(upbeat music) >> Hello and welcome to this CUBE conversation here presenting the "AWS Startup Showcase: "New Breakthroughs in DevOps, Data Analytics "and Cloud Management Tools" featuring Opsani for the cloud management and migration track here today, I'm your host John Furrier. Today, we're joined by Patrick Conte, Chief Commercial Officer, Opsani. Thanks for coming on. Appreciate you coming on. Future of AI operations. >> Thanks, John. Great to be here. Appreciate being with you. >> So congratulations on all your success being showcased here as part of the Startups Showcase, future of AI operations. You've got the cloud scale happening. A lot of new transitions in this quote digital transformation as cloud scales goes next generation. DevOps revolution as Emily Freeman pointed out in her keynote. What's the problem statement that you guys are focused on? Obviously, AI involves a lot of automation. I can imagine there's a data problem in there somewhere. What's the core problem that you guys are focused on? >> Yeah, it's interesting because there are a lot of companies that focus on trying to help other companies optimize what they're doing in the cloud, whether it's cost or whether it's performance or something else. We felt very strongly that AI was the way to do that. I've got a slide prepared, and maybe we can take a quick look at that, and that'll talk about the three elements or dimensions of the problem. So we think about cloud services and the challenge of delivering cloud services. You've really got three things that customers are trying to solve for. They're trying to solve for performance, they're trying to solve for the best performance, and, ultimately, scalability. I mean, applications are growing really quickly especially in this current timeframe with cloud services and whatnot. They're trying to keep costs under control because certainly, it can get way out of control in the cloud since you don't own the infrastructure, and more importantly than anything else which is why it's at the bottom sort of at the foundation of all this, is they want their applications to be a really a good experience for their customers. So our customer's customer is actually who we're trying to solve this problem for. So what we've done is we've built a platform that uses AI and machine learning to optimize, meaning tune, all of the key parameters of a cloud application. So those are things like the CPU usage, the memory usage, the number of replicas in a Kubernetes or container environment, those kinds of things. It seems like it would be simple just to grab some values and plug 'em in, but it's not. It's actually the combination of them has to be right. Otherwise, you get delays or faults or other problems with the application. >> Andrew, if you can bring that slide back up for a second. I want to just ask one quick question on the problem statement. You got expenditures, performance, customer experience kind of on the sides there. Do you see this tip a certain way depending upon use cases? I mean, is there one thing that jumps out at you, Patrick, from your customer's customer's standpoint? Obviously, customer experience is the outcome. That's the app, whatever. That's whatever we got going on there. >> Sure. >> But is there patterns 'cause you can have good performance, but then budget overruns. Or all of them could be failing. Talk about this dynamic with this triangle. >> Well, without AI, without machine learning, you can solve for one of these, only one, right? So if you want to solve for performance like you said, your costs may overrun, and you're probably not going to have control of the customer experience. If you want to solve for one of the others, you're going to have to sacrifice the other two. With machine learning though, we can actually balance that, and it isn't a perfect balance, and the question you asked is really a great one. Sometimes, you want to over-correct on something. Sometimes, scalability is more important than cost, but what we're going to do because of our machine learning capability, we're going to always make sure that you're never spending more than you should spend, so we're always going to make sure that you have the best cost for whatever the performance and reliability factors that you you want to have are. >> Yeah, I can imagine. Some people leave services on. Happened to us one time. An intern left one of the services on, and like where did that bill come from? So kind of looked back, we had to kind of fix that. There's a ton of action, but I got to ask you, what are customers looking for with you guys? I mean, as they look at Opsani, what you guys are offering, what's different than what other people might be proposing with optimization solutions? >> Sure. Well, why don't we bring up the second slide, and this'll illustrate some of the differences, and we can talk through some of this stuff as well. So really, the area that we play in is called AIOps, and that's sort of a new area, if you will, over the last few years, and really what it means is applying intelligence to your cloud operations, and those cloud operations could be development operations, or they could be production operations. And what this slide is really representing is in the upper slide, that's sort of the way customers experience their DevOps model today. Somebody says we need an application or we need a feature, the developers pull down something from get. They hack an early version of it. They run through some tests. They size it whatever way they know that it won't fail, and then they throw it over to the SREs to try to tune it before they shove it out into production, but nobody really sizes it properly. It's not optimized, and so it's not tuned either. When it goes into production, it's just the first combination of settings that work. So what happens is undoubtedly, there's some type of a problem, a fault or a delay, or you push new code, or there's a change in traffic. Something happens, and then, you've got to figure out what the heck. So what happens then is you use your tools. First thing you do is you over-provision everything. That's what everybody does, they over-provision and try to soak up the problem. But that doesn't solve it because now, your costs are going crazy. You've got to go back and find out and try as best you can to get root cause. You go back to the tests, and you're trying to find something in the test phase that might be an indicator. Eventually your developers have to hack a hot fix, and the conveyor belt sort of keeps on going. We've tested this model on every single customer that we've spoken to, and they've all said this is what they experience on a day-to-day basis. Now, if we can go back to the side, let's talk about the second part which is what we do and what makes us different. So on the bottom of this slide, you'll see it's really a shift-left model. What we do is we plug in in the production phase, and as I mentioned earlier, what we're doing is we're tuning all those cloud parameters. We're tuning the CPU, the memory, the Replicas, all those kinds of things. We're tuning them all in concert, and we're doing it at machine speed, so that's how the customer gets the best performance, the best reliability at the best cost. That's the way we're able to achieve that is because we're iterating this thing in machine speed, but there's one other place where we plug in and we help the whole concept of AIOps and DevOps, and that is we can plug in in the test phase as well. And so if you think about it, the DevOps guy can actually not have to over-provision before he throws it over to the SREs. He can actually optimize and find the right size of the application before he sends it through to the SREs, and what this does is collapses the timeframe because it means the SREs don't have to hunt for a working set of parameters. They get one from the DevOps guys when they send it over, and this is how the future of AIOps is being really affected by optimization and what we call autonomous optimization which means that it's happening without humans having to press a button on it. >> John: Andrew, bring that slide back up. I want to just ask another question. Tuning in concert thing is very interesting to me. So how does that work? Are you telegraphing information to the developer from the autonomous workload tuning engine piece? I mean, how does the developer know the right knobs or where does it get that provisioning information? I see the performance lag. I see where you're solving that problem. >> Sure. >> How does that work? >> Yeah, so actually, if we go to the next slide, I'll show you exactly how it works. Okay, so this slide represents the architecture of a typical application environment that we would find ourselves in, and inside the dotted line is the customer's application namespace. That's where the app is. And so, it's got a bunch of pods. It's got a horizontal pod. It's got something for replication, probably an HPA. And so, what we do is we install inside that namespace two small instances. One is a tuning pod which some people call a canary, and that tuning pod joins the rest of the pods, but it's not part of the application. It's actually separate, but it gets the same traffic. We also install somebody we call Servo which is basically an action engine. What Servo does is Servo takes the metrics from whatever the metric system is is collecting all those different settings and whatnot from the working application. It could be something like Prometheus. It could be an Envoy Sidecar, or more likely, it's something like AppDynamics, or we can even collect metrics off of Nginx which is at the front of the service. We can plug into anywhere where those metrics are. We can pull the metrics forward. Once we see the metrics, we send them to our backend. The Opsani SaaS service is our machine learning backend. That's where all the magic happens, and what happens then is that service sees the settings, sends a recommendation to Servo, Servo sends it to the tuning pod, and we tune until we find optimal. And so, that iteration typically takes about 20 steps. It depends on how big the application is and whatnot, how fast those steps take. It could be anywhere from seconds to minutes to 10 to 20 minutes per step, but typically within about 20 steps, we can find optimal, and then we'll come back and we'll say, "Here's optimal, and do you want to "promote this to production," and the customer says, "Yes, I want to promote it to production "because I'm saving a lot of money or because I've gotten "better performance or better reliability." Then, all he has to do is press a button, and all that stuff gets sent right to the production pods, and all of those settings get put into production, and now he's now he's actually saving the money. So that's basically how it works. >> It's kind of like when I want to go to the beach, I look at the weather.com, I check the forecast, and I decide whether I want to go or not. You're getting the data, so you're getting a good look at the information, and then putting that into a policy standpoint. I get that, makes total sense. Can I ask you, if you don't mind, expanding on the performance and reliability and the cost advantage? You mentioned cost. How is that impacting? Give us an example of some performance impact, reliability, and cost impacts. >> Well, let's talk about what those things mean because like a lot of people might have different ideas about what they think those mean. So from a cost standpoint, we're talking about cloud spend ultimately, but it's represented by the settings themselves, so I'm not talking about what deal you cut with AWS or Azure or Google. I'm talking about whatever deal you cut, we're going to save you 30, 50, 70% off of that. So it doesn't really matter what cost you negotiated. What we're talking about is right-sizing the settings for CPU and memory, Replica. Could be Java. It could be garbage collection, time ratios, or heap sizes or things like that. Those are all the kinds of things that we can tune. The thing is most of those settings have an unlimited number of values, and this is why machine learning is important because, if you think about it, even if they only had eight settings or eight values per setting, now you're talking about literally billions of combinations. So to find optimal, you've got to have machine speed to be able to do it, and you have to iterate very, very quickly to make it happen. So that's basically the thing, and that's really one of the things that makes us different from anybody else, and if you put that last slide back up, the architecture slide, for just a second, there's a couple of key words at the bottom of it that I want to want to focus on, continuous. So continuous really means that we're on all the time. We're not plug us in one time, make a change, and then walk away. We're actually always measuring and adjusting, and the reason why this is important is in the modern DevOps world, your traffic level is going to change. You're going to push new code. Things are going to happen that are going to change the basic nature of the software, and you have to be able to tune for those changes. So continuous is very important. Second thing is autonomous. This is designed to take pressure off of the SREs. It's not designed to replace them, but to take the pressure off of them having to check pager all the time and run in and make adjustments, or try to divine or find an adjustment that might be very, very difficult for them to do so. So we're doing it for them, and that scale means that we can solve this for, let's say, one big monolithic application, or we can solve it for literally hundreds of applications and thousands of microservices that make up those applications and tune them all at the same time. So the same platform can be used for all of those. You originally asked about the parameters and the settings. Did I answer the question there? >> You totally did. I mean, the tuning in concert. You mentioned early as a key point. I mean, you're basically tuning the engine. It's not so much negotiating a purchase SaaS discount. It's essentially cost overruns by the engine, either over burning or heating or whatever you want to call it. I mean, basically inefficiency. You're tuning the core engine. >> Exactly so. So the cost thing is I mentioned is due to right-sizing the settings and the number of Replicas. The performance is typically measured via latency, and the reliability is typically measured via error rates. And there's some other measures as well. We have a whole list of them that are in the application itself, but those are the kinds of things that we look for as results. When we do our tuning, we look for reducing error rates, or we look for holding error rates at zero, for example, even if we improve the performance or we improve the cost. So we're looking for the best result, the best combination result, and then a customer can decide if they want to do so to actually over-correct on something. We have the whole concept of guard rail, so if performance is the most important thing, or maybe some customers, cost is the most important thing, they can actually say, "Well, give us the best cost, "and give us the best performance and the best reliability, "but at this cost," and we can then use that as a service-level objective and tune around it. >> Yeah, it reminds me back in the old days when you had filtering white lists of black lists of addresses that can go through, say, a firewall or a device. You have billions of combinations now with machine learning. It's essentially scaling the same concept to unbelievable. These guardrails are now in place, and that's super cool and I think really relevant call-out point, Patrick, to kind of highlight that. At this kind of scale, you need machine learning, you need the AI to essentially identify quickly the patterns or combinations that are actually happening so a human doesn't have to waste their time that can be filled by basically a bot at that point. >> So John, there's just one other thing I want to mention around this, and that is one of the things that makes us different from other companies that do optimization. Basically, every other company in the optimization space creates a static recommendation, basically their recommendation engines, and what you get out of that is, let's say it's a manifest of changes, and you hand that to the SREs, and they put it into effect. Well, the fact of the matter is is that the traffic could have changed then. It could have spiked up, or it could have dropped below normal. You could have introduced a new feature or some other code change, and at that point in time, you've already instituted these changes. They may be completely out of date. That's why the continuous nature of what we do is important and different. >> It's funny, even the language that we're using here: network, garbage collection. I mean, you're talking about tuning an engine, am operating system. You're talking about stuff that's moving up the stack to the application layer, hence this new kind of eliminating of these kind of siloed waterfall, as you pointed out in your second slide, is kind of one integrated kind of operating environment. So when you have that or think about the data coming in, and you have to think about the automation just like self-correcting, error-correcting, tuning, garbage collection. These are words that we've kind of kicking around, but at the end of the day, it's an operating system. >> Well in the old days of automobiles, which I remember cause I'm I'm an old guy, if you wanted to tune your engine, you would probably rebuild your carburetor and turn some dials to get the air-oxygen-gas mix right. You'd re-gap your spark plugs. You'd probably make sure your points were right. There'd be four or five key things that you would do. You couldn't do them at the same time unless you had a magic wand. So we're the magic wand that basically, or in modern world, we're sort of that thing you plug in that tunes everything at once within that engine which is all now electronically controlled. So that's the big differences as you think about what we used to do manually, and now, can be done with automation. It can be done much, much faster without humans having to get their fingernails greasy, let's say. >> And I think the dynamic versus static is an interesting point. I want to bring up the SRE which has become a role that's becoming very prominent in the DevOps kind of plus world that's happening. You're seeing this new revolution. The role of the SRE is not just to be there to hold down and do the manual configuration. They had a scale. They're a developer, too. So I think this notion of offloading the SRE from doing manual tasks is another big, important point. Can you just react to that and share more about why the SRE role is so important and why automating that away through when you guys have is important? >> The SRE role is becoming more and more important, just as you said, and the reason is because somebody has to get that application ready for production. The DevOps guys don't do it. That's not their job. Their job is to get the code finished and send it through, and the SREs then have to make sure that that code will work, so they have to find a set of settings that will actually work in production. Once they find that set of settings, the first one they find that works, they'll push it through. It's not optimized at that point in time because they don't have time to try to find optimal, and if you think about it, the difference between a machine learning backend and an army of SREs that work 24-by-seven, we're talking about being able to do the work of many, many SREs that never get tired, that never need to go play video games, to unstress or whatever. We're working all the time. We're always measuring, adjusting. A lot of the companies we talked to do a once-a-month adjustment on their software. So they put an application out, and then they send in their SREs once a month to try to tune the application, and maybe they're using some of these other tools, or maybe they're using just their smarts, but they'll do that once a month. Well, gosh, they've pushed code probably four times during the month, and they probably had a bunch of different spikes and drops in traffic and other things that have happened. So we just want to help them spend their time on making sure that the application is ready for production. Want to make sure that all the other parts of the application are where they should be, and let us worry about tuning CPU, memory, Replica, job instances, and things like that so that they can work on making sure that application gets out and that it can scale, which is really important for them, for their companies to make money is for the apps to scale. >> Well, that's a great insight, Patrick. You mentioned you have a lot of great customers, and certainly if you have your customer base are early adopters, pioneers, and grow big companies because they have DevOps. They know that they're seeing a DevOps engineer and an SRE. Some of the other enterprises that are transforming think the DevOps engineer is the SRE person 'cause they're having to get transformed. So you guys are at the high end and getting now the new enterprises as they come on board to cloud scale. You have a huge uptake in Kubernetes, starting to see the standardization of microservices. People are getting it, so I got to ask you can you give us some examples of your customers, how they're organized, some case studies, who uses you guys, and why they love you? >> Sure. Well, let's bring up the next slide. We've got some customer examples here, and your viewers, our viewers, can probably figure out who these guys are. I can't tell them, but if they go on our website, they can sort of put two and two together, but the first one there is a major financial application SaaS provider, and in this particular case, they were having problems that they couldn't diagnose within the stack. Ultimately, they had to apply automation to it, and what we were able to do for them was give them a huge jump in reliability which was actually the biggest problem that they were having. We gave them 5,000 hours back a month in terms of the application. They were they're having pager duty alerts going off all the time. We actually gave them better performance. We gave them a 10% performance boost, and we dropped their cloud spend for that application by 72%. So in fact, it was an 80-plus % price performance or cost performance improvement that we gave them, and essentially, we helped them tune the entire stack. This was a hybrid environment, so this included VMs as well as more modern architecture. Today, I would say the overwhelming majority of our customers have moved off of the VMs and are in a containerized environment, and even more to the point, Kubernetes which we find just a very, very high percentage of our customers have moved to. So most of the work we're doing today with new customers is around that, and if we look at the second and third examples here, those are examples of that. In the second example, that's a company that develops websites. It's one of the big ones out in the marketplace that, let's say, if you were starting a new business and you wanted a website, they would develop that website for you. So their internal infrastructure is all brand new stuff. It's all Kubernetes, and what we were able to do for them is they were actually getting decent performance. We held their performance at their SLO. We achieved a 100% error-free scenario for them at runtime, and we dropped their cost by 80%. So for them, they needed us to hold-serve, if you will, on performance and reliability and get their costs under control because everything in that, that's a cloud native company. Everything there is cloud cost. So the interesting thing is it took us nine steps because nine of our iterations to actually get to optimal. So it was very, very quick, and there was no integration required. In the first case, we actually had to do a custom integration for an underlying platform that was used for CICD, but with the- >> John: Because of the hybrid, right? >> Patrick: Sorry? >> John: Because it was hybrid, right? >> Patrick: Yes, because it was hybrid, exactly. But within the second one, we just plugged right in, and we were able to tune the Kubernetes environment just as I showed in that architecture slide, and then the third one is one of the leading application performance monitoring companies on the market. They have a bunch of their own internal applications and those use a lot of cloud spend. They're actually running Kubernetes on top of VMs, but we don't have to worry about the VM layer. We just worry about the Kubernetes layer for them, and what we did for them was we gave them a 48% performance improvement in terms of latency and throughput. We dropped their error rates by 90% which is pretty substantial to say the least, and we gave them a 50% cost delta from where they had been. So this is the perfect example of actually being able to deliver on all three things which you can't always do. It has to be, sort of all applications are not created equal. This was one where we were able to actually deliver on all three of the key objectives. We were able to set them up in about 25 minutes from the time we got started, no extra integration, and needless to say, it was a big, happy moment for the developers to be able to go back to their bosses and say, "Hey, we have better performance, "better reliability. "Oh, by the way, we saved you half." >> So depending on the stack situation, you got VMs and Kubernetes on the one side, cloud-native, all Kubernetes, that's dream scenario obviously. Not many people like that. All the new stuff's going cloud-native, so that's ideal, and then the mixed ones, Kubernetes, but no VMs, right? >> Yeah, exactly. So Kubernetes with no VMs, no problem. Kubernetes on top of VMs, no problem, but we don't manage the VMs. We don't manage the underlay at all, in fact. And the other thing is we don't have to go back to the slide, but I think everybody will remember the slide that had the architecture, and on one side was our cloud instance. The only data that's going between the application and our cloud instance are the settings, so there's never any data. There's never any customer data, nothing for PCI, nothing for HIPPA, nothing for GDPR or any of those things. So no personal data, no health data. Nothing is passing back and forth. Just the settings of the containers. >> Patrick, while I got you here 'cause you're such a great, insightful guest, thank you for coming on and showcasing your company. Kubernetes real quick. How prevalent is this mainstream trend is because you're seeing such great examples of performance improvements. SLAs being met, SLOs being met. How real is Kubernetes for the mainstream enterprise as they're starting to use containers to tip their legacy and get into the cloud-native and certainly hybrid and soon to be multi-cloud environment? >> Yeah, I would not say it's dominant yet. Of container environments, I would say it's dominant now, but for all environments, it's not. I think the larger legacy companies are still going through that digital transformation, and so what we do is we catch them at that transformation point, and we can help them develop because as we remember from the AIOps slide, we can plug in at that test level and help them sort of pre-optimize as they're coming through. So we can actually help them be more efficient as they're transforming. The other side of it is the cloud-native companies. So you've got the legacy companies, brick and mortar, who are desperately trying to move to digitization. Then, you've got the ones that are born in the cloud. Most of them aren't on VMs at all. Most of them are on containers right from the get-go, but you do have some in the middle who have started to make a transition, and what they've done is they've taken their native VM environment and they've put Kubernetes on top of it so that way, they don't have to scuttle everything underneath it. >> Great. >> So I would say it's mixed at this point. >> Great business model, helping customers today, and being a bridge to the future. Real quick, what licensing models, how to buy, promotions you have for Amazon Web Services customers? How do people get involved? How do you guys charge? >> The product is licensed as a service, and the typical service is an annual. We license it by application, so let's just say you have an application, and it has 10 microservices. That would be a standard application. We'd have an annual cost for optimizing that application over the course of the year. We have a large application pack, if you will, for let's say applications of 20 services, something like that, and then we also have a platform, what we call Opsani platform, and that is for environments where the customer might have hundreds of applications and-or thousands of services, and we can plug into their deployment platform, something like a harness or Spinnaker or Jenkins or something like that, or we can plug into their their cloud Kubernetes orchestrator, and then we can actually discover the apps and optimize them. So we've got environments for both single apps and for many, many apps, and with the same platform. And yes, thanks for reminding me. We do have a promotion for for our AWS viewers. If you reference this presentation, and you look at the URL there which is opsani.com/awsstartupshowcase, can't forget that, you will, number one, get a free trial of our software. If you optimize one of your own applications, we're going to give you an Oculus set of goggles, the augmented reality goggles. And we have one other promotion for your viewers and for our joint customers here, and that is if you buy an annual license, you're going to get actually 15 months. So that's what we're putting on the table. It's actually a pretty good deal. The Oculus isn't contingent. That's a promotion. It's contingent on you actually optimizing one of your own services. So it's not a synthetic app. It's got to be one of your own apps, but that's what we've got on the table here, and I think it's a pretty good deal, and I hope your guys take us up on it. >> All right, great. Get Oculus Rift for optimizing one of your apps and 15 months for the price of 12. Patrick, thank you for coming on and sharing the future of AIOps with you guys. Great product, bridge to the future, solving a lot of problems. A lot of use cases there. Congratulations on your success. Thanks for coming on. >> Thank you so much. This has been excellent, and I really appreciate it. >> Hey, thanks for sharing. I'm John Furrier, your host with theCUBE. Thanks for watching. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent Executive Summit 2020 sponsored by Accenture and AWS. What? Welcome, everyone to the Cube Virtual and our coverage of the Accenture Executive Summit, part of AWS reinvent 2020. I'm your host, Rebecca Knight. Today we're talking about the power of three. And what happens when you bring together the scientific know how of a global bias Bio pharmaceutical powerhouse in Takeda, a leading cloud services provider in AWS and Accenture's ability to innovate, execute and deliver innovation, Joining me to talk about these things we have Aaron. Sorry. Arjun, baby. He is the senior managing director and chairman of Accenture's Diamond Leadership Council. Welcome margin, you Carl Hick. He is the chief digital and information officer at Takeda. >>Pleasure to be here. Thank you. Rebecca >>and Brian Bowen, global director and head of the Accenture AWS Business Group at Amazon Web services. Thanks so much for coming. Thank you. So, as I said, we're talking today about this relationship between your three organizations. Carl, I want to talk with you. I know you're at the beginning of your cloud journey. What was the compelling reason Why? Why I moved to the cloud and why now? >>Yeah. No, thank you for the question. So you know as ah, bio pharmaceutical leader were committed toe bringing better health and a brighter future to our patients. We're doing that by translating science and toe some really innovative and life transporting therapies. But throughout, you know, we believe that there's, ah responsible use of technology of data and of innovation. And those three ingredients air really key to helping us deliver on that promise. And so, you know, while I think I'll call it this Cloud Journeys already has always been a part of our strategy. Andi have made some pretty steady progress over the last years with a number of local it diverse approaches to the digital in AI. We just weren't seeing the impact at scale that we wanted to see. Andi, I think that you know, there's a there's a need ultimately to accelerate and broaden that shift. And, you know, we were commenting on this earlier, but there's, you know, it's been highlighted by a number of factors. One of those has been certainly a number of the large acquisitions we've made Shire being the most pressing example, but also the global pandemic. Both of those highlight the need for us to move faster at the speed of cloud ultimately on. So we started thinking outside of the box because it was taking us too long and we decided to leverage the strategic partner model on. It's giving us a chance to think about our challenges very differently. We call this the power of three on. Ultimately, our focus is singularly on our patients. I mean, they're waiting for us. We need Thio. Get there faster. It can take years. And so I think that there is a focus on innovation at a rapid speed so we can move ultimately from treating conditions to keeping people healthy. >>So as you are embarking on this journey, what are some of the insights you want to share about? About what you're seeing so far? >>Yeah. No, it's a great question. So I mean, look, maybe right before I highlight some of the key insights, I would say that, you know, with Cloud now as the as a launchpad for innovation, you know, our vision all along has been that in less than 10 years we want every single to Kito associate. We're employees to be empowered by an AI assistant. And I think that, you know that's gonna help us make faster, better decisions that will help us. Uh, fundamentally, you know, deliver transformative therapies and better experiences to to that ecosystem, to our patients, to positions to payers, etcetera much faster than we previously thought possible. Um, and I think that technologies like cloud and edge computing together with a very powerful or call it data fabric is gonna help us to create this this really time. I'll call it the digital ecosystem. The data has to flow ultimately seamlessly between our patients and providers or partners or researchers. Etcetera. Uh, and so we've been thinking about this, Uh, call it. We call it sort of this pyramid. Um, that helps us describe our vision on a lot of it has to do with ultimately modernizing the foundation, modernizing and re architect ing the platforms that drive the company, heightening our focus on data, which means that there's an accelerated shift towards enterprise data platforms and digital products. And then, ultimately, a, you know, really an engine for innovation. Sitting at the very top, um, and So I think with that, you know, there's a few different I'll call it insights that you know are quickly kind of come zooming into focus. I would say one is this need to collaborate very differently. Um, you know, not only internally, but you know, how do we define ultimately and build a connected digital ecosystem with the right partners and technologies? Externally, I think the second component that maybe people don't think as much about but, you know, I find critically important is for us to find ways of really transforming our culture. We have to unlock talent and shift the culture, certainly as a large biopharmaceutical, very differently. And then, lastly, you've touched on it already. Which is, you know, innovation at the speed of cloud. How do we re imagine that you know how Doe ideas go from getting tested in months? That kind of getting tested in days, you know, how do we collaborate very differently on So I think those air three, perhaps of the larger chocolate insights that you know the three of us are spending a lot of time thinking about right now. >>So, Arjun, I want to bring you into this conversation a little bit. Let's let's delve into those a bit. Talk first about the collaboration that Carl was referencing there. How how have you seen that it is enabling colleagues and teams to communicate differently, interact in new and different ways, both internally and externally. As Carl said, >>No, thank you for that. And I've got to give called a lot of credit because as we started to think about this journey, it was clear was a bold ambition. It was, uh, something that, you know, we had all to do differently. And so the concept of the power of three that Karl has constructed has become a label for us as a way to think about what are we going to do to collectively drive this journey forward? And to me, the unique ways of collaboration means three things. The first one is that what is expected is that the three parties they're going to come together, and it's more than just the sum of our resource is, and by that I mean that we have to bring all of ourselves all of our collective capabilities as an example. Amazon has amazing supply chain capabilities there. One of the best at supply chain. So in addition to Resource is when we have supply chain innovations, that's something that they're bringing in addition to just talent and assets. Similarly, for Accenture, right, we do a lot in the talent space. So how do we bring our thinking as to how we apply best practices for talent to this partnership? So as we think about this, so that's that's the first one. The second one is about shared success. Very early on in this partnership, we started to build some foundations and actually develop seven principles that all of us would look at it as the basis for this success shared success model. And we continue to hold that sort of in the forefront as we think about this collaboration. And maybe the third thing I would say is this one team mindset. So whether it's the three of our CEO's that get together every couple of months to think about this partnership or it is the governance model that Karl has put together, which has all three parties in the governance and every level of leadership, we always think about this as a collective group so that we can keep that front and center. And what this, I think, ultimately has enabled us to do Is it allowed us to move its speed, be more flexible and ultimately all be looking at the target the same way the North south? The same way. >>Brian. What? What about you? What have you observed? What are you thinking about? In terms of how this is helping teams collaborate differently? >>Yeah, absolutely. And Georgia made some great points there. And I think if you really think about what he's talking about, it's that diversity of talent, diversity of skill and viewpoint and even culture. Right? And so we see that in the power of three. And I think if we drilled down into what we see at Takeda and frankly, Takeda was really, I think, pretty visionary and on their way here, right, and taking this kind of cross functional approach and applying it to how they operate day to day. So moving from a more functional view of the world to more of a product oriented view of the world, right? So when you think about, we're gonna be organized around a product or service or capability that we're gonna provide to our customers are patients or donors. In this case, it implies a different structure, although altogether in a different way of thinking. Right, because now you've got technical people in business experts and marketing experts all working together in This is sort of a cross collaboration, and what's great about that is it's really the only way to succeed with Cloud, right, because the old ways of thinking where you've got application people in infrastructure, people and business people is sub optimal, right, because we can all access this tools and capabilities. And the best way to do that isn't across kind of a cross collaborative way. And so this is product oriented mindset of Takeda was already on, I think is allowed us to move faster in those areas. >>Carl, I wanna go back to this idea of unlocking talent and culture, and this is something that both Brian and origin have talked about. Two people are are an essential part of their at the heart of your organization. How will their experience of work change and how are you helping reimagine and reinforce a strong organizational culture, particularly at this time when so many people are working remotely. >>Yeah, that's a great question. And it's something that, you know, I think we all have to think a lot about. I mean, I think, you know, driving this this call this this digital and data kind of capability building takes a lot of a lot of thinking. So I mean, there's a few different elements in terms of how we're tackling this one is we're recognizing. And it's not just for the technology organization or for those actors that that we're innovating with. But it's really across, you know, all of Takeda. We're working through ways of raising what I'll call the overall digital leaders literacy of the organization. You know, what are the, You know, what are the skills that are needed almost at a baseline level, even for, ah, global biopharmaceutical company? And how do we deploy? I'll call it Those learning resource is very broadly, and then secondly, I think that, you know, we're very clear that there's a number of areas where they're very specialized skills that are needed. Uh, my organization is one of those, and so, you know, we're fostering ways in which you know, were very kind of quickly kind of creating avenues, excitement for for associates in that space. So one example specifically is we use, you know, during these very much sort of remote sort of days, we use what we call global it me days, and we set a day aside every single month and this last Friday. Um, you know, we create during that time, it's time for personal development. Um, and we provide active seminars and training on things like, you know, robotic process automation, Data Analytics Cloud. Uh, in this last month, we've been doing this for months and months now, but in his last month, more than 50% of my organization participated. And there's this huge positive shift, both in terms of access and excitement about really harnessing those new skills and being able to apply them on. So I think that that's, you know, 11 element that can be considered. And then thirdly, of course, every organization has to work on. How do you prioritize talent, acquisition and management and competencies that you can't re skill? I mean, there's just some new capabilities that we don't have, And so there's a large focus that I have with our executive team in our CEO and thinking through those critical roles that we need to activate in order kind of thio build on this, uh, this business led cloud transformation and lastly, probably the hardest one. But the one that I'm most jazzed about is really this focus on changing the mindsets and behaviors. Andi, I think there, you know, this is where the power of three is really kind of coming together nicely. E mean, we're working on things like, you know, how do we create this patient obsessed curiosity? Um, and really kind of unlock innovation with a really kind of a growth mindset, Uh, and the level of curiosity that's needed not to just continue to do the same things, but to really challenge the status quo. So that's one big area of focus. We're having the agility toe act just faster. I mean, toe worry less. I guess I would say about kind of the standard chain of command, but how do you make more speedy, more courageous decisions? And this is places where we can emulate the way that ah, partner like AWS works? Or how do we collaborate across the number of boundaries, you know, and I think origin spoke eloquently to a number of partnerships that we can build so we can break down some of these barriers and use these networks. Um, whether it's within our own internal ecosystem or externally, to help to create value faster. So a lot of energy around ways of working we'll have to check back in. But, I mean, we're early in on this mindset and behavioral shift, but a lot of good early momentum. >>Carl, you've given me a good segue to talk to Brian about innovation because you said a lot of the things that I was the customer obsession and this idea of innovating much more quickly. Obviously. Now the world has its eyes on drug development, and we've all learned a lot about it in the past few months. And accelerating drug development is all of is of great interest to all of us. Brian How does a transformation like this help a company's ability to become more agile and more innovative? Add quicker speed to >>Yeah, No, absolutely. And I think some of the things that Karl talked about just now are critical to that. Right? I think, where sometimes you know, folks fall short is they think, you know, we're going to roll out the technology and the technology is going to be the Silver Bullet, where, in fact it is. The culture it is is the talent, and it's the focus on that. That's going to be, you know, the determinant of success. And I will say, You know, in this power of three arrangement, Karl talked a lot about the pyramid, um, talent and culture and that change. And that kind of thinking about that has been a first class citizen since the very beginning. Right? That absolutely is critical for being there. Um and so that's been that's been key. And so we think about innovation at Amazon and AWS, and Carl mentioned some of things that, you know, partner like AWS can bring to the table is we talk a lot about builders, right? So we're kind of obsessive about builders, Onda. We mean what we mean by that is way at Amazon, we hire for builders, we cultivate builders and we like to talk to our customers about it as well. And it also implies a different mindset. Right? When you're a builder, you have that curiosity. You have that ownership. You have that steak and whatever I am creating. I'm going to be a co owner of this product or the service right getting back to that kind of product oriented mindset. And it's not just the technical people or the I t. People who are builders. It is also the business people, as Karl talked about right. So when we start thinking about innovation again, where we see folks kind of get into a little bit of innovation, pilot paralysis is that you can focus on the technology. But if you're not focusing on the talent and the culture and the processes and the mechanisms, you're gonna be putting out technology. But you're not gonna have an organization that's ready to take it and scale and accelerated right, and so that's that's been absolutely critical. So just a couple of things we've been doing with with Takeda indicate, has really been leading the way is think about a mechanism and a process, and it's really been working backwards from the customer, right? In this case again, the patient and the donor. And that was an easy one because a key value of decadas is to be a patient focused biopharmaceutical, right? So that was embedded in their DNA. So that working back from that, that patient, that donor was a key part of that process. And that's really deep in our DNA as well in eccentrics. And so we're able to bring that together. The other one is, is getting used to experimenting and even perhaps failing right and being able to reiterate and fail fast and experiment and understanding that you know some decisions, what we call it at Amazon or to a doors meaning you could go through that door not like what you see and turn around and go back. And cloud really helps there. Because the cost of experimenting and the cost of failure is so much lower than it's ever been. You could do it much faster, and the implications there so much less so just a couple of things that we've been really driving with a kid around innovation that's been really critical. >>Carl, where are you already seeing signs of success? >>Yeah, No, it's a great question. And so we chose, you know, with our focus on innovation to try to unleash maybe the power of data digital in uh, focusing on what I call sort of a maid. And so we chose our plasma derived therapy business. Um and you know, the plasma drive therapy business unit? It develops critical lifesaving therapies for patients with a rare and complex diseases. Um, but what we're doing is by bringing kind of our energy together, we're focusing on creating called State of the art digitally connected donation centers. And we're really modernizing. You know, the donor experience right now we're trying Thio improve. Also, I'll call it the overall Plasma Collection process. And so we've selected a number of uncle at very high speed pilots that were working through right now specifically in this in this area, and we're seeing really great results already on DSO. That's that's one specific area of focus. >>Arjun, I want you to close this out here. Any ideas? Any best practices advice you would have for other pharmaceutical companies that are that are at the early stage of their cloud journey. >>Sorry. Was that for me? >>Yes. Sorry. Urgent? >>Yeah. No, I was breaking up a bit. No, I think the key is what sort of been great for me to see is that when people think about cloud, you know, you always think about infrastructure technology. The reality is that the cloud is really the true enabler for innovation at innovating at scale. And if you think about that, right and all the components that you need, ultimately, that's where the value is for the company, right? Because, yes, you're gonna get some cost synergies, and that's great. But the true value is And how do we transform the organization? The case of Takeda and a life sciences clients, right. We're trying to take a 14 year process of research and development that takes billions of dollars and compress that right. Tremendous amounts of innovation, opportunity. You think about the commercial aspect, lots of innovation can come that the plasma derived therapy is a great example of how we're gonna really innovate to change the trajectory of that business. So I think innovation is at the heart of what most organizations need to do. And the formula the cocktail that Takeda has constructed with this Fuji program really has all the ingredients, um, that are required for that success. >>Great. Well, thank you so much. Arjun, Brian and Carl was really an enlightening conversation. >>Thank you. It's been a lot of >>fun. Thank you. >>Uh, been fun. Thanks, Rebecca. >>And thank you for tuning into the Cube. Virtual is coverage of the Accenture Executive Summit.

>>from around the globe. It's the Cube covering HP. Discover Virtual experience Brought to you by HP. >>Welcome to the Cube's coverage of HP Discover 2020 Virtual Experience. I'm Lisa Martin, and I'm pleased to be joined by two guests from HP longtime partner Tech Data. We have calling Blair the vice president of sales and marketing of I. O. T. And Data Solutions and David Smith, H P E Pre Sales Field Solutions are common. And David, Welcome to the Cube. Thanks, Lisa. Great to see. So let's start with the calling. HP and Technical have been partners for over 40 years, but tell our audience a little bit about tech data before we get into the specifics of what you're doing and some of the cool I o. T. Stuff with HP. I >>think that the Tech data is a Fortune 100 distributor. We continued to evolved to be a solutions aggregator in these next generation technology businesses. As you've mentioned, we've been serving the I T distribution markets globally for for 40 plus years, and we're now moving into next generation technologies like Wild Analytics, I O. T and Security bubble Lifecycle Management services. But to be able todo position ourselves with our customer base and the needs of their clients have. So I'm excited to be here today to talk a little bit about what we're doing in I, O. T. And Analytics with David on the HPC side >>and in addition to the 40 plus years of partnership calling that you mentioned that Detected and HP have you've got over 200 plus hp. Resource is David, you're one of those guys in the field. Talk to us about some of the things that you're working on with Channel Partners Table David to enable them, especially during such crazy times of living and now >>absolutely, absolutely so. What we can do is we can provide strong sales and technical enablement if your team, for example, wants to better understand how to position HP portfolio if they require assistance and architect ing a secure performance i o t. Solution. We can help ensure that you're technical team is fully capable of having that conversation, and it's one that they're able to have of confidence, weaken validate the proposed HP solutions with the customers, technical requirements and proposed use case. We can even exist on a customer calls, if it would, would benefit our partner to kind of extend out to that. We also have a a a deep technical bench that Colin can speak to in the OT space toe lean on as well. For so solution is that kind of span into the space beyond where HP typically operates, which would be edge, compute computing and network. Sic security. >>Excellent call and tell me a little bit about Tech Data's investments in I o. T. When did this start? What are you guys doing today? >>Sure, we started in the cloud space. First tackle this opportunity in data center modernization and hybrid cloud. That was about seven years ago. Shortly thereafter we started investing very materially in the security cyber security space. And then we follow that with Data Analytics and then the Internet of things. Now we've been in those spaces with our long term partners for some time. But now that we're seeing this movement to the intelligent edge and a real focus on business outcomes and specialization, we've kind of tracked with the market, and we feel like we've invested a little bit ahead of where the channel is in terms of supporting our ecosystem of partners in this space. >>So the intelligent edge has been growing for quite some time. Poland in the very unique times that we're living in in 2020 how are you seeing that intelligent edge expand even more? And what are some of the pressing opportunities that tech data and HPC i O T solutions together can address? >>So a couple. So the first is a Xai mentioned earlier just data center modernization. And so, in the middle of code 19 and perhaps postcode 19 we're going to see a lot of clients that are really focused on monetizing the things that they've got. But doing so to drive business outcomes. We believe that increasingly, the predominance of use cases and compute and analytics is going to move to the edge. And HP has got a great portfolio for not just on premise high performance computing but also hybrid cloud computing. And then when we get into the edge with edge line and networking with Aruba and devices that need to be a digitized and sense arised, it's a really great partnership. And then what we're able to do also, Lisa, is we've been investing in vertical markets since 2000 and seven, and I've been a long the ride with that team, most all of that way. So we've got deep specialization and healthcare and industrial manufacturing, retail and then public sector. And then the last thing we've kind of turned on here recently just last month is a strategic partnership in the smarter cities space. So we're able to leverage a lot of those vertical market capabilities. Couple that with our HP organization and really drive specialized repeatable solutions in these vertical markets, where we believe increasingly, customers are going to be more interested in a repeatable solutions that can drive quick proof of value proof of concepts with minimal viable what kinds of products. And that's that's kind of the apartment today with RHB Organization and the HP Corporation >>David. Let's double click into some of those of vertical markets that Colin mentioned some of the things that pop into minor healthcare manufacturing. As we know, supply chains have been very challenged during covered. Give us an insight into what you're hearing from channel partners now virtually, but what are some of the things that are pressing importance? >>So from a pressing and important to Collins exact point, and your exact point as well is really it's all about the edge computing space now from a product perspective Azaz Colin had mentioned earlier. HP has their edge line converged systems, which is kind of taking the functionality of OT and edge T Excuse me of OT and I t and combine it into a single edge processing compute solution. You kind of couple that with the ability to configure components such as Tesla GP, use in specific excellent offerings to offer an aid and things like realtime, video processing and analytics. Uh, and a perfect example of this is, ah so for dissing and covert space. If if I need to be able to analyze a group of people to ensure they're staying as far apart as possible or, you know within self distant guidelines, that is where kind of the real time that's like an aspect of things can be taken advantage of same things with with the leveraging cameras where you could actually take temperature detection as as well, so it's really kind of best to think of Edge Lines Solutions is data center computing at the edge kind of transition into the Aruba space. Uh Rubio says offerings aid in the island Security is such a clear pass device inside, which allows for device discovery of network and monitoring of wired and wireless devices. There's also Aruba asset tracking and real time location of solutions, and that's particularly important in the healthcare space as well. If I have a lot of high value assets, things like wheelchairs, things like ventilation devices, where these things low located within my facilities and how can I keep keep track of them? They also, and by that I mean HP. They also kind of leveraging expanse ecosystem of partners. As an example, they leverage thing works allow their i o t solutions as well, when you kind of tying it all together with HP Point. Next to the end, customers provided with comprehensive loyalty solution. >>So, Colin, how ready? Our channel partners and the end user customers to rapidly pivot and start either deploying more technologies at the edge to be able to deliver some of the capabilities that David talked about in terms of analytics and sensors for social distancing. How ready are the channel partners and customers to be able to understand, adopt and execute this technology. >>So I think on the understanding side, I think the partners are there. We've been talking about digital transformation in the channel for a couple of years now, and I think what's happened through the 19 Pandemic is that it's been a real spotlight on the need for those business outcomes to to solve for very specific problems. And that's one of the values that we serve in the channel. So we've got a solution offering that we call our solution factory. And what we do really says is we leverage a process to look outside the industry. At Gartner, Magic Quadrant Solutions forced a Wave G two crowd. You know, top leaders, visionaries and understand What are those solutions that are in demand in these vertical markets that we talked about? And then we do a lot of work with David and his team internally in the HP organization to be able to do that and then build out that reference architectures so that we know that there's a solution that drives a bill of materials and a reference architecture that's going to work that clients are going to need and then we can do it quickly. You know, Tech data. Everything's about being bold, acting now getting scale. And we've got a large ecosystem partners that already have great relationships. So we pride ourselves on being able to identify what are those solutions that we can take to our partners that they can quickly take to their end users where you know we've We've kind of developed out what we think the 70 or 80% of that solution is going to look like. And then we drive point next and other services capabilities to be able to complete that last mile, if you will, of some of the customization. So we're helping them. For those who aren't ready, we're helping them. For those who already have very specific use cases and a practice that they drive with repeatable solutions were coming alongside them and understanding. What can we do? Using a practice builder approach, which is our consultative approach to understand where our partners are going in the market, who their clients are, what skill sets do they have? What supplier affinities do they want to drive? What brand marketing or demand generation support do they need? And that's where we can take some of these solutions, bring them to bear and engage in that consultative engagement to accelerate being ready as, as you rightly say, >>so tech. It has a lot of partners. You in general. You also have a lot of partners in the i o T space calling What? How do you from a marketing hat perspective? How do you describe the differentiation that Tech data and HP ease Iot solutions delivered to the channel to the end user? >>A couple of different things? I think that's that's differentiation. And that's one of the things that we strive for in the channel is to be specialized and to be competitively differentiated. And so the first part, I say to all of my team, Lisa, is you know, whether it's our solution consultants or our technical consultants, our solutions to the developers or the software development team that works my organization. Our goal is to be specialized in such a way that we're having relevant value added conversations not only our channel partners, but also end users of our partners want to bring us into those conversations, and many do. The next is really education and enablement as you would expect. And so there's a lot of things that are specialized in our technical. We drive education certification programs, roadshows, seminars, one of the things that we're seeing a lot of interest now. Lisa is for a digital marketing, and we're driving. Some really need offerings around digital marketing platforms that not only educate our partners but also allow our partners to bring their end users and tour some of this some of these technologies. So whether it's at our Clearwater office, where we've got an I. O T. Solution center, that we we take our partners and their clients through or we're using our facilities Teoh to do executive briefings and ideation as a service that, you know, kind of understanding the art of the possible. With both our resellers and their clients work, we're using our solution. Our solution catalogs that we've built an interactive pdf that allows our partners to understand over 50 solutions that we've got and then be able to identify. Where would they like to bring in David and his team and then my consultants to do that, that deep planning on business development, uh, that we talked about a little bit earlier. >>So the engagement right now is maybe even more important than it has been in a while because it's all hands off and virtual David. Talk to me about some of the engagement and the enablement piece that call and talked about. How are you able to really keep a channel partner and their end user customers engaged and interested in what you're able to deliver through this from New Virtual World? >>That's a great, great question. And we work in conjunction with our marketing teams to make sure that as new technologies and quite in I O. T space as well as within the HP East base as well that that our channel partners are educated and aware that these solutions exist. I know for a fact that for the majority of them you kind of get this consistent bombardment of new technology. But being able to actually have someone go out and explain it and then being able to correspondingly position it's use case and it's functionality and why it would provide value for your end customer is one of the benefits of tech data ads to kind of build upon that previous statement. The fact that We have such a huge portfolio of partners, so you kind of have HP and the edge compute space. But we have so many different partners in the OT space where it's really just a phone call, an email, a Skype message, a way to have that conversation around interoperability and then provide those responses back to our partners. >>Excellent. One more question before we go. Colin for you, A lot of partners. Why HP fry Mt. >>So a couple of reasons? One of the one of the biggest reasons as HP is just a great partner. And so when you look at evaluating I. O. T solutions that tend to be pretty comprehensive in many cases, Lisa it takes 10 or 12 partners to complete a really i o t solution and address that use case that that's in the field. And so when you have a partner like HP who's investing in these programs, investing in demand generation, investing in the spectrum of technology, whether it's hybrid Cloud Data Center, compute storage or your edge devices and Iot gateways, then to be able to contextualize those into what we call market ready solutions in each one of these vertical markets where there's references and there's use cases. And there were coupling education that specific rest of solutions. You know HP can do all of those things, and that's very important. Because in this new world, no one can go it alone anymore. It takes it takes partnerships, and we're all better together. And HP really does embrace that philosophy. And they've been a great partner for us in the Iot space. >>Excellent. Well, Colin and David, thank you so much for joining me today on the Cube Tech data. H p e i o t better together. Thank you so much. It's been a pleasure talking with you. >>Thank you. >>Thank you. Lisa. >>And four Collet and David. I am Lisa Martin. You're watching the Cube's virtual coverage of HP Discover 2020. Thanks for watching. Yeah, yeah, yeah, yeah.

>>live from Las Vegas. It's the Cube covering Splunk dot com. 19. Brought to you by spunk >>Welcome back Everyone's two cubes Live coverage from Las Vegas. Four Splunk dot com 2019 The 10th anniversary of their and user conference I'm John Free host of the key that starts seventh year covering Splunk Riding the wave of Big Data Day three of our three days were winding down. Our show are great to have on next guest Didn't Medoc executive director be Ibis Intelligence? Advanced Data Analytics at Clemson University Big A C C. Football team Everyone knows that. Great stadium. Great to have you on. Thanks for spending the time to come by and on Day three coverage. >>Thanks, John, for having me over. >>So, you know, hospitals, campuses, some use cases just encapsulate the digital opportunities and challenges. But you guys air have that kind of same thing going on. You got students, you got people who work there. You got a I ot or campus to campus is you guys are living the the real life example of physical digital coming together. Tell us about what's going on in your world that Clemson wouldn't your job there. What's your current situation? >>So, like you mentioned, we have a lot of students. So Clemson's about 20,000 undergraduate, children's and 5000 graduate students way faculty and staff. So you're talking about a lot of people every semester. We have new devices coming in. We have to support the entire network infrastructure, our student information systems on and research computing. So way we're focused on how convene make students lives better than experience. Better on how convene facilitated education for them. So way try toe in my role. Specifically, I'm responsible for the advanced eight analytics, the data that we're collecting from our systems. How can we? How can you use that on get more insides for better decision making? So that's that's >>Is a scope university wide, or is it specifically targeted for certain areas? >>So it does interest divide. So we have. We have some key projects going on University wide way, have a project for sure and success. There's a project for space utilization and how how, how we can utilize space and campus more efficiently. And then we're looking at energy energy usage across buildings campus emergency management idea. So we've got a couple of projects, and then Pettersson projects that most hired edge motion overseas work on this father's retention enrollment, graduation rates. How how the academics are. So so we're doing the same thing. >>What's interesting is that the new tagline for Splunk is data to everything. You got a lot of things. Their data. Ah, lot of horizontal use cases. So it seems to me that you have, ah, view and we're kind of talking on camera before we went live here was Dana is a fluid situation is not like just a subsystem. It's gotta be every native everywhere in the organization on touched, touches everything. How do you guys look at the data? Because you want to harness the data? Because data getting gathering on, say, energy. Your specialization might be great data to look at endpoint protection, for instance. I don't know. I'm making it up, but data needs to be workable. Cross. How do you view that? What's what's the state of the art thinking around data everywhere? >>So the key thing is, we've got so many IOC's. We've got so many sensors, we've got so many servers, it's it's hard when you work with different technologies to sort of integrate all of them on in the industry that have bean Some some software companies that try to view themselves as being deking, but really the way to dress it does you look at each system, you look at how you can integrate all of that, all of that data without being deking. So you basically analyze the data from different systems. You figured out a way to get it into a place where you can analyze it on, then make decisions based on that. So so that's essentially what we've been focused on. Working on >>Splunk role in all this is because one of things that we've been doing spot I've been falling spunk for a long time in a very fascinated with law. How they take log files and make make value out of that. And their vision now is that Grew is grow is they're enabling a lot of value of the data which I love. I think it's a mission that's notable, relevant and certainly gonna help a lot of use cases. But their success has been about just dumping data on display and then getting value out of it. How does that translate into this kind of data space that you're looking at, because does it work across all areas? What should what specifically are you guys doing with Splunk and you talk about the case. >>So we're looking at it as a platform, like, how can we provide ah self service platform toe analysts who can who can go into system, analyze the data way not We're not focusing on a specific technology, so our platform is built up of multiple technologies. We have tableau for visual analytics. We're also using Splunk. We also have a data warehouse. We've got a lot of databases. We have a Kafka infrastructure. So how can we integrate all of these tools and give give the choice to the people to use the tools, the place where we really see strong helping us? Originally in our journey when we started, our network team used to long for getting log data from switches. It started off troubleshooting exercise of a switch went down. You know what was wrong with it? Eventually we pulled in all for server logs. That's where security guard interested apart from the traditional idea of monitoring security, saw value in the data on. And then we talked about the whole ecosystem. That that's one provides. It gives you a way to bring in data withdrawal based access control so you can have data in a read only state that you can change when it's in the system and then give access to people to a specific set of data. So so that's that's really game changing, even for us. Like having having people be comfortable to opening data to two analysts for so that they can make better decisions. That's that's the key with a lot of product announcements made during dot com, I think the exciting thing is it's Nargis, the data that you index and spunk anymore, especially with the integration with With Dew and s three. You don't have to bring in your data in response. So even if you have your data sitting in history, our audio do cluster, you can just use the data fabric search and Sarge across all your data sets. And from what I hear that are gonna be more integrations that are gonna be added to the tool. So >>that's awesome. Well, that's a good use. Case shows that they're thinking about it. I got to ask you about Clemson to get into some of the things that you guys do in knowing Clemson. You guys have a lot of new things. You do your university here, building stuff here, you got people doing research. So you guys are bringing on new stuff, The network, a lot of new technology. Is there security concerns in terms of that, How do you guys handle that? Because you want to encourage innovation, students and faculty at the same time. You want gonna have the data to make sure you get the security without giving away the security secrets are things that you do. How do you look at the data when you got an environment that encourages people to put more stuff on the network to generate more data? Because devices generate data project, create more data. How do you view that? How do you guys handle that? >>So our mission and our goal is not to disrupt the student experience. Eso we want to make it seem less. And as we as we get influx of students every semester, we have way have challenges that the traditional corporate sector doesn't have. If you think about our violence infrastructure. We're talking about 20 25,000 students on campus. They're moving around. When, when? When they move from one class to another, they're switching between different access points. So having a robust infrastructure, how can we? How can we use the data to be more proactive and build infrastructure that's more stable? It also helps us plan for maintenance is S O. We don't destruct. Children's so looking at at key usage patterns. How what time's Our college is more active when our submissions happening when our I. D. Computing service is being access more and then finding out the time, which is gonna be less disruptive, do the students. So that's that's how we what's been >>the biggest learnings and challenges that you've overcome or opportunities that you see with data that Clemson What's the What's the exciting areas and or things that you guys have tripped over on, or what I have learned from? We'll share some experiences of what's going on in there for you, >>So I think Sky's the limit here. Really like that is so much data and so less people in the industry, it's hard to analyze all of the data and make sense of it. And it's not just the people who were doing the analysis. You also need people who understand the data. So the data, the data stores, the data trustees you need you need buy in from them. They're the ones who understand what data looks like, how how it should be structured, how, how, how it can be provided for additional analysis s Oh, that's That's the key thing. What's >>the coolest thing you're working on right now? >>So I'm specifically working on analyzing data from our learning management system canvas. So we're getting data informer snapshots that we're trying to analyze, using multiple technologies for that spunk is one of them. But we're loading the data, looking at at key trends, our colleges interacting, engaging with that elements. How can we drive more adoption? How can we encourage certain colleges and departments, too sort of moved to a digital classroom Gordon delivery experience. >>I just l a mess part of the curriculum in gym or online portion? Or is it integrated into the physical curriculum? >>So it's at this time it's more online, But are we trying to trying to engage more classes and more faculty members to use the elements to deliver content. So >>right online, soon to be integrated in Yeah, you know, I was talking with Dawn on our team from the Cube and some of the slum people this week. Look at this event. This is a physical event. Get physical campuses digitizing. Everything is kind of a nirvana. It's kind of aspiration is not. People aren't really doing 100% but people are envisioning that the physical and digital worlds are coming together. If that happens and it's going to happen at some point, it's a day that problem indeed, Opportunity date is everything right? So what's your vision of that as a professional or someone in the industry and someone dealing with data Clemson Because you can digitize everything, Then you can instrument everything of your instrument, everything you could start creating an official efficiencies and innovations. >>Yes, so the way I think you you structure it very accurately. It's amalgam of the physical world and the digital world as the as the as the world is moving towards using more more of smartphones and digital devices, how how can we improve experience by by analyzing the data on and sort of be behind the scenes without even having the user. The North is what's going on trading expedience. If the first expedience is in good that the user has, they're not going to be inclined to continue using the service that we offer. >>What's your view on security now? Splunk House League has been talking about security for a long time. I think about five years ago we started seeing the radar data. Is driving a lot of the cyber security now is ever Everyone knows that you guys have a lot of endpoints. Security's always a concern. How do you guys view the security of picture with data? How do you guys talk about that internally? How do you guys implement data without giving me a secret? You know, >>way don't have ah ready Good Cyber Security Operation Center. That's run by students on. And they do a tremendous job protecting our environment. Way monitored. A lot of activity that goes on higher I deserve is a is a challenge because way have in the corporate industry, you can you can have a set of devices in the in the higher education world We have students coming in every semester that bringing in new, important devices. It causes some unique set of challenges knowing where devices are getting on the network. If if there's fishing campaigns going on, how can be, How can we protect that environment and those sort of things? >>It is great to have you on. First of all, love to have folks from Clemson ons great great university got a great environment. Great Great conversation. Congratulations on all your success on their final question for you share some stories around some mischief that students do because students or students, you know, they're gonna get on the network and most things down. Like when when I was in school, when we were learning they're all love coding. They're all throwing. Who knows? Kitty scripts out there hosting Blockchain mining algorithms. They gonna cause some creek. Curiosity's gonna cause potentially some issues. Um, can you share some funny or interesting student stories of caught him in the dorm room, but a server in there running a Web farm? Is there any kind of cool experiences you can share? That might be interesting to folks that students have done that have been kind of funny mistress, but innovative. >>So without going into Thio, I just say, Like most universities, we have, we have students and computer science programs and people who were programmers and sort of trying to pursue the security route in the industry. So they, um, way also have a lot of research going on the network on. And sometimes research going on may affect our infrastructure environment. So we tried toe account for those use cases and on silo specific use cases and into a dedicated network. >>So they hit the honeypot a lot. They're freshmen together. I'll go right to the kidding, of course. >>Yes. So way do we do try to protect that environment on Dhe. Makes shooting experience better. >>I know you don't want to give any secrets. Thanks for coming on. I always find a talk tech with you guys. Thanks so much appreciated. Okay. Cube coverage. I'm shot for a year. Day three of spunk dot com for more coverage after this short break

>> From Cambridge, Massachusetts, it's theCUBE covering MIT Chief Data Officer and Information Quality Symposium 2019 brought to you by SiliconANGLE Media. (upbeat techno music) >> Hi everybody, welcome back to Cambridge, Massachusetts. You're watching theCUBE, the leader in tech coverage. We go out to the events. We extract the signal from the noise, and we're here at the MIT CDOIQ Conference, Chief Data Officer Information Quality Conference. It is the 13th year here at the Tang building. We've outgrown this building and have to move next year. It's fire marshal full. Gokula Mishra is here. He is the Senior Director of Global Data and Analytics and Supply Chain-- >> Formerly. Former, former Senior Director. >> Former! I'm sorry. It's former Senior Director of Global Data Analytics and Supply Chain at McDonald's. Oh, I didn't know that. I apologize my friend. Well, welcome back to theCUBE. We met when you were at Oracle doing data. So you've left that, you're on to your next big thing. >> Yes, thinking through it. >> Fantastic, now let's start with your career. You've had, so you just recently left McDonald's. I met you when you were at Oracle, so you cut over to the dark side for a while, and then before that, I mean, you've been a practitioner all your life, so take us through sort of your background. >> Yeah, I mean my beginning was really with a company called Tata Burroughs. Those days we did not have a lot of work getting done in India. We used to send people to U.S. so I was one of the pioneers of the whole industry, coming here and working on very interesting projects. But I was lucky to be working on mostly data analytics related work, joined a great company called CS Associates. I did my Master's at Northwestern. In fact, my thesis was intelligent databases. So, building AI into the databases and from there on I have been with Booz Allen, Oracle, HP, TransUnion, I also run my own company, and Sierra Atlantic, which is part of Hitachi, and McDonald's. >> Awesome, so let's talk about use of data. It's evolved dramatically as we know. One of the themes in this conference over the years has been sort of, I said yesterday, the Chief Data Officer role emerged from the ashes of sort of governance, kind of back office information quality compliance, and then ascended with the tailwind of the Big Data meme, and it's kind of come full circle. People are realizing actually to get value out of data, you have to have information quality. So those two worlds have collided together, and you've also seen the ascendancy of the Chief Digital Officer who has really taken a front and center role in some of the more strategic and revenue generating initiatives, and in some ways the Chief Data Officer has been a supporting role to that, providing the quality, providing the compliance, the governance, and the data modeling and analytics, and a component of it. First of all, is that a fair assessment? How do you see the way in which the use of data has evolved over the last 10 years? >> So to me, primarily, the use of data was, in my mind, mostly around financial reporting. So, anything that companies needed to run their company, any metrics they needed, any data they needed. So, if you look at all the reporting that used to happen it's primarily around metrics that are financials, whether it's around finances around operations, finances around marketing effort, finances around reporting if it's a public company reporting to the market. That's where the focus was, and so therefore a lot of the data that was not needed for financial reporting was what we call nowadays dark data. This is data we collect but don't do anything with it. Then, as the capability of the computing, and the storage, and new technologies, and new techniques evolve, and are able to handle more variety and more volume of data, then people quickly realize how much potential they have in the other data outside of the financial reporting data that they can utilize too. So, some of the pioneers leverage that and actually improved a lot in their efficiency of operations, came out with innovation. You know, GE comes to mind as one of the companies that actually leverage data early on, and number of other companies. Obviously, you look at today data has been, it's defining some of the multi-billion dollar company and all they have is data. >> Well, Facebook, Google, Amazon, Microsoft. >> Exactly. >> Apple, I mean Apple obviously makes stuff, but those other companies, they're data companies. I mean largely, and those five companies have the highest market value on the U.S. stock exchange. They've surpassed all the other big leaders, even Berkshire Hathaway. >> So now, what is happening is because the market changes, the forces that are changing the behavior of our consumers and customers, which I talked about which is everyone now is digitally engaging with each other. What that does is all the experiences now are being captured digitally, all the services are being captured digitally, all the products are creating a lot of digital exhaust of data and so now companies have to pay attention to engage with their customers and partners digitally. Therefore, they have to make sure that they're leveraging data and analytics in doing so. The other thing that has changed is the time to decision to the time to act on the data inside that you get is shrinking, and shrinking, and shrinking, so a lot more decision-making is now going real time. Therefore, you have a situation now, you have the capability, you have the technology, you have the data now, you have to make sure that you convert that in what I call programmatic kind of data decision-making. Obviously, there are people involved in more strategic decision-making. So, that's more manual, but at the operational level, it's going more programmatic decision-making. >> Okay, I want to talk, By the way, I've seen a stat, I don't know if you can confirm this, that 80% of the data that's out there today is dark data or it's data that's behind a firewall or not searchable, not open to Google's crawlers. So, there's a lot of value there-- >> So, I would say that percent is declining over time as companies have realized the value of data. So, more and more companies are removing the silos, bringing those dark data out. I think the key to that is companies being able to value their data, and as soon as they are able to value their data, they are able to leverage a lot of the data. I still believe there's a large percent still not used or accessed in companies. >> Well, and of course you talked a lot about data monetization. Doug Laney, who's an expert in that topic, we had Doug on a couple years ago when he, just after, he wrote Infonomics. He was on yesterday. He's got a very detailed prescription as to, he makes strong cases as to why data should be valued like an asset. I don't think anybody really disagrees with that, but then he gave kind of a how-to-do-it, which will, somewhat, make your eyes bleed, but it was really well thought out, as you know. But you talked a lot about data monetization, you talked about a number of ways in which data can contribute to monetization. Revenue, cost reduction, efficiency, risk, and innovation. Revenue and cost is obvious. I mean, that's where the starting point is. Efficiency is interesting. I look at efficiency as kind of a doing more with less but it's sort of a cost reduction, but explain why it's not in the cost bucket, it's different. >> So, it is first starts with doing what we do today cheaper, better, faster, and doing more comes after that because if you don't understand, and data is the way to understand how your current processes work, you will not take the first step. So, to take the first step is to understand how can I do this process faster, and then you focus on cheaper, and then you focus on better. Of course, faster is because of some of the market forces and customer behavior that's driving you to do that process faster. >> Okay, and then the other one was risk reduction. I think that makes a lot of sense here. Actually, let me go back. So, one of the key pieces of it, of efficiency is time to value. So, if you can compress the time, or accelerate the time and you get the value that means more cash in house faster, whether it's cost reduction or-- >> And the other aspect you look at is, can you automate more of the processes, and in that way it can be faster. >> And that hits the income statement as well because you're reducing headcount cost of your, maybe not reducing headcount cost, but you're getting more out of different, out ahead you're reallocating them to more strategic initiatives. Everybody says that but the reality is you hire less people because you just automated. And then, risk reduction, so the degree to which you can lower your expected loss. That's just instead thinking in insurance terms, that's tangible value so certainly to large corporations, but even midsize and small corporations. Innovation, I thought was a good one, but maybe you could use an example of, give us an example of how in your career you've seen data contribute to innovation. >> So, I'll give an example of oil and gas industry. If you look at speed of innovation in the oil and gas industry, they were all paper-based. I don't know how much you know about drilling. A lot of the assets that goes into figuring out where to drill, how to drill, and actually drilling and then taking the oil or gas out, and of course selling it to make money. All of those processes were paper based. So, if you can imagine trying to optimize a paper-based innovation, it's very hard. Not only that, it's very, very by itself because it's on paper, it's in someone's drawer or file. So, it's siloed by design and so one thing that the industry has gone through, they recognize that they have to optimize the processes to be better, to innovate, to find, for example, shale gas was a result output of digitizing the processes because otherwise you can't drill faster, cheaper, better to leverage the shale gas drilling that they did. So, the industry went through actually digitizing a lot of the paper assets. So, they went from not having data to knowingly creating the data that they can use to optimize the process and then in the process they're innovating new ways to drill the oil well cheaper, better, faster. >> In the early days of oil exploration in the U.S. go back to the Osage Indian tribe in northern Oklahoma, and they brilliantly, when they got shuttled around, they pushed him out of Kansas and they negotiated with the U.S. government that they maintain the mineral rights and so they became very, very wealthy. In fact, at one point they were the wealthiest per capita individuals in the entire world, and they used to hold auctions for various drilling rights. So, it was all gut feel, all the oil barons would train in, and they would have an auction, and it was, again, it was gut feel as to which areas were the best, and then of course they evolved, you remember it used to be you drill a little hole, no oil, drill a hole, no oil, drill a hole. >> You know how much that cost? >> Yeah, the expense is enormous right? >> It can vary from 10 to 20 million dollars. >> Just a giant expense. So, now today fast-forward to this century, and you're seeing much more sophisticated-- >> Yeah, I can give you another example in pharmaceutical. They develop new drugs, it's a long process. So, one of the initial process is to figure out what molecules this would be exploring in the next step, and you could have thousand different combination of molecules that could treat a particular condition, and now they with digitization and data analytics, they're able to do this in a virtual world, kind of creating a virtual lab where they can test out thousands of molecules. And then, once they can bring it down to a fewer, then the physical aspect of that starts. Think about innovation really shrinking their processes. >> All right, well I want to say this about clouds. You made the statement in your keynote that how many people out there think cloud is cheaper, or maybe you even said cheap, but cheaper I inferred cheaper than an on-prem, and so it was a loaded question so nobody put their hand up they're afraid, but I put my hand up because we don't have any IT. We used to have IT. It was a nightmare. So, for us it's better but in your experience, I think I'm inferring correctly that you had meant cheaper than on-prem, and certainly we talked to many practitioners who have large systems that when they lift and shift to the cloud, they don't change their operating model, they don't really change anything, they get a bill at the end of the month, and they go "What did this really do for us?" And I think that's what you mean-- >> So what I mean, let me make it clear, is that there are certain use cases that cloud is and, as you saw, that people did raise their hand saying "Yeah, I have use cases where cloud is cheaper." I think you need to look at the whole thing. Cost is one aspect. The flexibility and agility of being able to do things is another aspect. For example, if you have a situation where your stakeholder want to do something for three weeks, and they need five times the computing power, and the data that they are buying from outside to do that experiment. Now, imagine doing that in a physical war. It's going to take a long time just to procure and get the physical boxes, and then you'll be able to do it. In cloud, you can enable that, you can get GPUs depending on what problem we are trying to solve. That's another benefit. You can get the fit for purpose computing environment to that and so there are a lot of flexibility, agility all of that. It's a new way of managing it so people need to pay attention to the cost because it will add to the cost. The other thing I will point out is that if you go to the public cloud, because they make it cheaper, because they have hundreds and thousands of this canned CPU. This much computing power, this much memory, this much disk, this much connectivity, and they build thousands of them, and that's why it's cheaper. Well, if your need is something that's very unique and they don't have it, that's when it becomes a problem. Either you need more of those and the cost will be higher. So, now we are getting to the IOT war. The volume of data is growing so much, and the type of processing that you need to do is becoming more real-time, and you can't just move all this bulk of data, and then bring it back, and move the data back and forth. You need a special type of computing, which is at the, what Amazon calls it, adds computing. And the industry is kind of trying to design it. So, that is an example of hybrid computing evolving out of a cloud or out of the necessity that you need special purpose computing environment to deal with new situations, and all of it can't be in the cloud. >> I mean, I would argue, well I guess Microsoft with Azure Stack was kind of the first, although not really. Now, they're there but I would say Oracle, your former company, was the first one to say "Okay, we're going to put the exact same infrastructure on prem as we have in the public cloud." Oracle, I would say, was the first to truly do that-- >> They were doing hybrid computing. >> You now see Amazon with outposts has done the same, Google kind of has similar approach as Azure, and so it's clear that hybrid is here to stay, at least for some period of time. I think the cloud guys probably believe that ultimately it's all going to go to the cloud. We'll see it's going to be a long, long time before that happens. Okay! I'll give you last thoughts on this conference. You've been here before? Or is this your first one? >> This is my first one. >> Okay, so your takeaways, your thoughts, things you might-- >> I am very impressed. I'm a practitioner and finding so many practitioners coming from so many different backgrounds and industries. It's very, very enlightening to listen to their journey, their story, their learnings in terms of what works and what doesn't work. It is really invaluable. >> Yeah, I tell you this, it's always a highlight of our season and Gokula, thank you very much for coming on theCUBE. It was great to see you. >> Thank you. >> You're welcome. All right, keep it right there everybody. We'll be back with our next guest, Dave Vellante. Paul Gillin is in the house. You're watching theCUBE from MIT. Be right back! (upbeat techno music)

>> live from San Francisco, California. It's the key. You covering the IBM chief Data officer? Someone brought to you by IBM. >> We're back at the IBM CDO conference. Fisherman's Worf Worf in San Francisco. You're watching the Cube, the leader in life tech coverage. My name is David Dante. Glenn Finches. Here's the global leader of Big Data Analytics and IBM, and we're pleased to have Mark Clare. He's the head of data enablement at AstraZeneca. Gentlemen, welcome to the Cube. Thanks for coming on my mark. I'm gonna start with this head of data Data Enablement. That's a title that I've never heard before. And I've heard many thousands of titles in the Cube. What is that all about? >> Well, I think it's the credit goes to some of the executives at AstraZeneca when they recruited me. I've been a cheap date officer. Several the major financial institutions, both in the U. S. And in Europe. Um, AstraZeneca wanted to focus on how we actually enable our business is our science areas in our business is so it's not unlike a traditional CDO role, but we focus a lot more on what the enabling functions or processes would be >> So it sounds like driving business value is really the me and then throw. Sorry. >> I've always looked at this role in three functions value, risk and cost. So I think that in any CDO role, you have to look at all three. I think the you'd slide it if you didn't. This one with the title. Obviously, we're looking at quite a bit at the value we will drive across the the firm on how to leverage our date in a different way. >> I love that because you can quantify all three. All right, Glenn. So you're the host of this event. So awesome. I love that little presentation that you gave. So for those you didn't see it, you gave us pay stubs and then you gave us a website and said, Take a picture of the paste up, uploaded, and then you showed how you're working with your clients. Toe. Actually digitize that and compress all kinds of things. Time to mortgage origination. Time to decision. So explain that a little bit. And what's that? What's the tech behind that? And how are people using it? You know, >> for three decades, we've had this OCR technology where you take a piece of paper, you tell the machine what's on the paper. What longitudinal Enter the coordinates are and you feed it into the hope and pray to God that it isn't in there wrong. The form didn't change anything like that. That's what that's way. We've lived for three decades with cognitive and a I, but I read things like the human eye reads things. And so you put the page in and the machine comes back and says, Hey, is this invoice number? Hey, is this so security number? That's how you train it as compared to saying, Here's what it So we use this cognitive digitization capability to grab data that's locked in documents, and then you bring it back to the process so that you can digitally re imagine the process. Now there's been a lot of use of robotics and things like that. I'm kind of taken existing processes, and I'm making them incrementally. Better write This says look, you now have the data of the process. You can re imagine it. However, in fact, the CEO of our client ADP said, Look, I want you to make me a Netflix, not a blood Urbach Blockbuster, right? So So it's a mind shift right to say we'll use this data will read it with a I will digitally re imagine the process. And it usually cuts like 70 or 80% of the cycle time, 50 to 75% of the cost. I mean, it's it's pretty groundbreaking when you see it. >> So markets ahead of data neighborhood. You hear something like that and you're not. You're not myopically focused on one little use case. You're taking a big picture of you doing strategies and trying to develop a broader business cases for the organization. But when you see an example like that and many examples out there, I'm sure the light bulbs go off. So >> I wrote probably 10 years cases down while >> Glenn was talking about you. You do get tactical, Okay, but but But where do you start when you're trying to solve these problems? >> Well, I look att, Glenn's example, And about five and 1/2 years ago, Glenn was one I went to had gone to a global financial service, firms on obviously having scale across dozens of countries, and I had one simple request. Thio Glenn's team as well as a number of other technology companies. I want cognitive intelligence for on data in Just because the process is we've had done for 20 years just wouldn't scale not not its speed across many different languages and cultures. And I now look five and 1/2 years later, and we have beginning of, I would say technology opportunities. When I asked Glenn that question, he was probably the only one that didn't think I had horns coming out of my head, that I was crazy. I mean, some of the leading technology firms thought I was crazy asking for cognitive data management capabilities, and we are five and 1/2 years later and we're seeing a I applied not just on the front end of analytics, but back in the back end of the data management processes themselves started automate. So So I look, you know, there's a concept now coming out day tops on date offices. You think of what Dev Ops is. It's bringing within our data management processes. It's bringing cognitive capabilities to every process step, And what level of automation can we do? Because the, you know, for typical data science experiment 80 to 90% of that work Estate engineering. If I can automate that, then through a date office process, then I could get to incite much faster, but not in scale it and scale a lot more opportunities and have to manually do it. So I I look at presentations and I think, you know, in every aspect of our business, where we clear could we apply >> what you talk about date engineering? You talk about data scientist spending his or her time just cleaning the wrangling data, All the all the not fun stuff exactly plugging in cables back in the infrastructure date. >> You're seeing horror stories right now. I heard from a major academic institution. A client came to them and their data scientists. They had spent several years building. We're spending 99% of their time trying to cleanse and prep data. They were spend 90% cleansing and prepping, and of the remaining 10% 90% of that fixing it where they fix it wrong and the first time so they had 1% of their job doing their job. So this is a huge opportunity. You can start automating more of that and actually refocusing data science on data >> science. So you've been a chief data officer number of financial institutions. You've got this kind of cool title now, which touches on some of the things a CDO might do and your technical. We got a technical background. So when you look a lot of the what Ginny Rometty calls incumbents, call them incumbent Disruptors two years ago at Ivy and think they've got data that has been hardened, you know, in all these projects and use cases and it's locked and people talk about the silos, part of your role is to figure out Okay, how do we get that data out? Leverage. It put it at the core. Is that is that fair? >> Well, and I'm gonna stay away from the word core cause to make core Kenan for kind of legacy processes of building a single repositories single warehouse, which is very time consuming. So I think I can I leave it where it is, but find a wayto to unify it. >> Not physically, exactly what I say. Corny, but actually the court, that's what we need >> to think about is how to do this logically and cream or of Ah unification approach that has speed and agility with it versus the old physical approaches, which took time. And resource is >> so That's a that's a computer science problem that people have been trying to solve for years. Decentralized, distributed, dark detectors, right? And why is it that we're now able Thio Tap your I think it's >> a perfect storm of a I of Cloud, the cloud native of Io ti, because when you think of I o. T, it's a I ot to be successful fabric that can connect millions of devices or millions of sensors. So you'd be paired those three with the investment big data brought in the last seven or eight years and big data to me. Initially, when I started talking to companies in the Valley 10 years ago, the early days of, um, apparatus, what I saw or companies and I could get almost any of the digital companies in the valley they were not. They were using technology to be more agile. They were finding agile data science. Before we call the data signs the map produce and Hadoop, we're just and after almost not an afterthought. But it was just a mechanism to facilitate agility and speed. And so if you look at how we built out all the way up today and all the convergence of all these new technologies, it's a perfect storm to actually innovate differently. >> Well, what was profound about my producing in the dupe? It was like leave the data where it is and shipped five megabytes a code two upended by the data and that you bring up a good point. We've now, we spent 10 years leveraging that at a much lower cost. And you've got the cloud now for scale. And now machine intelligence comes in that you can apply in the data causes. Bob Pityana once told me, Data's plentiful insights aren't Amen to that. So Okay, so this is really interesting discussion. You guys have known each other for a couple of couple of decades. How do you work together toe to solve problems Where what is that conversation like, Do >> you want to start that? >> So, um, first of all, we've never worked together on solving small problems, not commodity problems. We would usually tackle something that someone would say would not be possible. So normally Mark is a change agent wherever he goes. And so he usually goes to a place that wants to fix something or change something in an abnormally short amount of time for an abnormally small amount of money. Right? So what's strange is that we always find that space together. Mark is very judicious about using us as a service is firm toe help accelerate those things. But then also, we build in a plan to transition us away in transition, in him into full ownership. Right. But we usually work together to jump start one of these wicked, hard, wicked, cool things that nobody else >> was. People hate you. At first. They love you. I would end the one >> institution and on I said, OK, we're going to a four step plan. I'm gonna bring the consultants in day one while we find Thailand internally and recruit talent External. That's kind of phases one and two in parallel. And then we're gonna train our talent as we find them, and and Glenn's team will knowledge transfer, and by face for where, Rayna. And you know, that's a model I've done successfully in several organizations. People can. I hated it first because they're not doing it themselves, but they may not have the experience and the skills, and I think as soon as you show your staff you're willing to invest in them and give them the time and exposure. The conversation changes, but it's always a little awkward. At first, I've run heavy attrition, and some organizations at first build the organizations. But the one instance that Glen was referring to, we came in there and they had a 4 1 1 2 1 12 to 15 year plan and the C I O. Looked at me, he says. I'll give you two years. I'm a bad negotiator. I got three years out of it and I got a business case approved by the CEO a week later. It was a significant size business case in five minutes. I didn't have to go back a second or third time, but we said We're gonna do it in three years. Here's how we're gonna scale an organization. We scaled more than 1000 person organization in three years of talent, but we did it in a planned way and in that particular organization, probably a year and 1/2 in, I had a global map of every data and analytics role I need and I could tell you were in the US they set and with what competitors earning what industry and where in India they set and in what industry And when we needed them. We went out and recruited, but it's time to build that. But you know, in any really period, I've worked because I've done this 20 plus years. The talent changes. The location changes someone, but it's always been a challenge to find him. >> I guess it's good to have a deadline. I guess you did not take the chief data officer role in your current position. Explain that. What's what. What's your point of view on on that role and how it's evolved and how it's maybe being used in ways that don't I >> mean, I think that a CDO, um on during the early days, there wasn't a definition of a matter of fact. Every time I get a recruiter, call me all. We have a great CDO row for first time I first thing I asked him, How would you define what you mean by CDO? Because I've never seen it defined the same way into cos it's just that way But I think that the CDO, regardless of institutions, responsibility end in to make sure there's an Indian framework from strategy execution, including all of the governance and compliance components, and that you have ownership of each piece in the organization. CDO most companies doesn't own all of that, but I think they have a responsibility and too many organizations that hasn't occurred. So you always find gaps and each organization somewhere between risk costs and value, in terms of how how they're, how the how the organization's driving data and in my current role. Like I said, I wanted to focus. We want the focus to really be on how we're enabling, and I may be enabling from a risk and compliance standpoint, Justus greatly as I'm enabling a gross perspective on the business or or cost management and cost reductions. We have been successful in several programs for self funding data programs for multi gears. By finding and costs, I've gone in tow several organizations that it had a decade of merger after merger and Data's afterthought in almost any merger. I mean, there's a Data Silas section session tomorrow. It'd be interesting to sit through that because I've found that data data is the afterthought in a lot of mergers. But yet I knew of one large health care company. They've made data core to all of their acquisitions, and they was one the first places they consolidated. And they grew faster by acquisition than any of their competitors. So I think there's a There's a way to do it correctly. But in most companies you go in, you'll find all kinds of legacy silos on duplication, and those are opportunities to, uh, to find really reduce costs and self fund. All the improvements, all the strategic programs you wanted, >> a number inferring from the Indian in the data roll overlaps or maybe better than gaps and data is that thread between cost risk. And it is >> it is. And I've been lucky in my career. I've report toe CEOs. I reported to see Yellows, and I've reported to CEO, so I've I've kind of reported in three different ways, and each of those executives really looked at it a little bit differently. Value obviously is in a CEO's office, you know, compliance. Maurizio owes office and costs was more in the c i o domain, but you know, we had to build a program looking >> at all three. >> You know, I think this topic, though, that we were just talking about how these rules are evolving. I think it's it's natural, because were about 5 2.0. to 7 years into the evolution of the CDO, it might be time for a CDO Um, and you see Maur CEOs moving away from pure policy and compliance Tomb or value enablement. It's a really hard change, and that's why you're starting to Seymour turnover of some of the studios because people who are really good CEOs at policy and risk and things like that might not be the best enablers, right? So I think it's pretty natural evolution. >> Great discussion, guys. We've got to leave it there, They say. Data is the new oil date is more valuable than oil because you could use data to reduce costs to reduce risk. The same data right toe to drive revenue, and you can't put a gallon of oil in your car and a quart of oil in the car quarter in your house of data. We think it's even more valuable. Gentlemen, thank you so much for coming on the cues. Thanks so much. Lot of fun. Thanks. Keep right, everybody. We'll be back with our next guest. You're watching the Cube from IBM CDO 2019 right back.

>> Live from Las Vegas, it's theCUBE. Covering Dell Technologies World 2019. Brought to you by Dell Technologies and its ecosystem partners. >> Welcome back everyone to theCUBE's live coverage of Dell World Technologies here in Las Vegas. I'm your host, Rebecca Knight along with my cohost, Stu Miniman. We have two guests for this segment. We have Honore LaBourdette. She is the VP, Global Market Development, Telco Business Group. Welcome, VMware, thank you, sorry. >> Thank you, yes. >> Welcome. And we have Kevin Shatzkamer, Senior VP, Networking and Solutions, Dell EMC. Thank you both so much for coming on the show. >> Our pleasure. >> Thank you. >> So Kevin I want to start with you. There was a big announcement this morning, signing with Orange of France. Tell our viewers a little bit more about this. >> Yeah, sure. So, I think as overall Dell Technologies continues to focus on helping our service providers through what is a very complex transition, both in their business, in their operations, in their technology investments, in the operational skill set gaps, in the business models, the architecture's use cases kind of comes across the board of how their businesses are evolving. What we continue to do is focus on a core set of telecommunication service providers that we can partner with very deeply to help in that transformation and use the knowledge gained through that collaboration as a means to expand the Dell Technologies capabilities globally. So, I think that the belief is that when we help solve problems, it not only benefits the service provider we're working with, it benefits the industry as a whole with the lessons learned, so that we can then contribute back. >> And so far, there's been some enthusiasm about this? >> There certainly has. I think it's been a big day for us. Obviously, the first two days at Dell Technologies World, we're extremely focused on new product introductions across the Dell portfolio, and today, with the opportunity to expand the messaging and announce some of the great things we're doing with partners, we're doing with out customers, and we're doing within the ecosystem, I think we continue to drive a very positive message. >> Honore, the networking component is something that we know service providers have a need and is ever-changing. We've watched that expand greatly in the VMware portfolio over the years. I've done plenty of interviews with telcos talking about things like NFV, network functions virtualization, but the big thing everybody's been talking about, the last couple of years it feels like, is 5G. So, maybe we could start there, but talk a little bit about what you're hearing from service providers and how VMware and VMware plus Dell are helping to meet some of those requirements. >> Sure, well, needless to say, 5G is the topic of every conversation we have with our telecommunication customers, and I think that there's a number of areas around 5G that are most prevalent in those conversations. One is really how does the service provider get a return on investment for the huge amounts of monies that they're investing in this infrastructure, right? So, 5G is a new infrastructure, a new technology, that's going to require a refresh of the entire infrastructure. And so, while they're making all of those investments, and they are doing so very aggressively to have a first-mover advantage, in terms of the first to deliver on a 5G technology, they want to work with vendors who can, in fact, accelerate their time to a return on the investment for that infrastructure. So, many of our conversations are really focused around how can we help these service providers actually accomplish that, right? Not just build out, or take advantage of a software-defined infrastructure and all of the technologies that both Dell and WMware offer to them under the umbrella of the Dell Technology Companies, but also, how can we help them accelerate services that they want to to put on top of the 5G technology? I think one of the key differentiators of 5G over its predecessors is that the industry has recognized that it's going to require partnerships in order for the service providers to really get their return on investment. And that's where the partnership with VMware and with Dell and the work that Kevin and I are doing together to focus on service providing is really anchored, right? It's bringing together those partnerships, so that these telecommunication customers can take advantage of our technology and do it very quickly. >> So, there's a real acknowledgement of the need for partnerships? >> Yes. >> So then, how do you show customers that the VMware-Dell partnership is the right direction? >> Well, needless to say, it's anchored in our technology. Kevin and I have been working together for a number of years now, and our partnership really started out focusing on just making sure that the components of the stack worked as promised, right? That we could deliver a high degree of confidence to our customers that when they software-defined the infrastructure on Dell Technology hardware, and then layered on top of that, their virtual network functions, that it would perform our outperform their legacy, bare-metal, vertical-stack equipment. Over time, however, our partnership has progressed to where we're actually collaborating to bring new technology to market together. And one example of that is the City of Las Vegas. We recently announced a Smart City IoT use case, and that technology, that solution, was co-developed with NTT, Dell EMC, and VMware using VMware software, Dell hardware, as well as Dell Storage, Dell Data Analytics and Intelligence, and NTT's infrastructure and points of presence. >> Yeah, I think there's both a technical reality and an operational reality to the technologies that we speak of, right? The technical reality is that the transformation that the telcos are going through around NFV and the direction toward network edge, edge computing, cloud environments, is really just software-defined data center similar to what we've done on the IT side for a long time. So, the technologies that the telecommunications industry is adopting are the technologies that both Dell EMC and VMware have been working on for a very long time. The operational reality is that just taking what you've done in IT and applying it into a telco network is not sufficient. Understanding of the workloads, how those workloads layer on top of infrastructure, understanding that those workloads are in a transformation of their own, and that virtual network functions were not designed to natively consume and compute. They were designed for network appliances, and that there are still requirements that they drive down to the infrastructure was, I think, where Honore and I have been investing for the last several years, right? How do we complement the broad capabilities of both Dell EMC and VMware in IT virtualization software-defined data center, and bring in telco service provider networking expertise and domain knowledge that we can use to be able to really ramp up and accelerate the partnerships we have in the service provider industry? >> That's great stuff. We actually got to do an interview on the smarter cities earlier this week, and a fascinating discussion to see how there's, Kevin, I like what you laid out there. When I look at this space, scale gets talked about a lot, but you talk to telcos, they have a little bit of a different scale, and the management for these kind of environments is also quite a bit different than if you were talking to the enterprise. Are those some of the key items? Where would you say your focus? >> I also think that even further. That the challenges of scale that have been solved in the public cloud are a different set of challenges than the telco industry is really trying to wrestle with, right? In the public cloud, we're taking about a very small number of facilities, and we can build a homogenous architecture within there. We define a standard server. We replicate that server across a rack, replicate that rack across rows, replicate those rows across a data center. The reality is, as we get further and further towards the edge of the telco network, it looks more heterogeneous, right? I need GPUs for particular instances. I have cloud-native applications. I have virtualized applications that sit inside of VMs. I have native Linux environments. I need to handle dense networking topologies. I have east-west traffic, north-south traffic that I need to take into account. And I think that what we've figured out and what we've learned in automating and orchestrating the public cloud is how to handle hundreds of thousands of things at single-digit number of locations. And what we're talking about here is hundreds of thousands of locations with single-digit number of things. >> And that's another key area of the collaboration between the two groups, in terms of how we deliver value to our telco customers. So, rather than us working in silos and delivering yet another disparate technology for managing the edge, cloud, or all these different locations, we're working together so we can bring a cohesive technology to market for them. >> That's right, I think the infrastructure demands and openness and a willingness to be a productive member of a complex and consistently changing ecosystem, and I think that, obviously, Dell EMC does that in our way. VMware does it in their way, but there's clear recognition that the better capabilities are when we work together to really drive the platform and bring the true capabilities of the broader Dell technologies together. >> So, telcoms is a hugely competitive industry, and as you've talked about, there's a lot of challenges, and it's a real transformative moment for this sector. Can you lay out some of sort of what you're thinking about for after 5G, which as you've said, is a hugely expensive investment for these companies? But sort of post-5G, what are we looking at? What's on their minds of your customers? >> So, I don't know that there's going to be distinct, post-5G event, right? I think that 5G, in and of itself, is going to take some time to roll out and proliferate, to the extent that its predecessors is now deployed across all locations all over the world. I do think that 5G, in addition to the infrastructure technology, or the refresh of that technology, a lot of what is going to happen around 5G is, in fact, the applications and use cases that's going to take advantage of 5G. If we about what 5G is capable of enabling, it doesn't just address consumer applications. 5G also will address enterprise applications. And that opens up a whole world of innovation, and again, applications, partnerships, and vendors coming together, who can really help the service providers put those pieces together and deliver on those applications. There's already talk about 6G, although it's very limited. So, it's easy for me to say what's coming next after 5G will be 6G, but I think that there's still a lot of activity and a lot of innovation that will happen around 5G for some time to come. >> Yeah, we know that standards and the consortiums always have to be working. I was looking at terabit ethernet on the networking side. So, I wanted to help kind of bring this conversation together. If you have maybe a customer example, love if you could share who it is, but if not, give us a little bit of anonymity around what it is to help highlight this partnership. >> Sure, I think Honore shared the City of Las Vegas as a great example of where we're enabling the Smart Cities use case. We can speak to MetTel, in terms of the capabilities of Dell Technologies to be able to transform their NFV offerings and really help them bring NFV to market at scale. We can speak to at least one tier-one service provider in AMIA that is delivering a full-stack offering, in which we extended the capabilities of our Ericsson partnership that both Dell EMC, as well as VMware have, to build a complete stack offering of Ericsson, VMware, as well as Dell EMC. >> Yeah, and to Ericsson, there's some of the edge computing in there. I've talked to them quite a bit about what they're doing on their edge offering. >> Yeah, so I think we have a number of examples that we also can't share as publicly. But we continue to collaborate. I think we're driving fantastic innovation. The industry is responding extremely favorably across the board, and I think that the strategy that we have jointly to not just develop technology, but really change the way we engage with telecommunications organizations and service providers to work with them well before they're ready to deploy technology, and also, help them scale their own operations and understand this transformation is really key to the success here. Because just having the best technology at this inflection point in the industry is not enough. We really have to partner to help them understand how to operationalize and monetize that infrastructure. >> And we do have a number of innovation projects, with regards to the edge and far edge with some of the top-tier service providers, in particular, in the Americas, where we're working together for edge solutions. I've got to hear what this far-edge is in a future conversation, because I thought I was getting my arms around it, but -- >> I know, it was edge, and now it's edge and far-edge. >> That's for Dell Technologies World 2020. >> That's right. >> Honore, Kevin, thank you both so much for coming on theCUBE. >> My pleasure. >> Thank you. >> Thank you. >> It's a great time. >> You are watching theCUBE's live coverage of Dell Technologies World. There's more to come after this. (upbeat music)

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen, Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas, Lisa Martin. With the cue, we're live Day one of our duel set coverage of Del Technologies World twenty nineteen student a menace here with me, and we're welcoming back a couple of alumni. But for the first time together on our set, we've got Terry Pellegrino, the BP of high performance computing at Delhi Emcee and Nick, who grew VP of Data Analytics and Cyber Securities just at MasterCard. Did I get that right? All right, good. So, guys, thanks for joining Suited me this afternoon, by the way. So we will start with you High performance computing. Talk about that a lot. I know you've been on the Cube talking about HPC in the Innovation lab down in in Austin, high performance computing, generating a ton of data really requiring a I. We talk a lot of it II in machine learning, but let's look at it in the context of all this data. Personal data data from that word, you know, it turns out do with mastercard, for example How are you guys working together? Dell Technologies and MasterCard to ensure that this data is protected. It secure as regulations come up as fraud, is a huge, expensive >> issue. Well, I think make way worked together to really well worry about the data being secure, but also privacy being a key item that we worry about every day you get a lot of data coming through, and if we let customer information or any kind of information out there, it can be really detrimental. So we've really spent a lot of time not only helping manage and worked through the data through the infrastructure and the solutions that we've put together for. For Nick, who also partnered with the consortium project that got started Mosaic Crown to try to focus even more on data privacy on Mosaic Crown is is really interesting because it's getting together and making sure that the way we keep that privacy through the entire life cycle of the data that we have the right tools tio have other folks understand that critical point. That's that's how we got all the brains working together. So it's not just Delon DMC with daily emcee and MasterCard It's also ASAP We have use of Milan, you're sort of bergamot and we'Ll solve the only three c and all together back in January decided to get together and out of Nick's idea. Think about how we could put together with all those tools and processes to help everybody have more private data. Other. >> I think this was your idea. >> I can't say it was my idea. The European Union itself with what? The advent of Judy parent privacy. Their biggest concern was we don't want people to stop sharing. Data began with artificial intelligence. The great things that we do with it from the security, you know, carrying diseases all the way through, making sure transactions are safe and secure. Look, we don't want people to stop our organizations to stop sharing that data because they have fear of the regulations. How do we create a date on market? So the U has something called Horizon twenty twenty on one of their initiatives. Wass Way wanted to understand what a framework for data market would look like where organizations can share that data with confidence that they're complying to all the regulations there, doing the anonymous ization of that data, and the framework itself allows someone to say, I could do analysis without worrying that if it's surfacing personally identifiable information or potentially financial information, but I can share it so that it can progress the market data economy. So as a result of that, what we did is we put the guilt. I said, This is a really good idea for us. Went to the partners at del. That's it, guys, this is something we should consider doing now. Organization always been looking at privacy, and as a result, we've done a very good job of putting that consortium together. >> So, Nick, we've talked with you on the Cuba quite a few times about security. >> Can you just give >> us? You know, you talked about that opportunity of a I We don't want people to stop giving data in. There was concerned with GPR that Oh, wait, I need you to stop collecting information because I'm going to get sued out of existence. If it happened, how do we balance that? You know, data is the new oil I need, you know, keep not flowing and oh, my God. I'm going to get hacked. I'm going to get sued. I'm going to have the regulation, You know, people's personal information. I'm goingto walk down the grocery store and they're going to be taking it from me. How do we balance that? >> Well, the nice part is, since State is the new oil, well, we considered it is artificial intelligences that refinery for that oil. So, for our perspective, is the opportunity to say we can use a eye to help. Somebody says, Hey, I don't want you to share my data information. I want to be private, but I can use a I d. S. Okay, let's filter those out so I can use a I'd actually sit on top of that. I can sit down and say, Okay, how do I keep that person's safe, secure and only share the necessary data that will solve the problem again, using artificial intelligence through different types of data classifications, whoever secure that data with different methods of data security, how we secure those types of things come into play. And again, there's also people say, I don't ever want my data to be we identified so we can use different methods to do complete anonymous ation. >> How do you do that when there are devices that are listening constantly, what Walmart's doing? Everybody that has those devices at home with the lady's name. I won't say it. I know it activates it. How How do you draw the line with ensuring that those folks that don't want certain things shared if they're in the island Walmart talking about something that they don't want shared? How do you facilitate that? >> Well, part of that is okay. At a certain point, when it comes to privacy, you've gotta have a little bit of parenting. Just because you have that information doesn't mean you need to use that information. So that's where we as humans have to come into play and start thinking about what is the data that we're collecting And how should we use that information on that person and who is walking through a store? And we say we are listening to what their conversations are? Well, I don't need to identify that you or you. I just didn't know what is the top talking about? Maybe that's the case, but again, you have to make that decision again. It's about being a parent at this point. That's the ethical part of data which we've discussed on this program before. Alright, >> so teary. Talkto us some about the underlying architecture that's going to drive all of this. You know, we we love the shift. For years ago, it was like storing my data. You know, Now we're talking about how do we extract the value of the data? We know data's moving a lot, So you know what's changing And I talk every infrastructure company I talked to, it's like, Oh, well, we've got the best ai ai, you know, x, whatever. So you know what kind of things should custom be looking for To be able to say, Oh, this is something, really. It's about scale. It's about, you know, really focused on my data. Yeah, absolutely. Well, I will say first, the end of underlying infrastructure. We have our set of products that have security intrinsic in the way they're designed. I really worry about ki management for software we have silicon based would have trust throughout a lot of our portfolio. We also think about secure supply chain, even thinking through security race. If you lose your hard drive on, we can go and make sure that the data is not removed. So that's on the security front. On the privacy side, as a corporation, William C. Is very careful about the data that we have access to on. Then you think about a HBC. So being in charge of H. P. C for Cordelia emcee way actually are part of how the data gets created, gets transferred, gets generated, curated and then stored. Of course, storage s O. What we want to make sure is our customers feel like where that one company that can help them through their journey for their data. And as you heard Michael this morning during keynote, >> uh, getting that value out of the data because it's really where that little transformation is going to get everybody to the next level. But right now there's a lot of data. Has Nick stated this data has more personal information at times? Andan i'll add one more thing way. Want to really make sure that innovation is not stifled and the way we get there is to make sure >> that the data sets are as broad as possible, and today it's very difficult to share data. Sets mean that there are parts of the industry there are so worried about data that they will not even get it anywhere else than their own data center and locked behind closed doors. But if you think about all the data scientists, they're craving more data. And the way we can get there is with what make it talked about is making sure that the data that is collected is free of personal information and can still be qualified for some analysis and letting all the data scientists out there to get a lot of value out of it. >> So HBC can help make the data scientist job simpler or simplify evaluating this innumerable amun of data. >> Correct. So what in the days you had an Excel spreadsheet and wanted to run and put the table on it, you could do that on a laptop for end up tablet. When you start thinking about finding a black hole in the galaxy, you can do that on tablet. So you're gonna have to use several computers in a cluster with the right storage of the right interconnect. And that's why it's easy comes in place. >> I mean, if I man a tactical level, what you'LL see with HBC computing is when someone's in the moment, right? You want to be able to recognize that person has given me the right to communicate to them or has not given me the right to communicate to them, even though they're trying to do something that could be a transaction. The ability to say Hey, I have I know that this person's or this device is operating here is this and they have given me these permissions. You've got to do that in real time, and that's what you're looking for. HBC competing to do. That's what you're saying. I need my G p you to process in that way, and I need that cpt kind of meat it from the courts. The edges say Yep, you can't communicate. No, you can't. Here's where your permissions like. So, >> Nick, what should we >> be looking for? Coming out of this consortium is people are watching around the industry. You know what, what, what >> what expect for us? The consortium's about people understand that they can trust that they're data's being used properly, wisely, and it's being used in the way it was intended to be used so again, part of the framework is what do you expect to do with the data so that the person understands what their data is being used for the analysis being done? So they have full disclosure. So the goal here is you can trust your data's being used. The way was intended. You could trust that. It's in a secure manner. You can trust that your privacy is still in place. That's what we want this construction to create that framework to allow people to have that trust and confidence. And we want the organization to be able to not, you know, to be able to actually to share that information to again move that date economy forward. >> That trust is Nirvana. Well, Nick Terry, thank you so much for joining suing me on the cue this afternoon. Fascinating conversation about HPC data security and privacy. We can't wait to hear what's in store next for this consortium. So you're gonna have to come back. Thank >> you. We'LL be back. Excellent. Thanks so much. >> Our pleasure. First Minutemen, I'm Lisa Martin. You're watching us live from Las Vegas. The keeps coverage of day one of del technology World twenty nineteen. Thanks for watching

>> Live, from Bahrain it's theCUBE. Covering AWS Summit Bahrain. Brought to you by Amazon Web Services. >> Okay, welcome back everyone. It's the theCUBE's coverage here, in Bahrain, in the Middle East, for our coverage of AWS Summit and the announcement, and now soon to be up-and-running in 2019 in Q1, Amazon Web Services, full region here in the Middle East. Should have a massive impact to the ecosystem, and companies and entrepreneurs from around the borders. We've got great conversations all day. And today we've got to great guests here, Emilio Valdes, VP of EMEA South and Latin America for Informatica. Thank you for theCUBE sponsorships over the years. We've covered Informatica shows all over the world. Mathew Joseph, business head of Data Analytics for Wipro. Good to see you, thanks for joining us. >> It's a pleasure. >> Same >> Great to be here. >> So, Informatica, we know a lot about you. We cover all of your big events in North America, I interviewed your CEO, I've been following the value proposition, growing really well, you've got a good product offering. But we're in the Middle East, okay? And what I've learned here is that there's a thirst for entrepreneurship. There's a thirst for cloud. But everyone's talking about data. And if data's the new oil, no better place to be than in the Middle East. They know the value of oil. What's going on in town here? What's happening in the Middle East? >> Right, so, as I cover a pretty big area within Informatica, I used to travel the world and meet many customers, in many places, many customers and many industries here in the Middle East. And I can tell you that, you know, the story, the messages are very consistent, you know? Every company, every industry, is going through a massive period of change, and companies are reacting to this change very differently. What we've seen is that the disrupters are going to be the ones that will, you know, implement digital transformation consistently, and we believe that data is the key driver for intelligent digital transformation. Here in the Middle East is no different. We've been seeing this across the different countries, in Dubai, in Bahrain, in Kuwait, in Saudi Arabia, exactly the same as everywhere else in the world. >> And cloud's now coming in full throttle at Amazon, You guys are not new to Amazon. I know you guys do a ton of work with Amazon integrating and putting all this together, what do you think is going to happen, here? Now Amazon gets up and running, they're already using a cloud now, so Bahrain's clear, cloud first. Saudi's got the cloud bug too, they're doing great things. So when an actual region comes here, what do you think is going to happen? An explosion of innovation and more business? What's going to be the impact? >> Well I think, I think the market knows what the benefits they can get out of the AWS platform, and I believe the challenges are related to get the most out of this AWS platform. At Informatica, we are going to help customers to move their data to the cloud in a consistent manner that is connected, articulated, properly governed, and not only this, but also we believe that the key value is in the hybrid world. The world hasn't moved to the cloud yet, entirely, so most companies continue to have some on-premise applications, as well as their cloud applications. So I believe that Informatica can help customers here in the Middle East, by connecting the on-premise world with the cloud world. And at the same time, the value they can get from our platform is by making AWS easy to operate, and, you know, move data to the cloud in a consistent, quick, and sustainable manner. >> So Matthew Joseph, you're with Wipro, why are you guys together, what's the relationship? Obviously we know what you guys do, you guys do great work, global, around the world. We see you at all the events. From SAP Sapphire, EMC World, now Dell World, Reinvent, you guys are everywhere. So here, what's going on here? I mean, analytics, you need analytics. You're good at analytics >> First of all, John, thanks a lot. A couple of thoughts. One, Wipro has been a global partner of AWS. Wipro's a global partner of Informatica. And the region is going through massive change of innovation, of using, consuming data. And at this point we really feel that both the expertises should come together to manage the change. And that's the simple reason why Informatica and Wipro are together, along with AWS and this, I would say a historical movement of this part of the world, to actually consume this rate and transfer the data for all of us. >> So if I asked you a question that said, hey, tell me about your relationship with Informatica. What's in it for me? What do you do for me? Are you, are you bringing it together? Are you guys going to market together? How do you, how do I win with you and Informatica? >> So what we have done is, as I told, the global partnership, across the globe, the best practices we're bringing back to this part of the world, to make sure that we have a similar set of stories across the global sphere. This certainly means more repeatability, less risk, and for the entire government to go through a small transition of going to the cloud. >> And data disruption is huge. You guys have Informatica 3.0, and you guys have your practice. When you put that together, what's the go to market? What's the value proposition? What's the pitch to the customer? >> So the key part is the IPaas method, the platform as a service message, right? With the platform as a service, it's a market that Gartner has identified as a $12.5 billion market. And it's growing very rapidly. Just to give you an idea, we process three trillion transactions per month, and this number is being multiplied by three every three to four months, right? So the iPaaS platform is what is going to help customers to move from the on-premise world, to the cloud. And this is where the key value Informatica, and Wipro, can put together to facilitate and to help enable customers in their journey to the cloud. >> So talk about the Amazon impact, obviously you guys do work with Amazon. What, specifically, does Amazon have that you guys like? That you work with the most with customers? Obviously they want to know, obviously you know, I got data, a ton of data. I've got to manage it. I mean, analytics are pretty good. You've got Sagemakers, Hotrock, on fire. Redshift everyone knows is doing well. Kinesis, with streaming. What's some of the Amazon tools you guys are working with around some of these day-to-day opportunities? >> Yeah, so there are multiple of them. In fact today's the day when the big data is pouring in, for example, right? So how do I really bring in all the data into a common platform? And today the customer is also talking about how do they really consume it? So consumption is a major attraction for AWS and how they really consume this data. The extraction, making sure the data is available, furthers decision making in the second part. The way Wipro and Informatica positions this entire journey is not just about putting the data into a common place and building up a transformation, right? What you're looking at is how do I really change the way the business works? And elements of design principal come in on it. And what Wipro has literally done is, we've done a lot of investments around how to I really make this transformation from a design-thinking point of view? How do I make sure the best practices of data science, and governance comes into it? How do I make sure that the press points for the customer are so clear and so vivid that decisions are made based on that? And I feel AWS, out in the region, is doing a great work on that. And that's the simple reason why all of us are together with that. >> That's great. And cloud, you guys are no stranger to Amazon. >> We are partner of Amazon. And we've been a partner of Amazon AWS for awhile. As well as Wipro is a partner of Amazon. And Informatica and Wipro are global partners as well. We're quite excited about bringing this partnership to the region. >> What sort of things that you guys have done together, can you share some examples of some awesome implementation and use cases? >> A few of them. So to me, what is happening, as I was earlier telling is that most of the government entities are talking about how do I really consume this data. How do I really think of it as an experience? So what we have really done is pull up this data, look at various models on how I can do revenue generation for the customer. How can I bring in more customers' recommendation? How do I make impactful decisions based on those data? And the ample amount of programs use cases that you have already implemented in this part of the world, and certainly Informatica has been a great help in this journey of ours. So the teams around which we look out, is data monetization, customizability, researching degree of the customer, operating efficiency, and this is true across industries. Government is doing a fabulous job of going on this journey but certainly we do a lot of work in the oil and gas sector, in the healthcare, and similar things like that. >> Awesome, and what's core value proposition that you guys are offering customers out here? >> I believe it's the messages we discussed earlier. It's having a consistent platform where data gets together and can be used across different applications, business units, et cetera. At the end of the day, end users will need to use data and they don't care where this data is stored. It could be in the cloud, it could on premise, it could be in a big data application, it doesn't really matter, you know? >> It could be addressable. >> Exactly >> In real time too in low latency. It can't be some data warehousing thing that takes, you know, real time application like a car needs data. IoT, a huge growth area. I mean these are new cloud architectural opportunities. You can't be having the old way. >> The data has to be connected, and secure, and clean, and available, and consistent. This is what we do for a business. >> Yeah you guys have got some good story there. Good luck with everything. I want to get your final questions as we kind of round down the day here. The day's kind of cleaning out here behind us. You can see it's getting quieter. What do you think about what's happening here? Amazon Web Services Summit, mix a little public sector, you've got some commercial, but this region pulsing with cloud demand. What do you think, guys? What's your thoughts? >> I think we're going to help the government to move to the cloud. We're very excited about the announcement that we heard this morning. The cloud-first policy. I think that Wipro and Informatica are uniquely positioned to give the government what they need to be successful in their cloud-first policy >> Thoughts? >> Same here, I think the last 24 months we have seen a lot of initiative from the government. Both across the artificial and then about data being the center of all things. And cloud is going to be a very pivotal role in this. And I think we are geared very well to take care of it. >> I think you guys are well positioned enough, you know. My translation is you see their cloud-first policy, they want to be involved in FinTech in the future, you got to have a data strategy to center the value proposition everything's got to be built around how that data's going to move, how it's going to be addressed, how it's going to be consumed, shared, connected. Across the board, IoT, on premises, real-time mobile, everything. >> And John, one more point, to close, would be what we see is the hybrid architecture coming up, alright? So cloud being one of them, the customers still want data inside the premises as well, so how do you really look at the hybrid architecture, and the challenges around it. I don't think there are many companies in this part of the world who are geared up to that. Wipro has done it multiple times, Informatica has been a leader in that. And I think that is going to be a game changer for all of us. >> You know Mathew you made me smile because, thank you for making me smile, because we always joke, and I always talk on theCUBE, and usually Dave Vellante's here and we kind of argue about it, because I say data is the new oil, he says it's not the new oil because oil can only be used in the car I guess, we can always go back and forth. But I've been saying that cloud is the future, I've been saying it for many years. Amazon certainly is more hardcore, Andy Jassy, all data systems moved to the cloud, What does that mean? Just announced RDS on VMware on premises, so it kind of like, takes that window, but I say that the cloud, operationally, is what's going on. People are moving to operations that are cloud-linked. So if everything is running cloud operations, DevOps, infrastructure as code, AI, all the things that you guys are working on, that means that the data center and on-premises, is an edge device. Or is it? It's a big fat edge. Or what's the difference between a windmill and an on-premise campus? I mean, edges? So, this is the debate we've been having. What is an edge? >> The way we see it is customers having a journey, in a journey to the cloud. And the state of the art is very different. We're happy to help customers to go through this journey efficiently, quickly, and in a consistent manner. >> And all serious, putting the fun kind of comment aside about the argument we had about the edge, is that the architecture that we see people are going to is, don't let some pre-defined thing define where the data has to go. So this data out there, it's got to move around. And if you don't want it to move around, then you put Compute to it. So there's all kinds of things going on where you don't have to get dogmatic about it. >> Absolutely >> What the definition is. It's all running cloud operations, then it's cloud, right? I mean it's not on-premises operations, no one says that. Anyway thanks for coming on theCUBE, thanks for sharing. Great to see Informatica here, great to see Wipro. We've got to get more of these use cases, if we had more time we would. This is theCUBE coverage, here, in Bahrain for Amazon Web Services Summit. Stay with us for more coverage after this break. (electronic music)

(bright orchestra music) >> Hi, I'm Peter Burris. Welcome to a special digital community event brought to you by Wikibon and theCUBE. Sponsored by Dell EMC. Today we're gonna spend quite some time talking about some of the trends in the relationship between hardware and AI. Specifically, we're seeing a number of companies doing some masterful work incorporating new technologies to simplify the infrastructure required to take full advantage of AI options and possibilities. Now at the end of this conversation, series of conversations, we're gonna run a CrowdChat, which will be your opportunity to engage your peers and engage thought leaders from Dell EMC and from Wikibon SiliconANGLE and have a broader conversation about what does it mean to be better at doing AI, more successful, improving time to value, et cetera. So wait 'til the very end for that. Alright, let's get it kicked off. Tom Burns is my first guest. And he is the Senior Vice President and General Manager of Networking Solutions at Dell EMC. Tom, it's great to have you back again. Welcome back to theCUBE. >> Thank you very much. It's great to be here. >> So Tom, this is gonna be a very, very exciting conversation we're gonna have. And it's gonna be about AI. So when you go out and talk to customers specifically, what are you hearing then as they describe their needs, their wants, their aspirations as they pertain to AI? >> Yeah, Pete, we've always been looking at this as this whole digital transformation. Some studies say that about 70% of enterprises today are looking how to take advantage of the digital transformation that's occurring. In fact, you're probably familiar with the Dell 2030 Survey, where we went out and talked to about 400 different companies of very different sizes. And they're looking at all these connected devices and edge computing and all the various changes that are happening from a technology standpoint, and certainly AI is one of the hottest areas. There's a report I think that was co-sponsored by ServiceNow. Over 62% of the CIO's and the Fortune 500 are looking at AI as far as managing their business in the future. And it's really about user outcomes. It's about how do they improve their businesses, their operations, their processes, their decision-making using the capability of compute coming down from a class perspective and the number of connected devices exploding bringing more and more data to their companies that they can use, analyze, and put to use cases that really make a difference in their business. >> But they make a difference in their business, but they're also often these use cases are a lot more complex. They're not, we have this little bromide that we use that the first 50 years of computing were about known process, unknown technology. We're now entering into an era where we know a little bit more about the technology. It's gonna be cloud-like, but we don't know what the processes are, because we're engaging directly with customers or partners in much more complex domains. That suggests a lot of things. How are customers dealing with that new level of complexity and where are they looking to simplify? >> You actually nailed it on the head. What's happening in our customers' environment is they're hiring these data scientists to really look at this data. And instead of looking at analyzing the data that's being connected, that's being analyzed and connected, they're spending more time worried about the infrastructure and building the components and looking about allocations of capacity in order to make these data scientists productive. And really, what we're trying to do is help them get through that particular hurdle. So you have the data scientists that are frustrated, because they're waiting for the IT Department to help them set up and scale the capacity that they need and infrastructure that they need in order to do their job. And then you got the IT Departments that are very frustrated, because they don't know how to manage all this infrastructure. So the question around do I go to the cloud? Do I remain on-prem? All of this is things that our companies, our customers, are continuing to be challenged with. >> Now, the ideal would be that you can have a cloud experience but have the data reside where it most naturally resides, given physics, given the cost, given bandwidth limitations, given regulatory regimes, et cetera. So how are you at Dell EMC helping to provide that sense of an experience based on what the work load is and where the data resides, as opposed to some other set of infrastructure choices? >> Well, that's the exciting part is that we're getting ready to announce a new solution called the Ready Solutions for AI. And what we've been doing is working with our customers over the last several years looking at these challenges around infrastructure, the data analytics, the connected devices, but giving them an experience that's real-time. Not letting them worry about how am I gonna set this up or management and so forth. So we're introducing the Ready Solutions for AI, which really focuses on three things. One is simplify the AI process. The second thing is to ensure that we give them deep and real-time analytics. And lastly, provide them the level of expertise that they need in a partner in order to make those tools useful and that information useful to their business. >> Now we want to not only provide AI to the business, but we also wanna start utilizing some of these advanced technologies directly into the infrastructure elements themselves to make it more simple. Is that a big feature of what the ready system for AI is? >> Absolutely, as I said, one of the key value propositions is around making AI simple. We are experts at building infrastructure. We have IP around compute, storage, networking, infinity band. The things that are capable of putting this infrastructure together. So we have tested that based upon customers' input, using traditional data analytics, libraries, and tool sets that the data scientists are gonna use, already pre-tested and certified. And then we're bringing this to them in a way which allows them through a service provisioning portal to basically set up and get to work much faster. The previous tools that were available out there, some from our competition. There were 15, 20, 25 different steps just to log on, just to get enough automation or enough capability in order to get the information that they need. The infrastructure allocated for this big data analytics through this service portal we've actually gotten it down to around five clicks with a very user-friendly GUI, no CLI required. And basically, again, interacting with the tools that they're used to immediately right out of the gate like in stage three. And then getting them to work in stage four and stage five so that they're not worried about the infrastructure, not worried about capacity, or is it gonna work. They basically are one, two, three, four clicks away, and they're up and working on the analytics that everyone wants them to work on. And heaven knows, these guys are not cheap. >> So you're talking about the data scientists. So presumably when you're saying they're not worried about all those things, they're also not worried about when the IT Department can get around to doing it. So this gives them the opportunity to self-provision. Have I got that right? >> That's correct. They don't need the IT to come in and set up the network to do the CLI for the provisioning, to make sure that there is enough VM's or workloads that are properly scheduled in order to give them the capacity that they need. They basically are set with a preset platform. Again, let's think about what Dell EMC is really working towards and that's becoming the infrastructure provider. We believe that the silos, the service storage, and networking are becoming eliminated, that companies want a platform that they can enable those capabilities. So you're absolutely right. The part about the simplicity or simplifying the AI process is really giving the data scientists the tools they need to provision the infrastructure they need very quickly. >> And so that means that the AI or rather the IT group can actually start acting more like a DevOps organization as opposed to a specialist in one or another technology. >> Correct, but we've also given them the capability by giving the usual automation and configuration tools that they're used to coming from some of our software partners, such as Cloudera. So in other words, you still want the IT Department involved, making sure that the infrastructure is meeting the requirements of the users. They're giving them what they want, but we're simplifying the tools and processes around the IT standpoint as well. >> Now we've done a lot of research into what's happening in the big data now is likely to happen in the AI world. And a lot of the problems that companies had with big data was they conflated or they confused the objectives, the outcome of a big data project, with just getting the infrastructure to work. And they walked away often, because they failed to get the infrastructure to work. So it sounds though what you're doing is you're trying to take the infrastructure out of the equation while at the same time going back to the customer and saying, "Wherever you want this job "to run or this workload to run, you're gonna get the same "experience irregardless." >> Correct, but we're gonna get an improved experience as well. Because of the products that we've put together in this particular solution, combined with our compute, our scale-out mass solution from a storage perspective, our partnership with Mellon Oshman infinity band or ethernet switch capability. We're gonna give them deeper insights and faster insights. The performance and scalability of this particular platform is tremendous. We believe in certain benchmark studies based upon the Reznik 50 benchmark. We've performed anywhere between two and half to almost three times faster than the competition. In addition from a storage standpoint, all of these workloads, all of the various characteristics that happen, you need a ton of IOPS. >> Yeah. >> And there's no one in the industry that has the IOP performance that we have with our All-Flash Isilon product. The capabilities that we have there we believe are somewhere around nine times the competition. Again, the scale-out performance while simplifying the overall architecture. >> Tom Burns, Senior Vice President of Networking and Solutions at Dell EMC. Thanks for being on theCUBE. >> Thank you very much. >> So there's some great points there about this new class of technology that dramatically simplifies how hardware can be deployed to improve the overall productivity and performance of AI solutions. But let's take a look at a product demo. >> Every week, more customers are telling us they know AI is possible for them, but they don't know where to start. Much of the recent progress in AI has been fueled by open source software. So it's tempting to think that do-it-yourself is the right way to go. Get some how-to references from the web and start building out your own distributive deep-learning platform. But it takes a lot of time and effort to create an enterprise-class AI platform with automation for deployment, management, and monitoring. There is no easy solution for that. Until now. Instead of putting the burden of do-it-yourself on your already limited staff, consider Dell EMC Ready Solutions for AI. Ready Solutions are complete software and hardware stacks pre-tested and validated with the most popular open source AI frameworks and libraries. Our professional services with proven AI expertise will have the solution up and running in days and ready for data scientists to start working in weeks. Data scientists will find the Dell EMC data science provisioning portal a welcome change for managing their own hardware and software environments. The portal lets data scientists acquire hardware resources from the cluster and customize their software environment with packages and libraries tested for compatibility with all dependencies. Data scientists choose between JupyterHub notebooks for interactive work, as well as terminal sessions for large-scale neural networks. These neural networks run across a high-performance cluster of power-edge servers with scalable Intel processors and scale-out Isilon storage that delivers up to 18 times the throughput of its closest all-flash competitor. IT pros will experience that AI is simplified as Bright Cluster Manager monitors your cluster for configuration drift down to the server BIOS using exclusive integration with Dell EMC's open manage API's for power-edge. This solution provides comprehensive metrics along with automatic health checks that keep an eye on the cluster and will alert you when there's trouble. Ready Solutions for AI are the only platforms that keep both data center professionals and data scientists productive and getting along. IT operations are simplified and that produces a more consistent experience for everyone. Data scientists get a customizable, high-performance, deep-learning service experience that can eliminate monthly charges spent on public cloud while keeping your data under your control. (upbeat guitar music) >> It's always great to see the product videos, but Tom Burns mentioned something earlier. He talked about the expansive expertise that Dell EMC has and bringing together advanced hardware and advanced software into more simple solutions that can liberate business value for customers, especially around AI. And so to really test that out, we sent Jeff Frick, who's the general manager and host of theCUBE down to the bowels of Dell EMC's operations in Austin, Texas. Jeff went and visited the Dell EMC HPC and AI Innovation Lab and met with Garima Kochhar, who's a tactical staff Senior Principal Engineer. Let's hear what Jeff learned. >> We're excited to have with us our next guest. She's Garima Kochhar. She's on the tactical staff and the Senior Principal Engineer at Dell EMC. Welcome. >> Thank you. >> From your perspective what kinda changing in the landscape from high-performance computing, which has been around for a long time, into more of the AI and machine learning and deep learning and stuff we hear about much more in business context today? >> High-performance computing has applicability across a broad range industries. So not just national labs and supercomputers, but commercial space as well. And our lab, we've done a lot of that work in the last several years. And then the deep learning algorithms, those have also been around for decades. But what we are finding right now is that the algorithms and the hardware, the technologies available, have hit that perfect point, along with industries' interest with the amount of data we have to make it more, what we would call, mainstream. >> So you can build an optimum solution, but ultimately you wanna build industry solutions. And then even subset of that, you invite customers in to optimize for what their particular workflow or their particular business case which may not match the perfect benchmark spec at all, right? >> That's exactly right. And so that's the reason this lab is set up for customer access, because we do the standard benchmarking. But you want to see what is my experience with this, how does my code work? And it allows us to learn from our customers, of course. And it allows them to get comfortable with their technologies, to work directly with the engineers and the experts so that we can be their true partners and trusted advisors and help them advance their research, their science, their business goals. >> Right. So you guys built the whole rack out, right? Not just the fun shiny new toys. >> Yeah, you're right. So typically, when something fails, it fails spectacularly. Right, so I'm you've heard horror stories where there was equipment on the dock and it wouldn't fit in the elevator or things like that, right? So there are lots of other teams that handle, of course Dell's really good at this, the logistics piece of it, but even within the lab. When you walk around the lab, you'll see our racks are set up with power meters. So we do power measurements. Whatever best practices in tuning we come up with, we feed that into our factories. So if you buy a solution, say targeted for HPC, it will come with different BIOS tuning options than a regular, say Oracle, database workload. We have this integration into our software deployment methods. So when you have racks and racks of equipment or one rack of equipment or maybe even three servers, and you're doing an installation, all the pieces are baked-in already and everything is easy, seamless, easy to operate. So our idea is... The more that we can do in building integrated solutions that are simple to use and performant, the less time our customers and their technical computing and IT Departments have to spend worrying about the equipment and they can focus on their unique and specific use case. >> Right, you guys have a services arm as well. >> Well, we're an engineering lab, which is why it's really messy, right? Like if you look at the racks, if you look at the work we do, we're a working lab. We're an engineering lab. We're a product development lab. And of course, we have a support arm. We have a services arm. And sometimes we're working with new technologies. We conduct training in the lab for our services and support people, but we're an engineering organization. And so when customers come into the lab and work with us, they work with it from an engineering point of view not from a pre-sales point of view or a services point of view. >> Right, kinda what's the benefit of having the experience in this broader set of applications as you can apply it to some of the newer, more exciting things around AI, machine learning, deep learning? >> Right, so the fact that we are a shared lab, right? Like the bulk of this lab is High Performance Computing and AI, but there's lots of other technologies and solutions we work on over here. And there's other labs in the building that we have colleagues in as well. The first thing is that the technology building blocks for several of these solutions are similar, right? So when you're looking at storage arrays, when you're looking at Linux kernels, when you're looking at network cards, or solid state drives, or NVMe, several of the building block technolgies are similar. And so when we find interoperability issues, which you would think that there would never be any problems, you throw all these things together, they always work like-- >> (laughs) Of course (laughs). >> Right, so when you sometimes, rarely find an interoperability issue, that issue can affect multiple solutions. And so we share those best practices, because we engineers sit next to each other and we discuss things with each other. We're part of the larger organization. Similarly, when you find tuning options and nuances and parameters for performance or for energy efficiency, those also apply across different domains. So while you might think of Oracle as something that it's been done for years, with every iteration of technology there's new learning and that applies broadly across anybody using enterprise infrastructure. >> Right, what gets you excited? What are some of the things that you see, like, "I'm so excited that we can now apply "this horsepower to some of these problems out there?" >> Right, so that's a really good point, right? Because most of the time when you're trying to describe what you do, it's hard to make everybody understand. Well, not what you're doing, right? But sometimes with deep technology it's hard to explain what's the actual value of this. And so a lot of work we're doing in terms of excess scale, it's to grow like the... Human body of knowledge forward, to grow the science happening in each country moving that forward. And that's kind of, at the higher end when you talk about national labs and defense and everybody understands that needs to be done. But when you find that your social media is doing some face recognition, everybody experiences that and everybody sees that. And when you're trying to describe the, we're all talking about driverless cars or we're all talking about, "Oh, it took me so long, "because I had this insurance claim and then I had "to get an appointment with the appraisor "and they had to come in." I mean, those are actual real-world use cases where some of these technologies are going to apply. So even industries where you didn't think of them as being leading-edge on the technical forefront in terms of IT infrastructure and digital transformation, in every one of these places you're going to have an impact of what you do. >> Right. >> Whether it's drug discovery, right? Or whether it's next-generation gene sequencing or whether it's designing the next car, like pick your favorite car, or when you're flying in an aircraft the engineers who were designing the engine and the blades and the rotors for that craft were using technologies that you worked with. And so now it's everywhere, everywhere you go. We talked about 5G and IoT and edge computing. >> Right. >> I mean, we all work on this collectively. >> Right. >> So it's our world. >> Right. Okay, so last question before I let you go. Just being, having the resources to bear, in terms of being in your position, to do the work when you've got the massive resources now behind you. You have Dell, the merger of EMC, all the subset brands, Isilon, so many brands. How does that help you do your job better? What does that let you do here in this lab that probably a lot of other people can't do? >> Yeah, exactly. So when you're building complex solutions, there's no one company that makes every single piece of it, but the tighter that things work together the better that they work together. And that's directly through all the technologies that we have in the Dell technologies umbrella and with Dell EMC. And that's because of our super close relationships with our partners that allows us to build these solutions that are painless for our customers and our users. And so that's the advantage we bring. >> Alright. >> This lab and our company. >> Alright, Garima. Well, thank you for taking a few minutes. Your passion shines through. (laughs) >> Thank you. >> I really liked hearing about what Dell EMC's doing in their innovation labs down at Austin, Texas, but it all comes together for the customer. And so the last segment that we wanna bring you here is a great segment. Nick Curcuru, who's the Vice President of Big Data Analytics at Mastercard is here to talk about how some of these technologies are coming together to speed value and realize the potential of AI at Mastercard. Nick, welcome to theCUBE. >> Thank you for letting me be here. >> So Mastercard, tell us a little bit about what's going on at Mastercard. >> There's a lot that's going on with Mastercard, but I think the most exciting things that we're doing out of Mastercard right now is with artificial intelligence and how we're bringing the ability for artificial intelligence to really allow a seamless transition when someone's actually doing a transaction and also bringing a level of security to our customers and our banks and the people that use Mastercards. >> So AI to improve engagement, provide a better experience, but that's a pretty broad range of things. What specifically kinds of, when you think about how AI can be applied, what are you looking to? Especially early on. >> Well, let's actually take a look at our core business, which is being able to make sure that we can secure a payment, right? So at this particular point, people are used to, we're applying AI to biometrics. But not just a fingerprint or a facial recognition, but actually how you interact with your device. So you think of like the Internet of Things and you're sitting back saying, "I'm using, "I'm swiping my device, my mobile device, "or how I interact with a keyboard." Those are all key signatures. And we, with our company, new data that we've just acquired are taking that capability to create a profile and make that a part of your signature. So it's not just beyond a fingerprint. It's not just beyond a facial. It's actually how you're interacting so that we know it's you. >> So there's a lot of different potential sources of information that you can utilize, but AI is still a relatively young technology and practice. And one of the big issues for a lot of our clients is how do you get time to value? So take us through, if you would, a little bit about some of the challenges that Mastercard and anybody would face to try to get to that time to value. >> Well, what you're really seeing is looking for actually a good partner to be with when you're doing artificial intelligence, because again, at that particular point, you try to get to scale. For us, it's always about scale. How can we roll this across 220 countries? We're 165 million transactions per hour, right? So what we're looking for is a partner who also has that ability to scale. A partner who has the global presence, who's learning. So that's the first step. That's gonna help you with your time to value. The other part is actually sitting back and actually using those particular partners to bring their expertise that they're learning to combine with yours. It's no longer just silos. So when we talk about artificial intelligence, how can we be learning from each other? Those open source systems that are out there, how do we learn from that community? It's that community that allows you to get there. Again, those that are trying to do it on their own, trying to do it by themselves, they're not gonna get to the point where they need to be. In other words, in a six month time to value it's gonna take them years. We're trying to accelerate that, you say, "How can we get out of those algorithms operating for us "the way we need them to provide the experiences "that people want quickly." And that's with good partners. >> 165 million transactions per hour is only likely to go up over the course of the next few years. That creates an operational challenge. AI is associated with a probabilistic set of behaviors as opposed to categorical. Little bit more difficult to test, little bit more difficult to verify, how is the introduction of some of these AI technologies impacting the way you think about operations at Mastercard? >> Well, for the operations, it's actually when you take a look there's three components, right? There's right there on the edge. So when someone's interacting and actually doing the transaction, and then we'll look at it as we have a core. So that core sits there, right? Basically, that's where you're learning, right? And then there's actually, what we call, the deep learning component of it. So for us, it's how can we move what we need to have in the core and what we need to have on the edge? So the question for us always is we want that algorithm to be smart. So what three to four things do we need that algorithm to be looking for within that artificial intelligence needs to know that it then goes back into the core and retrieves something, whether that's your fingerprint, your biometrics, how you're interacting with that machine, to say, "Yes, that's you. "Yes, we want that transaction to go through." Or, "No, stop it before it even begins." It's that interaction and operational basis that we're always have a dynamic tension with, but it's how we get from the edge to the core. And it's understanding what we need it to do. So we're breaking apart what we have to have that intelligence to be able to create a decision for us. So that's how we're trying to manage it, as well as of course, the hardware that goes with it and the tools that we need in order to make that happen. >> When we get on the hardware just a little bit, so that historically different applications put pressure on different components within a stack. One of the observations that we've made is that the transition from spinning disk to flash allows companies like Mastercard to think about just persisting data to actually delivering data. >> Yeah. >> Much more rapidly. How does some of the, how does these AI technologies, what kinda new pressures do they put on storage? >> Well, they put a tremendous pressure, because that's actually again, the next tension or dynamics that you have to play with. So what do you wanna have on disk? What do you need flash to do? Again, if you look at some people, everyone's like, "Oh, flash will take over everything." It's like no, flash has, there's a reason for it to exist, and understanding what that reason is and understanding, "Hey, I need that to be able to do this "in sub-seconds, nanoseconds," I've heard the term before. That's what you're asking flash to do. When you want deep learning, that, I want it on disk. I want to be taking all those millions of billions of transactions that we're gonna see and learn from them. All the ways that people will be trying to attack me, right? The bad guys, how am I learning from everything that I'm having that can sit there on disk and let it continue to run, that's the deep learning. The flash is when I wanna create a seamless transaction with a customer, or a consumer, or from a business to business. I need to have that decision now. I need to know it is you who is trying to swipe or purchase something with my mobile device or through the, basically through the Internet. Or how am I actually even swiping or inserting, tipping my card in that particular machine at a merchant. That's we're looking at how we use flash. >> So you're looking at perhaps using older technologies or different classes technologies for some of the training elements, but really moving to flash for the interfacing piece where you gotta deliver the real-time effort right now. >> And that's the experience. And that's what you're looking for. And that's you're looking, you wanna be able to make sure you're making those distinctions. 'Cause again there's no longer one or the other. It's how they interact. And again, when you look at your partners, it's the question now is how are they interacting? Am I actually, has this been done at scale somewhere else? Can you help me understand how I need to deploy this so that I can reduce my time to value, which is very, very important to create that seamless, frictionless transaction we want our consumers to have. >> So Nick, you talked about how you wanna work with companies that demonstrate that they have expertise, because you can't do it on your own. Companies that are capable of providing the scale that you need to provide. So just as we talk about how AI is placing pressure on different parts of the technology stack, it's got also to be putting pressure on the traditional relationships you have with technology suppliers. What are you looking for in suppliers as you think about these new classes of applications? >> Well, the part is you're looking at, for us it's do you have that scale that we're looking at? Have you done this before, that global scale? Again, in many cases you can have five guys in a garage that can do great things, but where has it been tested? When we say tested, it's not just, "Hey, we did this "in a pilot." We're talking it's gotta be robust. So that's one thing that you're looking for. You're looking for also a partner we can bring, for us, additional information that we don't have ourselves, right? In many cases, when you look at that partner they're gonna bring something that they're almost like they are an adjunct part of your team. They are your bench strength. That's what we're looking for when we look at it. What expertise do you have that we may not? What are you seeing, especially on the technology front, that we're not privy to? What are those different chips that are coming out, the new ways we should be handling the storage, the new ways the applications are interacting with that? We want to know from you, because again, everyone's, there's a talent, competition for talent, and we're looking for a partner who has that talent and will bring it to us so that we don't have to search it. >> At scale. >> Yeah, especially at scale. >> Nick Curcuro, Mastercard. Thanks for being on theCUBE. >> Thank you for having me. >> So there you have a great example of what leading companies or what a leading company is doing to try to take full advantage of the possibilities of AI by utilizing infrastructure that gets the job done simpler, faster, and better. So let's imagine for a second how it might affect your life. Well, here's your opportunity. We're now gonna move into the CrowdChat part of the event, and this is your chance to ask peers questions, provide your insights, tell your war stories. Ultimately, to interact with thought leaders about what it means to get ready for AI. Once again, I'm Peter Burris, thank you for watching. Now let's jump into the CrowdChat.

>> Live from the MIT campus in Cambridge, Massachusetts, it's theCUBE, covering the 12th Annual MIT Chief Data Officer and Information Quality Symposium. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host Rebecca Knight along with my cohost Peter Burris. We have two guests on this segment. We have Cortnie Abercrombie, she is the founder of the nonprofit AI Truth, and Carl Gerber, who is the managing partner at Global Data Analytics Leaders. Thanks so much for coming on theCUBE Cortnie and Carl. >> Thank you. >> Thank you. >> So I want to start by just having you introduce yourselves to our viewers, what you do. So tell us a little bit about AI Truth, Cortnie. >> So this was born out of a passion. As I, the last gig I had at IBM, everybody knows me for chief data officer and what I did with that, but the more recent role that I had was developing custom offerings for Fortune 500 in the AI solutions area, so as I would go meet and see different clients, and talk with them and start to look at different processes for how you implement AI solutions, it became very clear that not everybody is attuned, just because they're the ones funding the project or even initiating the purpose of the project, the business leaders don't necessarily know how these things work or run or what can go wrong with them. And on the flip side of that, we have very ambitious up-and-comer-type data scientists who are just trying to fulfill the mission, you know, the talent at hand, and they get really swept up in it. To the point where you can even see that data's getting bartered back and forth with any real governance over it or policies in place to say, "Hey, is that right? Should we have gotten that kind of information?" Which leads us into things like the creepy factor. Like, you know target (laughs) and some of these cases that are well-known. And so, as I saw some of these mistakes happening that were costing brand reputation, our return on investment, or possibly even creating opportunities for risk for the companies and for the business leaders, I felt like someone's got to take one for the team here and go out and start educating people on how this stuff actually works, what the issues can be and how to prevent those issues, and then also what do you do when things do go wrong, how do you fix it? So that's the mission of AI Truth and I have a book. Yes, power to the people, but you know really my main concern was concerned individuals, because I think we've all been affected when we've sent and email and all of a sudden we get a weird ad, and we're like, "Hey, what, they should not, is somebody reading my email?" You know, and we feel this, just, offense-- >> And the answer is yes. >> Yes, and they are, they are. So I mean, we, but we need to know because the only way we can empower ourselves to do something is to actually know how it works. So, that's what my missions is to try and do. So, for the concerned individuals out there, I am writing a book to kind of encapsulate all the experiences that I had so people know where to look and what they can actually do, because you'll be less fearful if you know, "Hey, I can download DuckDuckGo for my browser, or my search engine I mean, and Epic for my browser, and some private, you know, private offerings instead of the typical free offerings. There's not an answer for Facebook yet though. >> So, (laughs) we'll get there. Carl, tell us a little bit about Global Data Analytics Leaders. >> So, I launched Analytics Leaders and CDO Coach after a long career in corporate America. I started building an executive information system when I was in the military for a four-star commander, and I've really done a lot in data analytics throughout my career. Most recently, starting a CDO function at two large multinational companies in leading global transformation programs. And, what I've experienced is even though the industries may vary a little bit, the challenges are the same and the patterns of behavior are the same, both the good and bad behavior, bad habits around the data. And, through the course of my career, I've developed these frameworks and playbooks and just ways to get a repeatable outcome and bring these new technologies like machine learning to bear to really overcome the challenges that I've seen. And what I've seen is a lot of the current thinking is we're solving these data management problems manually. You know, we all hear the complaints about the people who are analysts and data scientists spending 70, 80% of their time being a data gatherer and not really generating insight from the data itself and making it actionable. Well, that's why we have computer systems, right? But that large-scale technology in automation hasn't really served us well, because we think in silos, right? We fund these projects based on departments and divisions. We acquire companies through mergers and acquisitions. And the CDO role has emerged because we need to think about this, all the data that an enterprise uses, horizontally. And with that, I bring a high degree of automation, things like machine learning, to solve those problems. So, I'm now bottling that and advising my clients. And at the same time, the CDO role is where the CIO role was 20 years ago. We're really in it's infancy, and so you see companies define it differently, have different expectations. People are filling the roles that may have not done this before, and so I provide the coaching services there. It's like a professional golfer who has a swing coach. So I come in and I help the data executives with upping their game. >> Well, it's interesting, I actually said the CIO role 40 years ago. But, here's why. If we look back in the 1970s, hardcore financial systems were made possible by the technology which allowed us to run businesses like a portfolio: Jack Welch, the GE model. That was not possible if you didn't have a common asset management system, if you didn't have a common cached management system, etc. And so, when we started creating those common systems, we needed someone that could describe how that shared asset was going to be used within the organization. And we went from the DP manager in HR, the DP manager within finance, to the CIO. And in many respects, we're doing the same thing, right? We're talking about data in a lot of different places and now the business is saying, "We can bring this data together in new and interesting ways into more a shared asset, and we need someone that can help administer that process, and you know, navigate between different groups and different needs and whatnot." Is that kind of what you guys are seeing? >> Oh yeah. >> Yeah. >> Well you know once I get to talking (laughs). For me, I can going right back to the newer technologies like AI and IOT that are coming from externally into your organization, and then also the fact that we're seeing bartering at an unprec... of data at an unprecedented level before. And yet, what the chief data officer role originally did was look at data internally, and structured data mostly. But now, we're asking them to step out of their comfort zone and start looking at all these unknown, niche data broker firms that may or may not be ethical in how they're... I mean, I... look I tell people, "If you hear the word scrape, you run." No scraping, we don't want scraped data, no, no, no (laugh). But I mean, but that's what we're talking about-- >> Well, what do you mean by scraped data, 'cause that's important? >> Well, this is a well-known data science practice. And it's not that... nobody's being malicious here, nobody's trying to have a malintent, but I think it's just data scientists are just scruffy, they roll up their sleeves and they get data however they can. And so, the practice emerged. Look, they're built off of open-source software and everything's free, right, for them, for the most part? So they just start reading in screens and things that are available that you could see, they can optical character read it in, or they can do it however without having to have a subscription to any of that data, without having to have permission to any of that data. It's, "I can see it, so it's mine." But you know, that doesn't work in candy stores. We can't just go, or jewelry stores in my case, I mean, you can't just say, "I like that diamond earring, or whatever, I'm just going to take it because I can see it." (laughs) So, I mean, yeah we got to... that's scraping though. >> And the implications of that are suddenly now you've got a great new business initiative and somebody finds out that you used their private data in that initiative, and now they've got a claim on that asset. >> Right. And this is where things start to get super hairy, and you just want to make sure that you're being on the up-and-up with your data practices and you data ethics, because, in my opinion, 90% of what's gone wrong in AI or the fear factor of AI is that your privacy's getting violated and then you're labeled with data that you may or may not know even exists half the time. I mean. >> So, what's the answer? I mean as you were talking about these data scientists are scrappy, scruffy, roll-up-your-sleeves kind of people, and they are coming up with new ideas, new innovations that sometimes are good-- >> Oh yes, they are. >> So what, so what is the answer? Is this this code of ethics? Is it a... sort of similar to a Hippocratic Oath? I mean how would you, what do you think? >> So, it's a multidimensional problem. Cortnie and I were talking earlier that you have to have more transparency into the models you're creating, and that means a significant validation process. And that's where the chief data officer partners with folks in risk and other areas and the data science team around getting more transparency and visibility into what's the data that's feeding into it? Is it really the authoritative data of the company? And as Cortnie points out, do we even have the rights to that data that's feeding our models? And so, by bringing that transparency and a little more validation before you actually start making key, bet-the-business decisions on the outcomes of these models, you need to look at how you're vetting them. >> And the vetting process is part technology, part culture, part process, it goes back to that people process technology trying. >> Yeah, absolutely, know where your data came from. Why are you doing this model? What are you doing to do with the outcomes? Are you actually going to do something with it or are you going to ignore it? Under what conditions will you empower a decision-maker to use the information that is the output of the model? A lot of these things, you have to think through when you want to operationalize it. It's not just, "I'm going to go get a bunch of data wherever I can, I put a model together. Here, don't you like the results?" >> But this is Silicon Valley way, right? An MVP for everything and you just let it run until... you can't. >> That's a great point Cortnie (laughs) I've always believed, and I want to test this with you, we talk about people process technology about information, we never talk about people process technology and information of information. There's a manner of respects what we're talking about is making explicit the information about... information, the metadata, and how we manage that and how we treat that, and how we defuse that, and how we turn that, the metadata itself, into models to try to govern and guide utilization of this. That's especially important in AI world, isn't it? >> I start with this. For me, it's simple, I mean, but everything he said was true. But, I try to keep it to this: it's about free will. If I said you can do that with my data, to me it's always my data. I don't care if it's on Facebook, I don't care where it is and I don't care if it's free or not, it's still my data. Even if it's X23andMe, or 23andMe, sorry, and they've taken the swab, or whether it's Facebook or I did a google search, I don't care, it's still my data. So if you ask me if it's okay to do a certain type of thing, then maybe I will consent to that. But I should at least be given an option. And no, be given the transparency. So it's all about free will. So in my mind, as long as you're always providing some sort of free will (laughs), the ability for me to having a decision to say, "Yes, I want to participate in that," or, "Yes, you can label me as whatever label I'm getting, Trump or a pro-Hillary or Obam-whatever, name whatever issue of the day is," then I'm okay with that as long as I get a choice. >> Let's go back to it, I want to build on that if I can, because, and then I want to ask you a question about it Carl, the issue of free will presupposes that both sides know exactly what's going into the data. So for example, if I have a medical procedure, I can sit down on that form and I can say, "Whatever happens is my responsibility." But if bad things happen because of malfeasance, guess what? That piece of paper's worthless and I can sue. Because the doctor and the medical provider is supposed to know more about what's going on than I do. >> Right. >> Does the same thing exist? You talked earlier about governance and some of the culture imperatives and transparency, doesn't that same thing exist? And I'm going to ask you a question: is that part of your nonprofit is to try to raise the bar for everybody? But doesn't that same notion exist, that at the end of the day, you don't... You do have information asymmetries, both sides don't know how the data's being used because of the nature of data? >> Right. That's why you're seeing the emergence of all these data privacy laws. And so what I'm advising executives and the board and my clients is we need to step back and think bigger about this. We need to think about as not just GDPR, the European scope, it's global data privacy. And if we look at the motivation, why are we doing this? Are we doing it just because we have to be regulatory-compliant 'cause there's a law in the books, or should we reframe it and say, "This is really about the user experience, the customer experience." This is a touchpoint that my customers have with my company. How transparent should I be with what data I have about you, how I'm using it, how I'm sharing it, and is there a way that I can turn this into a positive instead of it's just, "I'm doing this because I have to for regulatory-compliance." And so, I believe if you really examine the motivation and look at it from more of the carrot and less of the stick, you're going to find that you're more motivated to do it, you're going to be more transparent with your customers, and you're going to share, and you're ultimately going to protect that data more closely because you want to build that trust with your customers. And then lastly, let's face it, this is the data we want to analyze, right? This is the authenticated data we want to give to the data scientists, so I just flip that whole thing on its head. We do for these reasons and we increase the transparency and trust. >> So Cortnie, let me bring it back to you. >> Okay. >> That presupposes, again, an up-leveling of knowledge about data privacy not just for the executive but also for the consumer. How are you going to do that? >> Personally, I'm going to come back to free will again, and I'm also going to add: harm impacts. We need to start thinking impact assessments instead of governance, quite frankly. We need to start looking at if I, you know, start using a FICO score as a proxy for another piece of information, like a crime record in a certain district of whatever, as a way to understand how responsible you are and whether or not your car is going to get broken into, and now you have to pay more. Well, you're... if you always use a FICO score, for example, as a proxy for responsibility which, let's face it, once a data scientist latches onto something, they share it with everybody 'cause that's how they are, right? They love that and I love that about them, quite frankly. But, what I don't like is it propagates, and then before you know it, the people who are of lesser financial means, it's getting propagated because now they're going to be... Every AI pricing model is going to use FICO score as a-- >> And they're priced out of the market. >> And they're priced out of the market and how is that fair? And there's a whole group, I think you know about the Fairness Accountability Transparency group that, you know, kind of watch dogs this stuff. But I think business leaders as a whole don't really think through to that level like, "If I do this, then this this and this could incur--" >> So what would be the one thing you could say if, corporate America's listening. >> Let's do impact. Let's do impact assessments. If you're going to cost someone their livelihood, or you're going to cost them thousands of dollars, then let's put more scrutiny, let's put more government validation. To your point, let's put some... 'cause not everything needs the nth level. Like, if I present you with a blue sweater instead of a red sweater on google or whatever, (laughs) You know, that's not going to harm you. But it will harm you if I give you a teacher assessment that's based on something that you have no control over, and now you're fired because you've been laid off 'cause your rating was bad. >> This is a great conversation. Let me... Let me add something different, 'cause... Or say it a different way, and tell me if you agree. In many respects, it's: Does this practice increase inclusion or does this practice decrease inclusion? This is not some goofy, social thing, this is: Are you making your market bigger or are you making your market smaller? Because the last thing you want is that the participation by people ends with: You can't play because of some algorithmic response we had. So maybe the question of inclusion becomes a key issue. Would you agree with that? >> I do agree with it, and I still think there's levels even to inclusion. >> Of course. >> Like, you know, being a part of the blue sweater club versus the (laughs) versus, "I don't want to be a convict," you know, suddenly because of some record you found, or association with someone else. And let's just face it, a lot of these algorithmic models do do these kinds of things where they... They use n+1, you know, a lot... you know what I'm saying. And so you're associated naturally with the next person closest to you, and that's not always the right thing to do, right? So, in some ways, and so I'm positing just little bit of a new idea here, you're creating some policies, whether you're being, and we were just talking about this, but whether you're being implicit about them or explicit, more likely you're being implicit because you're just you're summarily deciding. Well, okay, I have just decided in the credit score example, that if you don't have a good credit threshold... But where in your policies and your corporate policy did it ever say that people of lesser financial means should be excluded from being able to have good car insurance for... 'cause now, the same goes with like Facebook. Some people feel like they're going to have to opt of of life, I mean, if they don't-- >> (laughs) Opt out of life. >> I mean like, seriously, when you think about grandparents who are excluded, you know, out in whatever Timbuktu place they live, and all their families are somewhere else, and the only way that they get to see is, you know, on Facebook. >> Go back to the issue you raised earlier about "Somebody read my email," I can tell you, as a person with a couple of more elderly grandparents, they inadvertently shared some information with me on Facebook about a health condition that they had. You know how grotesque the response of Facebook was to that? And, it affected me to because they had my name in it. They didn't know any better. >> Sometimes there's a stigma. Sometimes things become a stigma as well. There's an emotional response. When I put the article out about why I left IBM to start this new AI Truth nonprofit, the responses I got back that were so immediate were emotional responses about how this stuff affects people. That they're scared of what this means. Can people come after my kids or my grandkids? And if you think about how genetic information can get used, you're not just hosing yourself. I mean, breast cancer genes, I believe, aren't they, like... They run through families, so, I-- >> And they're pretty well-understood. >> If someone swabs my, and uses it and swaps it with other data, you know, people, all of a sudden, not just me is affected, but my whole entire lineage, I mean... It's hard to think of that, but... it's true (laughs). >> These are real life and death... these are-- >> Not just today, but for the future. And in many respects, it's that notion of inclusion... Going back to it, now I'm making something up, but not entirely, but going back to some of the stuff that you were talking about, Carl, the decisions we make about data today, we want to ensure that we know that there's value in the options for how we use that data in the future. So, the issue of inclusion is not just about people, but it's also about other activities, or other things that we might be able to do with data because of the nature of data. I think we always have to have an options approach to thinking about... as we make data decisions. Would you agree with that? Yes, because you know, data's not absolute. So, you can measure something and you can look at the data quality, you can look at the inputs to a model, whatever, but you still have to have that human element of, "Are you we doing the right thing?" You know, the data should guide us in our decisions, but I don't think it's ever an absolute. It's a range of options, and we chose this options for this reason. >> Right, so are we doing the right thing and do no harm too? Carl, Cortnie, we could talk all day, this has been a really fun conversation. >> Oh yeah, and we have. (laughter) >> But we're out of time. I'm Rebecca Knight for Peter Burris, we will have more from MIT CDOIQ in just a little bit. (upbeat music)

>> Narrator: From Berlin, Germany, it's theCUBE (relaxing music) covering DataWorks Summit Europe 2018. (relaxing music) Brought to you by Hortonworks. (quieting music) >> Well, hello and welcome to theCUBE. We're here at DataWorks Summit 2018 in Berlin, Germany, and it's been a great show. Who we have now is we have IBM. Specifically we have Dave McDonnell of IBM, and we're going to be talkin' with him for the next 10 minutes or so about... Dave, you explain. You are in storage for IBM, and IBM of course is a partner of Hortonworks who are of course the host of this show. So Dave, have you been introduced, give us your capacity or roll at IBM. Discuss the partnership of Hortonworks, and really what's your perspective on the market for storage systems for Big Data right now and going forward? And what kind of work loads and what kind of requirements are customers coming to you with for storage systems now? >> Okay, sure, so I lead alliances for the storage business unit, and Hortonworks, we actually partner with Hortonworks not just in our storage business unit but also with our analytics counterparts, our power counterparts, and we're in discussions with many others, right? Our partner organization services and so forth. So the nature of our relationship is quite broad compared to many of our others. We're working with them in the analytics space, so these are a lot of these Big Data Data Lakes, BDDNA a lot of people will use as an acronym. These are the types of work loads that customers are using us both for. >> Mm-hmm. >> And it's not new anymore, you know, by now they're well past their first half dozen applications. We've got customers running hundreds of applications. These are production applications now, so it's all about, "How can I be more efficient? "How can I grow this? "How can I get the best performance and scalability "and ease of management to deploy these "in a way that's manageable?" 'cause if I have 400 production applications, that's not off in any corner anymore. So that's how I'd describe it in a nutshell. >> One of the trends that we're seeing at Wikibon, of course I'm the lead analyst for Big Data Analytics at Wikibon under SiliconANGLE Media, we're seeing a trend in the marketplace towards I wouldn't call them appliances, but what I would call them is workload optimized hardware software platforms so they can combine storage with compute and are optimized for AI and machine learning and so forth. Is that something that you're hearing from customers, that they require those built-out, AI optimized storage systems, or is that far in the future or? Give me a sense for whether IBM is doing anything in that area and whether that's on your horizon. >> If you were to define all of IBM in five words or less, you would say "artificial intelligence and cloud computing," so this is something' >> Yeah. that gets a lot of thought in Mindshare. So absolutely we hear about it a lot. It's a very broad market with a lot of diverse requirements. So we hear people asking for the Converged infrastructure, for Appliance solutions. There's of course Hyper Converged. We actually have, either directly or with partners, answers to all of those. Now we do think one of the things that customers want to do is they're going to scale and grow in these environments is to take a software-defined strategy so they're not limited, they're not limited by hardware blocks. You know, they don't want to have to buy processing power and spend all that money on it when really all they need is more data. >> Yeah. >> There's pros and cons to the different (mumbles). >> You have power AI systems, I know that, so that's where they're probably heading, yeah. >> Yes, yes, yes. So of course, we have packages that we've modeled in AI. They feed off of some of the Hortonworks data lakes that we're building. Of course we see a lot of people putting these on new pieces of infrastructure because they don't want to put this on their production applications, so they're extracting data from maybe a Hortonworks data lake number one, Hortonworks data lake number two, some of the EDWs, some external data, and putting that into the AI infrastructure. >> As customers move their cloud infrastructures towards more edge facing environments, or edge applications, how are storage requirements change or evolving in terms of in the move to edge computing. Can you give us a sense for any sort of trends you're seeing in that area? >> Well, if we're going to the world of AI and cognitive applications, all that data that I mighta thrown in the cloud five years ago I now, I'm educated enough 'cause I've been paying bills for a few years on just how expensive it is, and if I'm going to be bringing that data back, some of which I don't even know I'm going to be bringing back, it gets extremely expensive. So we see a pendulum shift coming back where now a lot of data is going to be on host, ah sorry, on premise, but it's not going to stay there. They need the flexibility to move it here, there, or everywhere. So if it's going to come back, how can we bring customers some of that flexibility that they liked about the cloud, the speed, the ease of deployment, even a consumption based model? These are very big changes on a traditional storage manufacturer like ourselves, right? So that's requiring a lot of development in software, it's requiring a lot of development in our business model, and one of the biggest thing you hear us talk about this year is IBM Cloud Private, which does exactly that, >> Right. and it gives them somethin' they can work with that's flexible, it's agile, and allows you to take containerized based applications and move them back and forth as you please. >> Yeah. So containerized applications. So if you can define it for our audience, what is a containerized application? You talk about Docker and orchestrate it through Kubernetes and so forth. So you mentioned Cloud Private. Can you bring us up to speed on what exactly Cloud Private is and in terms of the storage requirements or storage architecture within that portfolio? >> Oh yes, absolutely. So this is a set of infrastructure that's optimized for on-premise deployment that gives you multi-cloud access, not just IBM Cloud, Amazon Web Services, Microsoft Azure, et cetera, and then it also gives you multiple architectural choices basically wrapped by software to allow you to move those containers around and put them where you want them at the right time at the right place given the business requirement at that hour. >> Now is the data storager persisted in the container itself? I know that's fairly difficult to do in a Docker environment. How do ya handle persistence of data for containerized applications within your architecture? >> Okay, some of those are going to be application specific. It's the question of designing the right data management layer depending on the application. So we have software intelligence, some of it from open source, some of which we add on top of open source to bring some of the enterprise resilience and performance needed. And of course, you have to be very careful if the biggest trend in the world is unstructured data. Well, okay fine, it's a lot of sensor data. That's still fairly easy to move around. But once we get into things like medical images, lots of video, you know, HD video, 4K video, those are the things which you have to give a lot of thought to how to do that. And that's why we have lots of new partners that we work with the help us with edge cloud, which gives that on premise-like performance in really a cloud-like set up. >> Here's a question out of left field, and you may not have the answer, but I would like to hear your thoughts on this. How has Blockchain, and IBM's been making significant investments in blockchain technology database technology, how is blockchain changing the face of the storage industry in terms of customers' requirements for a storage systems to manage data in distributed blockchains? Is that something you're hearing coming from customers as a requirement? I'm just tryin' to get a sense for whether that's, you know, is it moving customers towards more flash, towards more distributed edge-oriented or edge deployed storage systems? >> Okay, so yes, yes, and yes. >> Okay. So all of a sudden, if you're doing things like a blockchain application, things become even more important than they are today. >> Yeah. >> Okay, so you can't lose a transaction. You can't have a storage going down. So there's a lot more care and thought into the resiliency of the infrastructure. If I'm, you know, buying a diamond from you, I can't accept the excuse that my $100,000 diamond, maybe that's a little optimistic, my $10,000 diamond or yours, you know, the transaction's corrupted because the data's not proper. >> Right. >> Or if I want my privacy, I need to be assured that there's good data governance around that transaction, and that that will be protected for a good 10, 20, and 30 years. So it's elevating the importance of all the infrastructure to a whole different level. >> Switching our focus slightly, so we're here at DataWorks Summit in Berlin. Where are the largest growth markets right now for cloud storage systems? Is it Apache, is it the North America, or where are the growth markets in terms of regions, in terms of vertical industries right now in the marketplace for enterprise grade storage systems for big data in the cloud? >> That's a great question, 'cause we certainly have these conversations globally. I'd say the place where we're seeing the most activity would be the Americas, we see it in China. We have a lot of interesting engagements and people reaching out to us. I would say by market, you can also point to financial services in more than those two regions. Financial services, healthcare, retail, these are probably the top verticals. I think it's probably safe to assume, and we can the federal governments also have a lot of stringent requirements and, you know, requirements, new applications around the space as well. >> Right. GDPR, how is that impacting your customers' storage requirements. The requirement for GDPR compliance, is that moving the needle in terms of their requirement for consolidated storage of the data that they need to maintain? I mean obviously there's a security, but there's just the sheer amount of, there's a leading to consolidation or centralization of storage, of customer data, that would seem to make it easier to control and monitor usage of the data. Is it making a difference at all? >> It's making a big difference. Not many people encrypt data today, so there's a whole new level of interest in encryption at many different levels, data at rest, data in motion. There's new levels of focus and attention on performance, on the ability for customers to get their arms around disparate islands of data, because now GDPR is not only a legal requirement that requires you to be able to have it, but you've also got timelines which you're expected to act on a request from a customer to have your data removed. And most of those will have a baseline of 30 days. So you can't fool around now. It's not just a nice to have. It's an actual core part of a business requirement that if you don't have a good strategy for, you could be spending tens of millions of dollars in liability if you're not ready for it. >> Well Dave, thank you very much. We're at the end of our time. This has been Dave McDonnell of IBM talking about system storage and of course a big Hortonworks partner. We are here on day two of the DataWorks Summit, and I'm James Kobielus of Wikibon SiliconANGLE Media, and have a good day. (upbeat music)

>> Narrator: From Berlin, Germany, it's theCUBE. Covering Dataworks Summit Europe 2018. Brought to you by Hortonworks. >> Hello, welcome to theCUBE. We're here at Dataworks Summit 2018 in Berlin, Germany. I'm James Kobielus. I'm the lead analyst for Big Data Analytics, within the Wikibon team of SiliconAngle Media. Our guest is John Kreisa. He's the VP for Marketing at Hortonworks, of course, the host company of Dataworks Summit. John, it's great to have you. >> Thank you Jim, it's great to be here. >> We go long back, so you know it's always great to reconnect with you guys at Hortonworks. You guys are on a roll, it's been seven years I think since you guys were founded. I remember the founding of Hortonworks. I remember when it splashed in the Wall Street Journal. It was like oh wow, this big data thing, this Hadoop thing is actually, it's a market, it's a segment and you guys have built it. You know, you and your competitors, your partners, your ecosystem continues to grow. You guys went IPO a few years ago. Your latest numbers are pretty good. You're continuing to grow in revenues, in customer acquisitions, your deal sizes are growing. So Hortonworks remains on a roll. So, I'd like you to talk right now, John, and give us a sense of where Hortonworks is at in terms of engaging with the marketplace, in terms of trends that you're seeing, in terms of how you're addressing them. But talk about first of all the Dataworks Summit. How many attendees do you have from how many countries? Just give us sort of the layout of this show. >> I don't have all of the final counts yet. >> This is year six of the show? >> This is year six in Europe, absolutely, thank you. So it's great, we've moved it around different locations. Great venue, great host city here in Berlin. Super excited about it, I know we have representatives from more than 51 countries. If you think about that, drawing from a really broad set of countries, well beyond, as you know, because you've interviewed some of the folks beyond just Europe. We've had them from South America, U.S., Africa, and Asia as well, so really a broad swath of the open-source and big data community, which is great. The final attendance is going to be 1,250 to 1,300 range. The final numbers, but a great sized conference. The energy level's been really great, the sessions have been, you know, oversubscribed, standing room only in many of the popular sessions. So the community's strong, I think that's the thing that we really see here and that we're really continuing to invest in. It's something that Hortonworks was founded around. You referenced the founding, and driving the community forward and investing is something that has been part of our mantra since we started and it remains that way today. >> Right. So first of all what is Hortonworks? Now how does Hortonworks position itself? Clearly Hadoop is your foundation, but you, just like Cloudera, MapR, you guys have all continued to evolve to address a broader range of use-cases with a deeper stack of technology with fairly extensive partner ecosystems. So what kind of a beast is Hortonworks? It's an elephant, but what kind of an elephant is it? >> We're an elephant or riding on the elephant I'd say, so we're a global data management company. That's what we're helping organizations do. Really the end-to-end lifecycle of their data, helping them manage it regardless of where it is, whether it's on-premise or in the cloud, really through hybrid data architectures. That's really how we've seen the market evolve is, we started off in terms of our strategy with the platform based on Hadoop, as you said, to store, process, and analyze data at scale. The kind of fundamental use-case for Hadoop. Then as the company emerged, as the market kind of continued to evolve, we moved to and saw the opportunity really, capturing data from the edge. As IOT and kind of edge-use cases emerged it made sense for us to add to the platform and create the Hortonworks DataFlow. >> James: Apache NiFi >> Apache NiFi, exactly, HDF underneath, with associated additional open-source projects in there. Kafka and some streaming and things like that. So that was now move data, capture data in motion, move it back and put it into the platform for those large data applications that organizations are building on the core platform. It's also the next evolution, seeing great attach rates with that, the really strong interest in the Apache NiFi, you know, the meetup here for NiFi was oversubscribed, so really really strong interest in that. And then, the markets continued to evolve with cloud and cloud architectures, customers wanting to deploy in the cloud. You know, you saw we had that poll yesterday in the general session about cloud with really interesting results, but we saw that there was really companies wanting to deploy in a hybrid way. Some of them wanted to move specific workloads to the cloud. >> Multi-cloud, public, private. >> Exactly right, and multi-data center. >> The majority of your customer deployments are on prem. >> They are. >> Rob Bearden, your CEO, I think he said in a recent article on SiliconAngle that two-thirds of your deployments are on prem. Is that percentage going down over time? Are more of your customers shifting toward a public cloud orientation? Does Hortonworks worry about that? You've got partnerships, clearly, with the likes of IBM, AWS, and Microsoft Dasher and so forth, so do you guys see that as an opportunity, as a worrisome trend? >> No, we see it very much as an opportunity. And that's because we do have customers who are wanting to put more workloads and run things in the cloud, however, there's still almost always a component that's going to be on premise. And that creates a challenge for organizations. How do they manage the security and governance and really the overall operations of those deployments as they're in the cloud and on premise. And, to your point, multi-cloud. And so you get some complexity in there around that deployment and particularly with the regulations, we talked about GDPR earlier today. >> Oh, by the way, the Data Steward Studio demo today was really, really good. It showed that, first of all, you cover the entire range of core requirements for compliance. So that was actually the primary announcement at this show; Scott Gnau announced that. You demoed it today, I think you guys are off on a good start, yeah. We've gotten really, and thank you for that, we've gotten really good feedback on our DataPlane Services strategy, right, it provides that single pane of glass. >> I should say to our viewers that Data Steward Studio is the second of the services under the DataPlane, the Hortonworks DataPlane Services Portfolio. >> That's right, that's exactly right. >> Go ahead, keep going. >> So, you know, we see that as an opportunity. We think we're very strongly positioned in the market, being the first to bring that kind of solution to the customers and our large customers that we've been talking about and who have been starting to use DataPlane have been very, very positive. I mean they see it as something that is going to help them really kind of maintain control over these deployments as they start to spread around, as they grow their uses of the thing. >> And it's built to operate across the multi-cloud, I know this as well in terms of executing the consent or withdrawal of consent that the data subject makes through what is essentially a consent portal. >> That's right, that's right. >> That was actually a very compelling demonstration in that regard. >> It was good, and they worked very hard on it. And I was speaking to an analyst yesterday, and they were saying that they're seeing an increasing number of the customers, enterprises, wanting to have a multi-cloud strategy. They don't want to get locked into any one public cloud vendor, so, what they want is somebody who can help them maintain that common security and governance across their different deployments, and they see DataPlane Services is the way that's going to help them do that. >> So John, how is Hortonworks, what's your road map, how do you see the company in your go to market evolving over the coming years in terms of geographies, in terms of your focuses? Focus, in terms of the use-cases and workloads that the Hortonworks portfolio addresses. How is that shifting? You mentioned the Edge. AI, machine learning, deep learning. You are a reseller of IBM Data Science Experience. >> DSX, that's right. >> So, let's just focus on that. Do you see more customers turning to Hortonworks and IBM for a complete end-to-end pipeline for the ingest, for the preparation, modeling, training and so forth? And deployment of operationalized AI? Is that something you see going forward as an evolution path for your capabilities? >> I'd say yes, long-term, or even in the short-term. So, they have to get their data house in order, if you will, before they get to some of those other things, so we're still, Hortonworks strategy has always been focused on the platform aspect, right? The data-at-rest platform, data-in-motion platform, and now a platform for managing common security and governance across those different deployments. Building on that is the data science, machine learning, and AI opportunity, but our strategy there, as opposed to trying to trying to do it ourselves, is to partner, so we've got the strong partnership with IBM, resell their DSX product. And also other partnerships around to deliver those other capabilities, like machine learning and AI, from our partner ecosystem, which you referenced. We have over 2,300 partners, so a very, very strong ecosystem. And so, we're going to stick to our strategy of the platforms enabling that, which will subsequently enable data science, machine learning, and AI on top. And then, if you want me to talk about our strategy in terms of growth, so we already operate globally. We've got offices in I think 19 different countries. So we're really covering the globe in terms of the demand for Hortonworks products and beginning implements. >> Where's the fastest growing market in terms of regions for Hortonworks? >> Yeah, I mean, international generally is our fastest growing region, faster than the U.S. But we're seeing very strong growth in APAC, actually, so India, Asian countries, Singapore, and then up and through to Japan. There's a lot of growth out in the Asian region. And, you know, they're sort of moving directly to digital transformation projects at really large scale. Big banks, telcos, from a workload standpoint I'd say the patterns are very similar to what we've seen. I've been at Hortonworks for six and a half years, as it turns out, and the patterns we saw initially in terms of adoption in the U.S. became the patterns we saw in terms of adoption in Europe and now those patterns of adoption are the same in Asia. So, once a company realizes they need to either drive out operational costs or build new data applications, the patterns tend to be the same whether it's retail, financial services, telco, manufacturing. You can sort of replicate those as they move forward. >> So going forward, how is Hortonworks evolving as a company in terms of, for example with GDPR, Data Steward, data governance as a strong focus going forward, are you shifting your model in terms of your target customer away from the data engineers, the Hadoop cluster managers who are still very much the center of it, towards more data governance, towards more business analyst level of focus. Do you see Hortonworks shifting in that direction in terms of your focus, go to market, your message and everything? >> I would say it's not a shifting as much as an expansion, so we definitely are continuing to invest in the core platform, in Hadoop, and you would have heard of some of the changes that are coming in the core Hadoop 3.0 and 3.1 platform here. Alan and others can talk about those details, and in Apache NiFi. But, to your point, as we bring and have brought Data Steward Studio and DataPlane Services online, that allows us to address a different user within the organization, so it's really an expansion. We're not de-investing in any other things. It's really here's another way in a natural evolution of the way that we're helping organizations solve data problems. >> That's great, well thank you. This has been John Kreisa, he's the VP for marketing at Hortonworks. I'm James Kobielus of Wikibon SiliconAngle Media here at Dataworks Summit 2018 in Berlin. And it's been great, John, and thank you very much for coming on theCUBE. >> Great, thanks for your time. (techno music)

(techno music) >> (announcer) From Berlin, Germany it's theCUBE covering DataWorks Summit Europe 2018. Brought to you by Hortonworks. >> Well hello, welcome to theCUBE. We're here on day two of DataWorks Summit 2018 in Berlin, Germany. I'm James Kobielus. I'm lead analyst for Big Data Analytics in the Wikibon team of SiliconANGLE Media. And who we have here today, we have Alan Gates whose one of the founders of Hortonworks and Hortonworks of course is the host of DataWorks Summit and he's going to be, well, hello Alan. Welcome to theCUBE. >> Hello, thank you. >> Yeah, so Alan, so you and I go way back. Essentially, what we'd like you to do first of all is just explain a little bit of the genesis of Hortonworks. Where it came from, your role as a founder from the beginning, how that's evolved over time but really how the company has evolved specifically with the folks on the community, the Hadoop community, the Open Source community. You have a deepening open source stack with you build upon with Atlas and Ranger and so forth. Gives us a sense for all of that Alan. >> Sure. So as I think it's well-known, we started as the team at Yahoo that really was driving a lot of the development of Hadoop. We were one of the major players in the Hadoop community. Worked on that for, I was in that team for four years. I think the team itself was going for about five. And it became clear that there was an opportunity to build a business around this. Some others had already started to do so. We wanted to participate in that. We worked with Yahoo to spin out Hortonworks and actually they were a great partner in that. Helped us get than spun out. And the leadership team of the Hadoop team at Yahoo became the founders of Hortonworks and brought along a number of the other engineering, a bunch of the other engineers to help get started. And really at the beginning, we were. It was Hadoop, Pig, Hive, you know, a few of the very, Hbase, the kind of, the beginning projects. So pretty small toolkit. And we were, our early customers were very engineering heavy people, or companies who knew how to take those tools and build something directly on those tools right? >> Well, you started off with the Hadoop community as a whole started off with a focus on the data engineers of the world >> Yes. >> And I think it's shifted, and confirm for me, over time that you focus increasing with your solutions on the data scientists who are doing the development of the applications, and the data stewards from what I can see at this show. >> I think it's really just a part of the adoption curve right? When you're early on that curve, you have people who are very into the technology, understand how it works, and want to dive in there. So those tend to be, as you said, the data engineering types in this space. As that curve grows out, you get, it comes wider and wider. There's still plenty of data engineers that are our customers, that are working with us but as you said, the data analysts, the BI people, data scientists, data stewards, all those people are now starting to adopt it as well. And they need different tools than the data engineers do. They don't want to sit down and write Java code or you know, some of the data scientists might want to work in Python in a notebook like Zeppelin or Jupyter but some, may want to use SQL or even Tablo or something on top of SQL to do the presentation. Of course, data stewards want tools more like Atlas to help manage all their stuff. So that does drive us to one, put more things into the toolkit so you see the addition of projects like Apache Atlas and Ranger for security and all that. Another area of growth, I would say is also the kind of data that we're focused on. So early on, we were focused on data at rest. You know, we're going to store all this stuff in HDFS and as the kind of data scene has evolved, there's a lot more focus now on a couple things. One is data, what we call data-in-motion for our HDF product where you've got in a stream manager like Kafka or something like that >> (James) Right >> So there's processing that kind of data. But now we also see a lot of data in various places. It's not just oh, okay I have a Hadoop cluster on premise at my company. I might have some here, some on premise somewhere else and I might have it in several clouds as well. >> K, your focus has shifted like the industry in general towards streaming data in multi-clouds where your, it's more stateful interactions and so forth? I think you've made investments in Apache NiFi so >> (Alan) yes. >> Give us a sense for your NiFi versus Kafka and so forth inside of your product strategy or your >> Sure. So NiFi is really focused on that data at the edge, right? So you're bringing data in from sensors, connected cars, airplane engines, all those sorts of things that are out there generating data and you need, you need to figure out what parts of the data to move upstream, what parts not to. What processing can I do here so that I don't have to move upstream? When I have a error event or a warning event, can I turn up the amount of data I'm sending in, right? Say this airplane engine is suddenly heating up maybe a little more than it's supposed to. Maybe I should ship more of the logs upstream when the plane lands and connects that I would if, otherwise. That's the kind o' thing that Apache NiFi focuses on. I'm not saying it runs in all those places by my point is, it's that kind o' edge processing. Kafka is still going to be running in a data center somewhere. It's still a pretty heavy weight technology in terms of memory and disk space and all that so it's not going to be run on some sensor somewhere. But it is that data-in-motion right? I've got millions of events streaming through a set of Kafka topics watching all that sensor data that's coming in from NiFi and reacting to it, maybe putting some of it in the data warehouse for later analysis, all those sorts of things. So that's kind o' the differentiation there between Kafka and NiFi. >> Right, right, right. So, going forward, do you see more of your customers working internet of things projects, is that, we don't often, at least in the industry of popular mind, associate Hortonworks with edge computing and so forth. Is that? >> I think that we will have more and more customers in that space. I mean, our goal is to help our customers with their data wherever it is. >> (James) Yeah. >> When it's on the edge, when it's in the data center, when it's moving in between, when it's in the cloud. All those places, that's where we want to help our customers store and process their data. Right? So, I wouldn't want to say that we're going to focus on just the edge or the internet of things but that certainly has to be part of our strategy 'cause it's has to be part of what our customers are doing. >> When I think about the Hortonworks community, now we have to broaden our understanding because you have a tight partnership with IBM which obviously is well-established, huge and global. Give us a sense for as you guys have teamed more closely with IBM, how your community has changed or broadened or shifted in its focus or has it? >> I don't know that it's shifted the focus. I mean IBM was already part of the Hadoop community. They were already contributing. Obviously, they've contributed very heavily on projects like Spark and some of those. They continue some of that contribution. So I wouldn't say that it's shifted it, it's just we are working more closely together as we both contribute to those communities, working more closely together to present solutions to our mutual customer base. But I wouldn't say it's really shifted the focus for us. >> Right, right. Now at this show, we're in Europe right now, but it doesn't matter that we're in Europe. GDPR is coming down fast and furious now. Data Steward Studio, we had the demonstration today, it was announced yesterday. And it looks like a really good tool for the main, the requirements for compliance which is discover and inventory your data which is really set up a consent portal, what I like to refer to. So the data subject can then go and make a request to have my data forgotten and so forth. Give us a sense going forward, for how or if Hortonworks, IBM, and others in your community are going to work towards greater standardization in the functional capabilities of the tools and platforms for enabling GDPR compliance. 'Cause it seems to me that you're going to need, the industry's going to need to have some reference architecture for these kind o' capabilities so that going forward, either your ecosystem of partners can build add on tools in some common, like the framework that was laid out today looks like a good basis. Is there anything that you're doing in terms of pushing towards more Open Source standardization in that area? >> Yes, there is. So actually one of my responsibilities is the technical management of our relationship with ODPI which >> (James) yes. >> Mandy Chessell referenced yesterday in her keynote and that is where we're working with IBM, with ING, with other companies to build exactly those standards. Right? Because we do want to build it around Apache Atlas. We feel like that's a good tool for the basis of that but we know one, that some people are going to want to bring their own tools to it. They're not necessarily going to want to use that one platform so we want to do it in an open way that they can still plug in their metadata repositories and communicate with others and we want to build the standards on top of that of how do you properly implement these features that GDPR requires like right to be forgotten, like you know, what are the protocols around PIII data? How do you prevent a breach? How do you respond to a breach? >> Will that all be under the umbrella of ODPI, that initiative of the partnership or will it be a separate group or? >> Well, so certainly Apache Atlas is part of Apache and remains so. What ODPI is really focused up is that next layer up of how do we engage, not the programmers 'cause programmers can gage really well at the Apache level but the next level up. We want to engage the data professionals, the people whose job it is, the compliance officers. The people who don't sit and write code and frankly if you connect them to the engineers, there's just going to be an impedance mismatch in that conversation. >> You got policy wonks and you got tech wonks so. They understand each other at the wonk level. >> That's a good way to put it. And so that's where ODPI is really coming is that group of compliance people that speak a completely different language. But we still need to get them all talking to each other as you said, so that there's specifications around. How do we do this? And what is compliance? >> Well Alan, thank you very much. We're at the end of our time for this segment. This has been great. It's been great to catch up with you and Hortonworks has been evolving very rapidly and it seems to me that, going forward, I think you're well-positioned now for the new GDPR age to take your overall solution portfolio, your partnerships, and your capabilities to the next level and really in terms of in an Open Source framework. In many ways though, you're not entirely 100% like nobody is, purely Open Source. You're still very much focused on open frameworks for building fairly scalable, very scalable solutions for enterprise deployment. Well, this has been Jim Kobielus with Alan Gates of Hortonworks here at theCUBE on theCUBE at DataWorks Summit 2018 in Berlin. We'll be back fairly quickly with another guest and thank you very much for watching our segment. (techno music)

>> Voiceover: From Berlin, Germany, it's theCUBE, covering DataWorks Summit Europe 2018, brought to you by Hortonworks. >> Hi, hello, welcome to theCUBE, I'm James Kobielus. I'm the lead analyst for Big Data Analytics at the Wikibon, which is the team inside of SiliconANGLE Media that focuses on emerging trends and technologies. We are here, on theCUBE at DataWorks Summit 2018 in Berlin, Germany. And I have a guest here. This is, Muggie, and if I get it wrong, Muggie Van Staden >> That's good enough, yep. >> Who is with Obsidian, which is a South Africa-based partner of Hortonworks. And I'm not familiar with Obsidian, so I'm going to ask Muggie to tell us a little bit about your company, what you do, your focus on open source, and really the opportunities you see for big data, for Hadoop, in South Africa, really the African continent as a whole. So, Muggie? >> Yeah, James great to be here. Yes, Obsidian, we started it 23 years ago, focusing mostly on open source technologies, and as you can imagine that has changed a lot over the last 23 years when we started the concept of selling Linux was basically a box with a hat and maybe a T-shirt in it. Today that's changed. >> James: Hopefully there's a stuffed penguin in there, too. (laughing) I could use that right now. >> Maybe a manual. So our business has evolved a lot over the last 23 years. And one of the technologies that has come around is Hadoop. And we actually started with some of the other Hadoop vendors out there as our first partnerships, and probably three or four years ago we decided to take on Hortonworks as one of our vendors. We found them an amazing company to work with. And together with them we've now worked in four of the big banks in South Africa. One of them is actually here at DataWorks Summit. They won an award last night. So it's fantastic to be part of all of that. And yes, South Africa being so far removed from the rest of the world. They have different challenges. Everybody's nervous of Cloud. We have the joys that we don't really have any Cloud players locally yet. The two big players are in Microsoft and Amazon are planning some data centers soon. So the guys have different challenges to Europe and to the States. But big data, the big banks are looking at it, starting to deploy nice Hadoop clusters, starting to ingest data, starting to get real business value out of it, and we're there to help, and hopefully the four is the start for us and we can help lots of customers on this journey. >> Are South African-based companies, because you are so distant in terms of miles on the planet from Europe, from the EU, is any company in South Africa, or many companies, concerned at all about the global, or say the general data protection regulation, GDPR? US-based companies certainly are 'cause they operate in Europe. So is that a growing focus for them? And we have five weeks until GDPR kicks in. So tell me about it. >> Yeah, so from a South African point of view, some of the banks and some of the companies would have subsidiaries in Europe. So for them it's a very real thing. But we have our own Act called PoPI, which is the protection of private information, so very similar. So everybody's keeping an eye on it. Everybody's worried. I think everybody's worried for the first company to be fined. And then they will all make sure that they get their things right. But, I think not just because of a legislation, I think it's something that everybody should worry about. How do we protect data? How do we make sure the right people have access to the correct data when they should and nobody violates that because I mean, in this day and age, you know, Google and Amazon and those guys probably know more about me than my family does. So it's a challenge for everybody. And I think it's just the right thing for companies to do is to make sure that the data that they do have that they really do take good care of it. We trust them with our money and now we're trusting them with our data. So it's a real challenge for everybody. >> So how long has Obsidian been a partner of Hortonworks and how has your role, or partnership I should say, evolved over that time, and how do you see it evolving going forward. >> We've been a partner about three or four years now. And started off as a value added reseller. We also a training partner in South Africa for them. And as they as company have evolved, we've had to evolve with them. You know, so they started with HTTP as the Hadoop platform. Now they're doing NiFi and HDF, so we have to learn all of those technologies as well. But very, very excited where they're going with DataPlane service just managing a customer's data across multiple clusters, multiple clouds, because that's realistically where we see all the customers going, is you know clusters, on-premise clusters in typically multiple Clouds and how do you manage that? And we are very excited to walk this road together with Hortonworks and all the South African customers that we have. >> So you say your customers are deploying multiple Clouds. Public Clouds or hybrid private-public Clouds? Give us a sense, for South Africa, whether public Cloud is a major, or is a major deployment option or choice for financial services firms that you work with. >> Not necessarily financial services, so most of them are kicking tires at this stage, nobody's really put major work loads in there. As I mentioned, both Amazon and Microsoft are planning to put data centers down in South Africa very soon, and I think that will spur a big movement towards Cloud, but we do have some customers, unfortunately not Hortonworks customers, that are actually mostly in the Cloud. And they are now starting to look at a multi-Cloud strategy. So to ideally be in the three or four major Cloud providers and spinning up the right workloads in the right Cloud, and we're there to help. >> One of the most predominant workloads that your customers are running in the Cloud, is it backend in terms of data ingest and transformation? Is it a bit of maybe data warehousing with unstructured data? Is it a bit of things like queriable archiving. I want to get a sense for, what is predominant right now in workloads? >> Yeah I think most of them start with (mumble) environments. (mumbles) one customer that's heavily into Cloud from a data point of view. Literally it's their data warehouse. They put everything in there. I think from the banking customers, most of them are considering DR of their existing Hadoop clusters, maybe a subset of their data and not necessarily everything. And I think some of them are also considering putting their unstructured data outside on the Cloud because that's where most of it's coming from. I mean, if you have Twitter, Facebook, LinkedIn data, it's a bit silly to pull all of that into your environment, why not just put it in the Cloud, that's where it's coming from, and analyze that and connect it back to your data where relevant. So I think a lot of the customers would love to get there, and now Hortonworks makes it so much easier to do that. I think a lot of them will start moving in that direction. Now, excuse me, so are any or many of your customers doing development and training of machine learning algorithms and models in their Clouds? And to the extent that they are, are they using tools like the IBM Data Science Experience that Hortonworks resells for that? >> I think it's definitely on the radar for a lot of them. I'm not aware of anybody using it yet, but lots of people are looking at it and excited about the partnership between IBM and Hortonworks. And IBM has been a longstanding player in the South African market, and it's exciting for us as well to bring them into the whole Hortonworks ecosystem, and together solve real world problems. >> Give us a sense for how built out the big data infrastructure is in neighboring countries like Botswana or Angola or Mozambique and so forth. Is that an area that your company, are those regions that your company operates in? Sells into? >> We don't have offices, but we don't have a problem going in and helping customers there, so we've had projects in the past, not data related, that we've flown in and helped people. Most of the banks from a South African point of view, have branches into Africa. So it's on the roadmap, some are a little bit ahead of others, but definitely on the roadmap to actually put down Hadoop clusters in some of the major countries all throughout Africa. There's a big debate, do you put it down there, do you leave the data in South Africa? So they're all going through their own legislation, but it's definitely on the roadmap for all of them to actually take their data, knowledge in data science, up into Africa. >> Now you say that in South Africa Proper, there are privacy regulations, you know, maybe not the same as GDPR, but equivalent. Throughout Africa, at least throughout Southern Africa, how is privacy regulation lacking or is it emerging? >> I think it's emerging. A lot of the countries do have the basic rule that their data shouldn't leave the country. So everybody wants that data sovereignty and that's why a lot of them will not go to Cloud, and that's part of the challenges for the banks, that if they have banks up in Botswana, etc. And Botswana rules are our data has to stay in country. They have to figure out a way how do they connect that data to get the value for all of their customers. So real world challenges for everybody. >> When you're going into and selling into an emerging, or developing nation, of you need to provide upfront consulting to help the customer bootstrap their own understanding of the technology and making the business case and so forth. And how consultative is the selling process... >> Absolutely, and what we see with the banks, most of them even have a consultative approach within their own environment, so you would have the South African team maybe flying into the team at (mumbles) Botswana, and share some of the learnings that they've had. And then help those guys get up to speed. The reality is the skills are not necessarily in country. So there's a lot of training, a lot of help to go and say, we've done this, let us upscale you. And be a part of that process. So we sometimes send in teams to come and do two, three day training, basics, etc., so that ultimately the guys can operationalize in each country by themselves. >> So, that's very interesting, so what do you want to take away from this event? What do you find most interesting in terms of the sessions you've been in around the community showcase that you can take back to Obsidian, back in your country and apply? Like the announcement this morning of the Data Steward Studio. Do you see a possible, that your customers might be eager to use that for curation of their data in their clusters? >> Definitely, and one of the key messages for me was Scott, the CTO's message about your data strategy, your Cloud strategy, and your business strategy. It is effectively the same thing. And I think that's the biggest message that I would like to take back to the South African customers is to go and say, you need to start thinking about this. You know, as Cloud becomes a bigger reality for us, we have to align, we have to go and say, how do we get your data where it belongs? So you know, we like to say to our customers, we help the teams get the right code to the right computer and the right data, and I think it's absolutely critical for all of the customers to go and say, well, where is that data going to sit? Where is the right compute for that piece of data? And can we get it then, can we manage it, etc.? And align to business strategy. Everybody's trying to do digital transformation, and those three things go very much hand-in-hand. >> Well, Muggie, thank you very much. We're at the end of our slot. This has been great. It's been excellent to learn more about Obsidian and the work you're doing in South Africa, providing big data solutions or working with customers to build the big data infrastructure in the financial industry down there. So this has been theCUBE. We've been speaking with Muggie Van Staden of Obsidian Systems, and here at DataWorks Summit 2018 in Berlin. Thank you very much.