>>Welcome to this cube conversation. I'm Dave Nicholson and I am joined by Lisa O'Malley senior director of product management, Google cloud, specializing in industry solutions. Lisa, welcome. Welcome to the >>Cube. Thank you, David. Great to be here. >>So let's, let's dive right into it. What makes an industry solution and what makes for a poser of an industry solution? >>Um, I think industry solutions are really all about driving business outcomes, that individual industries and individual companies within those industries really, really care about. Um, you know, uh, an alternative might be to take a horizontal solution, whether it's a CRM or an ERP and slap some industry labels on it and pose it as an industry solution. We like to do the hard engineering work, which is really going and figuring out what are the key outcomes that industries care about and spending time understanding the root causes and helping them with a Google cloud platform and all of the security data and analytics and AI capabilities that we have helping them really deeply solve those problems, um, at a, at a level that makes a difference and transforms their industry. >>So can you give us an example of something that's engineered in for a specific industry? When someone tells me they're engineering something in a, I think of a, I think of my car seat and if you're going to engineer in comfort, you better provide some controls for adjustability for me. So how, how do you strike that balance between hard engineered and sort of the bespoke services and customization that are always going to be necessary? >>Yeah, so clearly we don't want to create bespoke solutions for individual customers. We like to take, you know, industry wide problems and think about them a different way. You know, one example might be retail search. Um, you've probably all gone to a retailer, uh, typed items into the search bar and had an unsuccessful result. Um, and then maybe you've gone to Google and Googled it there and come back with the item in that retailer as one of the results. So search within individual retailers, websites historically has not been great. However, we've delivered a solution that brings Google quality search to an individual retailers, catalog and website. Um, and what we see is that this really helps them with what we call search abandonment. So it's like $300 billion a year is lost every year to people having unsuccessful, uh, searches on various websites. And so by, by delivering our product discovery solution, which incorporates, uh, retail that really solves an industry-wide problem. >>So Google is considered to be at the forefront of artificial intelligence. AI ML gets tossed around a lot, um, GCP, Google cloud, it's the real deal. Uh, how does, how does AI factor into some of these industry specific solutions? >>Uh, great question. Um, and not all of them are based on AI, but clearly, you know, when we think about Google, you think about, you know, data analytics, our ability to manipulate data and to apply AI and ML to real-world problems. Um, I'll give you an example of where we're using some of our core AI technology. And so that would be a product like visual inspection, where on a manufacturing line, you want to be able to, um, identify defects very effectively. Um, existing systems require a ton of training data. Whereas our machine learning allows us to deliver very high quality, like 10 X reduction in defects, um, with about, you know, 300 times less training data. And so that's where we've applied both our vision technology and our machine learning capabilities, uh, to come up with a great solution that fundamentally changes how inspection is done on manufacturing lines. >>So visual inspection inspection is one category, uh, recommendations is also often cited as another example. Uh, do, do you have any specific, uh, customer examples either with names or without are fine? Um, where, where recommendations come into play and, and, and, and what are some of the, the shades of difference when you talk about, um, the kind of intelligence that goes into visual inspection versus recommendations? Okay. >>So, uh, recommendations, one customer that I can talk about is Ikea. Um, they have implemented recommendations for a number of months at this stage, and they've seen an increase in click-through rate of about 30%. Um, we measure about 400% increase in, um, you know, relevant recommendations and overall that's, that's delivered at a 2% increase in average order value. Um, and so that's just one example of how recommendations and recommendations technology obviously has been with Google for a long time, when you think across search and YouTube, and a lot of the capabilities that are core to Google. And so being able to apply that more broadly to an industry circumstances is really, really powerful, um, on the visual inspection side, uh, Foxconn deployed this technology within their phone manufacturing process and that, um, uh, increased the accuracy of their defect detection by about 10 X. >>So, so you touched on this a little bit already, but if someone is trying to evaluate the difference between a real industry solution or industry cloud versus something that's just slapping a label on top of another label of, you know, for something that's generic, um, what are the sort of litmus tests that they should apply? What are the things that you look for? What are the criteria you think are important? >>Yeah. Um, I think it's really important to, you know, to really dig down, to identify as this just a horizontal solution, or has a company done the real hard engineering work to solve the problem? The way I think about it is I ask several questions, you know, do we think that these products have been engineered from the ground up to solve a specific industry problem? Are they just selling, you know, horizontal capabilities like CRM or ERP, um, and putting an industry label on it, have they actually being built for real world companies, you know, can they demo it in a real world example? Um, how much of it is, you know, original code code or is it, you know, just a reference architecture, how much, um, must a customer pay or work to actually implement that solution? Does it work out of the box or is there, you know, a big implementation with lots of system integrator, uh, spend required? And then I think lastly, and maybe most importantly is, is the, is the pricing connected to the value that you're bringing and is that pricing transparent? Um, and is it easy to understand for the customer and where the value, uh, where the value lies based on the pricing? >>What does the process look like, uh, within Google, within Google cloud, when you're considering what to categorize as an industry, what level of granularity do you get down to? How, how do you, how do you figure out what makes sense? Is it a level of effort in terms of engineering? Is it total addressable market? I'd love to, I'd love to be a fly on the wall. In some of those conversations, you think of the obvious categories, like financial services, retail, um, but give us an idea of what those conversations look like when you're trying to determine what constitutes an industry. >>So I think there's what constitutes an industry. And then there's what constitutes an industry that we want to build a specific productized solution for in terms of what constitutes an industry. I mean, I think those are pretty established in the market. They are things like financial services, healthcare, retail, and then there may be sub-verticals within those industries. So within financial services, you might have banking, retail banking, and commercial banking. You might have payments, you might have capital markets. Um, and I think those are, are, are pretty well established. I think you would expect the similar, the types of conversation that we have around what's the total addressable market. What's the level of technical sophistication within those industries. And then what are the problems that they're really seeking to solve? And do we have solutions that we think can make a difference there? >>So example of AI applied in an industry solution is the area around documents. Uh, and, uh, so, uh, again, if you can give us an example of, of >>Document AI, uh, how it's brought to bear, how it's different from making recommendations and product searches, and maybe a customer example, if you have one. Yeah. So, um, document, uh, AI is really, really phenomenal space in that the, the efficiency gains and operational efficiency, essentially around taking paper out of the process or taking people who are reviewing paper out of the process, uh, the opportunity is immense. You know, when you think about mortgage applications and the hundreds and hundreds of pages that we all have to sign up for, whether it's a refinance or a new buy, um, and then some poor person within the institution has to go and review all of that documentation. Um, we can turn that into, you know, something really phenomenal by using the document AI technology that Google has developed over time and training it on mortgage documentation. For example, um, Mr. Cooper uses our document AI technology, and they were able to, uh, increase their efficiency by about 400%, uh, in terms of their mortgage application process. So that's pretty phenomenal, but, you know, documents, don't just show up in mortgages nor do they just show up in financial services. There are documents all over the healthcare system. There are documents all over a public sector system. Um, and we believe that there's immense opportunity to take, uh, to take much of that paper and that re manual review of paper out of the system. >>So Lisa what's next, what kinds of industry solutions are you're working on that you can share a glimpse into? We're not asking for secrets that we can't, they can't be shared here just to be clear, but what's on the what's on the horizon. >>So I think there's some exciting things happening in our retail environment. Um, you can imagine that, you know, in the post COVID world, retail is very different from where it has been. And so the ability to bring your online and offline business and your consumer journeys through that business, um, really together is, is going to be super important. And so we're working on a lot of things there around understanding a full 360 view of your customer and how we might help them through their shopping journeys. Um, on, in, uh, in healthcare, we have, um, some phenomenal products in the market like our healthcare data engine, which helps take, um, sources of data from the many silos that exist across our healthcare system and bring them into one longitudinal view of the data. And so you can imagine that there would be many, um, diagnostic and operational opportunities to use that data in a much more efficient way than there are than the, than it's being used today, >>Specifically in healthcare, has that, have we seen a pivot, um, uh, because of the pandemic? >>So I don't know that it was specific to the pandemic. I think that the, um, the healthcare industry is, uh, is undergoing a lot of change, uh, in general across the board. And so the, the realization is that with, um, uh, I think the, what the, what the pandemic has done is it has accelerated some existing trends around movement towards telehealth, um, movement towards dispersed, um, healthcare within communities, as opposed to big centers. Uh, and so, you know, we find then that the, the data becomes even more fragmented and becomes more siloed and lots of, um, companies are solving small pieces of the problem. And so what Google would like to be able to do is to bring all of that data together, harmonize it, understanding all of the regulatory and compliance issues and opportunities that there are within the healthcare area and enable not just Google to build solutions on top of this data, but also to enable partners, um, and, uh, and, and providers our, uh, our payers themselves to, uh, to build solutions on top of the data. >>Lisa, it looks like it's time to wrap up. Do you have any final thoughts on, especially a, you know, where, where does AI progress us in industry solutions moving forward? >>So, you know, I think that AI is a tool that we should use wisely. I think it's something that we should understand how we, you know, understand that the, um, uh, our customer's deep needs, um, their business. I would come and sit there hoping to drive and where careful application of AI and machine learning can really benefit everybody in transforming their industries, whether that's through increasing top line revenue, taking cost out of the system, or generally being more efficient. >>Fantastic. Lisa, thank you for joining us for this cube conversation from the cube until next time. This is Dave Nicholson. >>Thank you, David. It was a pleasure. >>Thank you, Lisa.

>> Okay. We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid right. So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manuvir Das who's the head of enterprise computing at NVIDIA. And before I hand it off to Rob, I just want to say for those of you who follow me at the Cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise and it's being driven by the emergence of data intensive applications and workloads. No longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like NVIDIA. So let's learn more about this collaboration and what it means to your data business. Rob, take it away. >> Thanks Mick and Dave. That was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy in accelerating the path to value and hybrid environments. And I want to drill down on this aspect. Today, every business is facing accelerating change. Everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor now. Every engagement with coworkers, customers and partners is virtual. From website metrics to customer service records and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? At Cloudera, we believe this onslaught of data offers an opportunity to make better business decisions faster and we want to make that easier for everyone, whether it's fraud detection, demand forecasting, preventative maintenance, or customer churn. Whether the goal is to save money or produce income, every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit that cloud provides. And with security and edge to AI data intimacy, that's why the partnership between Cloudera and NVIDIA together means so much. And it starts with a shared vision, making data-driven decision-making a reality for every business. And our customers will now be able to leverage virtually unlimited quantities and varieties of data to power an order of magnitude faster decision-making. And together we turbo charged the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. We're joined today by NVIDIA's Manduvir Das, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, Manuvir, thank you for joining us. Over to you now. >> Thank you Rob, for having me. It's a pleasure to be here on behalf of NVIDIA. We're so excited about this partnership with Cloudera. You know, when, when NVIDIA started many years ago, we started as a chip company focused on graphics. But as you know, over the last decade, we've really become a full stack, accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, AI being a prime example. And when we think about Cloudera, and your company, your great company, there's three things we see Rob. The first one is that for the companies that were already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing we've seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about NVIDIA's mission going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies to date who have been the early adopters using the power acceleration by changing their technology and their stacks. But more and more we see the opportunity of meeting customers where they are with tools that they're familiar with, with partners that they trust. And of course, Cloudera being a great example of that. The second part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through. But as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. And so again, the power of your platform is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute, the machine learning compute, needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And, and Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where, literally, they took the workflow they had, they took the servers they had, they added GPUs into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >> How you doing? My name's Joe Ansaldi. I'm the branch chief of the technical branch in RAS. It's actually the research division, research and statistical division of the IRS. Basically, the mission that RAS has is we do statistical and research on all things related to taxes, compliance issues, fraud issues, you know, anything that you can think of basically, we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those algorithms, the number of parameters each of those algorithms have. So that's, that's really been our challenge now. The expectation was that with NVIDIA and Cloudera's help and with the cluster, we actually build out to test this on the actual fraud detection algorithm. Our expectation was we were definitely going to see some speed up in computational processing times. And just to give you context, the size of the data set that we were, the SME was actually working her algorithm against was around four terabytes. If I recall correctly, we had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them. It was really, really quick. The definite now term, short term, what's next is going to be the subject matter expert is actually going to take our algorithm run with that. So that's definitely the now term thing we want to do. Going down, go looking forward, maybe out a couple of months, we're also looking at procuring some A-100 cards to actually test those out. As you guys can guess, our datasets are just getting bigger and bigger and bigger, and it demands to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward and then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run a, you know, run to our heart's desire, wherever our imaginations takes our SMEs to actually develop solutions. Now have the platforms to run them on. Just kind of to close out, we really would be remiss, I've worked with a lot of companies through the year and most of them been spectacular. And you guys are definitely in that category, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't thank you guys. So thank you for the opportunity. Doing fantastic. and I'd have to also, I want to thank my guys. my staff, Raul, David worked on this, Richie worked on this, Lex and Tony just, they did a fantastic job and I want to publicly thank them for all the work they did with you guys and Chev, obviously also is fantastic. So thank you everyone. >> Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and NVIDIA? Is it primarily go to market or are you doing engineering work? What's the story there? >> It's really both. It's both go to market and engineering The engineering focus is to optimize and take advantage of NVIDIA's platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both. >> Great. Thank you. Manuvir, maybe you could talk a little bit more about why can't we just use existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do NVIDIA and Cloudera bring to the table that goes beyond the conventional systems that we've known for many years? >> Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So, the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now NVIDIA has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform so that regardless of the technique the customer is using to get insight from the data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >> So, I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going towards doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start. You think about AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems, and real-time AI inference, at least even at the edge, huge potential for business value. In a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the liking. So you're putting AI into these data intensive apps within the enterprise. The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >> Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint. And new platforms like these being developed by Cloudera and NVIDIA will dramatically lower the cost of enabling this type of workload to be done. And what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation engine, supply chain management, drug province. And increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots. That AR, VR and manufacturing so driving better quality. The power grid management, automated retail, IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >> I mean, Manufir, this is like your wheelhouse. Maybe you could add something to that. >> Yeah. I mean, I agree with Rob. I mean he listed some really good use cases, you know, The way we see this at NVIDIA, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled use cases, particular use cases like a chat bot from the ground up with the hardware and the software. Almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. Now, I think we are in the first phase of the democratization. For example, the work we do with Cloudera, which is for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. And you still come home and assemble it, but all the parts are there, the instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought a table and it showed up and somebody placed it in the right spot. Right? And they didn't really have to learn how to do AI. So these are the phases. And I think we're very excited to be going there. >> You know, Rob, the great thing about, for your customers is they don't have to build out the AI. They can, they can buy it. And just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations, and Mick I talked about this, the GIGO problem that we've all, you know, studied in college, you know, garbage in, garbage out. But, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob, over the next several years? >> So yeah, the combination of massive amounts of data that had been gathered across the enterprise in the past 10 years with an open APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency. And that's allowing us as an industry to democratize the data access while at the same time delivering the federated governance and security models. And hybrid technologies are playing a key role in making this a reality and enabling data access to be quote, hybridized, meaning access and treated in a substantially similar way, irrespective of the physical location of where that data actually resides. >> And that's great. That is really the value layer that you guys are building out on top of all this great infrastructure that the hyperscalers have have given us. You know, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you could go first and then Manuvir, you could bring us home. Where do you guys want to see the relationship go between Cloudera and NVIDIA? In other words, how should we as outside observers be, be thinking about and measuring your project, specifically in the industry's progress generally? >> Yes. I think we're very aligned on this and for Cloudera, it's all about helping companies move forward, leverage every bit of their data and all the places that it may be hosted and partnering with our customers, working closely with our technology ecosystem of partners, means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >> Yeah and I agree with Rob and for us at NVIDIA, you know, we, this partnership started with data analytics. As you know, Spark is a very powerful technology for data analytics. People who use Spark rely on Cloudera for that. And the first thing we did together was to really accelerate Spark in a seamless manner. But we're accelerating machine learning. We're accelerating artificial intelligence together. And I think for NVIDIA it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity.

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

(bright music) >> Welcome back to HPE Discover 2021. My name is Dave Vellante and you're watching theCUBE's virtual coverage of HPE's big customer event. Of course, the virtual edition and we're going to dig into transformations, the role of technology and the role of senior technology leadership. Look, let's face it, HPE has gone through a pretty dramatic transformation itself in the past few years so it makes a great example in case study and with me is Rashmi Kumar who is the senior vice president and CIO at HPE, Rashmi welcome come on inside theCUBE. >> Hi Dave nice to be here. >> Well it's been almost a year since COVID you know changed the world as we know it. How would you say the role of the CIO specifically in generally IT has changed? I mean you got digital, zero trust has gone from buzzword to mandate, digital, everybody was you know complacent about digital in many ways and now it's really accelerated, remote work, hybrid, how do you see it? >> Absolutely, as I said in the last Discover that COVID has been the biggest reason to accelerate digital transformation in the companies. I see CIO's role has changed tremendously in the last 15 months. It's no more just keep the operations running, that's become a table stake. Our roles have become not only to create digital customer experience, engage with our customers in different ways, but also to transform the company operations from inside out to be able to give that digital experience from beginning to end of the customer engagement going forward. We have also become responsible for switching our strategies around the companies as the COVID hit in different parts of the world at different times and how companies structured their operations to go from one region to another, a global company like HPE had to look into its supply chain differently, had to look into strategies to mitigate the risk that was created because of the supply chain disruptions, as well as you go to taking care of our employees. How do you create this digital collaboration experience where teams can still come together and make the work happen for our end customers? How do we think about future employee engagement when people are not coming into these big buildings and offices and working together, but how do you create the same level of collaboration, coordination, as well as delivery of faster, good and services which is enabled by technology going forward. So CIO and IT's role has gone from giving a different level of customer experience to different level of employee experience, as well as enabling day-to-day operations of the companies. CEOs have realized that digital is the way to go forward, it does not matter what industry you are in and now CIOs have their seat at the table to define what the future of every company now which is a technology company irrespective you are in oil and gas, or mining, or a technical product, or a car or a mobility company, end of the day you have to act and behave like a technology company. >> So I want to ask you about that because you've been a CIO at a leading technology provider now for the last three years and you've had previous roles and were, you know non-technical, technology, you know, selling to IT companies and as you point out those worlds are coming together. Everybody's a technology company today. How do you think that changes the role of the CIO because it would always seem to me that there was a difference between a CIO at a tech company you know what I mean by that and a CIO at sort of every other company is, are those two worlds converging? >> Absolutely and it's interesting you pointed out that I have worked in many different industries from healthcare and pharma, to entertainment, to utilities and now at a technology company. End of the day the issues that IT deals with are pretty similar across the organization. What is different here is now my customers are people like me in other industries and I have little bit of an advantage because just having the experience across various ecosystem even that HPE look I was fortunate at HPE because of Antonio's leadership we had top-down mandate to transform how we did business and I talked about my NextGEN IT program in last year's CUBE interview. But at the same time while we were changing our customer, partner's experience from ordering, to order processing, to supply chain, to finance, we decided this pivot of becoming as a service company. And if you think about that pivot, it's pretty common. If it was a technology company or non-technology company. At HPE we were very used to selling a product and coming back three years later at the time of refresh of infrastructure or hardware. That's no more true for us. Now we are becoming an as a service or a subscription company and IT played a major role to enable that quote-to-cash experience which is very different than the traditional experience, around how we stay connected with our customer, how we proactively understand their behavior. I always talk about this term digital exhaust which results into data, which can result into better insight and you can not only upsell, cross-sell because now you have more data about your product usage, but first and foremost give what your customer wants in a much better way because you can proactively understand their needs and wants because you are providing a digital product versus a physical product. So this is the change that most of the companies are now going through. If you look at Domino's transition, they are pizza sellers but they did better because they had better digital experience. If you look at Chipotle, these are food service companies. Ikea which is a furniture manufacturer, across the board we have helped our customers and industries to understand how to become a more digital provider. And remember when HPE says edge to cloud platform as a service, edge is the product, the customers is what we deal with and how do we get that, help them get that data, understand how the product is behaving and then get the information to cloud for further analysis and understanding from the data that comes out of the products that they sell. >> I think you've been at HPE now I think around three years and I've been watching of course for decades, you know HPE, well HP then HPE is, I feel like it's entering now that sort of third phase of its transformation, your phase one was okay we got to figure out how to deal or operate as separate companies, okay, that took some time and then it was okay, now how do we align our resources? And you know what are the waves that we're going to ride? And how do we take our human capital, our investments and what bets do we place? And you're all in on as a service and now it's like okay, you know how do we deliver on all those promises? So pretty massive transformations. You talked about edge to cloud as a service so you've got this huge pivot in your business. What's the technology strategy to support that transformation? >> Yeah, that's a great question. So as I mentioned first, your second phase which was becoming a stand-alone company was the NextGEN IT program where we brought in S4 and 60 related ecosystem application where even in the traditional business there was a realization that we were 120 billion company, we are a 30 billion company, we need different types of technologies as well as more integrated across our product line, across the globe and we, I'm very happy to report that we are the last leg of NextGEN IT transformation. Where we have brought in new customer experience through low-touch or no-touch order processing, a very strong S4 capabilities where we are now able to run all global orders across all our hardware and services business together and I'm happy to report that we have been able to successfully run through the transformation which a typical company of our size would take five or six years to do in around close to three years. But at the same time while we were building this foundation and the capabilities to be able to do order management supply chain and data and analytics platforms, we also made the pivot to go to as a service. Now for as a service and subscription selling, it needs a very different quote-to-cash experience for our customers. And that's where we had bring in platforms like BRIM to do subscription billing, convergent charging and a whole different way to address. But we were lucky to have this transformation completed on which we could bolt on this new capability and we had the data analytics platform built which now these as a service products can also use to drive better insight into our customer behavior as well as how they're using our product real time for our operations teams. >> Well they say follow the money, in theCUBE we love to say follow the data. I mean data is obviously a crucial component of competitive advantage, business value, so talk a little bit more about the role of data, I'm interested in where IT fits. You know a lot of companies they'll have a chief data officer, or a CIO, sometimes they're separate sometimes they work, you know for each other, or CDO works for CIO, how do you guys approach the whole data conversation? >> Yeah that's a great question and has been top of the mind of a lot of CEOs, CIOs, chief digital officers in many different companies. The way we have set it up here is we do have a chief data officer and we do have a head of technology and platform and data lake within IT. Look the way I see is that I call the term data torture. If they have multiple data lakes, if they have multiple data locations and the data is not coming together at one place at the first time that it comes out to the source system, we end up with data swamps and it's very difficult to drive insights, it's very difficult to have single version of truth. So HPE had two-pronged approach. First one was as part of this NextGEN IT transformation we embarked upon the journey first of all to define our customers and products in a very uniform way across the globe. It's called entity master data and product master data program. These were very, very difficult program. We are now happy to report that we can understand the customer from cold stage to servicing stage beginning to end across all our system. It's been a tough journey but it was effort well spent. At the same time while we were building this master data capability we also invested time in our analytics platform. Because we are generating so much data now globally as one footprint, how do we link our data lake to our SAP and Salesforce and all these systems where our customer data flows through and create analytics and insight from it from our customers or our operations team. At the same time we also created a chief data officer role where the responsibility is really to drive business from understanding what decision making and analytics they need around product, around customer, around their usage around their experience to be able to drive better alignment with our customers and products going forward. So this creates efficiencies in the organization. If you have a leader who is taking care of your platforms and data, building single source of truth and you have a leader who is propagating this mature notion of handling data as enterprise data and driving that focus on understanding the metrics and the insight that the businesses need to drive better customer alignment, that's when we gain those efficiencies and behind the scenes the chief data officer and the data leader within my organization work very, very closely to understand each other needs, sometimes art of the possible, where do we need the data processing? Is it at the edge? Is it in the cloud? What's the best way to drive the technology and the platform forward? And they kind of rely on each other's knowledge and intelligence to give us superior results. And I have done data analytics in many different companies, this model works. Where you have focus on insight and analytics without, because data without insight is of no value. But at the same time you need clean data, you need efficient, fast platforms to process that insight at the functional non-functional requirement that our business partners have. And that's how we have established in here and we have seen many successes recently as of now. >> I want to ask you a kind of a harder, maybe it's not a harder question it's a weird question around single version of the truth. 'Cause it's clearly a challenge for organizations and there's many applications, workloads that require that single version of the truth, the operational systems, the transaction systems, the HR, the Salesforce and clearly you have to have a single version of the truth. I feel like, however we're on the cusp of a new era where business lines see an opportunity for whatever, their own truth to work with a partner to create some kind of new data product. And it's early days in that but I wonder, maybe not the right question for HPE but I wonder if you see it with in your ecosystems where it's yes, single version of truth is sort of one class of data and analytics got to have that nailed down, data quality, everything else. But then there's this sort of artistic version of the data where business people need more freedom, they need more latitude to create. Are you seeing that? Maybe you can help me put that into context. >> That's a great question Dave and I'm glad you asked it so. I think Tom Davenport, who is known in the data space talks about the offensive and the defensive use cases of leveraging data. I think the piece that you talked about where it's clean, it's pristine, it's quality, it's all that, most of those offer the offensive use cases where you are improving companies' operations incrementally because you have very clean data, you have very good understanding of how my territories are doing, how my customers are doing, how my products are doing, how am I meeting my SLAs or how my financials are looking, there's no room for failure in that area. The other area is though which works on the same set of data. It's not a different set of data but the need is more around finding needles in the haystack to come up with new needs, new wants in customers or new business models that we go with. The way we have done it is we do take this data, take out what's not allowed for everybody to be seen and then what we call is a private space but that's this entire data available to our business leader not real time, because the need is not as real time because they are doing more, what we call this predictive analytics to be able to leverage the same data set and run their analytics. And we work very closely with business units, we educate them, we tell them how to leverage this data set and use it and gather their feedback to understand what they need in that space to continue to run with their analytics. I think as we talk about hindsight, insight and foresight, hindsight and insight happens more from this clean data lakes where you have authenticity, you have quality and then most of the foresight happens in a different space where the users have more leverage to use data in many different ways to drive analytics and insights which is not readily available. >> Great thank you for that. That's an interesting discussion. You know digital transformation it's a journey and it's going to take you know many years. I know a lot of ways, not a lot of ways, 2020 was a forced march to digital you know. If you weren't a digital business you were out of business and so you really didn't have much time to plan. So now organizations are stepping back saying, okay, let's really lean into our strategy, the journey and along the way, there's going to be blind spots, there's bumps in the road, when you look out what are the potential disruptions that you see maybe in terms of how companies are currently approaching their digital transformations? >> That's a great question Dave and I'm going to take a little bit more longer-term view on this topic, right? And what's top of my mind recently is the whole topic of ESG, environmental, social and governance. Most of the companies have governance in place right? Because they are either public companies, or they're under some kind of scrutiny from different regulatory bodies or whatnot even if you're a startup you need to do things with our customers and whatnot. It has been there for companies, it continues to be there. We the public companies are very good at making sure that we have the right compliance, right privacy, right governance in place. Now we'll talk about cybersecurity I think that creates a whole new challenge in that governance space, however we have the setup within our companies to be able to handle that challenge. Now, when we go to social, what happened last year was really important. And now as each and every company we need to think about what are we doing from our perspective to play our part in that and not only the bigger companies, leaders at our level I would say that between last March and this year I have hired more than 400 people during pandemic which was all virtual, but me and my team have made sure that we are doing the right thing to drive inclusion and diversity which is also very big objective for HPE and Antonio himself has been very active in various round tables in US at the World Economic Forum level and I think it's really important for companies to create that opportunity, remove that disparity that's there for the underserved communities. If we want to continue to be successful in this world to create innovative product and services we need to sell it to the broader cross section of populations and to be able to do that we need to bring them in our fold and enable them to create that equal consumption capabilities across different sets of people. HPE has taken many initiatives and so are many companies. I feel like the momentum that companies have now created around the topic of equality is very important. I'm also very excited to see that a lot of startups are now coming up to serve that 99% versus just the shiny ones as you know in the Bay Area to create better delivery methods of food or products right? But the third piece which is environmental is extremely important as well. As we have seen recently in many companies and where even the dollar or the economic value is flowing are around the companies which are serious about environmental. HPE recently published it's a Living Progress Report, we have been in the forefront of innovation to reduce carbon emissions, we help our customers through those processes. Again, if we don't, if our planet is on fire none of us will exist right? So we all have to do that every little part to be able to do better. And I'm happy to report I myself as a person solar panels, battery, electric cars, whatever I can do. But I think something more needs to happen right? Where as an individual I need to pitch in but maybe utilities will be so green in the future that I don't need to put panels on my roof which again creates a different kind of race going forward. So when you ask me about disruptions, I personally feel that successful company like ours have to have ESG top of their mind and think of product and services from that perspective, which creates equal opportunity for people, which creates better environment sustainability going forward and you know our customers, our investors are very interested in seeing what we are doing to be able to serve that cause for bigger cross section of companies. And I'm most of the time very happy to share with my CIO cohort around how our HPEFS capabilities creates or feeds into the circular economy, how much e-waste we have recycled or kept it off of landfills, our green lake capabilities, how it reduces the e-waste going forward, as well as our sustainability initiatives which can help other CIOs to be more carbon neutral going forward as well. >> You know that's a great answer Rashmi thank you for that 'cause I got to tell you I hear a lot of mumbo jumbo about ESG but that was a very substantive, thoughtful response that I think tech companies in particular are, have to lead and are leading in this area. So I really appreciate that sentiment. I want to end with a very important topic which is cyber it's, obviously you know escalated in the news the last several months, it's always in the news but, you know 10 or 15 years ago there was this mentality of failure equals fire. And now we realize, hey they're going to get in, it's how you handle it. Cyber has become a board-level topic. You know years ago there was a lot of discussion, oh you can't have the SecOps team working for the CIO because that's like the fox watching the hen house that's changed. It's been a real awakening, a kind of a rude awakening so the world is now more virtual, you've got a secure physical assets. I mean any knucklehead can now become a ransomware attacker, they can buy ransomware as a service in the dark web so that's something we've never seen before. You're seeing supply chains get hacked and self-forming malware I mean it's a really scary time. So you've got these intellectual assets it's a top priority for organizations. Are you seeing a convergence of the CISO role, the CIO role, the line of business roles relative to sort of prior years in terms of driving security throughout organizations? >> Yeah this is a great question and this was a big discussion at my public board meeting a couple of days ago. It's, as I talk about many topics, if you think digital, if you think data, if you think ESG, it's no more one organization's business, it's now everybody's responsibility. I saw a Wall Street Journal article a couple of days ago where somebody has compared cyber to 9/11 type scenario that if it happens for a company that's the level of impact you feel on your operations. So, you know all models are going to change where CISO reports to CIO, at HPE we are also into product security and that's why CISO is a peer of mine who I work with very closely, who also worked with product teams where we are saving our customers from lot of pain in this space going forward and HPE itself is investing enormous amount of efforts and time in coming out of products which are secure and are not vulnerable to these types of attacks. The way I see it is CISO role has become extremely critical in every company and a big part of that role is to make people understand that cybersecurity is also everybody's responsibility. That's why an IT we propagate DevSecOps, as we talk about it we are very, very careful about picking the right products and services. This is one area where companies cannot shy away from investing. You have to continuously looking at cybersecurity architecture, you have to continuously look at and understand where the gaps are and how do we switch our product or service that we use from the providers to make sure our companies stay secure. The training not only for individual employees around anti-phishing or what does cybersecurity mean, but also to the executive committee and to the board around what cyber security means, what zero trust means, but at the same time doing drive-ins. We did it for business continuity and disaster recovery before, now it is time we do it for a ransomware attack and stay prepared. As you mentioned and we all say in tech community, it's always if not when. No company can take them their chest and say, "oh we are fully secure," because something can happen going forward. But what is the readiness for something that can happen? It has to be handled at the same risk level as a pandemic, or a earthquake, or a natural disaster and assume that it's going to happen and how as a company we will behave when something like this happens. So I'm huge believer in the framework of protect, detect, govern and respond as these things happen. So we need to have exercises within the company to ensure that everybody's aware of the part that they play day to day but at the same time when some event happen and making sure we do very periodic reviews of IT and cyber practices across the company, there is no more differentiation between IT and OT. That was 10 years ago. I remember working with different industries where OT was totally out of reach of IT and guess what happened? WannaCry and Petya and XP machines were still running your supply chains and they were not protected. So, if it's a technology it needs to be protected. That's the mindset people need to go with. Invest in education, training, awareness of your employees, your management committee, your board and do frequent exercises to understand how to respond when something like this happen. See it's a big responsibility to protect our customer data, our customer's operations and we all need to be responsible and accountable to be able to provide all our product and services to our customers when something unforeseen like this happens. >> Rashmi you're very generous with your time thank you so much for coming back in theCUBE it was great to have you again. >> Thank you Dave, it was really nice chatting with you. >> And thanks for being with us for our ongoing coverage of HPE Discover '21. This is Dave Vellante you're watching the virtual CUBE, the leader in digital tech coverage we'll be right back. (bright music)

>> Okay. Now we're going to dig deeper into HPE Ezmeral and try to better understand how it's going to impact customers. And with me to do that are Robert Christiansen, who is the Vice President of Strategy in the office of the CTO and Kumar Sreekanti, who is the Chief Technology Officer and Head of Software, both of course, with Hewlett Packard Enterprise. Gentlemen, welcome to the program. Thanks for coming on. >> Good seeing you, Dave. Thanks for having us. >> It's always good to see you guys. >> Thanks for having us. >> So, Ezmeral, kind of an interesting name, catchy name, but Kumar, what exactly is HPE Ezmeral? >> It's indeed a catchy name. Our branding team has done fantastic job. I believe it's actually derived from Esmeralda, is the Spanish for emarald. Often it's supposed some very mythical bars, and they derived Ezmeral from there. And we all initially when we heard, it was interesting. So, Ezmeral was our effort to take all the software, the platform tools that HPE has and provide this modern operating platform to the customers and put it under one brand. So, it has a modern container platform, it does persistent storage with the data fabric and it doesn't include as many of our customers from that. So, think of it as a modern container platform for modernization and digitazation for the customers. >> Yeah, it's an interesting, you talk about platform, so it's not, you know, a lot of times people say product, but you're positioning it as a platform so that has a broader implication. >> That's very true. So, as the customers are thinking of this digitazation, modernization containers and Microsoft, as you know, there is, has become the stable all. So, it's actually a container orchestration platform with golfers open source going into this as well as the persistence already. >> So, by the way, Ezmeral, I think Emerald in Spanish, I think in the culture, it also has immunity powers as well. So immunity from lock-in, (Robert and Kumar laughing) and all those other terrible diseases, maybe it helps us with COVID too. Robert, when you talk to customers, what problems do you probe for that Ezmeral can do a good job solving? >> Yeah, that's a really great question because a lot of times they don't even know what it is that they're trying to solve for other than just a very narrow use case. But the idea here is to give them a platform by which they can bridge both the public and private environment for what they do, the application development, specifically in the data side. So, when yo're looking to bring containerization, which originally got started on the public cloud and it has moved its way, I should say it become popular in the public cloud and it moved its way on premises now, Ezmeral really opens the door to three fundamental things, but, you know, how do I maintain an open architecture like you're referring to, to some low or no lock-in of my applications. Number two, how do I gain a data fabric or a data consistency of accessing the data so I don't have to rewrite those applications when I do move them around. And then lastly, where everybody's heading, the real value is in the AI ML initiatives that companies are really bringing and that value of their data and locking that data at where the data is being generated and stored. And so the Ezmeral platform is those multiple pieces that Kumar was talking about stacked together to deliver the solutions for the client. >> So Kumar, how does it work? What's the sort of IP or the secret source behind it all? What makes HPE different? >> Yeah. Continuing on (indistinct) it's a modern glass form of optimizing the data and workloads. But I think I would say there are three unique characteristics of this platform. Number one is that it actually provides you both an ability to run statefull and stateless as workloads under the same platform. And number two is, as we were thinking about, unlike another Kubernete is open source, it actually add, use you all open-source Kurbenates as well as an orchestration behind them so you can actually, you can provide this hybrid thing that Robert was talking about. And then actually we built the workflows into it, for example, they'll actually announced along with it Ezmeral, ML expert on the customers can actually do the workflow management around specific data woakload. So, the magic is if you want to see the secrets out of all the efforts that has been going into some of the IP acquisitions that HPE has done over the years, we said we BlueData, MAPR, and the Nimble, all these pieces are coming together and providing a modern digitization platform for the customers. >> So these pieces, they all have a little bit of a machine intelligence in them, you have people, who used to think of AI as this sort of separate thing, I mean the same thing with containers, right? But now it's getting embedded into the stack. What is the role of machine intelligence or machine learning in Ezmeral? >> I would take a step back and say, you know, there's very well the customers, the amount of data that is being generated and 95% or 98% of the data is machine generated. And it does a series of a window gravity, and it is sitting at the edge and we were the only one that had edge to the cloud data fabric that's built to it. So, the number one is that we are bringing computer or a cloud to the data that taking the data to the cloud, right, if you will. It's a cloud like experience that provides the customer. AI is not much value to us if we don't harness the data. So, I said this in one of the blog was we have gone from collecting the data, to the finding the insights into the data, right. So, that people have used all sorts of analysis that we are to find data is the new oil. So, the AI and the data. And then now your applications have to be modernized and nobody wants write an application in a non microservices fashion because you wanted to build the modernization. So, if you bring these three things, I want to have a data gravity with lots of data, I have built an AI applications and I want to have those three things I think we bring to the customer. >> So, Robert let's stay on customers for a minute. I mean, I want to understand the business impact, the business case, I mean, why should all the cloud developers have all the fun, you've mentioned it, you're bridging the cloud and on-prem, they talk about when you talk to customers and what they are seeing is the business impact, what's the real drivers for that? >> That's a great question cause at the end of the day, I think the recent survey that was that cost and performance are still the number one requirement for this, just real close second is agility, the speed at which they want to move and so those two are the top of mind every time. But the thing we find Ezmeral, which is so impactful is that nobody brings together the Silicon, the hardware, the platform, and all of that stack together work and combine like Ezmeral does with the platforms that we have and specifically, we start getting 90, 92, 93% utilization out of AI ML workloads on very expensive hardware, it really, really is a competitive advantage over a public cloud offering, which does not offer those kinds of services and the cost models are so significantly different. So, we do that by collapsing the stack, we take out as much intellectual property, excuse me, as much software pieces that are necessary so we are closest to the Silicon, closest to the applications, bring it to the hardware itself, meaning that we can interleave the applications, meaning that you can get to true multitenancy on a particular platform that allows you to deliver a cost optimized solution. So, when you talk about the money side, absolutely, there's just nothing out there and then on the second side, which is agility. One of the things that we know is today is that applications need to be built in pipelines, right, this is something that's been established now for quite some time. Now, that's really making its way on premises and what Kumar was talking about with, how do we modernize? How do we do that? Well, there's going to be some that you want to break into microservices containers, and there's some that you don't. Now, the ones that they're going to do that they're going to get that speed and motion, et cetera, out of the gate and they can put that on premises, which is relatively new these days to the on-premises world. So, we think both won't be the advantage. >> Okay. I want to unpack that a little bit. So, the cost is clearly really 90 plus percent utilization. >> Yes. >> I mean, Kumar, you know, even pre virtualization, we know that it was like, even with virtualization, you never really got that high. I mean, people would talk about it, but are you really able to sustain that in real world workloads? >> Yeah. I think when you make your exchangeable cut up into smaller pieces, you can insert them into many areas. We have one customer was running 18 containers on a single server and each of those containers, as you know, early days of new data, you actually modernize what we consider week run containers or microbiome. So, if you actually build these microservices, and you all and you have versioning all correctly, you can pack these things extremely well. And we have seen this, again, it's not a guarantee, it all depends on your application and your, I mean, as an engineer, we want to always understand all of these caveats work, but it is a very modern utilization of the platform with the data and once you know where the data is, and then it becomes very easy to match those two. >> Now, the other piece of the value proposition that I heard Robert is it's basically an integrated stack. So I don't have to cobble together a bunch of open source components, there's legal implications, there's obviously performance implications. I would imagine that resonates and particularly with the enterprise buyer because they don't have the time to do all this integration. >> That's a very good point. So there is an interesting question that enterprises, they want to have an open source so there is no lock-in, but they also need help to implement and deploy and manage it because they don't have the expertise. And we all know that the IKEA desk has actually brought that API, the past layer standardization. So what we have done is we have given the open source and you arrive to the Kubernetes API, but at the same time orchestration, persistent stories, the data fabric, the AI algorithms, all of them are bolted into it and on the top of that, it's available both as a licensed software on-prem, and the same software runs on the GreenLake. So you can actually pay as you go and then we run it for them in a colo or, or in their own data center. >> Oh, good. That was one of my latter questions. So, I can get this as a service pay by the drink, essentially I don't have to install a bunch of stuff on-prem and pay it perpetualized... >> There is a lot of containers and is the reason and the lapse of service in the last discover and knowledge gone production. So both Ezmeral is available, you can run it on-prem, on the cloud as well, a congenital platform, or you can run instead on GreenLake. >> Robert, are there any specific use case patterns that you see emerging amongst customers? >> Yeah, absolutely. So there's a couple of them. So we have a, a really nice relationship that we see with any of the Splunk operators that were out there today, right? So Splunk containerized, their operator, that operator is the number one operator, for example, for Splunk in the IT operation side or notifications as well as on the security operations side. So we've found that that runs highly effective on top of Ezmeral, on top of our platforms so we just talked about, that Kumar just talked about, but I want to also give a little bit of backgrounds to that same operator platform. The way that the Ezmeral platform has done is that we've been able to make it highly active, active with HA availability at nine, it's going to be at five nines for that same Splunk operator on premises, on the Kubernetes open source, which is as far as I'm concerned, a very, very high end computer science work. You understand how difficult that is, that's number one. Number two is you'll see just a spark workloads as a whole. All right. Nobody handles spark workloads like we do. So we put a container around them and we put them inside the pipeline of moving people through that basic, ML AI pipeline of getting a model through its system, through its trained, and then actually deployed to our ML ops pipeline. This is a key fundamental for delivering value in the data space as well. And then lastly, this is, this is really important when you think about the data fabric that we offer, the data fabric itself doesn't necessarily have to be bolted with the container platform, the container, the actual data fabric itself, can be deployed underneath a number of our, you know, for competitive platforms who don't handle data well. We know that, we know that they don't handle it very well at all. And we get lots and lots of calls for people saying, "Hey, can you take your Ezmeral data fabric "and solve my large scale, "highly challenging data problems?" And we say, "yeah, "and then when you're ready for a real world, "full time enterprise ready container platform, "we'd be happy to prove that too." >> So you're saying you're, if I'm inferring correctly, you're one of the values as you're simplifying that whole data pipeline and the whole data science, science project pun intended, I guess. (Robert and Kumar laughing) >> That's true. >> Absolutely. >> So, where does a customer start? I mean, what, what are the engagements like? What's the starting point? >> It's means we're probably one of the most trusted and robust supplier for many, many years and we have a phenomenal workforce of both the (indistinct), world leading support organization, there are many places to start with. One is obviously all these salaries that are available on the GreenLake, as we just talked about, and they can start on a pay as you go basis. There are many customers that actually some of them are from the early days of BlueData and MAPR, and then already running and they actually improvise on when, as they move into their next version more of a message. You can start with simple as well as container platform or system with the store, a computer's operation and can implement as an analyst to start working. And then finally as a big company like HPE as an everybody's company, that finance it's services, it's very easy for the customers to be able to get that support on day to day operations. >> Thank you for watching everybody. It's Dave Vellante for theCUBE. Keep it right there for more great content from Ezmeral.

(upbeat music) >> Announcer: Live from Barcelona, Spain it's theCUBE covering Cisco Live 2020, brought to you by Cisco and its ecosystem partners. >> Welcome back. This is theCUBE's live coverage of Cisco Live 2020 here in Barcelona, Spain. I'm Stu Miniman. My co-host for this segment is Dave Volante. John Furrier is also in the house. We're doing a little more than three days wall-to-wall coverage. One of the big themes we're talking about this week is in this complicated world, networking, containerization, applications going through transformation. Future work simplification is something that is very important and helping us to really tease through and understand some of the integration, some of the announcements where Cisco is helping to simplify the environment, happy to welcome back to the program one of our Cube alumni, Kaustubh Das who is a Vice President of Product Management at Cisco. KD, thanks so much for joining us. >> Oh, I'm delighted to be here, it's great to be here. >> All right. So but up on the main stage, they walk through a number of the announcement. Listen Tony, I was talking about some of the pieces and two of the announcements from the main stage are under your purview. So why don't we start there, walk us through the news. >> Yeah, so there's two two major announcements. The first one's called Cisco Intersight Workload Optimizer. And what it is, it's a way to have visibility into your data center, all the way from the applications and in fact, the user journeys within those applications, all the way down through the virtualization there, through the app servers, through the container platforms down into the servers, the networks, storage lands. So you have a map of the data center. You have a common data set that the application owner and the infrastructure owner can both look at and you finally have a common vocabulary so that it helps them to troubleshoot faster so on a fast reactor way, they talking the same language not pointing fingers at each other or do things proactively to prevent problems from happening when you see a server running hot, a virtual machine running hot, an application server running hot. You can diagnose it and have that conversation before it happens. >> My understanding is that Intersight and there's also some integrations with AppDynamics there, AppD which of course we know we talk to that team at the Amazon Cloud shows a lot. So that common vocabulary spans between my hybrid and multi cloud environments. Am I getting that right? >> Correct and there's two pieces even within that. So certainly that's integrations with AppD so from AppD we get information about the application performance. We get information about the business metrics associated with the application performance. We get information about the journeys that user take within the application and then we take that data then we stitch it together with infrastructure data to map how many applications are dependent on which application servers, how many VMs are those dependent on, what does those VMs run on? What hosts are they dependent on, what networks do they Traverse, what lands do they run on? And each one of these is an API call into that element in the infrastructure stack. Each API call gives us a little bit of data and then we piece together this data to create this map of the of the entire data center. There's a multi cloud aspect to it obviously and so we also make API calls into AWS and Azure and clouds out there and we get data about utilization of the various instance types. We get data about performance from the cloud as well. >> So two announcements. Insight Workload Optimizer and HyperFlex AppDynamics, is that right or they are separate? >> HyperFlex application platform. >> Okay. >> So if we look at the, let me just put these two in context. Every enterprise is doing two things. It's trying to run application that it already hosts and then it's writing some bespoke new applications. So the first announcement, the Cisco Intersight Workload Optimizer and the integration of the AppD, that helps us be more performant for applications we're running, to have troubleshoot faster, to have reduced cost in a multiply cloud environment. The second announcement Dave, the HyperFlex application platform, it's really targeted towards developers who are writing new applications on a container platform. And for those developers, IT needs to give them a simple appliance like easy to use container as a service platform. So what HX AP HyperFlex application platform is is a container as a service platform driven from the cloud so that the developer gets the same experience that they get when they go to an AWS and and request a pod. But they get it on-prem and it's fully 100% upstream Kubernetes compliant. It's curated by us so it's very simple appliance like feel for development environments on container. >> Okay. So Insight Workload Optimizer, it really attacks the problem of sort of the mystery of what goes on inside VMs and the application team, the infrastructure team, they're not talking to each other. You're bringing a common, like you said parlance together. >> Kaustubh: Correct. >> Really so they can solve problems and that that trickles down to cost optimization as well as performance. >> It does, aha. >> And I understand hyper HyperFlex app platform it's really bringing that cloud experience to on-prem for hybrid environments. >> For our new development. So if you're developing on containers, you're probably using Kubernetes but you're probably using this entire kind of ecosystem of open source tools. >> Yeah. >> And we make that simple. >> Okay. >> We make it simple for developers to use that and variety to provide that to developers. >> Okay. since underneath, there's HyperFlex. is there still virtualization involved in there and how does this tie in with the rest of the Kubernete solutions that we were talking about with your cloud partner? >> Great, great. Great question. So yes, there is HyperFlex underneath this. So to develop, you need a platform. The best platform we think is the elastic platform that is hyper-convergence. And with type of flex, we took storage networking and compute, packaged it together, made it super simple. We're doing the same thing with Kubernetes. So it's the same concept that how do you take complex things, package it together and make it almost appliance like. We said we're doing the same thing with Kubernetes. Now Stu, the point about virtualization is a good one. A lot of container deployments today are run in virtual machines. And they run in virtual machines for good reason, for isolation, for multi-tenancy, for all these kinds of ignition. However, the promise of containers was to sort of get rid of the tax that you pay when you deploy a virtualization environment. And what we're giving out right now is no tax, no virtualization tax virtualization environment. So we have a layer over transition in there. It's designed for this use case so it does give the isolation, it does give the multi-tenancy benefits but you don't need to need to pay additionally for it if you're deploying on containers-- >> Job wise it is some KB and base type solution >> Kaustubh: Correct. >> Underneath, it makes a lot of sense if you look at the large virtualization player out there. It's been talking about how do I enable the infrastructure that's all virtualized and everything and bring them along to that journey >> Correct. >> For that bridge if you will to the environment? Sure containerization sometimes I want to be able to spin it up super fast. It leaves, it dies, but if I'm putting something in my data center, probably the characteristics I'm looking at are a little bit different. >> Correct, correct. The other thing it does and you touched on it a little bit was we have a homogeneous environment with the major clouds out there. So one of the things developers want to do is they want to develop in one place and they want to deploy in another place so develop on Amazon and deploy on-prem or Azure. We've got an environment with very native integrations so that it's natively integrated into EKS and AKS. And we facilitate that develop anywhere, deploy anywhere motion for developers who are trying to build on this. >> So okay. What does the customer have to do to consume these solutions? >> So our customer right now for this one is IT operations. It maybe helps to bit back a little bit on why we did this. I had a lot of customers come to me and they said listen, I'm IT, I'm in the business of taking shrink-wrap software, taking enterprise-grade resilient infrastructure, putting that together. I'm not in the business of getting open source drops, every week, every day, every month, putting them together by making sure all the versions line up and doing that again and again and again. So the putting together an Ikea piece part of open source software has not been traditionally the IT operator's business. So our customer is that IT operator. What they need to do is they buy a, if they may have a HyperFlex system already, or they buy a HyperFlex effect system. They add on a license for the HyperFlex application platform. They have an Intersight license. This is delivered from the cloud so Intersight manages that deployment, manages the lifecycle, manages the upgrades and so forth. If they have a state that spreads across multiple sites, Intersight is cloud-based so it can actually reach all those sites and so they're in business. >> Okay, so very low prerequisite. You just got to have the product and you can add on to it. >> Yeah, I have the HyperFlex system, add on to the license, you're done. >> So I'm curious. How unique do you see this in the marketplace? I think the keynotes this morning is that there's no other company that can actually do this. I wonder if you can sort of add some color to that and just help our viewers understand the uniqueness of Cisco's offer. >> Sure. So I think it's unique on a number of different dimensions. The first dimension is HyperFlex itself. We've had an appliance mentality to this for a long time and we really co-designed the software and the hardware to build the most performance hyper-converged system out there. We took the same approach when we went down the path of Kubernetes and building this container platform. And so it's called design software and infrastructure together. The second thing is we said we're going to be 100% upstream Kubernetes compliant right, so if you look at the major offerings out there in this space, they're often several months actually behind where the open source is, where the upstream of the sources and developers don't want that. They want the latest and greatest, they want they want to be current, right. So we are far ahead of most of the other offerings out there in terms of how close they are to their upstream commodities. The final piece is Intersight. Intersight gives us immense ability to have scale where especially if you're developing on containers and micro services, you're talking tens of thousands, many tens of thousands of N nodes, maybe more. And being in the cloud, we have the scale and we have reached so a lot of our customers have distributed assets and branches and you know, hotel chains with hotels and so forth. Intersight allows us the ability to actually deploy across a distributed asset class with with the centralized kind of provisioning. >> You see a huge uptake right now and containers generally Kubernetes, specifically. It's sort of across the board but I wonder if you could comment on how much of that demand and activity is coming from sort of the traditional IT roles versus with other hoody developers? >> Yeah, that's that's a great question. So yes, there is a on a hype cycle it's at the top of the hype cycle. Everybody's in actual adoption. I think it's pretty good as well right. So that is every company I talk to is doing something in containers, every company. But usually, it starts at the developers. It starts with like you described with the folks in the hoodies and that's great. I mean they're experimenting, they're getting this thing. What hasn't happened is it hasn't gotten mainstream. And things can mainstream is when IT picks it up. It certifies hey this is resilient, this is enterprise-grade, I can stand behind it, I can manage the lifecycle of it. That's what we're enabling here. I'm giving IT a path to mainstream containers, to mainstream Kubernetes so that the adoption kind of takes it from that pipe cycle to mainstream adoption. >> Do you see K.D. new sort of data protection approaches or thinking as containers come into play? I mean they're ephemeral, you know microservices sometimes aren't so micro. Like you say, they're running often times inside a VM. So how are people thinking about protecting containers? >> Yeah, yeah, that's a big topic in itself. I mean one of the things that we found is even though they were supposed to be ephemeral, they require persistent storage so we've implemented within hyperflex a CSI plugin that provides that persistent storage layer to containers. Then once you do that, all of the data protection mechanism of HyperFlex come into play. So within the cluster, the resiliency, the triple replication, the backups, the partnerships we have with their other data protection pairs, all of those mechanisms become available instantly and those are enterprise-grade. Those are ones that IT knows and can stand behind. Those become available to containers right away >> Great. >> But it's great, great question. >> Awesome. >> Just want to go back to when you were talking about Intersight and the reach and the scale of the solution reminds me that Cisco has a strong legacy in global environment. What I'm curious about, we've talked a little bit about Edge computing in the past. >> Kaustubh: Yes. >> Where are you seeing Edge today? Where is that going? What should we be looking at in that space when it comes to Edge? >> Yeah, no, it's a big part of our customer demand. In fact, we haven't seen I think all flash was the other technology that took place so fast but Edge has been really phenomenal in its growth rate. Over the last year, we've seen I think probably up to 15% to 20% of my engagements are in this space on at least the hyper convert side. So we see that as a big growth area. More and more deployments are happening. They're being centrally managed, deployed at the edges and so the only solution that scales to something like that is something that's based on the cloud. But it's not just enough to be based in the cloud. You've got to maintain that entire lifecycle right? You've got to make sure you can do installs, upgrades, you know OS installs, health monitoring and so as we built that Intersight platform, we've added all those capabilities to it over time So we started with hey this is a SAS-based management platform and then we added telemetry and then we said if we can actually match signatures, now machines can manage machines. So a good amount of my support calls are now machines calling each other and then fixing themselves. So that's just path-breaking from an informant Edge environment. You don't have an IT person, add an Edge location. You want to drop, ship an appliance there, and you want to be able to see it remotely. So I think it's a completely new operating model. >> I know we got to go but I want to run your scenario by K.D.'s. Do share with me from one of my breaking analysis. Look Dave, you mentioned Flash, that's what triggered me. (laughing) So think of containers and Kubernetes, think of like Flash. Remember Flash used to be the separate thing which we used to think it was a separate market and now it's just everywhere, it's embedded in everything. >> Kaustubh: Yes. >> So the same thing is going to happen with Kubernetes. It's going to be embedded in solutions. This is exactly what it is. By 2023, we're probably not going to be talking about it as a separate thing, maybe that's sooner. It's really just going to be ubiquitous, yeah. >> No, I totally agree. I think the underpinnings that you need for that future, you need a common infrastructure platform and a common management platform. So you don't want to have a new Silo creator and this has been our philosophy even for hyperconvergence. We said hey, there's going to be converging infrastructure that will be hyper converted. But they need to be the same management system, they need to be the same fabric. And so if it's Silo is not going to work. Same thing for containers you know. It's got to be the same platform in this case, it's HyperFlex. Hyperflex runs virtualization, it runs containers with HXAP. You get all of those benefits that I've talked about. It's all management insights, it's a common management platform across both of those. At some point, these are all tools in somebody's tool kit and you pick the right one for the job. >> Kaustubh, it is wonderful to hear the company that has been dominant in one of the silos for so long of course helping to bring the silos together work across the domains. Congratulations on that good news, always great to have you. >> Yeah, always great to be here, thank you. >> Dave: Thank you. >> For Dave Folante, I'm Stu Miniman back from lunch where we hear more from Cisco live in Barcelona 2020. Thank you for watching theCUBE.

>>Live from San Diego, California at the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back. This is the cubes coverage of CubeCon cloud date of con 2019 in San Diego, 12,000 in attendance. I'm just two minute and my cohost is John trier. And welcome back to the program. A multi-time cube alumni. You're on Aviv, who is the CTO and cofounder of a Gwoza. We've had quite a lot of, you know, founders, CTOs, you know, their big brains at this show, your own. So you know, let, let, let's start, you know, there's, there's really a gathering, uh, there's a lot of effort building out, you know, a very complicated ecosystem. Give us first, kind of your overall impressions of the show in this ecosystem. Yeah, so we're very early on on Desecco system. We were one of the first in the first batch of CNCF members when there were a few dozens of those. Not like a thousand of those. Uh, so I've been, I've been to all those shows. >>Uh, we're part of the CNCF committees for different things. And any initiating, I think this has become much more mainstream. I told you before, it's sort of the new van world. You know, I lot a lot more, uh, all day infrastructure vendors along with middleware and application vendor are coming here. All right, so, so one of the things we like having you on the program you're on is you don't pull any punches. So we've seen certain waves of technology come with big promise and fall short, you know, big data was going to allow us to leverage everything and you know, large percentage of, uh, solutions, you know, had to stop or be pulled back. Um, give us, what's the cautionary tale that we should learn and make sure that we don't repeat, you know, so I've been a CTO for many years in different companies and, and what everyone used to say about it, I'm always right. >>I'm only one year off usually. I'm usually a little more optimistic. So, you know, we've been talking about Cloudera and Hadoop world sort of going down and Kubernetes and cloud services, essentially replacing them. We were talking about it four years ago and what do you see that's actually happening? You know, with the collapse of my par and whore, then we're going to Cloudera things are going down, customer now Denon guys, we need equivalent solution for Kubernetes. We're not going to maintain two clusters. So I think in general we've been, uh, picking on many of those friends. We've, we've invented serverless before it was even called serverless with, with nuclear and now we're expanding it further and now we see the new emerging trends really around machine learning and AI. That's sort of the big thing. I'm surprised, you know, that's our space where essentially you're doing a data science platform as a service fully automated around serverless constructs so people can, can develop things really, really quickly. >>And what I see that, you know, third of the people I talk to are, have some relations to machine learning and AI. Yeah. Maybe explain that for our audience a little bit. Because when, you know, Kubernetes first started very much an infrastructure discussion, but the last year or two, uh, very much application specific, we hear many people talking about those data use cases, AI and ML early days. But you know how, how does that fit into the overall? It's simple. You know there, if you're moving to the cloud are two workloads. There is lift and shift workloads and there are new workloads. Okay, lift and ship. Why? Why bother moving them to Kubernetes? Okay, so you end up with new workloads. Everyone is trying to be cloud native server, elastic services and all that. Everyone has to feed data and machine learning into those new applications. This is why you see those trends that talk about old data integration, various frameworks and all that in that space. >>So I don't think it's by coincidence. I think it's, that's because new applications incorporate the intelligence. That's why you hear a lot of the talk about those things. What I loved about the architecture, what you just said is like people don't want to run into another cluster. I don't want to run two versions of Kubernetes, you know, if I'm moving there you, because you, but you're still built on that, that kind of infrastructure framework and, and knowledge of, of how to do serverless and how to make more nodes and fewer nodes and persistent storage and all that sort of good stuff and uh, and, and run TensorFlow and run, you know, all these, all these big data apps. But you can, um, you can talk about that just as a, as a, the advantage to your customer cause you could, it seems like you could, you could run it on top of GKE. >>You could run it on prem. I could run my own Coobernetti's you could, you could just give me a, uh, so >> we, we say Kubernetes is not interesting. I didn't know. I don't want anyone to get offended. Okay. But Kubernetes is not the big deal. The big deal is organizations want to be competitive in this sort of digital world. They need to build new applications. Old ones are sort of in sort of a maintenance mode. And the big point is about delivering new application with elastic scaling because your, your customers may, may be a million people behind some sort of, uh, you know, uh, app. Okay. Um, so that's the key thing and Kubernetes is a way to deliver those microservices. But what we figured out, it's still very complicated for people. Okay. Especially in, in the data science work. Uh, he takes him a few weeks to deliver a model on a Jupiter notebook, whatever. >>And then productizing it is about the year. That's something we've seen between six months to a year to productize things that are relatively simple. Okay. And that's because people think about the container, the TensorFlow, the Kuda driver, whatever, how to scale it, how to make it perform, et cetera. So let's, we came up with is traditionally there's a notion of serverless, which is abstraction with very slow performance, very limited set of use cases. We sell services about elastic scaling paper, use, full automation around dev ops and all that. Okay. Why cannot apply to other use cases are really high concurrency, high-speed batch, no distributed training, distributed workload. Because we're coming, if you know my background, you know, been beeping in Mellanox and other high-performance companies. So where I have a, we have a high performance DNA so we don't know how to build things are extremely slow. >>It sort of irritates me. So the point is that how can we apply this notion of abstraction and scaling and all that to variety of workloads and this is essentially what it was. It is a combination of high speed data technology for like, you know, moving data around on between those function and extremely high speed set though functions that work on the different domains of data collection and ingestion, data analytics, you know, machine learning, training and CIN learning model serving. So a customer can come on on our platform and we have testimonials around that, that you know, things that they thought about building on Amazon or even on prem for months and months. They'd built in our platform in few weeks with fewer people because the focus is on building the application. The focus is not about joining your Kubernetes. Now we go to customers, some of them are large banks, et cetera. >>They say, Alrighty, likes Kubernetes, we have our own Kubernetes. So you know what, we don't butter. Initially we, we used to bring our own Kubernetes, but then you know, I don't mind, you know, we do struggle sometimes because our level of expertise in Coobernetti's is way more sophisticated than what they have to say. Okay, we've installed Kubernetes and we come with our software stack. No you didn't, you know, you didn't configure the security, they didn't configure ingress, et cetera. So sometimes it's easier for us to bring, but we don't want him to get into this sort of tension with it. Our focus is to accelerate development on the new application that are intelligent, you know, move applications from, if you think of the traditional data analytics and data science, it's about reporting and what people want to do. And some applications we've announced this week and application around real time cyber collection, it's being used in some different governments is that you can collect a lot of information, SMS, telephony, video, et cetera. >>And in real time you could detect terrorists. Okay. So those application requires high concurrency always on rolling upgrades, things that weren't there in the traditional BI, Oracle, you know, kind of reporting. So you have this wave of putting intelligence into more highly concurrent online application. It requires all the dev ops sort of aspects, but all the data analytics and machine learning aspects to to come to come along. Alright. So speaking of those workloads for, for machine learning, uh, cube flow is a project, uh, moving the, moving in that space along it. Give us the update there. Yeah. So, so there is sort of a rising star in the Kubernetes community around how to automate machine learning workflows. That's cube flow. Uh, I'm personally, I one of the committers and killed flow and what we've done, because it's very complicated cause Google developed the cube cube flow as one of the services on, on a GKE. >>Okay. And the tweaked everything. It works great in GK, even that it's relatively new technology and people want to move around it in a more generic. So one of the things in our platform is a managed cube flow that works natively with all the rest of the solutions. And other thing that we've done is we make it, we made it fully. So instead of queue flow approach is very con, you know, Kubernetes oriented containers, the ammos, all that. Uh, in our flavor of Coupa we can just create function and you just like chain functions and you click and it runs. Just, you've mentioned a couple of times, uh, how does serverless, as you defined it, fit in with, uh, Coobernetti's? Is that working together just functions on top or I'm just trying to make here, >> you'll, you'll hear different things. I think when most people say serverless, they mean sort of front end application things that are served low concurrency, a Terra, you know, uh, when we mean serverless, it's, we have eight different engines that each one is very good in, in different, uh, domain like distributed deep learning, you know, distributed machine learning, et cetera. >>And we know how to fit the thing into any workloads. So for me, uh, we deliver the elastic scaling, the paper use and the ease of use of sort of no dev ops across all the eight workloads that we're addressing. For most people it's like a single Dreek phony. And I think really that the future is, is moving to that. And if you think about serverless, there's another aspect here which is very important for machine learning and Israel's ability. I'm not going to develop any algorithm in the world. Okay. There are a bunch of companies or users or developers that can develop an algorithm and I can just consume it. So the future in data science but not just data science is essentially to have like marketplaces of algorithms premade or analytic tools or maybe even vendors licensing their technology through sort of prepackaged solution. >>So we're a great believer of forget about the infrastructure, focus on the business components and Daisy chain them in to a pipeline like UFO pipeline and run them. And that will allow you most reusability that, you know, lowest amount of cost, best performance, et cetera. That's great. I just want to double click on the serverless idea one more time, but, so you're, you're developing, it's an architectural pattern, uh, and you're developing these concepts yourself. You're not actually, sometimes the concept gets confused with the implementations of other people's serverless frameworks or things like that. Is that, is that correct? I think there are confusion. I'm getting asked a lot of times. How do you compare your technology compared to let's say a? You've heard the term gay native is just a technology or open FAS or, yeah. Hold on. Pfizer's a CGIs or Alito. An open community is very nice for hobbies, but if you're an enterprise and it's security, Eldep integration, authentication for anything, you need DUIs, you need CLI, you need all of those things. >>So Amazon provides that with Lambda. Can you compare Lambda to K native? No. Okay. Native is, I need to go from get and build and all that. Serverless is about taking a function and clicking and deploying. It's not about building. And the problem is that this conference is about people, it people in crowd for people who like to build. So they, they don't like to get something that work. They want to get the build the Lego building blocks so they can play. So in our view, serverless is not open FAS or K native. Okay. It's something that you click and it works and have all the enterprise set of features. We've extended it to different levels of magnitude of performance. I'll give you an anecdote. I did a comparison for our customer asking me the same question, not about Canadian, but this time Lambda. How do you guys compare with London? >>Know Nokia is extremely high performance. You know we are doing up to 400,000 events on a single process and the customer said, you know what, I have a use case. I need like 5,000 events per second. How do you guys compare a total across all my functions? How do you compare against Lambda? We went into, you know the price calculator, 5,000 events per second on Lambda. That's $50,000 okay. $50,000 we do about, let's say even in simple function, 60,000 per process, $500 VM on Amazon, $500 VM on Amazon with our technology stick, 2000 transactions per second, 5,000 events per second on Lambda. That's 50,000. Okay. 100 times more expensive. So it depends on the design point. We designed our solution to be extremely efficient, high concurrency. If you just need something to do a web hook, use Lambda, you know, if you are trying to build a high concurrency application efficient, you know, an enterprise application on it, on a serverless architecture construct come to us. >>Yeah. So, so just a, I'll pause at this for you because a, it reminds me what you were talking about about the builders here in the early days of VMware to get it to work the way I wanted to. People need to participate and build it and there's the Ikea effect. If I actually helped build it a little bit, I like it more to get to the vast majority, uh, to uh, adopt those things. It needs to become simplified and I can't have, you know, all the applications move over to this environment if I have to constantly tweak that. Everything. So that's the trend we've been really seeing this year is some of that simplification needs to get there. There's focus on, you know, the operators, the day two operations, the applications so that anybody can get there without having to build themselves. So we know there's still work to be done. >>Um, but if we've crossed the chasm and we want the majority to now adopt this, it can't be that I have to customize it. It needs to be more turnkey. Yeah. And I think it's a friendly and attitude between what you'll see in Amazon reinvent in couple of weeks. And then what you see here, because there is those, the focus of we're building application a what kind of tools and the Jess is gonna just launch today on the, on the floor. Okay. So we can just consume it and build our new application. They're not thinking, how did Andy just, he built his tools. Okay. And I think that's the opposite here is like how can you know Ali's is still working inside underneath dude who cares about his team. You know, you care about having connectivity between two points and and all that. How do you implement it that, you know, let someone else take care of it and then you can apply your few people that you have on solving your business problem, not on infrastructure. >>You know, I just met a guy, came to our booth, we've seen our demo. Pretty impressive how we rise people function and need scales and does everything automatically said we want to build something like you're doing, you know, not really like only 10% of what you just showed me. And we have about six people and for three months where it just like scratching our head. I said, okay, you can use our platform, pay us some software license and now you'll get, you know, 10 times more functionality and your six people can do something more useful. Says right, let's do a POC. So, so that's our intention and I think people are starting to get it because Kubernetes is not easy. Again, people tell me we installed Kubernete is now installed your stack and then they haven't installed like 20% of all the things that you need to stop so well your own have Eve always pleasure to catch up with you. Thanks for the all the updates and I know we'll catch up with you again soon. Sure. All right. For John Troyer, I'm Stu Miniman. We'll be back with more coverage here from CubeCon cloud date of con in San Diego. Thanks for watching the cube.

>> Hi, I'm Stu Miniman, and this is special On the Ground of theCUBE here at the VTUG Summer Slam 2019. We've had the pleasure of knowing the VTUG team for quite awhile back actually, when it was the New England VMUG was when I started attending. When it switched to the VTUG at Gillette Stadium's when we started doing theCUBE there. And happy to bring back to the program first, Chris Harney, who is the one who created this as a true user event. And joining him is his wife Dawn Harney, who we know is behind the scenes organizing all of this event. So, Dawn and Chris, thank you so much for joining us and thank you for sharing this community and educational process with all of us. >> Thanks Stu, it's been a pleasure. >> All right, so, Chris, we really want this, it's a celebration. Sixteen years; back in 2003 the number one movie of the year was actually Finding Nemo. Of course we waited a long time for there. It goes without saying that all of us were a little bit younger. And boy, in those days, I started working with VMR in 2002, so that journey of virtualization was real early. There was no cloud talking we had kind of the XSP's and some of the earlier things. But so much has changed, and what I have loved is this journey that the users that are attending here. We're actually here in the Expo hall, and if you look, why are there no people in here right now? Because they are all in the break out sessions understanding what are the skill sets that they need today and tomorrow to help them in their journey; virtualization, cloud, DevOps, all of these changes there. Chris, you started this as a user to help share with your peers, so, we've had you on the program many times, bring us back. >> Yeah, so think back to 2003. There was no way to share information. There's no Google, no YouTube, no Facebook groups, Meetups, no Game of Thrones. >> We had to go to books and stuff like that. >> Exactly. >> Read the paper. >> So white papers, those were the big deal. You had the Microsoft books that were two inches thick and glossy. >> Yeah, I wonder how many of our younger audience would know the acronym RTFM? Read The Fine Manual please, is what we're doing. Dawn, this event, as I said, we've been at the winter event at Gillette Stadium, you brought in some of the Patriot players we've had the pleasure of interviewing. This Summer event is epic. I know people that come from very long distances to swim in the community, get the information. There's a little bit of lobster at the end of the day. >> There's a lot of lobster at the end of the day. >> So give us the community that you look to help build and foster, and what this event has meant to you over the years. >> For me it's really a place for everybody in the community to come together and share their knowledge with their peers. Something may work for me maybe it will work for you. Let's get together and talk about it. The best way to learn something is from somebody that may have done it, or done it, messed it up, learned something, like to share it with you. So, it really is about working with your peers, learning something from your sponsors and all these companies that you work with everyday. What's new, what's going on. So this is the place to go to get all that. >> Wait, Dawn, I thought you weren't a tech person. >> I'm not a tech person. >> That answer was spot on because one of the things I loved about the virtualization community, is we were all learning in the early days. And it required a little bit of work. There's this theory known as the IKEA effect. Sometimes if you actually help build it a little bit, you actually like it a little bit more. And this community really epitomizes that in the virtualization community and cloud. We've been talking about cloud now for a decade but it's still relatively early days on how this multi-hybrid cloud fits together, how operations are changing, so, Chris, bring us through a little bit of that arc. >> Well, I'll think about it, back in 2003, there was only VMwire. There was only one virtualization platform, if you didn't use VMwire, you were doing bare metal Windows install or Unix install on physical servers. Well, back when we changed, there was Hyper-V, that was coming out, AWS was just coming out, so that's when we kind of made the jump from just being a VMwire user to a virtual technology. So we could talk about the cloud, we could share those experiences and have that same journey together, and hopefully learn and lead, get smarter together as a group, you can learn faster as a group than you can by yourself. >> Yeah, and as we know, Chris, and we've talked about this, the IT industry is never "Hey, give me a clean "sheet of paper and we'll start everything." We know it is additive and all of these things go together, so cloud did not obviate the need for virtualization, so all of these things go together, and how do I make sure as my job doesn't get completely eliminated or, I was talking to somebody who said "If I've been doing the same thing for 10 years, "will I be out of a job?" They said, "Well hopefully you really really like "what you're doing cause if you think "you can keep doing what you're doing, "that is all you will ever be able to do." And I thought that was a very poignant comment. >> Yeah, Matt Broberg's talk this morning about what's your next job going to be, what skillsets do you need to be relevant in 10 years, and it's the same thing, I mean we said the same thing 10, 15 years ago. You can't be a Windows admin anymore, you can't be a VMwire admin anymore, you can't be a cloud admin anymore in five years. >> Yeah, so Dawn, give our audience a little bit of the scope of this event, as I said, I know people that have flown in from the Carolinas, from Colorado, from all over, from California and the like, 16 years of this event, this community is not just New England, it really has had a broad impact. >> Right, and it's huge, people plan their vacations around this, I've had people come from Europe, they fly over here, stay in the state of Maine, they go to L.L. Bean, they do all those things because they plan their vacation, they know they need to be here for the VTUG event, so it's meant a lot, because you do get so many different variety of people, you have the sponsors, you have the end users, you have media, you have bloggers, you have pretty much just everybody comes together to really be that community, so it's meant a lot to me, it's been a long 16 years but it's meant a lot. >> All right, so the question people are asking, this is the final VTUG, so no more winter event at Gillette, this is the final event tonight at Gritty's, so explain to us how that happened. >> It is the final event, 16 years, we're all getting older, it's bittersweet, but we've just realized that it takes a lot of time to put these together, it takes a lot of sponsors, it takes a lot of users, the users continue to come, but unfortunately the sponsors pay for it, and really don't have that following with the sponsors that we used to have, unfortunately. >> There are a lot more events, there are a lot more ways to find customers, so they're going to the meetups and they're doing their own events. >> Yeah, to your opening point Chris, 16 years ago it was much tougher to find sources. Now the challenge we have is there's too many options out there, there are too many events, trust me, I go to too many events, but this one has always been one that we've always looked forward, so please from the community, want to say thank you so much, it has always been one of our favorite things to kick off the year with when we do the winter one, and the summer one, I've made this trip a couple of times, it is a little warm in here, I think brings back to the roots of this event, remember it was four or five years ago it was 110 degrees out, and then you switched to this facility, so of course the air conditioning decides to go out, because we know in IT, sometimes things break. >> Start in the heat, end in the heat. >> So Chris, want to give you the final word for the final VTUG. >> You know, I'm just very proud and happy with this community, it truly is a community, it wasn't us, it wasn't theCUBE, it wasn't the vendors, it was everyone working together to make a community that helped each other out, so thanks to everyone. >> Chris and Dawn, thank you so much, we're happy to be a small piece of this community, and look forward to staying in touch with you in your future endeavors. Thanks so much, I'm Stu Miniman, we have a full day of coverage here, keynote speaker, some of the users that have traveled around, really focusing on the community here at the VTUG Summer Slam, as always, thank you for watching theCUBE.

>> Live from Las Vegas it's theCUBE covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel and their ecosystem partners. >> Welcome back to the Sands. We're live in Las Vegas here on theCUBE as we continue our coverage of AWS re:Invent. Along with Justin Warren, I'm John Walls. We're now joined by Dave Shacochis, who is Vice President of Product and Hybrid IT at Centurylink. Dave, good to see you again. >> Yeah, great to be back. Good to be back on theCube, good to talk to you John. >> Excellent. And by the way, you win the GQ award. >> All right. >> Everybody raving about that black, velvet you've got going on. >> 50,000 people here at re:Invent. If I'm in the lead- >> That looks very strong. >> I'm in the lead at the turn, that's good to hear. >> Best Dressed award. >> Very nice. All right, well big news though for you guys. Obviously being designated as the managed services provider, reaching that certification with AWS, tell us about that, about that process and what it's meaning to your business, and what it means to your customers. >> Yeah, AWS is such a customer-focused organization. They're very passionate about their end customers, and solving problems. But they've also built up a huge partner network, and what Terry Wise and the team have built is a real partner-relevant organization. And so what they've really done to make it a level playing field, to be as passionate about their partners as they are about their end customers, hopefully intending to solve problems for customers as well, is really to put a lot of thought into making sure that when they have a competency or a certification, that it's no joke to get through. It's a serious exercise to go through something like a managed service provider or an MSP certification. We had that get finished up for us several months ago, and we've been rolling that into our managed cloud practice, and really helping our customers with the three key criteria of what AWS really wants to have its partners do, which is really design and plan and be able to orchestrate workloads, and model workloads for customers, and understand how and where they're going to deploy and migrate into the cloud. They really want to see and make sure that you're doing next generation work during the operational run phase. Not just are you monitoring and managing those workloads in those environments, but are you doing predictive analytics? Are you starting to take a look at trends inside the data? Are you using Big Data to actually augment your management practices? Right? Not traditional ITEL just in a cloud location, but really next generation managed services. They measure and they certify all that. And then the third thing they want to do is take a look at how are you reporting? How are you helping the customer optimize and analyze cost, and become as efficient as they can with their deployment of AWS services. So it's a significant exercise to go through. It really made our service better. And quite honestly, that's a great example of AWS being customer-focused by making sure that the partners they want to work with can hit a certain level-- >> Step up your game. >> Hit a certain bar to be able to drive that value for their end customers. >> Yep. >> Yeah. So for the customers who were choosing Centurylink to come to something like AWS, what is it about Centurylink that they like? That they would rather deal with you than go, say direct to AWS and try to do everything themselves? >> I think there's really sort of two or three real differentiators for Centurylink when we work with our customers. Probably the first and foremost is that hybrid nature of how we can meet the customers where they are. Centurylink has been running and managing and working in the data center space for a good 15 to 20 years. We've been running and managing private clouds and hosted compute environments for as long as there has been such a category in the industry. With all the different heritage that rolls into Centurylink from an IT Services perspective. So they really come for the experience and the pedigree, and the complexity, friendliness. But they also come for the fact that we can meet them where they are, whether it's inside their current data center, help them do data center consolidation, help them move into hosted centers, and then help them on that journey, 'cause so much of the enterprise is still very much on a journey, right? There may be projects that are firing up to the cloud, there are a lot of organizations that are ready to make the full leap and go all-in on the cloud, but by and large there's some kind of a hybrid environment where they're still looking at the different form factors, and they're very much on a journey to get from where they are today to where they can be more agile. >> Yeah. >> So this is the experience that we have. But then what we really, and there's lots of companies out there that have good experience, they have good tools, they have experience running and managing and monitoring. There's a lot of other companies that have the MSB certification. What Centurylink has that's really a deep investment is all the network optionality and the network control that we have. So not only can we do managed services inside AWS, we can also do the managed network that gets the traffic and gets the workload to AWS. >> Right. >> And that's a real critical differentiator. Not only can we get those connections set up and configured, we can also manage those environments and then secure those environments. So there's a lot of investment that Centurylink has put into our managed cloud practice, augmented by managed networking and managed security. The assets that Centurylink brings to bear with regards to our security portfolio and our network portfolio, come from years of significant investment. One of the largest global IP backbones in the world. We've been gleaming network and security telemetry from that network, and building threat patterns and threat management services inside the core of our network. So really, customers who work with us have a secure, consistent, reliable path to the cloud, and then they get the managed services, the MSB certified managed services, once they're there. >> Yeah. So speaking of connections, I believe that you've announced a product in, I think it was October, called Direct Connections. >> Yes. >> Is that right? Tell me more about what that is. >> Sure so that's that sort of, for those of you tracking my hand waving at home, you know the network stuff over here. Inside our network portfolio there is ... our cloud connect service is one of most deeply connected to AWS services out there. So we're a significant direct connect partner. We drive an ethernet-based service into AWS in all their major regions, and then we have that cloud connect service run to hundreds of global multi-tenant data centers as well as hundreds of thousands of enterprise locations. So we have, what we launched there back in October was the latest version of cloud connect, which we call Dynamic Connections. It's a feature within cloud connect that allows us to take a global ethernet circuit, tying into AWS, and make that happen in minutes. That didn't exist before. So a lot of people think about AWS direct connect, and they can configure direct connect and tie it up to their VPC, then they start the Telecom process that could take weeks and or months, and it depends on who they're working with, and who they're buying from. >> Yeah. >> If you're in a building that's on net with us, or your traditional data center is one of the data centers, the many hundreds of data centers that are on net with us, we can go and get that connection turned up, all of the automation, and once you get that circuit created, you can dial it up and dial it back, a gig, ten gigs, anywhere in between. You can go below a gig, wherever you need to. You have complete control over the creation of a new circuit, which is great for retail locations. Retail customers like this as they're bringing in new facilities, and bringing mixed-use facilities on net, and they're bringing new facilities that they need to be able to trunk back to their data center in the cloud. We can use dynamic connections to go and help them create new locations, but then as the business needs change at those locations, they can dial up and dial down bandwidth, and really have a rich level of control for how the traffic is being routed and passed. >> Yeah, having spoken to customers in the past, that is actually quite valuable. It has been quite painful to go through that process. >> A lot of big cloud migrations, once they're done with them, one of the problems you run into is, "Well, I never really thought through and anticipated what the network path would look like after I made that move to the cloud." >> Yeah. >> And that's one of things we try to do with our customers at the onset of an engagement, is not just say, "Let's start stampeding to the cloud right away." Nothing necessarily wrong with that, but let's think through the network design first. Who are your users? What are the new traffic patterns going to look like? And what are the hybrids that you're going to be building, where something that's in the cloud needs to talk back to your corporate data center? Do you have enough bandwidth and do you have a low enough latency connection between the two? >> Yeah. >> Early this week you were talking about Milliseconds Matter, right? You had a presentation that you were featuring that. So what does that mean to your AWS customers? That's kind of intuitive, they do matter. >> Sure. >> What was the perspective that you were bringing to that and the latency issue? >> Yeah, so we did a presentation here earlier in the show, where we really illustrated that combination I was referring to earlier, of our MSP certified managed offering coupled with our cloud connect network automation. What we've really done a lot of work with around cloud connect is creating a service that has a few different user experiences. If you're a network engineer, if you're somebody who's running a corporate network, you really want to get in and really just get a layer to interface from Centurylink, optimize your BGP routing and do all of those sort of Telecom-grade configurations, you can do that with Centurylink cloud connect. We also have a very straight-forward version. In Andy's keynote this morning, one of the things he was really talking about, it really spoke to me, was this idea of, well there are builders who want to use the tools, and there are builders who just want instructions. >> Yeah. >> Builders who just want to dump the IKEA parts out and put the thing together. And then there's some people that want to sit there with a lathe and handcraft everything. So different types of builders. We have a version of cloud connect that can appeal to the builder who doesn't necessarily want to get down in the weeds of networking, and they just want to basically take a workload and connect it to the right private network link. And so that higher level version of cloud connect is what we demonstrated earlier today, and really the fundamental premise of Milliseconds Matter is network orchestration and cloud orchestration coupled together gives you a whole lot greater level of control. And that's where we're starting to see all these emerging use cases, where you can certainly think about migrating everything to the cloud, but then you have to start thinking about where do those workloads need to run? What does the future look like, in terms of IoT devices and sensors and video telemetry and environmental telemetry, all the different sources of data that organizations can use to go and innovate around. Where you're going to run that business logic is going to run closer and closer to the edge for a lot industries: in retail, in healthcare, in a lot of government institutions, in hospitality. So basically the fundamental premise of Milliseconds Matter is have control of your cloud, but then also have control over your network, and hopefully have the two in concert with one another. And that's what we're fundamentally driving at with our service platform. >> Sounds great. >> And real quick, when you talk about all this, in a 5G world, all of a sudden when you talk about edge, you talk about- >> Sure. >> That's a game changer, is it not? >> Well it is. 5G is still so emergent. There's a lot that's there. There's a lot there that's 5G. There's still 4GLTE. There's still lots of different ways to go and get all that data trunked together. And it doesn't stay on LTE forever, right? Eventually it all starts to get to an IP backbone, and then that's where you still have a lot of latency optimizations and route optimizations that you want to be able to deal with. So we absolutely look at LTE as something that we think is a huge opportunity with a lot of our partners that we're working with across our network footprint, to be able to use LTE as a new access strategy, just like we've used just about all the other access strategies that are out there. >> Excellent. Good to see you again. >> Yeah, great to see you, John. >> See you down the road soon, I hope. >> For sure. >> All right, thank you for joining us. Dave Shacochis joining us here from Centurylink. Back with more from AWS re:Invent. You're watching theCube. (electronic music)

(tech music) >> Announcer: Live from Madrid, Spain, it's the CUBE, covering HPE Discover Madrid 2017 brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid everybody this is the CUBE. The leader in live tech coverage. This is day one of our coverage of HPE Discover 2017. I'm Dave Vollante with my co-host Peter Burris. Jim Jackson is here, he's the senior vice president of the Enterprise Group at Hewlett Packard Enterprise. >> Happy to be here. Good to see you again and Jason Newton, vice president of global marketing at Hewlett Packard Enterprise. Guys, it wouldn't be a Discover without some big news, transitioning to Antonio. We're about to hear the key note but Jim, set up the week for us. The big news that we can expect. Show us a little leg. >> Yeah well first of all, thanks for having us here guys. We're really excited for this week. It's gonna be probably one of our biggest weeks of innovation. We've got a pretty amazing Discover lined up. So you're gonna see us talk about AI in the data center, so bringing predictive analytics from our Nimble acquisition it's called info site. We're extending that to three par so that really helps our customers predict and anticipate problems and solve them in advance. So that's really software-based leading with that. Another area is we're bringing consumption-based capabilities. A whole new suite of consumption offerings. We're branding it HPE Green Lake and it's really, think of purpose-built solutions for things like backup, SAP, data like environments but it's really outcomes as a service. So we're not able to give our customers the ability to have infrastructure as a service, and now outcomes as a service. And the other part of making hybrid IT simple that you're gonna hear about is how we're really helping our customers unify and manage that multi cloud environment. So applications are sitting in public clouds, private clouds, what we're hearing from our customers is, hey we need to be able to manage this a lot easier and have holistic ability to see all of that. So you're gonna see us talk about that on main stage as well. So new brands, a lot of innovation. We've also got some partnerships that we'll be rolling out later today. So a lot happening. >> Jason, you've spent a lot of time, sweat, toil, blood on branding. Obviously you're a big part of the branding exercise. Up leveling the messaging, we had you on two or three years ago, and you said, look, we're gonna change things. We're gonna shift the focus from product and widgets and really talk about what customers care about. How has that gone? Where are you at with that? It resonates extremely well with customers. In fact we just got out of a panel where we had four of our top customers, ABV, Dreamworks, IKEA and Nokia. And we just spent an hour just talking about their digital transformation journey and what they're all about. The room was packed. I think we had over 400 people who were in there. That's showing that we can be an innovation partner to those customers enabling them to share their stories at a venue like this is really powerful. >> We're becoming much more software and services led and it's really all about experiences. Providing that experience that our customers are looking for. >> Just follow up to that, so a lot of people think oh well HP, spun merge it's software business but you're leading with services and software. So help us clear that. >> We're doing a ton in software today. So if you just think of our software portfolio. We have HP 1V to manage our customers complete infrastructure estate, service storage and networking. We extended that last year with composability so HP and Synergy, we have over a thousand new customers since we announced that last year actually at this event. So we're seeing a lot of progress. Synergy enables our customers to really have one environment that can flex to the needs of multiple different applications so reduces over provisioning. AI, I talked about AI in the data center. So what we're doing with info site, that's software based, we're extending that to 3PAR and you'll see us extend that to other parts of the portfolio going forward. Nyara, and on the Aruba side of the house, software based. Aruba is very software centric and then of course, we'll be announcing this afternoon our code name project new stack, really about helping to manage that multi cloud environment. A lot happening in the software space and an area that we're very focused on. >> One of the things... By the way, we think that those three things that you mentioned, automation in the data center, on-premise capabilities and a cross multi cloud approach to management and managing your assets, absolutely spot-on. And we think ultimately and here's a question, we think that what's going to drive the determination is what does the data need? So talk to us a little bit about how you are articulating the idea of data as the new value source, the new value and hardware infrastructure and software and these capabilities, making it possible for the work to exist where the data requires. >> Yeah and I'll start maybe you can pile on a little bit. Our conversation starts with apps and data so we're starting the dialogue there and you know what we're seeing is you know really moving from large data centers, or only large data centers to centers of data that are really everywhere, right? So we're starting to see that edge really starting to proliferate and drive a lot more change, and what our customers are saying is wherever might, regardless of where my data sits, I need to manage it, I need to secure it, I need to process it, I need to be able to translate it into insight and that's really what our strategy is all about. We've been talking for the last couple of years about making hybrid IT simple. and we're really doing a lot in that space. So for example, we announced the acquisition of cloud technology partners and really what we're trying to do there it's the foremost authority really in helping customers understand how to migrate applications to to AWS or even to Google or Azure, and when you combine that with our on-prem capabilities, it really now starts to talk about data, we want to say your data is what matters and we want to help you manage that holistically. The software investments that we're doing enable you to have that complete view. And then from a consumption perspective, some of the things I talked about earlier, rolling that out right, making it easier to consume this as a service and only pay for what I use. So, we are in alignment. It all starts with data and wherever that data sits, it's how do I manage it? >> And that's why Aruba is such a great asset for us, because a lot of people think about Aruba as you know, you just replace copper wire and WiFi ... And hey, don't get me wrong, it's a money-making great business, but if you'd asked Kierty, he'd probably say we're a data business, right? >> Peter: We did ask him, and that is what he said. >> Is that what he said? Well, good, we're on message then. We're on message today, alright, yeah. I mean, because that's where the action is happening, that's where the data is being created, and so everything that they're doing around the the security 360 platform, the mobile first platform, everything is centered around, how do I draw a value in context from that data? >> Well I want to ask you about Aruba, because when you acquired Aruba, we said wow, this is a great business, it's gonna be a growth business, but is it a strategic weapon for HPE? Is it a strategic infrastructure component? From a messaging standpoint, It's all about the intelligent edge, that you've up-leveled that. Where'd that come from? Maybe take us through sort of the anatomy of-- >> Well I mean, the message is just exactly what we were saying. That if if value is gonna be created at the edge, if the data's gonna be coming from the edge, we have to drive a whole lot more intelligence into that edge in order to collect, process, analyze, secure the data that's coming in and make use of it, right? So I mean, that's where the genesis of the intelligent edge came from. >> Yeah, I mean I would say the other thing about Aruba that we're really seeing is all about experiences. So when we talk to our customers about Aruba, they're looking to deliver a different experience. Whether it's in retail, whether it's in stadiums, whether it's in the campus space. It's all about delivering a better experience. And that's really the value prop behind Aruba. Very software centric, open software, mobile solution. The other thing is, it's enabling us to engage more and more with parts of the company, customers that we might not have had as much engagement before. You know, the c-suite, you know, talking more with the line of business. because what they're focused on is how do I deliver that better experience? And Aruba's really playing a key role in doing that. We also have the view that ultimately, and you started the conversation about data, and we totally agree. But it has to be thought of from the edge, to the core, to the cloud. So whether we engage with Aruba, whether we engage with our core data center, capabilities, and our strengths there, or with services ... That's enabling us to holistically have a much more strategic conversation with our customers. So we're excited about that. >> I'd like to dig a little bit on this notion of AI for the data center, or AI for managing IT (mumbles). We'd like to talk about the difference between a breadth-first, which is I'm gonna do this, like in this big broad way, and we'll figure out how we're gonna get the components to participate, versus a depth-first. Which is, let's lean on suppliers, who know that hardware, know the software best, and ask them to create simulacrums, you know, digital representations that then will allow me to apply AI machine learning, et cetera. We like the depth-first approach, but customers ultimately want to see this bloom into a breadth approach. Talk to us a little bit about how individual elements are being represented, but in a coherent consistent way, so that you can get to a broader, overall set of automation across entire infrastructure. >> Well, I mean, I think that you're seeing the paradigm shift now. I mean for decades we've been chasing this idea that we can make the one tool to rule them all, this sort of magic management environment, one single pane of glass, everyone says that right? >> I've written a lot of research papers that suggested that, right? >> Right? And look, I think that's, we're done, alright? And the only thing we can do now is, how do we embed intelligence to make the infrastructure so smart it can take care of itself? And that's ultimately the experience that our customers are telling us that they want, right? Is, I don't want to be an expert on IT anymore. I don't wanna touch this stuff, I don't want to deal with it. >> Peter: Not just want, need. >> Right? I can't handle it, right? I mean, the scale and speed of everything is beyond the capacity ... I can't hire enough people to take care of it. So you know, I think starting there and saying, okay we're gonna start embedding that type of intelligence. Right now it's mostly predictive analytics type of stuff, but increasingly you're gonna see more true AI come in not just in the data center, with what we're doing with Nimble, right? But also with Nyara. Now we call it introspect, right at the edge. How do we start weaving that across to do a variety of things? Whether it's maintenance or performance optimization, or security. I think thinking of it like a continuous platform across the infrastructure is gonna give you that depth and kind of breadth of control that you're looking for. >> So that leads to kind of an ecosystem question, and I liked your comments on that. Because the question of breadth or depth, the answer is yes, you got to have both. The ecosystem posture has totally changed in the last year or so, subsequent. Because we had PWC on today. We've had Veam on earlier. These are-- >> Jason: They love us. Partners that you're putting forth, yeah. >> Jason: We're making them money. >> For sure, right. But they are partners that previously, you know, you wouldn't have profiled. Whether on stage, on the Cube, wherever. >> Jason: Yeah. >> How has the ecosystem evolved? >> I mean it's opening up a whole new set of opportunities for us. You know, if you think of when we had ES, a lot of people just felt like, hey we were gonna compete with them, right? Now that ES has spun out, we actually created another great partner in ES, but we've got a whole host of other SIs that want to engage with us. They want to take our capabilities in IT systems. Our consumption capabilities, and then align it with a value prop that they'll bring. So you talked about Veam for example, right? Data availability is really, really important for customers. So taking HPE and Veam together, we're able to deliver a great solution from data protection to recovery. Really powerful stuff, and we're seeing some great opportunities out there in the marketplace, and a very strong ROI. I mean, we have some data that says, hey over five years, is a 200% ROI. Another area, when you think of just partnering, right? Is what we're doing with our channel partners. So we're giving them more solutions that are channel centric, that we're driving through our channel organization, yeah. And then, we just announced a relationship a couple weeks ago with Rackspace. It's a managed private cloud, open source solution. We're using our consumption capabilities, combined with with Rackspace, their environment. And this is giving our customers the flexibility to now spin up very quickly, a private cloud environment that they're looking for with a lot of the public cloud capabilities. Very strong economics behind it. And then the edge, that's the other area we're seeing lots of new partnering opportunities as the edge continues to expand. So we believe that innovation is a team sport, and we're leaning in really hard, and I know you know the Gartner's and the IDCs don't track who are the best partners, but I think if they did, we would be at the top of the list. >> Well, probably a lot of this activity was going on previously, so it's not like you're starting from ground zero. >> Jim: Correct. >> But you just, from a marketing standpoint, you really didn't talk about it, because you had colleagues, whether it was from EDS or the software division that's saying, hey, don't talk about that, help us out here. So, how has that changed the way in which you market? One of the big values is your go-to-market. I mean, people are drooling to now partner with HPE. >> Yeah, and one of the big reasons is honestly, is point next. Because they see the value in what Accenture or PwC, or Wipro can bring from understanding a business, or whatever, versus the deep technical knowledge of a point next to come in, and what they really love is the consumption model stuff that we've been able to wrap around it. They see that customers want, that in order to move fast with less risk, right? You've gotta have some sort of financial lever that says, okay, I can start small and I can grow over time. I'm not putting all my money out in one place and we've been building that with flex capacity over the last several years. You're gonna see, well, I guess we announced yesterday, a new Green Lake ... Making that even simpler to consume. Every one of our partner says, I wanna take your IT expertise in that consumption based model and wrap it around a total solution. And that's what's like white-hot right now, and there's unlimited opportunity right now from ... As Jim said, edge to core to cloud. >> And we have another one we're gonna announce on stage in a couple of hours, so we're pretty excited about that as well. >> Well, you see that in the numbers too, yeah. >> Jason: I think we might have a clue what that is. >> We're excited about that. >> Yeah, I know, it is. Well, look, and you kind of you kind of gave something of a preview when you talked about the three things that you want to be able to do. Because there's one brand that hasn't been mentioned yet. But ultimately the business is recognizing that the technology questions that we're raising here are crucial to their future success, but they don't want them to be a continuous source of antagonism. >> Group: Right. >> So they recognize that they need the capability, but they want to dramatically simplify the degree to which it's evasive. I once had a CIO tell me that the value of my infrastructure is adversely proportional to the degree to which anybody in my business knows anything about it. So how do you then take steps to ensure that your customers don't know anything about the infrastructure, even though they have the infrastructure where the data demands, which is gonna be at the edge, and on premise? >> I think that's some of the things we're focused on now. So software to make infrastructure much more frictionless. And you're not really worrying about managing that infrastructure, it's just there to power the business, to deliver the business. Consumption-based offerings with Green Lake, this is truly purpose-built stacks for specific things, because our customers are telling us, I don't want to have to set all that up and manage it, but I want that outcome, and I only want to pay for what I use. So those are just a couple of examples of how we're trying to simplify it. Because ultimately it's all about the experience and the outcome and being able to translate all that data into insight. >> Well, when you're simplifying your face to the world, we heard in the last earnings call, new reporting structure going forward. Hybrid IT ... intelligent edge, and financial services, which is exploding, the consumption base modeling 22% growth last quarter. So organizationally, presumably, you've started to take that shape, and that's how you're presenting your face to the world. Is that right? >> Yeah, and that's helping us to really break down some of the silos, that has existed in this company for a while. And you're seeing that really, really becoming much more unified in terms of how we go to market, and how we think about engaging with our partners how we engage with our customers. >> Are your customers breaking down those silos at a consistent rate? Are you a little bit ahead, a little bit behind? How would you evaluate that? I think it's a transition, it depends on which customer, which sector. We still see some of some of them that are maybe a little behind. Some that are a little bit ahead, but really everybody wants to start the conversation much more about, how do I move faster? How do I accelerate my business? It's all focused on outcomes starting at that data level, and then how can you help me? And this is where I think some of the acquisitions that we've made, like CTP are very empowerful, and then all the software capabilities that we're bringing as well. So we're leading the dialogue much more around that. >> And the only way they're gonna get there is to break down those silos. >> Jim: Absolutely, absolutely. And we have to help them do that, right? We have to help them do that and give them the solutions to do this. >> So Jim, I want to go back to a point that you made about those other two research firms, Gartner and IDC I think it was. But you said that if they were measuring the value, or if there was a magic quadrant for who is the best partner, you guys would be up in the upper right hand quadrant? But partners in this world, especially here in Europe, are more than just the big guys. >> Jim: Yes. >> How are you taking steps to ensure that that large mass of crucially important companies out there, that still where a lot of that innovation, a lot of that excitement really is, are coming with you, are able to move with you? Because your ability to certainly provide them with financial support is important, but your ability to show them the future, and have them see their business in the future, is going to be crucial to whether or not they stay with you. >> And I think we're doing a couple of things. We created our Pathfinder program, I'm sure you guys are aware of that, right? So these are some of the newer partners coming up, we're actually investing in them, helping to scale them, because we think it's going to be unique innovation. Another area is this program that we have called Cloud 28 Plus, where we have a whole network of providers, service providers, ISVs, SPs, that's part of a network that we're able to grow and kind of scale that ecosystem, so I don't know if you want to comment anything more on that, but-- >> Jason: Up to 700 now (mumbles). >> Yeah, so Saviea is very passionate about this obviously, but he's done some some really good things-- >> Peter: And he should be passionate about it. >> But that gives us an ecosystem now of partners who are part of that HPE ecosystem, but different use cases, different compliance needs, they sit in different regions, so we're able to give our customers a lot of that flexibility. >> Alright, gotta give us something on the key note. Just a tidbit. What can you share? A little nugget? >> I mean, you know-- >> Dave: Teaser. >> Some themes we've talked about. You'll hear the word friction free a lot, how do we make things invisible? And really demonstrating how with services and software, and consumption-based service models, can we do that for customers? You'll hear a lot of those themes. We'll highlight some of the things we've announced over the last 24 hours, a few weeks. So we'll emphasize what we've done around Nimble and info site, and the importance of AI in the data center. We'll obviously spotlight point next, and Anna and her energy, she's gonna be out there and really firing people up. And a few surprises in the software space that will come today, that it'll probably cause the market to do a bit of a double take and say who is that that's doing this again? Yeah, it's us, it's HPE doing that. >> And you'll see us also talk about a little bit of a vision in terms of how we see the market starting more at the edge, bringing in AI, composing for different kinds of environments, and then how HPE has really been able to invest, so we're gonna start to show that over the last couple years, we have had a very clear agenda where we want it to go, and now that's all coming to fruition, so we'll start to show all that holistically in terms of our technology vision. So that's another thing that we're gonna be highlighting. >> Great. Perfect timing, we can hear the announcement. Keynotes are coming up, we'll be broadcasting those on our twitch channel. Siliconangle.com/twitch You can go to HPE.com and see the keynotes as well. Gents, great energy, awesome to see you. >> It's great to see you guys, thank you. >> We'll be watching the college football ranks. You guys have a fun little rivalry of Ohio State here. >> The Ohio State. >> Dave: ... Yale, but nobody cares. >> Baker for Heisman. >> Dave: Gents, thanks very much for coming. >> Thanks guys, appreciate it. >> Keep right there everybody, we'll be back with our next guest right after this short break. (soft tech music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering VMworld 2017. Brought to by VMware and its ecosystem partners. (upbeat techno music) >> Okay, we're live here at VMworld 2017's theCUBE's coverage of VMworld 2017. I'm John Furrier. My hosts, Dave Vellante and Stu Miniman. We've got two sets kicking off live here in Las Vegas for our eighth year of coverage. Boomy, we're in the broadcast booth at the Mandalay Bay. Guys, we're here to kick off the show. Three days of wall-to-wall coverage. Three days of great keynotes. Today, big surprise, Andy Jassy, the CEO of Amazon Web Services joined Pat Gelsinger on stage in a surprise announcement together, hugging each other before they talked and even after they talked. This partnership is going to be big. We're going to have coverage, in-depth analysis of that. Dave, VMWorld is now the cloud show with re:Invent. If you look at what's going on, Stu, you've been to many, many shows. This is our eighth year. This was the show. Great community. Now re:Invent has been called the new VMWorld. You put 'em both together, it's really the only cloud show that matters. Google does not have yet a presence. Microsoft has all these shows that are kind of spread all over the place. All the top people are here in IT and cloud at VMWorld and at re:Invent coming up in December. >> Well, John, eight years ago we talked about is this the last stop for IT before cloud just decimates it? And if you go back two years ago, VMware was not in favor. The stock was half of what it is today. Licensed revenue was down 1%. Fast forward to today, it's growing at 10 to 12% a year. Licenses up 13%. It's throwing off operating cash flow at $3 Billion a year. The market's booming. Wall Street's talking VMware now being and undervalued stock. The big question is, is this a fundamental shift in customer mindsets? In other words, are they saying, "Hey, we want to bring the cloud operating model to the business and not try to force our business into the cloud." Or, is this the last gap of onprem. >> Stu, I want to get your thoughts cause I want, squinting through the announcements and all the hype and all the posturing from the vendors is I was looking for, where's hybrid in all this? Where's the growth? And, my validation point on the keynote was when we heard very few words hybrid. Private, on premise was the focus. You guys put out at Wikibon a report called True Private Cloud, Market Sizing. Kind of lay out, that's where the growth is. But, I tweeted private cloud is the gateway drug to hybrid. We're seeing customers now wanting to do hybrid, but they got to do their homework first. They got to do the building blocks on premise, and that is what your calling True Private Cloud. Do you agree? And your thoughts. >> Yeah, so, really good points, John. And the nuance here, 'cause if I'm VMware, I've got a great position in the data center. 500,000 customers. Absolutely, the growth is the move from legacy to True Private Cloud. The challenge for VMware is they already have 500,000 customers there. Those are the customers that are making that shift. So it does not increase vSphere. One of the key things for me, is Pat said, "What vSphere had done for the last 20 years, is what NSX is going to do for the next 10 years, or more." Because they're betting on networking, security, some of these multi-cloud services that they announced. How do those expand VMware so that as True Private Cloud grows and they also do public cloud, VMware has a bigger seat at the table, not just saying "Wait, my customers are shifting. Where are they going?" >> Dave, I want to get your thoughts. You and I talk about all the time on camera, and also privately, about waves. We've been through many waves in the industry. We've seen a lot of waves. Pat Gelsinger has seen many waves, too. Let's talk about Pat Gelsinger because, interesting little tidbits inside the stage area. One, he said "I want to thank you for being the CEO of this company." Stu, you made a comment that this is the first VMWorld where there's not a rumor that Pat's not going to be the CEO. He's kind of kickin' ass and takin' names right now. Stock's up and he put the wave slide out there. And wave slides to me, you can tell the senior management's kind of mojo by how well laid out the wave slide is. He put up a slide on one side. Mainframe mini computer cloud. And the other side client server, internet, IoT Edge. He nailed it, I think. Pat Gelsinger is going to go down as being one of the most brilliant stroke of genius by looking at either laying down what looked like a data center position, and some say capitulate, to Jassy, who's smiling up there saying, "Bring those customers to Amazon." But this is a real partnership. So, Pat Gelsinger, go big or go home. You can't be any bigger, bold bet that Pat Gelsinger right now with VMware, and it looks like it's paying out. What's your thoughts on Pat Gelsinger, the wave and his bold bet? >> Well, I think that businesses are configuring the cloud, John, to the realities of the data. And the data, most of the data, is on prem. So the big question I have it, how is Amazon going to respond to this? And Stu, you and Furrier have had debates over the years. Furrier has said flat out, Amazon is going to do a True Private Cloud, just like Azure Stack. You have said, no. But if Amazon doesn't do that, I think that Pat Gelsinger's going to look like a genius. If they do do that, it's going to become an increasingly more competitive relationship than it is right now. >> Yeah, just a little bit of the inside baseball. Kudos to VMware for getting this VMware on AW out. I hear it was a sprint to the finish because taking cloud foundation, which is kind of a big piece. It's got the VSAN, the NSX, all that stuff, and putting it in a virtual private data center. Amazon owns the data center. They give them servers. This was a heavy lift. NSX, some of the pieces are still kind of early, but getting this out the door, limited availability. It's one data center. They're going to roll out services, but to Dave's point, right, where does this go down the road? Is this Amazon sticking a straw into 500,000 data centers and saying, "Come on in. You know that we've got great services, and this is awesome." 'Cause, I don't see Amazon re-writing their linux stuff to be all native VMware, So, where will this partnership mature? Andy said, "We're going to listen to our customers." "We're going to do what you're asking us." And absolutely today VMware and Amazon, two of these strongest players in the ecosystem today, they're going to listen to their customers. Google, Oracle, IBM, Microsoft, all in the wings fighting for these customers, so it's battle royale. >> You know the straw is in there, John, what's your take, and where do the developers fit in this? >> Well Stu wrote a good point, inside baseball, the key is that success with Amazon was critical. Jassy said basically, this is not a Barney deal, which he kind of modernized by saying most deals are optical really hitting at Microsoft on this one and Google. I mean, they're groping for relevance. It's clear that they're way behind. Everyone's trying to follow these guys. But, on the heels of Vcloud Air, it was critical that they get stake in the ground with Amazon. They took a lot of heat for the Vcloud Air, Stu. This had to get done. Now, my take on this is that, I think it's a genius move. I think Pat Gelsinger, by betting the ranch on Amazon, will go down in history as being a great move. You heard that here, 2017. He's so smart, he wants to be a component of the Amazon takeover, which will happen. It'll be a two-cloud game, maybe three, maybe four, we'll see, but mainly two. But the ecosystem partners on this phase one is key. DXT, Deloitte, Accenture, Capgemini, and then you start to see the logos coming in. They have so many logos, you have to break them down. But more importantly the white space. devops, migration cost, network security and data protection are all filled in with plenty more room for more players. I think this is where the ecosystem was lagging just a few years ago. You saw the shift in the tide. Now you're seeing the ecosystem going, "Wow, I get what VMware's doing. I'm doubling down." It's an Amazon Web Services, VMWare world. All the other cloud players, in my opinion, are really fumbling the ball. >> So, I can infer from that, you see this as a balanced partnerhip i.e. that's not like one needs more than the other. I mean, clearly, Amazon needs VMWARE to reach those 500,000 customers, and clearly, VMware needs a cloud strategy because Vcloud Air and many other attempts have failed. Yes, we said that. It's failed, we asked Pat about that. So, you see it as a more balanced partnership. Do you see that balance of power shifting over time a that straw gets bigger and bigger and bigger. >> Well the Walking Dead or as the Game of Thrones reference going on is kind of the Gray War is happening in cloud. And it really is going to become Amazon versus whoever they can partner with, and the rest of the legacy world. I think the wave slide was impressive to me because this is such a shift from just distributed computing now decentralized with blockchain and AI looming as massive disrupts, I think this is only going to get more decentralized. So whoever has tech that's legacy, will ultimately be toast. And I think Gelsinger's smart to see that wave, and I'm starting to see the movement. It's super early, so, no big bets. It's just be directionally correct and ride that wave. >> Yeah, so, one of the things that got me is last year, it kind of went under the radar that VMware is starting to launch some cloud services, and were very direct, today, that they said there are seven, basically SaaS offerings. It's security, it's cost management. Now, VMWare on AWS, little expensive. We're starting to get the data on how much it is per month or per year or for three-year. But going to have the SaaS offerings. We know Vcloud Air failed, also Paul Moritz had played the Microsoft game. We're going to get this suite of applications. We're going to give you email. We're going to give you, you know, social. We're going to give you all these things. They're all gone. Kind of cleared the table of all those. Now they've got these SaaS applications, so how will that play. I kind of like Pat, very up front on security, and said, "As an industry, we have failed you." Dave, you've been looking at this for a long time. It's a board-level discussion. It's a do-over for security. Does VMware have the chops to play in this space, Dave? Do you buy them as a, you know, valid SaaS provider? >> Well, two questions there. One is in the security front that great tech is always going to get beat by bad user behavior. So this is a board-level issue. As far as SaaS, to me, it's a business model issue that VMware is migrating its business to a routable business model, which is smart. I don't see it as SaaS as an applications, but I see it as a monthly fee. Better to get ahead of it now, while you're hot, than get crushed by Wall Street as you're trying to make that transition like many other companies have failed to do. >> Guys, one thing I want to note is that VMware also laid out their strategy. You kind of heard it there even though that Jassy came on stage. A look it, Jassy's not an idiot, he's smart. He knows what's going on. He knows that he has to win VMware over because VMware ... he's got to balance it. Got 'em in the back pocket on one hand, got a great relationship, Stu, 500,000 customers. Remember, VMware is also an arms dealer. They got the ops, IT operations locked down with their customers. So they have other clouds they can go to. SO, the big trend that we didn't hear, that's out there kind of hiding in plain site is multi-cloud. Multi-cloud is ultimately VMware's strategy. He laid out, one, make private cloud easy. You guys reported on that. Two, deep partnerships with major cloud providers. And three, expand the ecosystem. >> John, so I mean a little bit of kind of rumors I heard. They were actually looking to make the partnership not with AWS at first, it was going to be Google. And Michael Dell said, "If we're going to start with a cloud deal, it's going to be Amazon." The right move, absolutely, that's where it's going to be. But you remember last year, we were here. John, you and I, the announcement was with IBM. Now, no offense to SoftLayer, great acquisition. It's doing well, but IBM does not play at the level of an Amazon. They might have the revenue of a Google in cloud, but, you know, very different positioning. They were up on stage talking about security today. Great position there with analytics. But, we'll see, there's two more days of keynotes. I expect we'll see another cloud provider making some announcements with VMware. And VMware absolutely an arms dealer. They put out on the slide all of their service providers. We've got people like CenturyLink and OVH and Rackspace on theCUBE this week, as well as how their going to play with the Microsoft Google. You've got Michael Dell on tomorrow. I know you're going to talk to him about how Dell fits with Azure Stack, and how the Dell whole family is going to play across all of these because at the end of the day, Michael Dell, and Pat working for him, they want to keep getting revenue no matter who's the winner out there. >> Okay final question as we wrap up the segment. Customers are that watching here, it's clear to me that, we even heard from one on stage, saying, "Well, we're taking baby steps." That wasn't her exact words, but, their going slow to hybrid cloud. All the actions on private as you guys pointed out in our True Private Cloud report on Wikibon.com If you haven't seen it, go check it out, it's going viral. But, this is classic slowness of most enterprise customers. When there's doubt, they slow down. And, one of the things that concerns me, Stu, about the cloud guys right now, whether it's AWS, Google, and Microsoft, is the market's moving so fast, that if these clouds aren't dead simple easy to use, the customers aren't going to go to hybrid. They're going to go back to their comfort zone, which is the true private cloud, going to build that base. It's just got to get easier to manage. It's got to get easier to multi-cloud. And the bottom line is that Amazon's clearly in the lead. So, Jassy has a window right now to run the table on enterprise. He's got about 18-24 months, but Google's putting the pedal to the metal. I mean they're pedaling as fast as they can. Microsoft's cobbling together their legacy, okay, running as fast as they can. But there's this economies of scale, Stu, for them. Your thoughts and reactions. >> Yeah, so, I always thought enterprise simplicity is actually an oxymoron, does not exist. This VMware community, one of the things people loved about it, they were builders. They were all like get in there, and I tweak that. Harvard Business School calls it the Ikea effect. If I help build it just enough, I actually love it a little bit more. VMware's not simple. NSX, hitting about a billion dollars when you get into it is not easy. Security and networking are never going to be you know dirt simple. Amazon, we thought it was real simple, now thousands of services. Absolutely, we've been at that ecosystem for many years. It gets tougher and tougher the more you get into it. And, John, some of the builders there, the developers there, they get in. There's lot of room for this ecosystem to build around that. Because one of the things we talk about as VMware goes to some of these clouds. Where do they get that ecosystem? You mentioned some of the systems integrators, but the rest of the channel, where can they make money? And trying to help, because it's not simple, how do they help get opinionated, make those choices, build it all together. There's professional services dollars there. There's ways to help consult with companies there. >> Ecosystem is the key point. Watch the ecosystem and how that's forming around cloud, hybrid cloud, true private cloud, whatever you want to call it. And then, again, the technology's maturing. It's all about the people and the process to actually affect so called cloud, hybrid cloud, bringing the cloud model to the data, not forcing your business into cloud. >> We got to wrap up here. We've also got Lisa Martin and Keith Townsend and John Troyer, and we got some community guests as well, joining like we did last year. So this will be great. But I want to put something out there, guys, so we can hit up tomorrow and tease it out. I worry about when you have these fast waves that are coming through and the velocity is phenomenal right now. Is that, what tends to crumble, Dave, to your ecosystem point, are these foundations. When you have these industry consortiums, it's kind of like it's political. They've got boards and multiple fingers in it. That could be the suffering point, in my opinion. And that points directly at Cloud Foundry. Cloud Foundry, OpenStack, some of these consortium groups are at risk, in my opinion, if it goes too fast. Stu, to your point. Kubernetes has got great traction. You've got Containers. Dockers got a new CEO. Uber's got a new CEO. I mean the world is moving so fast. So, rhetorical question, industry consortiums. Do they suffer, or do they win in this environment? >> Depends on what they're doing, right? If they're low-level technical standards that advance the industry, I think they do win. I think if it's posturing, and co-opetition, and trying to cut off the one vendor at the knees, it loses. >> Stu, real quick, consortiums. Win or lose in this environment? >> Yeah, we've seen some that have done quite well, and some that have been horrific. So, absolutely, if it gets way too political. Open source has done some really good things, but the foundations, once they get in there, it's challenging and, I'd say, more times than not, they don't help. >> Well, we're in theCUBE. We're breaking it down. We're going to be squinting through all the announcements looking at where the meat on the bone is, where the action is and the relevance and the impact to enterprises and emerging tech. This is theCUBE. I'm John Furrier with Stu Miniman and Dave Vellante. We're back with more live coverage. Day one, after this short break. (techy music)

live from Las Vegas Nevada it's the kue covering knowledge 15 brought to you by service now okay welcome back everyone you are watching SiliconANGLE weak bonds to cube our flagship program I go out for the events and extract the signal-to-noise i'm john furrier my coach dave vellante with Wikibon Darden we're pleased to have Pat Casey VP general manager of create now platform development early employee of service now great perspective we're gonna get geeky here but talk about some of the high-level stuff welcome back to the cube thank you very much so you've seen the evolution of service now from early days to public company scaling very cloud I mean it's inside the tornado to use that metaphor it's been so successful what do you feel what is what you're feeling right now and how much more work do you see on the horizon well I think probably the first thing I feel is shocked the things they honest answer this company was founded we didn't have office space so we borrowed office space in the basement of our vc and it had no windows so we're in this little tomb of a room and there were five people there one table we got from Ikea so to look out now we've got nine thousand customers who paid money to attend an event about this it's just it's shocking it's also humbling and it's also to be honest it's scary people are here because they are dependent on technology that we wrote and one of the things that just been always been sunk into my head and I believe this forum is I do not want to let anybody here who has put their faith in service now down so in terms of where the work is we've only just gotten started I get up every day and I am just I fundamentally want to make sure that this is the best product it can be that our customers get the basic question to me that's the startup cash but you guys know and starve your big company but you got some good things going on to get some wind at your back to use the French lupine sailing analogy the market is exploding with innovation so that's a challenge but it's also could be an upgrade opportunity so what's your take on it I mean you got the agile you got native we're hearing terms like microservices being kicked around in this native cloudapp swirl you guys better platform share with your take on some of those buzzwords of some of the big mega trends I think if you when this company was founded this was actually founded as a platform company which I think most people don't realize but when Fred sat down to design this his cocktail napkin design and there was actually no cocktail napkin but imagine there was it was we're gonna run enterprise business apps in the cloud that was the idea and the first few sales calls though selling a platform were kind of miserable because we'd go to the customers and we'd say hey we're here to see show you service now and they say well what does it do and we'd say well whatever you want it to do and they kind of cock their head and say what's your sales call guys you've got to talk to us so we built out a suite of applications on top of the platform so we'd have something concrete to sell and that's what the company sold for probably about eight years it was our itsm sweet incident management problem management change management that's what most of our customer base uses we're sort of pivoting back to focusing on the platform again though partly by building other apps we've got HR we've got facilities we've got legal we've got GRC but it's also about trying to get people just onto the platform itself and in terms of really big mega trends that is one of the mega trends we're seeing it's that people are not building everything from scratch anymore it's just not an efficient way to build things in the market anymore and people are also moving to more and more specialized pieces of tooling you don't start with a C compiler anymore you start with a higher-level language you start with Ruby on Rails you start with j2ee if your enterprise developer you pick a tool that's appropriate for the problem you want to solve and service now is a great tool for solving a lot of enterprise business application let's talk about developers because one of the things that I hear all the time is oh I built this on node i got this an angular get this in java there's love different stacks kind of being built but cobble together can you know i guess i'll put them in a container whatever they say these days there's a lot of cool stuff happening on the developer front open sources we're doing great what are you guys looking at in terms of leverage and oh by the way that enables non-programmers to do stuff that looks program to ethic so the innovation opportunity for create is huge so what's what's going on with you guys nice front we actually view the developer world is kind of being in three different groups you've got it's a Gartner term but I think it's a good term you've got locoed developers and that's someone they can make a form they can make a list they can potentiate a little bit of light scripting it's your kind of traditional system administrator archetype and that's who we founded the company to address that was the business idea we could enable loko developers we get enable administrators to build really meaningful business apps and that's really been the secret to our success we're really good at it because they're closer to the action but don't have to go in and just go out of bat and if you will the kind of develop requirements I think most people do their best work when they're scratching their own itch so if you're close to the problem you're like man I can solve this for myself and we've been very empowering to let administrators and loko developers do that but that's not the totality of people out there there's also people who can't even do that there are no code developers there my mother she can use Excel really well but she can't write code and my mom is a very bright woman she's a healthcare consultant but she's a no code developer but she can put a spreadsheet out there with column heading she can make forms using our no code tool she can actually put a business service out on the web with approval workflows notifications dynamic that's fever put out a HR appt in one day when he started playing with express absolutely that's the trend right it's that is definitely one of the futures you see is this democratization of access to development tools it used to be when I started in this industry you pretty much had to be an educated professional to build anything meaningful that's no longer the case you get kids today building great applications with real business value real value and that's the value of the modern era the barrier to entry has just declined and declines and declined because the tools have gotten so much better and so much more specialized the combination of the two is just incredibly empowering so what if we could talk about architecture maybe I don't know inside baseball or maybe maybe plumbing I don't know what you said in your keynote multi-tenant is the TV dinner of cloud vendor deployments what did you mean let's talk about multi-tenant versus multi-instance sure so traditionally in the in the SAS space there's really two different architectures people deploy the most common is something called multi tenant and multi tenant if you imagine a big old apartment building where there's one big construct is one big database some software on top of it and each individual customer is a separate software construct your sharing hardware you're sharing software you're sharing memory you're sharing an apartment in an apartment building it's really sort of efficient for the vendor it's certainly convenient for the vendor because they've got one thing to manage it you think about it though there's downsides though where if the water main breaks you have the entire apartment building or every customer in this case they don't have water so the failure modes tend to be really extreme with multi tenant environments and you can't do things like let people paint their apartment any color they want to or expand their apartment or cook foods that are really smelly you have to have apartment rules in place and you see the same thing with multi tenant architectures where in order to make it work you have to restrict what people can do within your platform you get licensing restrictions you get technical restrictions you get wrapped up in quotas that's part and parcel for multi-tenancy your service now is not multi-tenant we're multi-instance so every time a customer joins us they get a unique instance of service now it's just for them it's your own house and because of that we don't have to go in and tell you what you can do with your house there's no HOA you can paint it green you can paint it pink you can do whatever you want to because it's yours and that's the big freedom that we can do for the enterprise customer base for big customers and multi-tenancy does have its use case I don't want to oversell it if you're selling largely into kind of the SMB space for example it's a really good architecture but up at the enterprise level it's really not the multi-instance architecture we use is fundamentally I think superior okay so what what point did you make the decision to go to multi-instance obviously early on you were there early on and and why did you make that decision I think it's not as clear-cut as it is in history always look back and say well we had this great design system we set out knowing we wanted to address the enterprise space and we eventually figured out that in order to do this we couldn't do it with multi-tenancy but we sort of talked ourselves into kind of our own little version I know if you are watch south park but the underpants gnomes dilemma and if you remember that episode Cartman I think butters they decide they're going to stake out the underpants gnomes who sneak into your house and they steal your underwear and they follow them they watch them steal some underwear and they followed them down to their underground lair and they accost them and they say why have you been stealing everybody's underwear and so the gnomes take them to a small room and they show them powerpoints and the PowerPoint has three parts in part one the gnomes steal underpants and in part three the gnomes profit and then they skip back to part two and is a big question mark so we had the same problem we knew we wanted to go with multi-instance and we knew it was going to be great in the market we had no idea how to do it so we probably spent about three years of engineering effort figuring out how to make a multi instance architecture work well at scale because doing it once it's really easy we have 18,000 instances in the platform right now that's a lot things have to work with automation they have to work cleanly and they have to work all the time so it wasn't a matter of convenience for you just the opposite oh absolutely it's a terrible Jam it was a challenge we had to overcome I think it was necessary for our target audience and if you're listening to this and you're actually looking to start your own SAS company figure out who your SAS audiences if it's small business if it's medium business multi-tenancy may be absolutely the right answer okay in the trade-off is cost efficiency I mean it's more expensive right so not necessarily I think there's this myth that you know it's more expensive it's not convenient you did two more engineering work but in terms of what we actually spend on hardware and power and cooling the data center Computers Computers compute if I have to buy a lot of servers and plug them into one database or I have a lot of servers plugged into a lot of databases it generally equates to roughly the same hardware costs so it doesn't generally drive capex but what it does drive is you've got to put that engineering effort in its work up front and you're not a data intensive you have a lot of data and service now but if I remember my numbers rate were about 5 petabytes of storage so that's not how we are not saying Netflix you know we are not box you know we're not storage centric its transactions so it wasn't authorized for transaction absolutely but the the implication that you've made is that many of the clouds that are out there are fine for SMB maybe yeah if you're an SMB that is okay with that but many are not suitable for the enterprise absolutely and I think that's the big change we're seeing in the cloud space using different analogy but a hundred years ago just under half of all the cars on the road where one model is the ford model t say forty-eight percent and the best-selling car was actually a truck in 2014 was a Ford f-150 was two-point-three percent of the market the day when one car could dominate the market like that has long since passed but in the early days of the cloud there were only a few vendors so they were trying to address as much of the market as they possibly could so they built very general case solutions well time has changed people are getting much more specialized so if you wanted to surveys you probably use survey monkey they're really flip and good at surveys they're not claiming to do anything else the same thing is true with the cloud platforms the people who built general case platforms are generally getting kind of pushed a little aside by more specialized offerings that are addressing narrower market segments better how important is this issue of multi-tenant versus multi-instance you obviously feel it's important I mean you guys are talking about it now let me put you in a hypothetical situation you may or may not want to answer let's say you're a CIO you're bigger Oracle customer most your CIOs here I guarantee you're using Oracle in some way shape or form Oracle's making a big push to the cloud 12 cc4 cloud see four containers I don't know pick your poison but Oracle's generally considered a pretty you know reliable company sure um recovery is you know name of the game for them and you know they do a good job should I be concerned if they're going in a multi-tenant direction or is Oracle sort of an outlier in the cloud you honestly I'm not sure if they're an outlier but I would say that if I were hired by Oracle to run their our cloud I would not do that given their customer base I do think there's a case where the early cloud companies use sales forces with example we're a multi-tenant there multi-tenant because it was convenient there multi-tenant because that was their target audience and so they were pitching hey look the cloud and that message ultimately got tangled up with their deployment architecture so it's stuck in people's head that the cloud equals multi-tenant and it really does it SMB cloud multi-tenant is probably exactly what you want to do departmentally focus is probably right at the enterprise level it's not the right design decision them talk about what's new in the platform let's get into the platform what's happening give us the update give us the highlight reel real quick and then talk about what it's exciting you about the next evolution of the platform sure so a couple of different things I'll talk a little about what we're doing for developers historically i mentioned i talked about loko developers talks about no code developers there are also professionals I'm a professional developer i did this for 20 years of my life I lived in an IDE I started writing code I wrote C code I wrote 370 assembler I've done a lot of terrible horrifying stuff back in my day terrible is probably long school with no natural there you go that's where to put it here it was really hard you know I was being shot at but no the trick to that though is that if you were a professional and you wanted to use service now the tools were not familiar there was no IDE or single place you go to see your whole app so we built one the Geneva release the product actually has an in-browser IDE as code search it as editing it has code management you see your whole app in one place it's great and actually our teams use it to build itself it's a little bit self eating watermelon but the team working on the IDE actually programs in the IDE so they prefer that to programming and eclipse for example we're biased we like our IDE but it's actually very valuable that's for the developer side there's also a new developer program and go to developers service now calm join the program you don't need to be a customer just have an email address you can get a hold of a free instance you can get access to technology you can actually join the forums long as you use it it's yours it's really aimed everybody if you want to learn service now go to the developer program join it there's no requirements other than a willingness to learn on your part technology wise though talk about something else we live in a post Edward Snowden world and I don't really like Edward Snowden because it made my work harder but one of the things he's done is make the concept of data sovereignty and data privacy a foreground concern for a lot of people especially outside the US people don't want to put data in the cloud if there's fear of it a us-based vendor or us-based firms can potentially see it we're set aside the u.s. if it's just private information they don't want to put it in the cloud if anybody can see it one of the ways to solve that and we're addressing this is to allow the data to get encrypted before it comes to us so we're putting an encryption proxy inside the customers network along with its keys and data will pass through the proxy certain fields get encrypted and we see only ciphertext we literally can't read it so encryptions your solution there it is absolutely our solution side the international lies you go to create a replica have a cloud-based system potentially or do you can you store in the US oh it's stored in the US because the data is ciphertext we literally can't read it and that's their side effects there that are actually kind of cool in that because we can't read it you also can't use it in back-end workflows so you've got to design your wrap around the encryption but that is a hard guarantee of it is we don't have the keys it is not possible for service now to get your data back and the government subpoena you can't give it okay given really know either know that you have to supreme the cup of the company in question who had the keys and up to their legal department as to what they wanted to do with it okay so can I ask you kind of as we wrap up here a lot of great stuff containers are all the rage I think doc I just got another 95 million dollars 95 million they've raised so much funding over the years containers but promises interoperability I bring that up only as a way to tease out this notion of interoperability sure how does that how do you guys view that trend in the cloud is that something that's you change I've been around for a while sure you know programming but Dockers got the traction than you seeing security it was like lumio make it a lot of hype I think there's two different parts to bet you no one is there definitely is a push to keep applications from messing each other up and impact each other in bad ways either from a security standpoint or just from a architecture overload and you see that on back-end technologies you'll contain docker is a good example of that you know vmware's a little more mature technology doing something very similar then you know choose your virtualization layer in the more application space where service now fits we have the same problem in that we don't want a service now application to impact a different service now application so we actually invested very heavily in fuji something called scoping it allows for applications to be managed individually to be deployed individually and to be interact with each other only through defined api's and that means that you can actually deploy an application with a high degree of confidence it's not going to impact any of the other for lack of a better word innocent applications inside your system it's a very big improvement and one of the things actually allowed us to do the service now store how does open source evolution if you will you know we always talked about this but you know be me being computer science degree back in the 80s we lived in the same generations we're open source was new second classes and now its first classes and now you have beyond that now it's proven it's working is there new business models you're seeing kind of like pure pure red hat and you seeing you know open platforms like data platforms so what's the next evolution open source on how do you guys going to tap into that and what's the most relevant thing to for the folks to be looking at I think first what we're very big users of open source especially in our back-end I mean we're sent OS we're a little bit of red hat where or you know f5s we've got pixie we've got we got Python we got puppet we've got lots of open source environment and the product as well we're huge fans we think it really has brought a lot of really good technology out it's very accessible to the engineering community so we use a lot of it we even contribute back to some of them case maybe I think if you look at business models i'll be honest i have not seen a lot of open source companies do really well in the environment they built a lot of great technology and i think it's been very empowering for the developer community but even red hat has not really you know they're not huge it's not a 20 billion dollar company the case may be so I don't expect to see people flocking to the open source world to make money I see people flocking to the open source world for the same reason engineers have always built cool stuff it's that joy of creation that power of building to be of value creation and contribution it's absolute like a love innovation and it's not i think no one objects to money and that's why they call it money but the open source world from what I've seen it's not being driven by financials it's being driven by engineers wanting to solve problems it's kind of creativity brick it's also a great way to play ball and get a job and show you what you're worth it's like you know I'm sorry just like playing ball in the yard Sandlot baseball then you go pro right so it's a way for recruiting and also to meet people absolutely and we're actually as I said we're big users and we love a lot of its at knowledge we use my sequel community users as well so okay probably gonna get the hook here but I want to view the final word the future give us your take of the preferred future technology wise and just next five years ten years what's good what's the world going to be like I think five years out it's going to look fairly similar to it does today you're definitely going to see a push to drive the information you need to you without you having to go and look for it you're already seeing this you know Twitter pops when something happens data comes to you you don't have to go here hit refresh periodically that's going to drive itself into more and more parts of the world your iPhone dings when something comes up that's going to seep out away from the phone away from specialty platforms like Twitter and other applications and you're going to get more and more used to seeing things come to you other than you having to go out and look for information mission that's relevant it's going to be kind of a service-oriented internet it's going to kind of push stuff out to you ten years out I suspect there'll be more dramatic changes the big thing actually seen this is a little bit of inside baseball but operational architectures are getting much more standardized so I do suspect that the amount of compute people can throw at problems is going to continue to go up astronomically so right now big data solutions are generally applicable to fairly narrow companies who can apply a lot of data to it like a netflix can afford to optimize for recommendations for you that computes going to get cheaper and cheaper and more and more accessible and you will see that sort of solution get applied to more and more specialized problems so I think you're going to find that information is going to come to you and it's going to be more and more germane to you asynchronous definitely absolutely the value and the goodness of more and more cheap compute will create faster faster personalization faster personalization and it'll be it'll be real time there's no need for you to pull on it asynchronous it'll come to you and it'll be the information you're not near real-time real-time self-driving cars don't do very well in your that's how I okay thanks so much for sharing your time and insights here inside the Cuban my pleasure get the insight from the early days to what's going on now appreciate it this is the cube or live in Las Vegas for three days for no 15 I'm John for Dave vellante we right back with more cube signal from the noise after this short break you

okay we're back this is Dave vellante I'm with Wikibon organ this is silicon angles the cube the cube is a live mobile studio we come into events we're here at knowledge service now's big customer event we're here at the aria hotel in Las Vegas and we've got wall-to-wall coverage today tomorrow and part of thursday as many of you know we were at sapphire now the big SI p customer show were simulcasting that on SiliconANGLE too but we're here in Las Vegas the ServiceNow conference is all about transformation transforming from no to now we've kind of got a double whammy segment here virtually every industry is transforming and certainly Big Pharma is transforming quite dramatically as well as the IT components of many industries Ross rexer is here he's the managing director at kpmg the global consultancy and T Juan Lumpkin who's an IT practitioner for Eli Lilly gentlemen welcome to the cube okay thanks for you so Ross let's start with you at a high level what's happening in the pharmaceutical industry in general Big Pharma how is the industry itself transforming and then we'll get into the I TPS sure so many of the Big Pharma's find themselves today in a situation that is unique to their their business industry and market where a lot of blocked blockbuster drugs which have been significant sources of revenue over the years are starting to come on with that it brings competition and a loss of revenue so the big farmers are all in a very coordinated methodical process right now to resize their business and at the same time enable the R&D function to bring new drugs to market focusing on patient outcomes that will happen in different ways in them they probably ever done before so the business model itself has changed and along with it all the support functions like ITA of course too so in that so it's all about the pipeline right and and the challenge if I understand it is that historically you got the big pharma companies they would you know go do about go about their thing and develop these drugs and they get a blockbuster and it was a relative today a relatively slow paced environment that's that's changing if I understand it correctly what's driving that change so the the innovation around medicines today is much different than it has been over the last 10 20 years in that composition around in the use of different biotech components to create a to create medicines is now being sourced in different ways historically Pharma built itself and really invested and was really a research and development company almost entirely in-house right so all the support systems and everything the way that the business was run was around that nowadays these the farmers are collaborating with smaller providers many of them in ways that again they just historically have never done everything was done in house to build to bring drugs to market and now it's it's shifted absolute to the opposite side where big farmers are relying on these providers these third-party providers for all stages of R&D and ultimately FDA and the release of these so t1 I introduced you as an IT practitioner and Lily so talk about more specifically about your role there you focused on infrastructure I teach em a list or more about them yeah so my rules are about service integration think about those services that we deliver to our internal customers within lily and how do we do that across our complex ecosystem where you have multiple different IT departments you have multiple suppliers who have different rigs and complexities in that space and so our job is how do we minimize that complexity for our internal business partners and making sure that the way we build variety is seamless for our internal customers okay so we heard Ross talking about the the pressures in the in the industry from a from an IT practitioner standpoint what how does that change change your life what are the drivers and what's the business asking you to do but just like anyone we need more volume but we also have to do that under under constraint and so for us how do we get more fishing so you think about this basically gone under you can only do so much outsourcing you only do so much change and so you have to see how do I start running my business more efficiently and I think that's the big shift and I tias you're moving from a from an internal infrastructure towers are truly looking at how do we deliver IT services and part of living IT services and making sure that we're a value-added partner and also being assured that we're competitive with other sources of our businesses have to get services from an IT perspective yeah so 10 years ago we used to talk a lot about demand management and to me it's that's why i love this from now from know to now because demand management is actually ended up just being no we just can't handle the the volume so you mentioned constraints you've got constraints you've got to be more efficient so so talk a little bit about what you did to get more efficient for us it was all about standardization so how do we how do we build standardization across our IT infrastructure nikka system within our IT partners empower external partners what that does it gives us flexibility so that we can deliver our systems and be more agile they think about our internal space we had a lot of complexity we had multiple procedures multiple processes different business units operating or delivering IT services in a consistent manner what we've been able to do it being able to streamline that we've been able to be more consistent internally in a line on the comments that are processes and how we deliver those ikea services to our customers so Ross you're talking about the sort of changing dynamic of what I would call sort of the pharmaceutical ecosystem right so so that's that sounds like it's relatively new in pharma it used to be sort of a go-it-alone the big guys hey we're multi-billion dollar companies we don't need these little guys you see all these startups coming out there really innovative there faster so take us through sort of how that's evolving how companies are dealing with the ecosystem and what kind of pressures that puts on IT what are you seeing out there so as t1 was was mentioning as well this was pushing to IT service integration as a kind of one of the next frontiers of now right being able to have the single pane of glass single system of record of IT and our ability to bring standardized services up and down in a coordinating consistent way has allowed for the bigger more monolithic type companies in be able to interact with with these smaller more agile more tech-savvy appeal partners and be able to not overburden them so the little provider who has maybe less less overhead of IT infrastructure and their processes would find it hard to be able to collaborate electronically with a big pharma if we had to adopt the big pharma's old-style processes so service integration is all about allowing for the the easy plug-and-play of these providers and establishing the reference set of processes and the supporting data that's needed to govern those transactions or the length of the of the outsourcing arrangement with with that provider in a way that doesn't get overburden them but provides the company Big Pharma the ability to have transparency ability to see risks before they're happening and to enter manage the cause so talk about your practice a little bit how do you what's role do you play it's obviously you've got this increasingly complex ecosystem evolving they've definitely got different infrastructures um how do you sort of mediate all this so Kim G what are our go-to-market offering and our solution set is based around a set of leading practices that that we have established over the past 17 years for example that we've been in the IT service management consulting and advisory business so we have these accelerators that we can we bring to a project and engagement like like the one we're at Eli Lilly where we can quickly faster than ever establish a common ground for those processes the operational processes first and foremost that would don't require years and years of consultancy process engineering 20 years ago type of thing so our role in that is to provide the basis for the are the operating model that's going to go forward and allow the core customer as well as these other providers to get there fast to get operating faster so t1 we've been hearing a similar pattern from the customers that we've talked to a lot of stovepipes a lot of legacy you know tools a lot of uncoordinated sort of activities going going on is that what what Lily with you would you describe that as an accurate depiction of the pasture i think i think that i think you're being kind yeah I'm sure we kind on the cheer we don't like to feed our guests up what I think it not to over use the ERP for IT term but this is something I t we've done for our business partners over the years we haven't done for our so if you think about the essay peas of the world where you get your CI CFO a one-click look at the the financial assets of the company you think about from a CRM perspectively doing that for our sales force we've done that from an HR perspective but we haven't taken the time to look at from an IT perspective and how do i give the cio that same visibility across our portfolio services so that he can ask those same questions you can have that same visibility so i want to add a little color to this whole erp for IT though of course on the one hand you know the sort of single system of record that's a positive but when you think of erp i say we were at SI p sapphire there's a lot of complexity in erp and with that type of complexity you'd never succeed but so what's your experience been thus far with regard to you know the complexity in my senses it's not this big monolithic system it's a cloud-based SAS based system talk about that a little bit well for us it's getting to a set of standards it actually helped reduce the complexity where you have complexity when you have multiple business procedures across the organization delivering services and so to get to that single source that single record it is actually help to reduce a lot of complexity on our part help it make it easier for us to deliver customer service for customers the other piece of that to which is the the singularity of vision of how we deliver I team so right now within our business we're depending on what area in you may get IT servers that delivers slightly differently from each area we've been able to streamline that and say this is how you're going to receive IT services and make it a more predictable experience for our internal users I saw Rus I want to talk about this notion of a single system of record before I ask you why it's so important what are we talking about here because today you've got a single system of record for your transactions you might have a single system of record for your your data warehouse all these single systems are at a record so what do you mean by a single system of record so when we're talking with service now and specifically in the IT Service Management domain what we're talking about is having integrated the capability to see data across the different data domains if you like so operational data performance data service level data with that coupled with the IT finance data as well as a zesty one put 360-degree vision of your assets as well so linking all those sources of data together in a way that can be used for analytics maybe for the first time ever so we we we use the analogy of IT intelligence right so what we've given our business partners and business intelligence over the years mmm it's-- never had that so the ability to provide IT intelligence that allows for the leadership due to to have data have information that they can take decisions and then ultimately become predicted with that right so be able have the knowledge to know what we're doing to make the right choices and in the future be able to do some predictive analysis again back to the point about the demands really never got one hundred percent right over the years we've talked about a lot but having the ability to understand the consumption and have the levers to influence demand and see it grow I want to go back to this business process discussion you were sort of reference the 20 years ago the whole VPO of movement and you know business process reorganization it seems to me that what what occurred was you had let's say a database or some kind of system and maybe there was a module and then you build a business process around that and so you had relatively inflexible business process they were hard to change is are you seeing that change it we at the cusp of the dawn of a new era where I can actually create whatever business process I want to around that single system of record is that truly a vision that's coming to fruition we believe it is and our experience it is it is starting to happen and I think service now with their platform is one of the emerging leaders in this space that's allowing for that to happen percent of the day so you have you have a concise platform that allows you enough flexibility to build new processes but has the common data structure has the common user interface as the common workflow set in a and all wrapped in and easy to maintain type of platform is what I think 20 years ago we wished we had and we tried to build in many different ways and ended up mostly cobbling things together but we really believe that and again our starting to see success out there David the platform question is solved and that we're now able to get to the prosecutor historically we you know delivered value plenty of value the problem is so much of that value was sucked by the infrastructure and and and not enough went into the innovation around it do you want my question to you is so people don't like change naturally now maybe it's different and nit maybe they want change in IT but did you see initial resistance I'll know we have this way of doing it we don't want to change or are people enthusiastic about change talk about that a little quite you hit it spot-on and absolutely the technology is the easy part of it it's really the change part that that's the most difficult piece of it and I would say we've done to a lot of work just a line organization and we've had a lot of support for from not only our internal IT people but also our senior leadership team so we've gotten support we've seen a lot of buy-in not saying still them not going to be easy not gonna be easy but I feel that we've got the right momentum now to make this type of change to get the business volume part of its been able to articulate the value that we're going to receive from from from this initiative so it's early days for for Lily and you guys should just get started on this journey not yesterday but you know you you're in an inference perience to give some advice to your fellow practitioners so my ask you guys both start with t1 what advice would you give to fellow practitioners that are looking to move in this direction great I would say first of all you have to have the business alignment so I need to make sure that you can clearly articulate the value of the change of the company so I can I can talk not in terms of process but in terms of outcomes that we're going to drive for our business partners once you're able to describe those outcomes then you can have the conversation on what's the work it's going to take to get there it's not an easy journey to be able to paint that picture accurately for for our teams and also talk about how we're going to support them through the process and so we're going to talk about the value we're going to we're going to paint the picture the journey we're not going to tell you how I want to support you throughout that process okay Ross you're talking to CIOs what's your what's your main point of advice for CIOs in this regard is look at the transformation as transformational right so it's it's it can be a set of tactical projects and tactical wins based on outcomes that you're looking for however to in order to truly change the way your IT functions runs as a business do all these these great things that we're talking we're talking about today is you have to have the vision and understand that it is there are series of building blocks that we will get you incremental value along the way but this is not a quick you know product slam then again maybe 20 years ago was about let's swap this software for that software and we're going to be good it's not about that and that's not going to get you the transformation so it's about transformation it's about the metrics to be able to prove that you are transforming and continuous improvement Ross do you want thanks very much for coming on the cube and sharing your story we could go on forever we're getting the hook but really appreciate you guys coming up thanks thanks for having right thanks for watching everybody we right back with our next guest Chris Pope is here who's the director of product management for service now so we're going to double-click on the platform and share with you some greater information about that this is the cube I'm Dave vellante we're right back