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

(upbeat music) >> Welcome to transforming ideas into insights, presented with theCUBE and made possible by Cloudera. My name is Dave Vellante from theCUBE and I'll be your host for today. In the next hundred minutes, you're going to hear how to turn your best ideas into action using data and we're going to share the real-world examples of 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing efficiencies, better forecast retail demand, transform analytics, improve public sector service and so much more. How we use data is rapidly evolving. That is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to, rather we use terms like, digital transformation and digital business. But you think about it. What is a digital business? How is that different from just a business? Well, a digital business is a data business and it differentiates itself by the way it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such, the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings, but increasingly insights are leading to the development of data products and services that can be monetized. Or as you'll hear in our industry examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. Self-service investing, filing insurance claims on our smart phones and so many examples. IOT systems that communicate and act machine to machine. Real-time pricing actions, these are all examples of products and services that drive revenue, cut costs or create other value and they all rely on data. Now, while many business leaders sometimes express frustration that their investments in data, people and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data transformation and leadership. One thing is certain, the next 10 years of data and digital transformation won't be like the last 10. So let's into it. Please join us in the chat. You can ask questions. You can share your comments. Hit us up on Twitter. Right now, it's my pleasure to welcome Mick Holliston and he's the president of Cloudera. Mick, great to see you. >> Great to see you as well, Dave. >> Hey, so I call it the new abnormal, right? The world is kind of out of whack. Offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations, line cooks at restaurants. Everything that we consumers have missed, but here's the one thing, it seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling, their pricing algorithms, commodity prices, we don't know. Is inflation transitory? Is it a long-term threat, trying to forecast GDP? It seems like we have to reset all of our assumptions and Mick, I feel a quality data is going to be a key here. How do you see the current state of the industry in the role data plays to get us into a more predictable and stable future? >> Well, I can sure tell you this, Dave, out of whack is definitely right. I don't know if you know or not, but I happened to be coming to you live today from Atlanta and as a native of Atlanta, I can tell you there's a lot to be known about the airport here. It's often said that whether you're going to heaven or hell, you got to change planes in Atlanta and after 40 minutes waiting on an algorithm to be right for baggage claim last night, I finally managed to get some bag and to be able to show up, dressed appropriately for you today. Here's one thing that I know for sure though, Dave. Clean, consistent and safe data will be essential to getting the world and businesses as we know it back on track again. Without well-managed data, we're certain to get very inconsistent outcomes. Quality data will be the normalizing factor because one thing really hasn't changed about computing since the dawn of time, back when I was taking computer classes at Georgia Tech here in Atlanta and that's what we used to refer to as garbage in, garbage out. In other words, you'll never get quality data-driven insights from a poor dataset. This is especially important today for machine learning and AI. You can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid. As AI is increasingly used in every business app, you must build a solid data foundation. >> Mick, let's talk about hybrid. Every CXO that I talked to, they're trying to get hybrid right. Whether it's hybrid work, hybrid events, which is our business, hybrid cloud. How are you thinking about the hybrid everything, what's your point of view? >> With all those prescriptions of hybrid and everything, there was one item you might not have quite hit on, Dave and that's hybrid data. >> Oh yeah, you're right, Mick, I did miss that. What do you mean by hybrid data? >> Well, Dave, in Cloudera, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now, every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises, in a private cloud, in public cloud or perhaps even in a new open data exchange. Now, this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud. But either way, security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch or read a recent news story. Data breaches are at an all time high and the ethics of AI applications are being called into question everyday. And understanding lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know, Dave, that's the business Cloudera is in. On a serious note, from Cloudera's perspective, adopting a hybrid data strategy is central to every business' digital transformation. It will enable rapid adoption of new technologies and optimize economic models, while ensuring the security and privacy of every bit of data. >> Okay, Mick, I'm glad you brought in that notion of hybrid data because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You got the economics, the physics, the local laws come into play, so what about the rest of hybrid? >> Yeah, that's a great question, Dave and certainly, Cloudera itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind, the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office ethernet may not be happening quite so fast in somebody's rural home in the middle of Nebraska somewhere. So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, involve us kind of slowly coming back to work, beginning this fall. And we're looking forward to being able to shake hands and see one another again for the first time, in many cases, for more than a year and a half. But yes, hybrid work and hybrid data are playing an increasingly important role for every kind of business. >> Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, here's how I think about it. I mean, some industries have transformed. You think about retail, for example, it's pretty clear. Although, every physical retail brand I know has not only beefed up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence. And reverse, we see Amazon building out physical assets, so there's more hybrid going on. But when you look at healthcare, for example, it's just starting with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control of payment systems. In manufacturing, the pandemic highlighted, America's reliance on China as a manufacturing partner and supply chain. And so my point is, it seems at different industries, they're in different stages of transformation, but two things look really clear. One, you got to put data at the core of the business model, that's compulsory. It seems like embedding AI into the applications, the data, the business process, that's going to become increasingly important. So how do you see that? >> Wow, there's a lot packed into that question there, Dave. But yeah, at Cloudera, I happened to be leading our own digital transformation as a technology company and what I would tell you there that's been an interesting process. The shift from being largely a subscription-based model to a consumption-based model requires a completely different level of instrumentation in our products and data collection that takes place in real-time, both for billing for our customers and to be able to check on the health and wellness, if you will, of their Cloudera implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed the rate and pace of getting vaccines to market or we've been assisting with testing process that's taken place. Because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and healthy and safe outcomes for everyone. And Cloudera has been underneath a great deal of that type of work. And the financial services industry you pointed out, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are helping those kinds of organizations get through those difficult challenges. You also happened to mention public sector and in public sector, we're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. And while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, you can't build great ML apps unless you've got a strong data foundation underneath. It's back to that garbage in, garbage out comment that I made previously. And so, in order to do that, you've got to have a great hybrid data management platform at your disposal to ensure that your data is clean and organized and up to date. Just as importantly from that, that's kind of the freedom side of things. On the security side of things, you've got to ensure that you can see who's touched not just the data itself, Dave, but actually the machine learning models. And organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage in addition to data lineage. In other words, who's had access to the machine learning models? When and where and at what time and what decisions were made perhaps, by the humans, perhaps by the machines that may have led to a particular outcome? So, every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models, just as they can track the lineage of data. So, lots going on there across industries. Lots going on as those various industries think about how AI can be applied to their businesses. >> It's a pretty interesting concept you're bringing into the discussion, the hybrid data, sort of, I think new to a lot of people. And this idea of model lineage is a great point because people want to talk about AI ethics, transparency of AI. When you start putting those models into machines to do real-time inferencing at the edge, it starts to get really complicated. I wonder if we could talk, we're still on that theme of industry transformation. I felt like coming into the pre-pandemic, there was just a lot of complacency. Yeah, digital transformation and a lot of buzz words and then we had this forced march to digital, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? >> There definitely is a lot of a POC limbo or I think some of us internally have referred to as POC purgatory, just getting in that phase, not being able to get from point A to point B in digital transformation. And for every industry, transformation, change in general, is difficult and it takes time and money and thoughtfulness. But like with all things, what we've found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts. To say it another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts, as it relates to the underlying technology here and to bring it home a little bit more practically, I guess I would say. At Cloudera, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, right close to home. In place, they can kind of experiment a little bit more safely and economically and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplished, but kind of starting small and then drawing fast from there on customer's journey to the cloud. >> Well Mick, we've covered a lot of ground. Last question, what do you want people to leave this event, this session with and thinking about sort of the next era of data that we're entering? >> Well, it's a great question, but I think it could be summed up in two words. I want them to think about a hybrid data strategy. So, really hybrid data is a concept that we're bringing forward on this show really, for the first time, arguably. And we really do think that it enables customers to experience what we refer to, Dave, as the power of ANT. That is freedom and security and in a world where we're all still trying to decide whether each day when we walk out, each building we walk into, whether we're free to come in and out with a mask, without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe for ourselves and for others. And the same is true of organization's IT strategies. They want the freedom to choose, to run workloads and applications in the best and most economical place possible, but they also want to do that with certainty that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So, hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole. >> Nick, thanks so much, great conversation. I really appreciate the insights that you're bringing to this event, into the industry, really. Thank you for your time. >> You bet, Dave, pleasure being with you.

>> 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 imbalances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processings, 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 Cube alum and VP and principal analyst at Forrester, and doin' some groundbreaking work in this area. And Cindy Maike 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, it's not usable. Nobody's able to understand what's happening in the business and they don't understand 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 with their customers 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, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >> Yeah, there's quite a few. And especially as we look across all the industries that were actually working with customers in. A few that stand out in top of mind for me is one is IQVIA. And what they're doing with real-world evidence and bringing together data across the entire healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making it accessible by both internally, as well as for the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, they're are a European car manufacturer and how do they make sure that they have efficient and effective processes when it comes to fixing equipment and so forth. And then also there's an Indonesian based telecommunications company, Techsomel, who's bringing together over the last five years, all their data about their customers and how do they enhance a customer experience, how do they make information accessible, especially in these pandemic and post pandemic times. 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? Where do things like hybrid fit in? What's Cloudera's point of view here? >> Platforms are truly an enabler. And data needs to be accessible in many different fashions, and also what's right for the business. When I want it in a cost and efficient and effective manner. So, data resides everywhere, data is developed and it's brought together. So you need to be able to balance both real time, our batch, historical information. It all depends upon what your analytical workloads are and what types of analytical methods you're going to use to drive those business insights. So putting in placing data, landing it, making it accessible, analyzing it, needs to be done in any accessible platform, whether it be 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 processes, 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 'cause 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 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 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 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 at one end of the spectrum, autonomous vehicles. You've got to make a decision in real time but near real-time, or real-time, it's in the eyes of the beholder if you will. It might be before you lose the customer or before the market changes. So it's really defined on a case by case basis. I wonder Michelle, if you could talk about in working with a number of organizations I see folks, they sometimes get twisted up in understanding the dependencies that technology generally, and the technologies around data specifically can sometimes have on critical business processes. Can you maybe give some guidance as to where customers should start? Where can we find some of the quick wins and high returns? >> It comes first down to how does your business operate? So you're going yo 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 creating greater consistency by bringing all of those signals together 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 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, bring Cindy back into the conversation here. Cindy, we often talk about people, process, and technology and the roles they play in creating a data strategy 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 kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but the fuel behind the process and how do you actually become insight-driven. And you look at the capabilities that you're needing to enable from that business process, that insight process. Your extended ecosystem on how do I make that happen? Partners and picking the right partner is important because a partner is one that actually helps you implement what your decisions are. So looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data within your process that if you need to do it in a real-time fashion, that they can actually meet those needs of the business. And enabling on those process activities. So the ecosystem looking at how you look at your vendors, and fundamentally they need to be that trusted partner. 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 business person enable those capabilities. >> So Cindy 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. (Dave chuckling) So Michelle, let's talk about leadership. 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, 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 one month, two months, three months, or a year from now, but in a week or tomorrow. And so that 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 experiences 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 participation 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. >> Great, 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. A lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a maturity model. 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 an insight driven organization, Cindy what do you see as the major characteristics that define the differences between sort of the early beginners sort of fat middle, if you will, and then the more advanced constituents? >> Yeah, I'm going to build upon what Michelle was talking about is data as an asset. And I think also being data aware and trying to actually become insight driven. 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, you're not going to be insight-driven. So you've got to move beyond that data awareness, where you're looking at data just from an operational reporting. But data's fundamentally driving the decisions that you make as a business. You're using data in real time. You're 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 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 artificial intelligence and so forth being able to do that. If you're just data aware, I would not be embracing on leveraging artificial intelligence. Because that means I probably haven't embedded data into my processes. Yes, data could very well still be a liability in your organization, so how do you actually make it an asset? >> Yeah I think data aware it's like cable ready. (Dave chuckling) So Michelle, maybe you could 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? Bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing it 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 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 the gap between when you see it, know it, and then get the most value and really exploit what that 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 spreadsheets and PowerPoint presentations and lots of mapping to help make 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, it's like people are acting on perfect or near perfect information. Or machines are 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 was 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. Now 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 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. (upbeat music)

>>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 a particular expertise in, in, in data and finance and insurance. I mean, 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, 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 transformation 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, 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 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 data. 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, 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 they having multiple, uh, distributors, what did they have in stock? So there are millions of data points that you need to drill down, 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 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 businesses and >>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 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 in? 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 Mick Halston 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 a, 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, w what do you see in that regard? >>Yeah, I think it's, I mean, we're definitely not at a point where when I talk 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? Where 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 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 and 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 address actually, you know, a business that's a restaurant with indoor dining, does it have a bar is an 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 like 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 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 half 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, 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 public cloud 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 about? >>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. Then the salespeople, they know the CRM data and, you know, logistics folks. There they're very much in tune with ERP. I 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 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, >>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. 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 of 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, my data architecture is, is that kind of thinking starting to really hit the marketplace. >>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, you know, 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 are 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. Exactly. And 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 as to yeah. So, >>Um, I I'm in the executive sponsor for, um, the Accenture cloud era 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 errors, the right data platform for that. So, um, >>That'd be Cloudera ushered in the modern big data era. We, 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 apply. >>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. Thank you.