(upbeat soft intro music) >> Hey everyone. Welcome back to theCube's live coverage from Los Angeles of KubeCon and CloudNativeCon 2021. Lisa Martin with Dave Nicholson, Dave and I are pleased to welcome our next guest remotely. Steven Huels joins us, the senior director of Cloud Services at Red Hat. Steven, welcome to the program. >> Steven: Thanks, Lisa. Good to be here with you and Dave. >> Talk to me about where you're seeing traction from an AI/ML perspective? Like where are you seeing that traction? What are you seeing? Like it. >> It's a great starter question here, right? Like AI/ML is really being employed everywhere, right? Regardless of industry. So financial services, telco, governments, manufacturing, retail. Everyone at this point is finding a use for AI/ML. They're looking for ways to better take advantage of the data that they've been collecting for these years. It really, it wasn't all that long ago when we were talking to customers about Kubernetes and containers, you know, AI/ML really wasn't a core topic where they were looking to use a Kubernetes platform to address those types of workloads. But in the last couple of years, that's really skyrocketed. We're seeing a lot of interest from existing customers that are using Red Hat open shift, which is a Kubernetes based platform to take those AI/ML workloads and take them from what they've been doing for additionally, for experimentation, and really get them into production and start getting value out of them at the end of it. >> Is there a common theme, you mentioned a number of different verticals, telco, healthcare, financial services. Is there a common theme, that you're seeing among these organizations across verticals? >> ^There is. I mean, everyone has their own approach, like the type of technique that they're going to get the most value out of. But the common theme is really that everyone seems to have a really good handle on experimentation. They have a lot of very brig data scientists, model developers that are able to take their data and out of it, but where they're all looking to get, get our help or looking for help, is to put those models into production. So ML ops, right. So how do I take what's been built on, on somebody's machine and put that into production in a repeatable way. And then once it's in production, how do I monitor it? What am I looking for as triggers to indicate that I need to retrain and how do I iterate on this sequentially and rapidly applying what would really be traditional dev ops software development, life cycle methodologies to ML and AI models. >> So Steve, we're joining you from KubeCon live at the moment. What's, what's the connection with Kubernetes and how does Kubernetes enable machine learning artificial intelligence? How does it enable it and what are some of the special considerations to in mind? >> So the immediate connection for Red Hat, is Red Hat's open shift is basically an enterprise grade Kubernetics. And so the connection there is, is really how we're working with customers and how customers in general are looking to take advantage of all the benefits that you can get from the Kubernetes platform that they've been applying to their traditional software development over the years, right? The, the agility, the ability to scale up on demand, the ability to have shared resources, to make specialized hardware available to the individual communities. And they want to start applying those foundational elements to their AI/Ml practices. A lot of data science work traditionally was done with high powered monolithic machines and systems. They weren't necessarily shared across development communities. So connecting something that was built by a data scientist, to something that then a software developer was going to put into production was challenging. There wasn't a lot of repeatability in there. There wasn't a lot of scalability, there wasn't a lot of auditability and these are all things that we know we need when talking about analytics and AI/ML. There's a lot of scrutiny put on the auditability of what you put into production, something that's making decisions that impact on whether or not somebody gets a loan or whether or not somebody is granted access to systems or decisions that are made. And so that the connection there is really around taking advantage of what has proven itself in kubernetes to be a very effective development model and applying that to AI/ML and getting the benefits in being able to put these things into production. >> Dave: So, so Red Hat has been involved in enterprises for a long time. Are you seeing most of this from a Kubernetes perspective, being net new application environments or are these extensions of what we would call legacy or traditional environments. >> They tend to be net new, I guess, you know, it's, it's sort of, it's transitioned a little bit over time. When we first started talking to customers, there was desire to try to do all of this in a single Kubernetes cluster, right? How can I take the same environment that had been doing our, our software development, beef it up a little bit and have it applied to our data science environment. And over time, like Kubernetes advanced rights. So now you can actually add labels to different nodes and target workloads based on specialized machinery and hardware accelerators. And so that has shifted now toward coming up with specialized data science environments, but still connecting the clusters in that's something that's being built on that data science environment is essentially being deployed then through, through a model pipeline, into a software artifact that then makes its way into an application that that goes live. And, and really, I think that that's sensible, right? Because we're constantly seeing a lot of evolution in, in the types of accelerators, the types of frameworks, the types of libraries that are being made available to data scientists. And so you want the ability to extend your data science cluster to take advantage of those things and to give data scientists access to that those specialized environments. So they can try things out, determine if there's a better way to, to do what they're doing. And then when they find out there is, be able to rapidly roll that into your production environment. >> You mentioned the word acceleration, and that's one of the words that we talk about when we talk about 2020, and even 2021, the acceleration in digital transformation that was necessary really a year and a half ago, for companies to survive. And now to be able to pivot and thrive. What are you seeing in terms of customers appetites for, for adopting AI/ML based solutions? Has it accelerated as the pandemic has accelerated digital transformation. >> It's definitely accelerated. And I think, you know, the pandemic probably put more of a focus for businesses on where can they start to drive more value? How can they start to do more with less? And when you look at systems that are used for customer interactions, whether they're deflecting customer cases or providing next best action type recommendations, AI/ML fits the bill there perfectly. So when they were looking to optimize, Hey, where do we put our spend? What can help us accelerate and grow? Even in this virtual world we're living in, AI/ML really floated to the top there, that's definitely a theme that we've seen. >> Lisa: Is there a customer example that you think that you could mention that really articulates the value over that? >> You know, I think a lot of it, you know, we've published one specifically around HCA health care, and this had started actually before the pandemic, but I think especially, it's applicable because of the nature of what a pandemic is, where HCA was using AI/ML to essentially accelerate diagnosis of sepsis, right. They were using it for, for disease diagnoses. That same type of, of diagnosis was being applied to looking at COVID cases as well. And so there was one that we did in Canada with, it's called 'how's your flattening', which was basically being able to track and do some predictions around COVID cases in the Canadian provinces. And so that one's particularly, I guess, kind of close to home, given the nature of the pandemic, but even within Red Hat, we started applying a lot more attention to how we could help with customer support cases, right. Knowing that if folks were going to be out with any type of illness. We needed to be able to be able to handle that case, you know, workload without negatively impacting work-life balance for, for other associates. So we looked at how can we apply AI/ML to help, you know, maintain and increase the quality of customer service we were providing. >> it's a great use case. Did you have a keynote or a session, here at KubeCon CloudNative? >> I did. I did. And it really focused specifically on that whole ML ops and model ops pipeline. It was called involving Kubernetes and bracing model ops. It was for a Kubernetes AI day. I believe it aired on Wednesday of this week. Tuesday, maybe. It all kind of condenses in the virtual world. >> Doesn't it? It does. >> So one of the questions that Lisa and I have for folks where we sit here, I don't know, was it year seven or so of the Dawn of Kubernetes, if I have that, right. Where do you think we are, in this, in this wave of adoption, coming from a Red Hat perspective, you have insight into, what's been going on in enterprises for the last 20 plus years. Where are we in this wave? >> That's a great question. Every time, like you, it's sort of that cresting wave sort of, of analogy, right? That when you get to top one wave, you notice the next wave it's even bigger. I think we've certainly gotten to the point where, where organizations have accepted that Kubernetes can, is applicable across all the workloads that they're looking to put in production. Now, the focus has shifted on optimizing those workloads, right? So what are the things that we need to run in our in-house data centers? What are things that we need, or can benefit from using commodity hardware from one of the hyperscalers, how do we connect those environments and more effectively target workloads? So if I look at where things are going to the future, right now, we see a lot of things being targeted based on cluster, right? We say, Hey, we have a data science cluster. It has characteristics because of X, Y, and Z. And we put all of our data science workloads into that cluster. In the future, I think we want to see more workload specific, type of categorization of workloads so that we're able to match available hardware with workloads rather than targeting a workload at a specific cluster. So a developer or data scientist can say, Hey, my particular algorithm here needs access to GPU acceleration and the following frameworks. And then it, the Kubernetes scheduler is able to determine of the available environments. What's the capacity, what are the available resources and match it up accordingly. So we get into a more dynamic environment where the developers and those that are actually building on top of these platforms actually have to know less and less about the clusters they're running on. It just have to know what types of resources they need access to. >> Lisa: So sort of democratizing that. Steve, thank you for joining Dave and me on the program tonight, talking about the traction that you're seeing with AI/ML, Kubernetes as an enabler, we appreciate your time. >> Thank you. >> Thanks Steve. >> For Dave Nicholson. I'm Lisa Martin. You're watching theCube live from Los Angeles KubeCon and CloudNativeCon 21. We'll be right back with our next guest. (subtle music playing) >> Lisa: I have been in the software and technology industry for over 12 years now. So I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCube. Being a host on the cube has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, Hey, I'm really interested in this. I love talking with customers...

>>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.

>>It's the queue covering the virtual vertical Big Data Conference 2020. Brought to you by vertical. >>Hello, everybody. Welcome to the new Normal. You're watching the Cube, and it's remote coverage of the vertical big data event on digital or gone Virtual. My name is Dave Volante, and I'm here with Colin Mahoney, who's a senior vice president at Micro Focus and the GM of Vertical Colin. Well, strange times, but the show goes on. Great to see you again. >>Good to see you too, Dave. Yeah, strange times indeed. Obviously, Safety first of everyone that we made >>a >>decision to go Virtual. I think it was absolutely the right all made it in advance of how things have transpired, but we're making the best of it and appreciate your time here, going virtual with us. >>Well, Joe and we're super excited to be here. As you know, the Cube has been at every single BDC since its inception. It's a great event. You just you just presented the key note to your to your audience, You know, it was remote. You didn't have that that live vibe. And you have a lot of fans in the vertical community But could you feel the love? >>Yeah, you know, it's >>it's hard to >>feel the love virtually, but I'll tell you what. The silver lining in all this is the reach that we have for this event now is much broader than it would have been a Z you know, you know, we brought this event back. It's been a few years since we've done it. We're super excited to do it, obviously, you know, in Boston, where it was supposed to be on location, but there wouldn't have been as many people that could participate. So the silver lining in all of this is that I think there's there's a lot of love out there we're getting, too. I have a lot of participants who otherwise would not have been able to participate in this. Both live as well. It's a lot of these assets that we're gonna have available. So, um, you know, it's out there. We've got an amazing customers and of practitioners with vertical. We've got so many have been with us for a long time. We've of course, have a lot of new customers as well that we're welcoming, so it's exciting. >>Well, it's been a while. Since you've had the BDC event, a lot of transpired. You're now part of micro focus, but I know you and I know the vertical team you guys have have not stopped. You've kept the innovation going. We've been following the announcements, but but bridge the gap between the last time. You know, we had coverage of this event and where we are today. A lot has changed. >>Oh, yeah, a lot. A lot has changed. I mean, you know, it's it's the software industry, right? So nothing stays the same. We constantly have Teoh keep going. Probably the only thing that stays the same is the name Vertical. Um and, uh, you know, you're not spending 10 which is just a phenomenal released for us. So, you know, overall, the the organization continues to grow. The dedication and commitment to this great form of vertical continues every single release we do as you know, and this hasn't changed. It's always about performance and scale and adding a whole bunch of new capabilities on that front. But it's also about are our main road map and direction that we're going towards. And I think one of the things have been great about it is that we've stayed true that from day one we haven't tried to deviate too much and get into things that are barred to outside your box. But we've really done, I think, a great job of extending vertical into places where people need a lot of help. And with vertical 10 we know we're going to talk more about that. But we've done a lot of that. It's super exciting for our customers, and all of this, of course, is driven by our customers. But back to the big data conference. You know, everybody has been saying this for years. It was one of the best conferences we've been to just so really it's. It's developers giving tech talks, its customers giving talks. And we have more customers that wanted to give talks than we had slots to fill this year at the event, which is another benefit, a little bit of going virtually accommodate a little bit more about obviously still a tight schedule. But it really was an opportunity for our community to come together and talk about not just America, but how to deal with data, you know, we know the volumes are slowing down. We know the complexity isn't slowing down. The things that people want to do with AI and machine learning are moving forward in a rapid pace as well. There's a lot talk about and share, and that's really huge part of what we try to do with it. >>Well, let's get into some of that. Um, your customers are making bets. Micro focus is actually making a bet on one vertical. I wanna get your perspective on one of the waves that you're riding and where are you placing your bets? >>Yeah, No, it's great. So, you know, I think that one of the waves that we've been writing for a long time, obviously Vertical started out as a sequel platform for analytics as a sequel, database engine, relational engine. But we always knew that was just sort of takes that we wanted to do. People were going to trust us to put enormous amounts of data in our platform and what we owe everyone else's lots of analytics to take advantage of that data in the lots of tools and capabilities to shape that data to get into the right format. The operational reporting but also in this day and age for machine learning and from some pretty advanced regressions and other techniques of things. So a huge part of vertical 10 is just doubling down on that commitment to what we call in database machine learning and ai. Um, And to do that, you know, we know that we're not going to come up with the world's best algorithms. Nor is that our focus to do. Our advantage is we have this massively parallel platform to ingest store, manage and analyze the data. So we made some announcements about incorporating PM ML models into the product. We continue to deepen our python integration. Building off of a new open source project we started with uber has been a great customer and partner on This is one of our great talks here at the event. So you know, we're continuing to do that, and it turns out that when it comes to anything analytics machine learning, certainly so much of what you have to do is actually prepare the big shape the data get the data in the right format, apply the model, fit the model test a model operationalized model and is a great platform to do that. So that's a huge bet that were, um, continuing to ride on, taking advantage of and then some of the other things that we've just been seeing. You continue. I'll take object. Storage is an example on, I think Hadoop and what would you point through ultimately was a huge part of this, but there's just a massive disruption going on in the world around object storage. You know, we've made several bets on S three early we created America Yang mode, which separates computing story. And so for us that separation is not just about being able to take care of your take advantage of cloud economics as we do, or the economics of object storage. It's also about being able to truly isolate workloads and start to set the sort of platform to be able to do very autonomous things in the databases in the database could actually start self analysing without impacting many operational workloads, and so that continues with our partnership with pure storage. On premise, we just announced that we're supporting beyond Google Cloud now. In addition to Amazon, we supported on we've got a CFS now being supported by are you on mode. So we continue to ride on that mega trend as well. Just the clouds in general. Whether it's a public cloud, it's a private cloud on premise. Giving our customers the flexibility and choice to run wherever it makes sense for them is something that we are very committed to. From a flexibility standpoint. There's a lot of lock in products out there. There's a lot of cloud only products now more than ever. We're hearing our customers that they want that flexibility to be able to run anywhere. They want the ease of use and simplicity of native cloud experiences, which we're giving them as well. >>I want to stay in that architectural component for a minute. Talk about separating compute from storage is not just about economics. I mean apart Is that you, you know, green, really scale compute separate from storage as opposed to in chunks. It's more efficient, but you're saying there's other advantages to operational and workload. Specificity. Um, what is unique about vertical In this regard, however, many others separate compute from storage? What's different about vertical? >>Yeah, I think you know, there's a lot of differences about how we do it. It's one thing if you're a cloud native company, you do it and you have a shared catalog. That's key value store that all of your customers are using and are on the same one. Frankly, it's probably more of a security concern than anything. But it's another thing. When you give that capability to each customer on their own, they're fully protected. They're not sharing it with any other customers. And that's something that we hear a lot of insights from our customers. They want to be able to separate compute and storage. But they want to be able to do this in their own environment so that they know that in their data catalog there's no one else is. You share in that catalog, there's no single point of failure. So, um, that's one huge advantage that we have. And frankly, I think it just comes from being a company that's operating on premise and, uh, up in the cloud. I think another huge advantages for us is we don't know what object storage platform is gonna win, nor do we necessarily have. We designed the young vote so that it's an sdk. We started with us three, but it could be anything. It's DFS. That's three. Who knows what what object storage formats were going to be there and then finally, beyond just the object storage. We're really one of the only database companies that actually allows our customers to natively operate on data in very different formats, like parquet and or if you're familiar with those in the Hadoop community. So we not only embrace this kind of object storage disruption, but we really embrace the different data formats. And what that means is our customers that have data pipelines that you know, fully automated, putting this information in different places. They don't have to completely reload everything to take advantage of the Arctic analytics. We can go where the data is connected into it, and we offer them a lot of different ways to take advantage of those analytics. So there are a couple of unique differences with verdict, and again, I think are really advance. You know, in many ways, by not being a cloud native platform is that we're very good at operating in different environments with different formats that changing formats over time. And I don't think a lot of the other companies out there that I think many, particularly many of the SAS companies were scrambling. They even have challenges moving from saying Amazon environment to a Microsoft azure environment with their office because they've got so much unique Band Aid. Excuse me in the background. Just holding the system up that is native to any of those. >>Good. I'm gonna summarize. I'm hearing from you your Ferrari of databases that we've always known. Your your object store agnostic? Um, it's any. It's the cloud experience that you can bring on Prem to virtually any cloud. All the popular clouds hybrid. You know, aws, azure, now Google or on Prem and in a variety of different data formats. And that is, I think, you know, you need the combination of those I think is unique in the marketplace. Um, before we get into the news, I want to ask you about data silos and data silos. You mentioned H DFs where you and I met back in the early days of big data. You know, in some respects, you know, Hadoop help break down the silos with distributing the date and leave it in place, and in other respects, they created Data Lakes, which became silos. And so we have. Yet all these other sales people are trying to get to, Ah, digital transformation meeting, putting data at their core virtually obviously, and leave it in place. What's your thoughts on that in terms of data being a silo buster Buster, How does verdict of way there? >>Yeah, so And you're absolutely right, I think if even if you look at his due for all the new data that gets into the do. In many ways, it's created yet another large island of data that many organizations are struggling with because it's separate from their core traditional data warehouse. It's separate from some of the operational systems that they have, and so there might be a lot of data in there, but they're still struggling with How do I break it out of that large silo and or combine it again? I think some some of the things that verdict it doesn't part of the announcement just attend his migration tools to make it really easy. If you do want to move it from one platform to another inter vertical, but you don't have to move it, you can actually take advantage of a lot of the data where it resides with vertical, especially in the Hadoop brown with our external table storage with our building or compartment natively. So we're very pragmatic about how our customers go about this. Very few customers, Many of them tried it with Hadoop and realize that didn't work. But very few customers want a wholesale. Just say we're going to throw everything out. We're gonna get rid of our data warehouse. We're gonna hit the pause button and we're going to go from there. Just it's not possible to do that. So we've spent a lot of time investing in the product, really work with them to go where the data is and then seamlessly migrate. And when it makes sense to migrate, you mentioned the performance of America. Um, and you talked about it is the variety. It definitely is. And one other thing that we're really proud of this is that it actually is not a gas guzzler. Easy either One of the things that we're seeing, a lot of the other cloud databases pound for pound you get on the 10th the hardware vertical running up there. You get over 10 x performance. We're seeing that a lot, so it's Ah, it's not just about the performance, but it's about the efficiency as well. And I think that efficiency is really important when it comes to silos. Because there's there's just only so much horsepower out there. And it's easier for companies to play tricks and lots of servers environment when they start up for so many organizations and cloud and frankly, looking at the bills they're getting from these cloud workloads that are running. They really conscious of that. >>Yeah. The big, big energy companies love the gas guzzlers. A lot of a lot of cloud. Cute. But let's get into the news. Uh, 10 dot io you shared with your the audience in your keynote. One of the one of the highlights of data. What do we need to know? >>Yeah, so, you know, again doubling down on these mega trends, I'll start with Machine Learning and ai. We've done a lot of work to integrate so that you can take native PM ml models, bring them into vertical, run them massively parallel and help shape you know your data and prepare it. Do all the work that we know is required true machine learning. And for all the hype that there is around it, this is really you know, people want to do a lot of unsupervised machine learning, whether it's for healthcare fraud, detection, financial services. So we've doubled down on that. We now also support things like Tensorflow and, you know, as I mentioned, we're not going to come up with the best algorithms. Our job is really to ensure that those algorithms that people coming up with could be incorporated, that we can run them against massive data sets super efficiently. So that's that's number one number two on object storage. We continue to support Mawr object storage platforms for ya mode in the cloud we're expanding to Google G CPI, Google's cloud beyond just Amazon on premise or in the cloud. Now we're also supporting HD fs with beyond. Of course, we continue to have a great relationship with our partners, your storage on premise. Well, what we continue to invest in the eon mode, especially. I'm not gonna go through all the different things here, but it's not just sort of Hey, you support this and then you move on. There's so many different things that we learn about AP I calls and how to save our customers money and tricks on performance and things on the third areas. We definitely continue to build on that flexibility of deployment, which is related to young vote with. Some are described, but it's also about simplicity. It's also about some of the migration tools that we've announced to make it easy to go from one platform to another. We have a great road map on these abuse on security, on performance and scale. I mean, for us. Those are the things that we're working on every single release. We probably don't talk about them as much as we need to, but obviously they're critically important. And so we constantly look at every component in this product, you know, Version 10 is. It is a huge release for any product, especially an analytic database platform. And so there's We're just constantly revisiting you know, some of the code base and figuring out how we can do it in new and better ways. And that's a big part of 10 as well. >>I'm glad you brought up the machine Intelligence, the machine Learning and AI piece because we would agree that it is really one of the things we've noticed is that you know the new innovation cocktail. It's not being driven by Moore's law anymore. It's really a combination of you. You've collected all this data over the last 10 years through Hadoop and other data stores, object stores, etcetera. And now you're applying machine intelligence to that. And then you've got the cloud for scale. And of course, we talked about you bringing the cloud experience, whether it's on Prem or hybrid etcetera. The reason why I think this is important I wanted to get your take on this is because you do see a lot of emerging analytic databases. Cloud Native. Yes, they do suck up, you know, a lot of compute. Yeah, but they also had a lot of value. And I really wanted to understand how you guys play in that new trend, that sort of cloud database, high performance, bringing in machine learning and AI and ML tools and then driving, you know, turning data into insights and from what I'm hearing is you played directly in that and your differentiation is a lot of the things that we talk about including the ability to do that on from and in the cloud and across clouds. >>Yeah, I mean, I think that's a great point. We were a great cloud database. We run very well upon three major clouds, and you could argue some of the other plants as well in other parts of the world. Um, if you talk to our customers and we have hundreds of customers who are running vertical in the cloud, the experience is very good. I think it would always be better. We've invested a lot in taking advantage of the native cloud ecosystem, so that provisioning and managing vertical is seamless when you're in that environment will continue to do that. But vertical excuse me as a cloud platform is phenomenal. And, um, you know, there's a There's a lot of confusion out there, you know? I think there's a lot of marketing dollars spent that won't name many of the companies here. You know who they are, You know, the cloud Native Data Warehouse and it's true, you know their their software as a service. But if you talk to a lot of our customers, they're getting very good and very similar. experiences with Bernie comic. We stopped short of saying where software is a service because ultimately our customers have that control of flexibility there. They're putting verdict on whichever cloud they want to run it on, managing it. Stay tuned on that. I think you'll you'll hear from or more from us about, you know, that going going even further. But, um, you know, we do really well in the cloud, and I think he on so much of yang. And, you know, this has really been a sort of 2.5 years and never for us. But so much of eon is was designed around. The cloud was designed around Cloud Data Lakes s three, separation of compute and storage on. And if you look at the work that we're doing around container ization and a lot of these other elements, it just takes that to the next level. And, um, there's a lot of great work, so I think we're gonna get continue to get better at cloud. But I would argue that we're already and have been for some time very good at being a cloud analytic data platform. >>Well, since you open the door I got to ask you. So it's e. I hear you from a performance and architectural perspective, but you're also alluding two. I think something else. I don't know what you can share with us. You said stay tuned on that. But I think you're talking about Optionality, maybe different consumption models. That am I getting that right and you share >>your difficult in that right? And actually, I'm glad you wrote something. I think a huge part of Cloud is also has nothing to do with the technology. I think it's how you and seeing the product. Some companies want to rent the product and they want to rent it for a certain period of time. And so we allow our customers to do that. We have incredibly flexible models of how you provision and purchase our product, and I think that helps a lot. You know, I am opening the door Ah, a little bit. But look, we have customers that ask us that we're in offer them or, you know, we can offer them platforms, brawl in. We've had customers come to us and say please take over systems, um, and offer something as a distribution as I said, though I think one thing that we've been really good at is focusing on on what is our core and where we really offer offer value. But I can tell you that, um, we introduced something called the Verdict Advisor Tool this year. One of the things that the Advisor Tool does is it collects information from our customer environments on premise or the cloud, and we run through our own machine learning. We analyze the customer's environment and we make some recommendations automatically. And a lot of our customers have said to us, You know, it's funny. We've tried managed service, tried SAS off, and you guys blow them away in terms of your ability to help us, like automatically managed the verdict, environment and the system. Why don't you guys just take this product and converted into a SAS offering, so I won't go much further than that? But you can imagine that there's a lot of innovation and a lot of thoughts going into how we can do that. But there's no reason that we have to wait and do that today and being able to offer our customers on premise customers that same sort of experience from a managed capability is something that we spend a lot of time thinking about as well. So again, just back to the automation that ease of use, the going above and beyond. Its really excited to have an analytic platform because we can do so much automation off ourselves. And just like we're doing with Perfect Advisor Tool, we're leveraging our own Kool Aid or Champagne Dawn. However you want to say Teoh, in fact, tune up and solve, um, some optimization for our customers automatically, and I think you're going to see that continue. And I think that could work really well in a bunch of different wallets. >>Welcome. Just on a personal note, I've always enjoyed our conversations. I've learned a lot from you over the years. I'm bummed that we can't hang out in Boston, but hopefully soon, uh, this will blow over. I loved last summer when we got together. We had the verdict throwback. We had Stone Breaker, Palmer, Lynch and Mahoney. We did a great series, and that was a lot of fun. So it's really it's a pleasure. And thanks so much. Stay safe out there and, uh, we'll talk to you soon. >>Yeah, you too did stay safe. I really appreciate it up. Unity and, you know, this is what it's all about. It's Ah, it's a lot of fun. I know we're going to see each other in person soon, and it's the people in the community that really make this happen. So looking forward to that, but I really appreciate it. >>Alright. And thank you, everybody for watching. This is the Cube coverage of the verdict. Big data conference gone, virtual going digital. I'm Dave Volante. We'll be right back right after this short break. >>Yeah.

>> Live from Seattle, Washington. It's theCUBE, covering KubeCon and CloudNativeCon North America 2018, brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem of partners. >> Hello everyone, welcome to theCUBE. We are at CubeCon 2018 in Seattle, CloudNativeCon as well. We've been to every KubeCon and CloudNativeCon since inception. I'm John Furrier. My co-host Stu Miniman want to break down the three days of wall to wall coverage of the rise of kubernetes and the ecosystem and the industry consolidation and standardization around kubernetes for multi cloud, for hybrid cloud. We're here breaking down day one keynote, kicking everything off. Stu, it's fun to come here and watch words like expansion, Moore's law, expansive growth, doubling down. The attendance for KubeCon, CloudNativeCon, hockey stick growth chart on Twitter. 1200, 4000, 8000 up into the right. Global phenomenon, the team at CNC at KubeCon, huge presence in China this year, total expansion all to save, hold the line on the cloud tsunami that is Amazon's web services. >> Yeah. >> This is the massive cloud game going on, your thoughts. >> Yeah, John first of all. You have to start out just expansive growth and you can just feel the energy here. We're in the middle of the show floor. You were here two years ago in Seattle when I think they said, they were, was it 16? There weren't that many sponsors here. There's 180 booths at this show. The joke in the keynote this morning was if you want to replace your entire T-shirt wardrobe that's what you can do here. Everybody's got fun stickers. It's a good crowd. Those alpha geeks, this is where they are. >> And Stu, you're sporting a new T-shirt. >> Yeah, John so I want to thank our friends. >> Make sure they can see that. >> Our friends here, Women Who Go. They do the GoLang languages, the gopher is what they're doing here. So love that, if you're at the show, come by. Get our stickers. If you look up Women Who Go on thread list. They actually have an artist shop. The CNCF has their logo up there. We have their logo. There is blockchain. There's docker, there's all these and you can buy the shirts and the money for buying these shirts actually goes to bring women and underserved people to events like this. We also love John when they're supporting this. The CNCF actually, I think it was a 130 or so people that they brought to this conference through charitable donations from many of the sponsors. >> And that's one of the highlights I want to get to later is the mission driven and the social responsibility, scholarships, the money that's being donated to fund diversity inclusion in all walks of life to make CloudNative, but Stu lets get back to the core thing that's going on here at KubeCon, CloudNativeCon. A couple years ago, I said, we said on theCUBE that the Tsunami, that is Amazon Web Service is just going to just hit ashore and just wipe out the industry in IT as much as it can go unless someone builds a seawall. Builds a wall to stop that momentum. Kubernetes and KubeCon specifically has had that moment. This is the industry saying look it. Cloud is awesome. It's full validation of cloud but there is more than just AWS. This is about multi cloud, hybrid cloud, and a lot of forces are at play competitively to make sure that Amazon doesn't run the table. >> Yeah, John, it's good to do a little bit of compare and contrast here because if you go back to OpenStack, it was OpenStack is the hail Mary against Amazon, and it's going to help you get off your VMware licenses. Well that's not what kubernetes is, if you look both VMware required Heptio, and Amazon have a big presence at this show. Amazon, their hands were forced to be able to actually work with kubernetes. I remember I read an article that said, there were about 14 different ways you can run kubernetes on Amazon before they supported it. Now they fully support it. They're going even deeper, AWS Fargate. I know you spend a lot of time at re:Invent digging into some of this environment here so this isn't, portability is a piece of kubernetes. Kubernetes won the orchestrator battles out there. It is the de facto standard out there, and we're seeing how this stack can really be built up on top of it. The thing that I've been keying in on coming into this year is how Serverless plays into it. You heard a big push for Knative on the keynote which is Google, who of course brought us to kubernetes. IBM, SAP, Red Hat all there but I don't see Microsoft or AWS yet embracing how we can match up Serverless and kubernetes today with the Knative. >> I think if I'm Amazon or Microsoft, I might be a little bit afraid of this movement because when, we went through the multi vendor days. You had multi vendoring decades ago. Now, multi cloud is the multi vendoring story, and what's interesting is that choice becomes the key word in all this and a real enterprise that's out there. They got Cisco routers, they got tons of stuff that's actually running their business, powering their business. They need to integrate that so this idea that one cloud fits all certainly has been validated. I think to me the winner takes most but what this community is doing Stu around kubernetes is galvanizing around a new stack configuration with kubernetes at the center of it, and that will disintermediate services at AWS and at Microsoft. Microsoft stock price has put that company in a higher value position than Google or Apple. What has Microsoft actually done to make them go from a $26 stock price to $100 and change? What did they actually invent? What did they actually do? What did they disrupt? Was it just go in a cloud? Is it Office 365? This begs the question is it just the business model shift so certainly there is business in the cloud and it's showing here at KubeCon. >> Yeah John, there was a major cultural shift inside of Microsoft I was really excited. One of the shows I got to go to this year was Microsoft Ignite, and in many ways it's interesting. That show has been around for decades and in many ways, it was the Windows admin just getting the latest and greatest. From my standpoint, I think it was Microsoft fully embracing the move to SaaS. They're pushing everybody to Office 365. They are aggressively moving to expand their cloud that that hybrid environment Microsoft has the applications, and they're not waiting for customers to just leave them or hold onto whatever revenue stream. They're switching to that writable model. They're switching to SaaS model. They're pushing really hard on Azure. They're here in force. They're really embracing developers, all the .NET folks, they were-- >> They're moving the ball inch by inch down the fields slowly to that cadence and that in totality with social responsibility and commencement of the cloud. I think has been, there's not one thing that's happened. It's just a total transformation for Microsoft, and the results and the valuation are off the charts. Google, the same way. Diane Greene has, I think was unfairly categorized by the press in terms of her exit. She's been wanting to retire for years Stu. She has turned Google around. You look at Google where they are right now verses where they were two years ago. Two years ago, they were slinging cloud the Google way. Now they're saying hey, you know what. We know the enterprise. Jennifer Lin, Sarah Novotny, Dawn Chen. All those people over there are leading the way real enterprise just with tech and they got some big moves to make, and they're doing it. So Diane Greene did not fail. So that was one thing that's interesting in the ecosystem and in Amazon as you know just kick it out. So given all that Stu, how does that relate to this? >> Yeah, let's bring it back here. So first of all, kubernetes. It was interesting the keynote this morning. We spent a lot of time talking about things that built on top of and around what's happening with kubernetes. Talking about things like how Helm is moving forward. Onvoy, Prometheus all of these projects. There are a couple dozen incubating projects and a few of them are graduating up to be full flanked projects. We talked about the ecosystem and how many partners are here. There's around 80 service providers and about 80 platforms that have kubernetes baked in. I want to point out an interesting distinction. Some people said, it's like oh they're 75 or 80 different distributions of it. I don't think that anybody thinks that they're going to make a differentiated platform that people are going to buy what I'm doing because I have the best kubernetes. Really what the CNCF has done a good job is saying you're fully supported. You're inoperable, you meet the guidelines to say, I am kubernetes and therefore it's baked into what we're doing. So why do we have so many of them? It's well, there's a lot of clouds out there. There's service providers and even the infrastructure players are making sure that they're in there. Everybody from Intel, all the way through. Servers and storage and networking all making sure that they're doing they're pieces to make sure that they work in the kubernetes environment. >> So Stu, I got to ask you a question on the keynote. You were in the front row. I was watching online here. Kind of distraction, sold out in the keynote. I didn't get the whole gist of it. How much of the keynote was vendor or project expansion verses end user traction? Can you give some color on that? >> Yeah, so a lot of it was the projects. What's really good is there's not a lot of vendors. Sure there is here's the logo slide. Let's everybody give a big round of applause and thank you. But when they put the projects up there, many of these projects came out of a group but some of that is well Lyft. Lyft created one of these projects and who's involved in that. One of the big news announcement was FCD is being donated to the CNCS, and well that came out of CoreOS to solve a really needed problem that they had to make sure that when you're rolling upgrades that you don't reboot the entire cluster all at once, and then your application isn't able to be there. So why are they donating? Well it has reached the maturity level, and while CoreOS is inside of Red Hat, there is a broad adoption. Lots of people contributing and it just makes sense to hand it over. Red Hat, everything they've done always is 100% open source, so them making sure that they have a good relationship with the foundation and who should have the governs of that. Red Hat has a strong track record on that. I know we'll be talking a lot-- >> All right so Stu get your perspective on the big players. We saw Google up on Saint-operno. We saw VMware. Cisco is here. I saw some of the Cisco executives here earlier. You got Red Hat, you got the big dogs here, Microsoft. What's the trend on the big players and then what's the trend on the hot startups either companies and or new wave in here? You mentioned Knative. So big companies, what's the general trend there and then what are you seeing on the interests around startups. >> So John, last year when I talked to users at this show. It was most of the people that were using kubernetes were building their own stack. The exception to that was oh if I'm a Red Hat customer, open shift makes sense for me. I can built it into what my model is. Azure had just come out with their AKS support. AWS had just been figuring out their ECS verse EKS and what they had. We're going to do before Fargate was down there. Today, what I hear is maturation of the platform so I expect Amazon and Microsoft to win more, and just I'm on those platforms. I'm using it, oh I want to use their kubernetes service that's going to make sense. So the rich get richer in this a lot way. Red Hat is going to do well, IBM is a strong player here, and of course sometime in 2019, we expect that acquisition of Red Hat to close. From a start up standpoint, there are so many niches that can be filled here. The question is how many of them are going to end up as acquisitions inside some of these big ones. How much of them can monetize because as I said with kubernetes John, I don't see a company that's going to say oh, I'm going to be the kubernetes company and monetize. Mirantis for a year or so ago was pivoting to be from the OpenStack company to the kubernetes company. Heptio was an early player and they had a quick exit. They're bringing strong skill set to the VMware team to help VMware accelerate their CloudNative activities. So in many ways John, this is an evolution more than a revolution so I do not see a drastic change in the landscape. >> Well evolution is cloud computing. We know that's going to yield the edge of the network and then on premise is complete conversions. This evolution is interesting Stu because this is an open source community vibe. You have now two other things going on around it. You have the classic open source community event, and you've got on the other spectrum, normal app developers that just want to right code. Then you got this IT dynamic. So what's happening and that will be interesting and we'll be watching this is how does the CNCF KubeCon, CloudNativeCon involve, and you start to cross pollinate app developers who just want our infrastructure as code. IT people who want to take over a new IT and then pure open source community players. This has now become a melting pot. Is that an opportunity or a challenge for the CNCF and the Linux Foundation? >> The danger is that this just gets overruned by vendors. It becomes another OpenStack that people get disenfranchised through what they're doing so absolutely there's a threat here. To their credit, I think the CNCF has done a really good job of managing things. They're smart is how they're doing. They're community focused. I have to say in the keynote John, if we noticed the diversity was phenomenal. Most of the speakers were women. They were one from end users. There are a couple of dozen end users that are now members of the CNCF. >> I think they're all CUBE alumnis too. >> Absolutely, and John, we've been here since the early days been tracking the whole thing. >> It's fun to watch. My opinion on the whole the melting pot of those personas is I think the CNCF and the Linux Foundation has a winning formula by owning and nurturing the open source community side of it. I think that's where the data is going to be, that's where the action is and I think as a downstream benefit, the IT market and developers will win. I would not try to get enamored by the money, and the vendor participation hype. I don't think they are. I'm just saying I would advise them to stay the course. Make this the open source community show of course, that's what we believe and of course we're CubeNative this week. We are here at the CloudNative and now we're CubeNative. This is the first day of three days of coverage. I'm John Furrier and Stu Miniman breaking down the analysis, talking to the smartest people we can find, and also talk about some of the key players that are sponsoring the show. We'll be back with more coverage after this short break. (uptempo techno music)

>> Live from Washington, D.C., it's theCUBE! Covering Inforum D.C. 2018. Brought to you by Infor. >> Well, welcome back to Washington, D.C., We are alive here at the Convention Center at Inforum 18, along with Dave Vellante, I'm John Walls. It's a pleasure now, welcome to theCUBE, Leigh Martin, who is the Senior Director of the Dynamic Science Labs at Infor, and good afternoon to you Leigh! >> Good afternoon, thank you for having me. >> Thanks for comin' on. >> Thank you for being here. Alright, well tell us about the Labs first off, obviously, data science is a big push at Infor. What do you do there, and then why is data science such a big deal? >> So Dynamic Science Labs is based in Cambridge, Massachusetts, we have about 20 scientists with backgrounds in math and science areas, so typically PhDs in Statistics and Operations Research, and those types of areas. And, we've really been working over the last several years to build solutions for Infor customers that are Math and Science based. So, we work directly with customers, typically through proof of concept, so we'll work directly with customers, we'll bring in their data, and we will build a solution around it. We like to see them implement it, and make sure we understand that they're getting the value back that we expect them to have. Once we prove out that piece of it, then we look for ways to deliver it to the larger group of Infor customers, typically through one of the Cloud Suites, perhaps functionality, that's built into a Cloud Suite, or something like that. >> Well, give me an example, I mean it's so, as you think-- you're saying that you're using data that's math and science based, but, for application development or solution development if you will. How? >> So, I'll give you an example, so we have a solution called Inventory Intelligence for Healthcare, it's moving towards a more generalized name of Inventory Intelligence, because we're going to move it out of the healthcare space and into other industries, but this is a product that we built over the last couple of years. We worked with a couple of customers, we brought in their loss and data, so their loss in customers, we bring the data into an area where we can work on it, we have a scientist in our team, actually, she's one of the Senior Directors in the team, Dawn Rose, who led the effort to design and build this, design and build the algorithm underlying the product; and what it essentially does is, it allows hospitals to find the right level of inventory. Most hospitals are overstocked, so this gives them an opportunity to bring down their inventory levels, to a manageable place without increasing stockouts, so obviously, it's very important in healthcare, that you're not having a lot of stockouts. And so, we spent a lot of time working with these customers, really understanding what the data was like that they were giving to us, and then Dawn and her team built the algorithm that essentially says, here's what you've done historically, right? So it's based on historic data, at the item level, at the location level. What've you done historically, and how can we project out the levels you should have going forward, so that they're at the right level where you're saving money, but again, you're not increasing stockouts, so. So, it's a lot of time and effort to bring those pieces together and build that algorithm, and then test it out with the customers, try it out a couple of times, you make some tweaks based on their business process and exactly how it works. And then, like I said, we've now built that out into originally a stand-alone application, and in about a month, we're going to go live in Cloud Suite Financials, so it's going to be a piece of functionality inside of Cloud Suite Financials. >> So, John, if I may, >> Please. >> I'm going to digress for a moment here because the first data scientist that I ever interviewed was the famous Hilary Mason, who's of course now at Cloudera, but, and she told me at the time that the data scientist is a part mathematician, part scientist, part statistician, part data hacker, part developer, and part artist. >> Right. (laughs) >> So, you know it's an amazing field that Hal Varian, who is the Google Economist said, "It's going to be the hottest field, in the next 10 years." And this is sort of proven true, but Leigh, my question is, so you guys are practitioners of data science, and then you bring that into your product, and what we hear from a lot of data scientists, other than that sort of, you know, panoply of skill sets, is, they spend more time wrangling data, and the tooling isn't there for collaboration. How are you guys dealing with that? How has that changed inside of Infor? >> It is true. And we actually really focus on first making sure we understand the data and the context of the data, so it's really important if you want to solve a particular business problem that a customer has, to make sure you understand exactly what is the definition of each and every piece of data that's in all of those fields that they sent over to you, before you try to put 'em inside an algorithm and make them do something for you. So it is very true that we spend a lot of time cleaning and understanding data before we ever dive into the problem solving aspect of it. And to your point, there is a whole list of other things that we do after we get through that phase, but it's still something we spend a lot of time on today, and that has been the case for, a long time now. We, wherever we can, we apply new tools and new techniques, but actually just the simple act of going in there and saying, "What am I looking at, how does it relate?" Let me ask the customer to clarify this to make sure I understand exactly what it means. That part doesn't go away, because we're really focused on solving the customer solution and then making sure that we can apply that to other customers, so really knowing what the data is that we're working with is key. So I don't think that part has actually changed too much, there are certainly tools that you can look at. People talk a lot about visualization, so you can start thinking, "Okay, how can I use some visualization to help me understand the data better?" But, just that, that whole act of understanding data is key and core to what we do, because, we want to build the solution that really answers the answers the business problem. >> The other thing that we hear a lot from data scientists is that, they help you figure out what questions you actually have to ask. So, it sort of starts with the data, they analyze the data, maybe you visualize the data, as you just pointed out, and all these questions pop out. So what is the process that you guys use? You have the data, you've got the data scientist, you're looking at the data, you're probably asking all these questions. You get, of course, get questions from your customers as well. You're building models maybe to address those questions, training the models to get better and better and better, and then you infuse that into your software. So, maybe, is that the process? Is it a little more complicated than that? Maybe you could fill in the gaps. >> Yeah, so, I, my personal opinion, and I think many of my colleagues would agree with me on this is, starting with the business problem, for us, is really the key. There are ways to go about looking at the data and then pulling out the questions from the data, but generally, that is a long and involved process. Because, it takes a lot of time to really get that deep into the data. So when we work, we really start with, what's the business problem that the customer's trying to solve? And then, what's the data that needs to be available for us to be able to solve that? And then, build the algorithm around that. So for us, it's really starting with the business problem. >> Okay, so what are some of the big problems? We heard this morning, that there's a problem in that, there's more job openings than there are candidates, and productivity, business productivity is not being impacted. So there are two big chewy problems that data scientists could maybe attack, and you guys seem to be passionate about those, so. How does data science help solve those problems? >> So, I think that, at Infor, I'll start off by saying at Infor there's actually, I talked about the folks that are in our office in Cambridge, but there's quite a bit of data science going on outside of our team, and we are the data science team, but there are lots of places inside of Infor where this is happening. Either in products that contains some sort of algorithmic approach, the HCM team for sure, the talent science team which works on HCM, that's a team that's led by Jill Strange, and we work with them on certain projects in certain areas. They are very focused on solving some of those people-related problems. For us, we work a little bit more on the, some of the other areas we work on is sort of the manufacturing and distribution areas, we work with the healthcare side of things, >> So supply chain, healthcare? >> Exactly. So some of the other areas, because they are, like I said, there are some strong teams out there that do data science, it's just, it's also incorporated with other things, like the talent science team. So, there's lots of examples of it out there. In terms of how we go about building it, so we, like I was saying, we work on answering the business, the business question upfront, understanding the data, and then, really sitting with the customer and building that out, and, so the problems that come to us are often through customers who have particular things that they want to answer. So, a lot of it is driven by customer questions, and particular problems that they're facing. Some of it is driven by us. We have some ideas about things that we think, would be really useful to customers. Either way, it ends up being a customer collaboration with us, with the product team, that eventually we'll want to roll it out too, to make sure that we're answering the problem in the way that the product team really feels it can be rolled out to customers, and better used, and more easily used by them. >> I presume it's a non-linear process, it's not like, that somebody comes to you with a problem, and it's okay, we're going to go look at that. Okay now, we got an answer, I mean it's-- Are you more embedded into the development process than that? Can you just explain that? >> So, we do have, we have a development team in Prague that does work with us, and it's depending on whether we think we're going to actually build a more-- a product with aspects to it like a UI, versus just a back end solution. Depends on how we've decided we want to proceed with it. so, for example, I was talking about Inventory Intelligence for Healthcare, we also have Pricing Science for Distribution, both of those were built initially with UIs on them, and customers could buy those separately. Now that we're in the Cloud Suites, that those are both being incorporated into the Cloud Suite. So, we have, going back to where I was talking about our team in Prague, we sometimes build product, sort of a fully encased product, working with them, and sometimes we work very closely with the development teams from the various Cloud Suites. And the product management team is always there to help us, to figure out sort of the long term plan and how the different pieces fit together. >> You know, kind of big picture, you've got AI right, and then machine learning, pumping all kinds of data your way. So, in a historical time frame, this is all pretty new, this confluence right? And in terms of development, but, where do you see it like 10 years from now, 20 years from now? What potential is there, we've talked about human potential, unlocking human potential, we'll unlock it with that kind of technology, what are we looking at, do you think? >> You know, I think that's such a fascinating area, and area of discussion, and sort of thinking, forward thinking. I do believe in sort of this idea of augmented intelligence, and I think Charles was talking a little bit about, about that this morning, although not in those particular terms; but this idea that computers and machines and technology will actually help us do better, and be better, and being more productive. So this idea of doing sort of the rote everyday tasks, that we no longer have to spend time doing, that'll free us up to think about the bigger problems, and hopefully, and my best self wants to say we'll work on famine, and poverty, and all those problems in the world that, really need our brains to focus on, and work. And the other interesting part of it is, if you think about, sort of the concept of singularity, and are computers ever going to actually be able to think for themselves? That's sort of another interesting piece when you talk about what's going to happen down the line. Maybe it won't happen in 10 years, maybe it will never happen, but there's definitely a lot of people out there, who are well known in sort of tech and science who talk about that, and talk about the fears related to that. That's a whole other piece, but it's fascinating to think about 10 years, 20 years from now, where we are going to be on that spectrum? >> How do you guys think about bias in AI and data science, because, humans express bias, tribalism, that's inherent in human nature. If machines are sort of mimicking humans, how do you deal with that and adjudicate? >> Yeah, and it's definitely a concern, it's another, there's a lot of writings out there and articles out there right now about bias in machine learning and in AI, and it's definitely a concern. I actually read, so, just being aware of it, I think is the first step, right? Because, as scientists and developers develop these algorithms, going into it consciously knowing that this is something they have to protect against, I think is the first step, for sure. And then, I was just reading an article just recently about another company (laughs) who is building sort of a, a bias tracker, so, a way to actually monitor your algorithm and identify places where there is perhaps bias coming in. So, I do think we'll see, we'll start to see more of those things, it gets very complicated, because when you start talking about deep learning and networks and AI, it's very difficult to actually understand what's going on under the covers, right? It's really hard to get in and say this is the reason why, your AI told you this, that's very hard to do. So, it's not going to be an easy process but, I think that we're going to start to see that kind of technology come. >> Well, we heard this morning about some sort of systems that could help, my interpretation, automate, speed up, and minimize the hassle of performance reviews. >> Yes. (laughs) >> And that's the classic example of, an assertive woman is called abrasive or aggressive, an assertive man is called a great leader, so it's just a classic example of bias. I mentioned Hilary Mason, rock star data scientist happens to be a woman, you happen to be a woman. Your thoughts as a woman in tech, and maybe, can AI help resolve some of those biases? >> Yeah. Well, first of all I want to say, I'm very pleased to work in an organization where we have some very strong leaders, who happen to be women, so I mentioned Dawn Rose, who designed our IIH solution, I mentioned Jill Strange, who runs the talent science organization. Half of my team is women, so, particularly inside of sort of the science area inside of Infor, I've been very pleased with the way we've built out some of that skill set. And, I'm also an active member of WIN, so the Women's Infor Network is something I'm very involved with, so, I meet a lot of people across our organization, a lot of women across our organization who have, are just really strong technology supporters, really intelligent, sort of go-getter type of people, and it's great to see that inside of Infor. I think there's a lot of work to be done, for sure. And you can always find stories, from other, whether it's coming out of Silicon Valley, or other places where you hear some, really sort of arcane sounding things that are still happening in the industry, and so, some of those things it's, it's disappointing, certainly to hear that. But I think, Van Jones said something this morning about how, and I liked the way he said it, and I'm not going to be able say it exactly, but he said something along the lines of, "The ground is there, the formation is starting, to get us moving in the right direction." and I think, I'm hopeful for the future, that we're heading in that way, and I think, you know, again, he sort of said something like, "Once the ground swell starts going in that direction, people will really jump in, and will see the benefits of being more diverse." Whether it's across, having more women, or having more people of color, however things expand, and that's just going to make us all better, and more efficient, and more productive, and I think that's a great thing. >> Well, and I think there's a spectrum, right? And on one side of the spectrum, there's intolerable and unacceptable behavior, which is just, should be zero tolerance in my opinion, and the passion of ours in theCUBE. The other side of that spectrum is inclusion, and it's a challenge that we have as a small company, and I remember having a conversation, earlier this year with an individual. And we talk about quotas, and I don't think that's the answer. Her comment was, "No, that's not the answer, you have to endeavor to reach deeper beyond your existing network." Which is hard sometimes for us, 'cause you're so busy, you're running around, it's like okay it's the convenient thing to do. But you got to peel the onion on that network, and actually take the extra time and make it a priority. I mean, your thoughts on that? >> No, I think that's a good point, I mean, if I think about who my circle is, right? And the people that I know and I interact with. If I only reach out to the smallest group of people, I'm not getting really out beyond my initial circle. So I think that's a very good point, and I think that that's-- we have to find ways to be more interactive, and pull from different areas. And I think it's interesting, so coming back to data science for a minute, if you sort of think about the evolution of where we got to, how we got to today where, now we're really pulling people from science areas, and math areas, and technology areas, and data scientists are coming from lots of places, right? And you don't always have to have a PhD, right? You don't necessary have to come up through that system to be a good data scientist, and I think, to see more of that, and really people going beyond, beyond just sort of the traditional circles and the traditional paths to really find people that you wouldn't normally identify, to bring into that, that path, is going to help us, just in general, be more diverse in our approach. >> Well it certainly it seems like it's embedded in the company culture. I think the great reason for you to be so optimistic going forward, not only about your job, but about the way companies going into that doing your job. >> What would you advise, young people generally, who want to crack into the data science field, but specifically, women, who have clearly, are underrepresented in technology? >> Yeah, so, I think the, I think we're starting to see more and more women enter the field, again it's one of those, people know it, and so there's less of a-- because people are aware of it, there's more tendency to be more inclusive. But I definitely think, just go for it, right? I mean if it's something you're interested in, and you want to try it out, go to a coding camp, and take a science class, and there's so many online resources now, I mean there's, the massive online courses that you can take. So, even if you're hesitant about it, there are ways you can kind of be at home, and try it out, and see if that's the right thing for you. >> Just dip your toe in the water. >> Yes, exactly, exactly! Try it out and see, and then just decide if that's the right thing for you, but I think there's a lot of different ways to sort of check it out. Again, you can take a course, you can actually get a degree, there's a wide range of things that you can do to kind of experiment with it, and then find out if that's right for you. >> And if you're not happy with the hiring opportunities out there, just start a company, that's my advice. >> That's right. (laughing together) >> Agreed, I definitely agree! >> We thank you-- we appreciate the time, and great advice, too. >> Thank you so much. >> Leigh Martin joining us here at Inforum 18, we are live in Washington, D.C., you're watching the exclusive coverage, right here, on theCUBE. (bubbly music)

>> Announcer: Live from Boston, Massachusetts this is the CUBE covering Spark Summit East 2017 brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to Boston everybody this is the CUBE. We're live here at Spark Summit East in the Hynes Convention Center. This is the CUBE, check out SiliconANGLE.com for all the news of the day. Check out Wikibon.com for all the research. I'm really excited about this session here. Al Essa is here, he's the vice president of analytics and R&D at McGraw-Hill Education. And I'm so excited because we always talk about digital transformations and transformations. We have an example of 150 year old company that has been, I'm sure, through many transformations. We're going to talk about a recent one. Al Essa, welcome to the CUBE, thanks for coming on. >> Thank you, pleasure to be here. >> So you heard my little narrative up front. You, obviously, have not been with the company for 150 years (laughs), you can't talk about all the transformations, but there's certainly one that's recent in the last couple of years, anyway which is digital. We know McGraw Hill is a print publisher, describe your business. >> Yeah, so McGraw Hill Education has been traditionally a print publisher, but beginning with our new CEO, David Levin, he joined the company about two years ago and now we call ourselves a learning science company. So it's no longer print publishing, it's smart digital and by smart digital we mean we're trying to transform education by applying principles of learning science. Basically what that means is we try to understand, how do people learn? And how they can learn better. So there are a number of domains, cognitive science, brain sciences, data science and we begin to try to understand what are the known knowns in these areas and then apply it to education. >> I think Marc Benioff said it first, at least the first I heard he said there were going to be way more Saas companies that come out of non-tech companies than tech companies. We're talking off camera, you're a software company. Describe that in some detail. >> Yeah, so being a software company is new for us, but we've moved pretty quickly. Our core competency has been really expert knowledge about education. We work with educators, subject matter experts, so for over a hundred years, we've created vetted content, assessments, and so on. So we have a great deal of domain expertise in education and now we're taking, sort of the new area of frontiers of knowledge, and cognitive science, brain sciences. How can learners learn better and applying that to software and models and algorithms. >> Okay, and there's a data component to this as well, right? >> So yeah, the way I think about it is we're a smart digital company, but smart digital is fueled by smart data. Data underlies everything that we do. Why? Because in order to strengthen learners, provide them with the optimal pathway, as well as instructors. We believe instructors are at the center of this new transformation. We need to provide immediate, real-time data to students and instructors on, how am I doing? How can I do better? This is the predictive component and then you're telling me, maybe I'm not on the best path. So what's my, "How can I do better?" the optimal path. So all of that is based on data. >> Okay, so that's, I mean, the major reason. Do you do any print anymore? Yes, we still do print, because there's still a huge need for print. So print's not going to go away. >> Right. Okay, I just wanted to clarify that. But what you described is largely a business model change, not largely, it is a business model change. But also the value proposition is changing. You're providing a new service, related, but new incremental value, right? >> Yeah, yeah. So the value proposition has changed, and here again, data is critical. Inquiring minds want to know. Our customers want to know, "All right, we're going to use your technology "and your products and solutions, "show us "rigorously, empirically, that it works." That's the bottom line question. Is it effective? Are the tools, products, solutions, not just ours, but are our products and solutions have a context. Is the instruction effective? Is it effective for everyone? So all that is reliant on data. >> So how much of a course, how much of the content in a course would you prepare? Is it now the entire courseware and you instrument the students interaction with it? And then, essentially you're selling the outcomes, the improved outcomes. >> Yeah, I think that's one way to think about it. Here's another model change, so this is not so much digital versus non-digital, but we've been a closed environment. You buy a textbook from us, all the material, the assessments is McGraw Hill Education. But now a fundamental part of our thinking as a software company is that we have to be an open company. Doesn't mean open as in free, but it's an open ecosystem, so one of the things that we believe in very much is standards. So there's a standard body in education called IMS Global. My boss, Stephen Laster, is on the board of IMS Global. So think of that as, this encompasses everything from different tools working together, interoperability tools, or interoperability standards, data standards for data exchange. So, we will always produce great content, great assessments, we have amazing platform and analytics capability, however, we don't believe all of our customers are going to want to use everything from McGraw Hill. So interoperability standards, data standards is vital to what we're doing. >> Can you explain in some detail this learning science company. Explain how we learn. We were talking off camera about sort of the three-- >> Yeah, so this is just one example. It's well known that memory decays exponentially, meaning when you see some item of knowledge for the first time, unless something happens, it goes into short-term memory and then it evaporates. One of the challenges in education is how can I acquire knowledge and retain knowledge? Now most of the techniques that we all use are not optimal. We cram right before an exam. We highlight things and that creates the illusion that we'll be able to recall it. But it's an illusion. Now, cognitive science and research in cognitive science tells us that there are optimal strategies for acquiring knowledge and recalling it. So three examples of that are effort for recall. If you have to actively recall some item of knowledge, that helps with the stickiness. Another is space practice. Practicing out your recall over multiple sessions. Another one is interleaving. So what we do is, we just recently came out with a product last week called, StudyWise. What we've done is taken those principles, written some algorithms, applies those algorithms into a mobile product. That's going to allow learners to optimize their acquisition and recall of knowledge. >> And you're using Spark to-- >> Yeah, we're using Spark and we're using Databricks. So I think what's important there is not just Spark as a technology, but it's an ecosystem, it's a set of technologies. And it has to be woven together into a workflow. Everything from building the model and algorithm, and those are always first approximations. We do the best we can, in terms of how we think the algorithm should work and then deploy that. So our data science team and learning science team builds the models, designs the models, but our IT team wants to make sure that it's part of a workflow. They don't want to have to deal with a new set of technologies, so essentially pressing the button goes into production and then it doesn't stop there, because as Studywise has gone on the market last week, now we're collecting data real-time as learners are interacting with our products. The results of their interactions is coming in to our research environment and we're analyzing that data, as a way of updating our models and tuning the models. >> So would it be fair to say that it was interesting when you talked about these new ways of learning. If I were to create an analogy to Legacy Enterprise apps, they standardize business transactions and the workflows that went with them. It's like you're picking out the best practices in learning, codifying them into an application. And you've opened it up so other platforms can take some or all and then you're taking live feedback from the models, but not just tuning the existing model, but actually adding learning to the model over time as you get a better sense for how effort of recall works or interleaving works. >> Yeah, I think that's exactly right. I do want to emphasize something, an aspect of what you just said is we believe, and it's not just we believe, the research in learning science shows that we can get the best, most significant learning gains when we place the instructor, the master teacher, at the center of learning. So, doing that, not just in isolation, but what we want to do is create a community of practitioners, master teachers. So think of the healthcare analogy. We have expert physicians, so when we have a new technique or even an old technique, What's working? What's not working? Let's look at the data. What we're also doing is instrumenting our tools so that we can surface these insights to the master practitioners or master teachers. George is trying this technique, that's working or not working, what adjustments do we need to make? So it's not just something has to happen with the learner. Maybe we need to adjust our curriculum. I have to change my teaching practices, my assessments. >> And the incentive for the master practitioners to collaborate is because that's just their nature? >> I think it is. So let's kind of stand back, I think the current paradigm of instruction is lecture mode. I want to impart knowledge, so I'm going to give a lecture. And then assessment is timed tests. In the educational, the jargon for that is summit of assessments, so lecture and tests. That's the dominant paradigm in education. All the research evidence says that doesn't work. (laughs) It doesn't work, but we still do it. >> For how many hundreds of years? >> Yeah. Well, it was okay if we needed to train and educate a handful of people. But now, everyone needs to be educated and it's lifelong learning rate, so that paradigm doesn't work. And the research evidence is overwhelming that it doesn't work. We have to change our paradigm where the new paradigm, and this is again based on research, is differentiated instruction. Different learners are at different stages in their learning and depending on what you need to know, I'm at a different stage. So, we need assessments. Assessments are not punitive, they're not tests. They help us determine what kind of knowledge, what kind of information each learner needs to know. And the instructor helps with the differentiated instruction. >> It's an alignment. >> It's an alignment, yeah. Really to take it to the next stage, the master practitioners, if they are armed with the right data, they can begin to compare. All right, practices this way of teaching for these types of students works well, these are the adjustments that we need to make. >> So, bringing it down to earth with Spark, these models of how to teach, or perhaps how to differentiate the instruction, how to do differentiated assessments, these are the Spark models. >> Yeah, these are the Spark models. So let's kind of stand back and see what's different about traditional analytics or business intelligence and the new analytics enabled by Spark, and so on. First, traditional analytics, the questions that you need to be able to answer are defined beforehand. And then they're implemented in schemas in a data warehouse. In the new order of things, I have questions that I need to ask and they just arise right now. I'm not going to anticipate all the questions that I might want to be able to ask. So, we have to be enable the ability to ask new questions and be able to receive answers immediately. Second, the feedback loop, traditional analytics is a batch mode. Overnight, data warehouse gets updated. Imagine you're flying an airplane, you're the pilot, a new weather system emerges. You can't wait a week or six months to get a report. I have to have corrective course. I have to re-navigate and find a new course. So, the same way, a student encounters difficulty, tell me what I need to do, what course correction do I need to apply? The data has to come in real-time. The models have to run real-time. And if it's at scale, then we have to have parallel processing and then the updates, the round trip, data back to the instructor or the student has to be essentially real-time or near real-time. Spark is one of the technologies that's enabling that. >> The way you got here is kind of interesting. You used to be CIO, got that big Yale brain (laughs) working for you. You're not a developer, I presume, is that right? >> No. >> How did you end up in this role? >> I think it's really a passion for education and I think this is at McGraw Hill. So I'm a first generation college student, I went to public school in Los Angeles. I had a lot of great breaks, I had great teachers who inspired me. So I think first, it's education, but I think we have a major, major problem that we need to solve. So if we look at... So I spent five years with the Minnesota state colleges and university system, most of the colleges, community colleges are open access institutions. So let me just give you a quick statistic. 70% of students who enter community colleges are not prepared in math and english. So seven out of 10 students need remediation. Of the seven out of 10 students who need remediation, only 15% not 5-0, one-five succeed to the next level. This is a national tragedy. >> And that's at the community college level? >> That's at the community college level. We're talking about millions of students who are not making it past the first gate. And they go away thinking they've failed, they incurred debt, their life is now stuck. So this is playing itself out, not to tens of thousands of students, but hundreds of thousands of students annually. So, we've got to solve this problem. I think it's not technology, but reshaping the paradigm of how we think about education. >> It is a national disaster, because often times that's the only affordable route for folks and they are taking on debt, thinking okay, this is a gateway. Al, we have to leave it there. Awesome segment, thanks very much for coming to the CUBE, really appreciate it. >> Thank you very much. >> All right, you're welcome. Keep it right there, my buddy, George and I will be back with our next guest. This is the CUBE, we're live from Boston. Be right back. (techno music) >> Narrator: Since the dawn of the cloud