing, that you see coming up around the corner, you're constantly working on and new potentially new requests that are coming in that are relevant to your business. There's two or three big customer needs. The number one is governance. So while engineers are going fast, innovating, legal, finance and procurement need to be confident that the contracts are being written well and is the spend under control. And so we're doing a lot of work around tagging or the resources so that it's tagged to the right project. Did you overspend on the project? And then on the contracting inside we launched this thing called enterprise contract and we're continuing to work with customers. >>We just integrated into the leading procurement system called ACP a Reebok and we launched that last week. And so we know have a procurement workflow that says procurement's happy it finances happy legal needs to be happy because the engineers want to go quick, but we can't leave the it finance legal professionals behind because they protect the risks for the kinda, the contracts too are all there. So you're modernizing procurement. We are transforming the supply chain for data and for software, you know big. You know I'm a big fan of what you do and I know you got a lot of hard work, a lot of demand, there's a lot of money to be made there, water customers to make happy and you know we've got great customers that BP or shell or Coca Cola, Coke industries that are using marketplace on a regular basis and we have customers now with over a foes and subscriptions from over 50 vendors and that's a single customer. >>Dave, thank you so much for coming on. I know you're super busy and making the time for wrestling the cube means a lot. You've been with us the entire journey for the Ravens, our seventh reinvent. You've been a great one. I missed one but usually patients man it's just you. You saw it working backwards and it's happening. It's working well and you know we learn from our customers and I'm having a dinner tonight with 40 more and I'm sure they'll hit us with more requirements. I'll check my email for the invite. I'm sure it's in there somewhere. Dave McKenna inside the cube. Good friend of the cube, hardworking, billable in the next generation, the next gen marketplace. Check it out. Of course, the cube three 65 our new offering is up there as of Monday. It's kind of a soft launch, but we're telling you now, I'm John Freud. Dave Volante. Thanks for watching back with more. Thanks and have a short break.

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome back to the queue from Las Vegas. We are live at AWS reinvent 19 Lisa Martin with John furrier. We've been having lots of great conversations. John, we're about to have another one cause we always love to talk about customer proof in the putting. Please welcome a couple of guests. We have Rafael, director of analytics and data management from triple a life. Welcome. Thanks for having me. Really appreciate it. Our pleasure. And from Burkle anchor Jane, the SVP of cloud platforms. Welcome. Thank you. Thank you so much. Pleasure to be here. So here we are in this, I can't see of people around us as, as growing exponential a by the hour here, but awkward. Let's start with you give her audience an understanding of Merkel, who you are and what you do. >>Yeah, absolutely. So Marco is a global performance marketing agency. We are part of a dental agent network and a, it's almost about 9,000 to 10,000 people worldwide. It's a global agency. What differentiates Merkel from rest of the other marketing agencies is our deep roots and data driven approach. We embrace technology. It's embedded in all our, all our solutions that we take to market. Um, and that's what we pride ourselves with. So, um, that's basically a high level pitch about Merkel. What differentiates us, my role, uh, I lead the cloud transformation for Merkel. Um, uh, basically think of my team as the think tanks who bring in the new technology, come up with a new way of rolling out solutions product I solutions, uh, disruptive solutions, which helps our clients and big fortune brands such as triple life insurance, uh, to transform their marketing ecosystem. >>So let's go ahead and dig. A lot of folks probably know AAA life, but, but Raphael, give us a little bit of an overview. This is a 50 year old organization. >>So we celebrate our 50th 50 year anniversary this year. Actually, we're founded in 1969. So everybody life insurance, we endeavor to be the provider of choice for a AAA member. Tell them to protect what matters most to them. And we offer a diverse set of insurance products across just about every channel. Um, and um, we engage with Merkel, uh, earlier, the, um, in 2018 actually to, to, uh, to build a nice solution that allows us to even better serve the needs of the members. Uh, my role, I am the, I lead our analytics and data management work. So helping us collect data and manage better and better leverage it to support the needs of members. >>So a trip, I can't even imagine the volumes of data that you're dealing with, but it's also, this is people's data, right? This is about insurance, life insurance, the volume of it. How have you, what were some of the things that you said? All right guys, we need to change how we're managing the data because we know there's probably a lot more business value, maybe new services that we can get our on it or eyes >>on it. >>So, so that was, that was it. So as an organization, uh, I want to underscore what you said. We make no compromises when it comes to the safety of our, of our members data. And we take every step possible to ensure that it is managed in a responsible and safe way. But we knew that on, on the platform that we had prior to this, we weren't, we weren't as italics. We wanted to be. We would find that threaten processes would take spans of weeks in order to operate or to run. And that just didn't allow us to provide the member experience that we wanted. So we built this new solution and this solution updates every day, right? There's no longer multi-week cycle times and tumbler processes happen in real time, which allows us to go to market with more accurate and more responsive programs to our members. >>Can you guys talk about the Amazon and AWS solution? How you guys using Amazon's at red shift? Can he says, you guys losing multiple databases, give us a peek into the Amazon services that you guys are taking advantage of that anchor. >>Yeah, please. Um, so basically when we were approached by AAA life to kind of come in and you know, present ourselves our credentials, one thing that differentiated there in that solution page was uh, bringing Amazon to the forefront because cloud, you know, one of the issue that Ravel and his team were facing were scalability aspect. You know, the performance was, was not up to the par, I believe you guys were um, on a two week cycle. That data was a definition every two weeks. And how can we turn that around and know can only be possible to, in our disruptive technologies that Amazon brings to the forefront. So what we built was basically it's a complete Amazon based cloud native architecture. Uh, we leveraged AWS with our chip as the data warehouse platform to integrate basically billions and billions of rows from a hundred plus sources that we are bringing in on a daily basis. >>In fact, actually some of the sources are the fresh on a real time basis. We are catching real time interactions of users on the website and then letting Kimberly the life make real time decisions on how we actually personalize their experience. So AWS, Redshift, you know, definitely the center's centerpiece. Then we are also leveraging a cloud native ELT technology extract load and transform technology called. It's a third party tool, but again, a very cloud native technology. So the whole solution leverage is Python to some extent. And then our veil can talk about AI and machine learning that how they are leveraging AWS ecosystem there. >>Yeah. So that was um, so, uh, I anchor said it right. One thing that differentiated Merkel was that cloud first approach, right? Uh, we looked at it what a, all of the analysts were saying. We went to all the key vendors in this space. We saw the, we saw the architecture is, and when Merkel walked in and presented that, um, that AWS architecture, it was great for me because if nausea immediately made sense, there was no wizardry around, I hope this database scales. I was confident that Redshift and Lambda and dynamo would this go to our use cases. So it became a lot more about are we solving the right business problem and less about do we have the right technologies. So in addition to what Ankur mentioned, we're leveraging our sort of living RNR studio, um, in AWS as well as top low frat for our machine learning models and for business intelligence. >>And more recently we've started transition from R to a Python as a practitioner on the keynote today. Slew a new thing, Sage maker studio, an IDE for machine learning framework. I mean this is like a common set. Like finally, I couldn't have been more excited right? That, that was my Superbowl moment. Um, I was, I was as I was, we were actually at dinner yesterday and I was mentioning Tonker, this is my wishlist, right? I want AWS to make a greater investment in that end user data scientists experience in auto ML and they knocked it out of the park. Everything they announced today, I was just, I was texting frat. Wow, this is amazing. I can't wait to go home. There's a lot of nuances to, and a lot of these announcements, auto ML for instance. Yeah. Really big deal the way they did it. >>And again, the ID who would've thought, I mean this is duh, why didn't we think about this sooner? Yeah. With auto ML that that focus on transparency. Right. And then I think about a year ago we went to market and we ended up not choosing any solutions because they hadn't solved for once you've got a model built, how do you effectively migrated from let's say an analyst who might not have the, the ML expertise to a data science team and the fact that AWS understood out of the gate that you need that transparent all for it. I'm really excited for that. What do you think the impacts are going to be more uptake on the data science side? What do you think the impact of this and the, so I think for, I think we're going to see, um, that a lot of our use cases are going to part a lot less effort to spin up. >>So we're going to see much more, much faster pilots. We're going to have a much clearer sense of is this worth it? Is this something we should continue to invest in and to me we should drive and I expect that a lot, much larger percentage of my team, the analysts are going to be involved in data and data science and machine learning. So I'm really excited about that. And also the ability to inquire, to integrate best practices into what we're doing out of the gate. Right? So software engineers figured out profiling, they figured out the bugging and these are things that machine learners are picking up. Now the fact that you're front and center is really excited. Superbowl moment. You can be like the new England Patriots, 17 straight AFC championship games. Boston. Gosh, I could resist. Uh, they're all Seattle. They're all Seattle here and Amazon. I don't even bring Seattle Patriots up here and Amazon, >>we are the ESPN of tech news that we have to get in as far as conversation. But I want to kind of talk a little bit, Raphael about the transformation because presumably in, in every industry, especially in insurance, there are so many born in the cloud companies that are a lot, they're a lot more agile and they are chasing what AAA life and your competitors and your peers are doing. What your S establishing with the help of anchor and Merkel, how does this allow you to actually take the data that you had, expand it, but also extract insights from maybe competitive advantages that you couldn't think about before? >>Yeah, so I think, uh, so as an organization, even though we're 50 years old, one of the things that drew me to the company and it's really exciting is it's unrelated to thrusting on its laurels, right? I think there's tremendous hunger and appetite within our executive group to better serve our members and to serve more members. And what this technology is allowed is the technology is not a limiting factor. It's an enabling factors. We're able to produce more models, more performant models, process more of IO data, build more features. Um, we've managed to do away with a lot of the, you know, if you take it and you look at it this way and squeeze it and maybe it'll work and systematize more aspects of our reporting and our campaign development and our model development and the observability, the visibility of just the ability to be agile and have our data be a partner to what we're trying to accomplish. That's been really great. >>You talked about the significant reduction in cycle times. If we go back up to the executive suite from a business differentiation perspective, is the senior leadership at AAA understanding what this cloud infrastructure is going to enable their business to achieve? >>Absolutely. So, so our successes here I think have been instrumental in encouraging our organization to continue to invest in cloud. And uh, we're an active, we're actively considering and discussing additional cloud initiatives, especially around the areas of machine learning and AI. >>And the auger question for you in terms of, of your expertise, in your experience as we look at how cloud is changing, John, you know, educate us on cloud cloud, Tuto, AI machine learning. What are, as, as these, as businesses, as industries have the opportunity to for next gen cloud, what are some of the next industries that you think are really prime to be completely transformed? >>Um, I'm in that are so many different business models. If you look around, one thing I would like to actually touch upon what we are seeing from Merkel standpoint is the digital transformation and how customers in today's world they are, you know, how brands are engaging with their customers and how customers are engaging with the brands. Especially that expectations customer is at the center stage here they are the ones who are driving the whole customer engagement journey, right? How all I am browsing a catalog of a particular brand on my cell phone and then I actually purchased right then and there and if I have an issue I can call them or I can go to social media and log a complaint. So that's whole multi channel, you know, aspect of this marketing ecosystem these days. I think cloud is the platform which is enabling that, right? >>This cannot happen without cloud. I'm going to look at, Raphael was just describing, you know, real time interaction, real time understanding the behavior of the customer in real time and engaging with them based on their need at that point of time. If you have technologies like Sage maker, if you have technologies like AWS Redship you have technologies like glue, Kinesis, which lets you bring in data from all these disparate sources and give you the ability to derive some insights from that data in that particular moment and then interact with the customer right then and there. That's exactly what we are talking about. And this can only happen through cloud so, so that's my 2 cents are where they are, what we from Merkel standpoint, we are looking into the market. That's what we are helping our brands through to >>client. I completely agree. I think that the change from capital and operation, right to no longer house to know these are all the sources and all the use cases and everything that needs to happen before you start the project and the ability to say, Hey, let's get going. Let's deliver value in the way that we've had and continue to have conversations and deliver new features, new stores, a new functionality, and at the same time, having AWS as a partner who's, who's building an incremental value. I think just last week I was really excited with the changes they've made to integrate Sage maker with their databases so you can score from the directly from the database. So it feels like all these things were coming together to allow us as a company to better off on push our aims and exciting time. >>It is exciting. Well guys, I wish we had more time, but we are out of time. Thank you Raphael and anchor for sharing with Merkel and AAA. Pleasure. All right. Take care. Or John furrier. I am Lisa Martin and you're watching the cube from Vegas re-invent 19 we'll be right back.

>> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (futuristic music) >> Welcome back to Cambridge, Massachusetts everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. This is day two, we're sort of wrapping up the Chief Data Officer event. It's MIT CDOIQ, it started as an information quality event and with the ascendancy of big data the CDO emerged and really took center stage here. And it's interesting to know that it's kind of come full circle back to information quality. People are realizing all this data we have, you know the old saying, garbage in, garbage out. So the information quality worlds and this chief data officer world have really come colliding together. Robert Abate is here, he's the Vice President and CDO of Global IDS and also the co-chair of next year's, the 14th annual MIT CDOIQ. Robert, thanks for coming on. >> Oh, well thank you. >> Now you're a CDO by background, give us a little history of your career. >> Sure, sure. Well I started out with an Electrical Engineering degree and went into applications development. By 2000, I was leading the Ralph Lauren's IT, and I realized when Ralph Lauren hired me, he was getting ready to go public. And his problem was he had hired eight different accounting firms to do eight different divisions. And each of those eight divisions were reporting a number, but the big number didn't add up, so he couldn't go public. So he searched the industry to find somebody who could figure out the problem. Now I was, at the time, working in applications and had built this system called Service Oriented Architectures, a way of integrating applications. And I said, "Well I don't know if I could solve the problem, "but I'll give it a shot." And what I did was, just by taking each silo as it's own problem, which was what EID Accounting Firm had done, I was able to figure out that one of Ralph Lauren's policies was if you buy a garment, you can return it anytime, anywhere, forever, however long you own it. And he didn't think about that, but what that meant is somebody could go to a Bloomingdale's, buy a garment and then go to his outlet store and return it. Well, the cross channels were different systems. So the outlet stores were his own business, retail was a different business, there was a completely different, each one had their own AS/400, their own data. So what I quickly learned was, the problem wasn't the systems, the problem was the data. And it took me about two months to figure it out and he offered me a job, he said well, I was a consultant at the time, he says, "I'm offering you a job, you're going to run my IT." >> Great user experience but hard to count. >> (laughs) Hard to count. So that's when I, probably 1999 was when that happened. I went into data and started researching-- >> Sorry, so how long did it take you to figure that out? You said a couple of months? >> A couple of months, I think it was about two months. >> 'Cause jeez, it took Oracle what, 10 years to build Fusion with SOA? That's pretty good. (laughs) >> This was a little bit of luck. When we started integrating the applications we learned that the messages that we were sending back and forth didn't match, and we said, "Well that's impossible, it can't not match." But what didn't match was it was coming from one channel and being returned in another channel, and the returns showed here didn't balance with the returns on this side. So it was a data problem. >> So a forensics showdown. So what did you do after? >> After that I went into ICICI Bank which was a large bank in India who was trying to integrate their systems, and again, this was a data problem. But they heard me giving a talk at a conference on how SOA had solved the data challenge, and they said, "We're a bank with a wholesale, a retail, "and other divisions, "and we can't integrate the systems, can you?" I said, "Well yeah, I'd build a website "and make them web services and now what'll happen is "each of those'll kind of communicate." And I was at ICICI Bank for about six months in Mumbai, and finished that which was a success, came back and started consulting because now a lot of companies were really interested in this concept of Service Oriented Architectures. Back then when we first published on it, myself, Peter Aiken, and a gentleman named Joseph Burke published on it in 1996. The publisher didn't accept the book, it was a really interesting thing. We wrote the book called, "Services Based Architectures: A Way to Integrate Systems." And the way Wiley & Sons, or most publishers work is, they'll have three industry experts read your book and if they don't think what you're saying has any value, they, forget about it. So one guy said this is brilliant, one guy says, "These guys don't know what they're talking about," and the third guy says, "I don't even think what they're talking about is feasible." So they decided not to publish. Four years later it came back and said, "We want to publish the book," and Peter said, "You know what, they lost their chance." We were ahead of them by four years, they didn't understand the technology. So that was kind of cool. So from there I went into consulting, eventually took a position as the Head of Enterprise and Director of Enterprise Information Architecture with Walmart. And Walmart, as you know, is a huge entity, almost the size of the federal government. So to build an architecture that integrates Walmart would've been a challenge, a behemoth challenge, and I took it on with a phenomenal team. >> And when was this, like what timeframe? >> This was 2010, and by the end of 2010 we had presented an architecture to the CIO and the rest of the organization, and they came back to me about a week later and said, "Look, everybody agrees what you did was brilliant, "but nobody knows how to implement it. "So we're taking you away, "you're no longer Director of Information Architecture, "you're now Director of Enterprise Information Management. "Build it. "Prove that what you say you could do, you could do." So we built something called the Data CAFE, and CAFE was an acronym, it stood for: Collaborative Analytics Facility for the Enterprise. What we did was we took data from one of the divisions, because you didn't want to take on the whole beast, boil the ocean. We picked Sam's Club and we worked with their CFO, and because we had information about customers we were able to build a room with seven 80 inch monitors that surrounded anyone in the room. And in the center was the Cisco telecommunications so you could be a part of a meeting. >> The TelePresence. >> TelePresence. And we built one room in one facility, and one room in another facility, and we labeled the monitors, one red, one blue, one green, and we said, "There's got to be a way where we can build "data science so it's interactive, so somebody, "an executive could walk into the room, "touch the screen, and drill into features. "And in another room "the features would be changing simultaneously." And that's what we built. The room was brought up on Black Friday of 2013, and we were able to see the trends of sales on the East Coast that we quickly, the executives in the room, and these are the CEO of Walmart and the heads of Sam's Club and the like, they were able to change the distribution in the Mountain Time Zone and west time zones because of the sales on the East Coast gave them the idea, well these things are going to sell, and these things aren't. And they saw a tremendous increase in productivity. We received the 2014, my team received the 2014 Walmart Innovation Project of the Year. >> And that's no slouch. Walmart has always been heavily data-oriented. I don't know if it's urban legend or not, but the famous story in the '80s of the beer and the diapers, right? Walmart would position beer next to diapers, why would they do that? Well the father goes in to buy the diapers for the baby, picks up a six pack while he's on the way, so they just move those proximate to each other. (laughs) >> In terms of data, Walmart really learned that there's an advantage to understanding how to place items in places that, a path that you might take in a store, and knowing that path, they actually have a term for it, I believe it's called, I'm sorry, I forgot the name but it's-- >> Selling more stuff. (laughs) >> Yeah, it's selling more stuff. It's the way you position items on a shelf. And Walmart had the brilliance, or at least I thought it was brilliant, that they would make their vendors the data champion. So the vendor, let's say Procter & Gamble's a vendor, and they sell this one product the most. They would then be the champion for that aisle. Oh, it's called planogramming. So the planogramming, the way the shelves were organized, would be set up by Procter & Gamble for that entire area, working with all their other vendors. And so Walmart would give the data to them and say, "You do it." And what I was purporting was, well, we shouldn't just be giving the data away, we should be using that data. And that was the advent of that. From there I moved to Kimberly-Clark, I became Global Director of Enterprise Data Management and Analytics. Their challenge was they had different teams, there were four different instances of SAP around the globe. One for Latin America, one for North America called the Enterprise Edition, one for EMEA, Europe, Middle East, and Africa, and one for Asia-Pacific. Well when you have four different instances of SAP, that means your master data doesn't exist because the same thing that happens in this facility is different here. And every company faces this challenge. If they implement more than one of a system the specialty fields get used by different companies in different ways. >> The gold standard, the gold version. >> The golden version. So I built a team by bringing together all the different international teams, and created one team that was able to integrate best practices and standards around data governance, data quality. Built BI teams for each of the regions, and then a data science and advanced analytics team. >> Wow, so okay, so that makes you uniquely qualified to coach here at the conference. >> Oh, I don't know about that. (laughs) There are some real, there are some geniuses here. >> No but, I say that because these are your peeps. >> Yes, they are, they are. >> And so, you're a practitioner, this conference is all about practitioners talking to practitioners, it's content-heavy, There's not a lot of fluff. Lunches aren't sponsored, there's no lanyard sponsor and it's not like, you know, there's very subtle sponsor desks, you have to have sponsors 'cause otherwise the conference's not enabled, and you've got costs associated with it. But it's a very intimate event and I think you guys want to keep it that way. >> And I really believe you're dead-on. When you go to most industry conferences, the industry conferences, the sponsors, you know, change the format or are heavily into the format. Here you have industry thought leaders from all over the globe. CDOs of major Fortune 500 companies who are working with their peers and exchanging ideas. I've had conversations with a number of CDOs and the thought leadership at this conference, I've never seen this type of thought leadership in any conference. >> Yeah, I mean the percentage of presentations by practitioners, even when there's a vendor name, they have a practitioner, you know, internal practitioner presenting so it's 99.9% which is why people attend. We're moving venues next year, I understand. Just did a little tour of the new venue, so, going to be able to accommodate more attendees, so that's great. >> Yeah it is. >> So what are your objectives in thinking ahead a year from now? >> Well, you know, I'm taking over from my current peer, Dr. Arka Mukherjee, who just did a phenomenal job of finding speakers. People who are in the industry, who are presenting challenges, and allowing others to interact. So I hope could do a similar thing which is, find with my peers people who have real world challenges, bring them to the forum so they can be debated. On top of that, there are some amazing, you know, technology change is just so fast. One of the areas like big data I remember only five years ago the chart of big data vendors maybe had 50 people on it, now you would need the table to put all the vendors. >> Who's not a data vendor, you know? >> Who's not a data vendor? (laughs) So I would think the best thing we could do is, is find, just get all the CDOs and CDO-types into a room, and let us debate and talk about these points and issues. I've seen just some tremendous interactions, great questions, people giving advice to others. I've learned a lot here. >> And how about long term, where do you see this going? How many CDOs are there in the world, do you know? Is that a number that's known? >> That's a really interesting point because, you know, only five years ago there weren't that many CDOs to be called. And then Gartner four years ago or so put out an article saying, "Every company really should have a CDO." Not just for the purpose of advancing your data, and to Doug Laney's point that data is being monetized, there's a need to have someone responsible for information 'cause we're in the Information Age. And a CIO really is focused on infrastructure, making sure I've got my PCs, making sure I've got a LAN, I've got websites. The focus on data has really, because of the Information Age, has turned data into an asset. So organizations realize, if you utilize that asset, let me reverse this, if you don't use data as an asset, you will be out of business. I heard a quote, I don't know if it's true, "Only 10 years ago, 250 of the Fortune 10 no longer exists." >> Yeah, something like that, the turnover's amazing. >> Many of those companies were companies that decided not to make the change to be data-enabled, to make data decision processing. Companies still use data warehouses, they're always going to use them, and a warehouse is a rear-view mirror, it tells you what happened last week, last month, last year. But today's businesses work forward-looking. And just like driving a car, it'd be really hard to drive your car through a rear-view mirror. So what companies are doing today are saying, "Okay, let's start looking at this as forward-looking, "a prescriptive and predictive analytics, "rather than just what happened in the past." I'll give you an example. In a major company that is a supplier of consumer products, they were leading in the industry and their sales started to drop, and they didn't know why. Well, with a data science team, we were able to determine by pulling in data from the CDC, now these are sources that only 20 years ago nobody ever used to bring in data in the enterprise, now 60% of your data is external. So we brought in data from the CDC, we brought in data on maternal births from the national government, we brought in data from the Census Bureau, we brought in data from sources of advertising and targeted marketing towards mothers. Pulled all that data together and said, "Why are diaper sales down?" Well they were targeting the large regions of the country and putting ads in TV stations in New York and California, big population centers. Birth rates in population centers have declined. Birth rates in certain other regions, like the south, and the Bible Belt, if I can call it that, have increased. So by changing the marketing, their product sales went up. >> Advertising to Texas. >> Well, you know, and that brings to one of the points, I heard a lecture today about ethics. We made it a point at Walmart that if you ran a query that reduced a result to less than five people, we wouldn't allow you to see the result. Because, think about it, I could say, "What is my neighbor buying? "What are you buying?" So there's an ethical component to this as well. But that, you know, data is not political. Data is not chauvinistic. It doesn't discriminate, it just gives you facts. It's the interpretation of that that is hard CDOs, because we have to say to someone, "Look, this is the fact, and your 25 years "of experience in the business, "granted, is tremendous and it's needed, "but the facts are saying this, "and that would mean that the business "would have to change its direction." And it's hard for people to do, so it requires that. >> So whether it's called the chief data officer, whatever the data czar rubric is, the head of analytics, there's obviously the data quality component there whatever that is, this is the conference for, as I called them, your peeps, for that role in the organization. People often ask, "Will that role be around?" I think it's clear, it's solidifying. Yes, you see the chief digital officer emerging and there's a lot of tailwinds there, but the information quality component, the data architecture component, it's here to stay. And this is the premiere conference, the premiere event, that I know of anyway. There are a couple of others, perhaps, but it's great to see all the success. When I first came here in 2013 there were probably about 130 folks here. Today, I think there were 500 people registered almost. Next year, I think 600 is kind of the target, and I think it's very reasonable with the new space. So congratulations on all the success, and thank you for stepping up to the co-chair role, I really appreciate it. >> Well, let me tell you I thank you guys. You provide a voice at these IT conferences that we really need, and that is the ability to get the message out. That people do think and care, the industry is not thoughtless and heartless. With all the data breaches and everything going on there's a lot of fear, fear, loathing, and anticipation. But having your voice, kind of like ESPN and a sports show, gives the technology community, which is getting larger and larger by the day, a voice and we need that so, thank you. >> Well thank you, Robert. We appreciate that, it was great to have you on. Appreciate the time. >> Great to be here, thank you. >> All right, and thank you for watching. We'll be right back with out next guest as we wrap up day two of MIT CDOIQ. You're watching theCUBE. (futuristic music)