(upbeat music) >> Hello, everyone, welcome to theCUBE. I'm John Furrier, the host of theCUBE in Palo Alto, California. Also it's SiliconANGLE News. Got two great guests here to talk about AI, the impact of the future of the internet, the applications, the people. Amr Awadallah, the founder and CEO, Ed Alban is the CEO of Vectara, a new startup that emerged out of the original Cloudera, I would say, 'cause Amr's known, famous for the Cloudera founding, which was really the beginning of the big data movement. And now as AI goes mainstream, there's so much to talk about, so much to go on. And plus the new company is one of the, now what I call the wave, this next big wave, I call it the fifth wave in the industry. You know, you had PCs, you had the internet, you had mobile. This generative AI thing is real. And you're starting to see startups come out in droves. Amr obviously was founder of Cloudera, Big Data, and now Vectara. And Ed Albanese, you guys have a new company. Welcome to the show. >> Thank you. It's great to be here. >> So great to see you. Now the story is theCUBE started in the Cloudera office. Thanks to you, and your friendly entrepreneurship views that you have. We got to know each other over the years. But Cloudera had Hadoop, which was the beginning of what I call the big data wave, which then became what we now call data lakes, data oceans, and data infrastructure that's developed from that. It's almost interesting to look back 12 plus years, and see that what AI is doing now, right now, is opening up the eyes to the mainstream, and the application's almost mind blowing. You know, Sati Natel called it the Mosaic Moment, didn't say Netscape, he built Netscape (laughing) but called it the Mosaic Moment. You're seeing companies in startups, kind of the alpha geeks running here, because this is the new frontier, and there's real meat on the bone, in terms of like things to do. Why? Why is this happening now? What's is the confluence of the forces happening, that are making this happen? >> Yeah, I mean if you go back to the Cloudera days, with big data, and so on, that was more about data processing. Like how can we process data, so we can extract numbers from it, and do reporting, and maybe take some actions, like this is a fraud transaction, or this is not. And in the meanwhile, many of the researchers working in the neural network, and deep neural network space, were trying to focus on data understanding, like how can I understand the data, and learn from it, so I can take actual actions, based on the data directly, just like a human does. And we were only good at doing that at the level of somebody who was five years old, or seven years old, all the way until about 2013. And starting in 2013, which is only 10 years ago, a number of key innovations started taking place, and each one added on. It was no major innovation that just took place. It was a couple of really incremental ones, but they added on top of each other, in a very exponentially additive way, that led to, by the end of 2019, we now have models, deep neural network models, that can read and understand human text just like we do. Right? And they can reason about it, and argue with you, and explain it to you. And I think that's what is unlocking this whole new wave of innovation that we're seeing right now. So data understanding would be the essence of it. >> So it's not a Big Bang kind of theory, it's been evolving over time, and I think that the tipping point has been the advancements and other things. I mean look at cloud computing, and look how fast it just crept up on AWS. I mean AWS you back three, five years ago, I was talking to Swami yesterday, and their big news about AI, expanding the Hugging Face's relationship with AWS. And just three, five years ago, there wasn't a model training models out there. But as compute comes out, and you got more horsepower,, these large language models, these foundational models, they're flexible, they're not monolithic silos, they're interacting. There's a whole new, almost fusion of data happening. Do you see that? I mean is that part of this? >> Of course, of course. I mean this wave is building on all the previous waves. We wouldn't be at this point if we did not have hardware that can scale, in a very efficient way. We wouldn't be at this point, if we don't have data that we're collecting about everything we do, that we're able to process in this way. So this, this movement, this motion, this phase we're in, absolutely builds on the shoulders of all the previous phases. For some of the observers from the outside, when they see chatGPT for the first time, for them was like, "Oh my god, this just happened overnight." Like it didn't happen overnight. (laughing) GPT itself, like GPT3, which is what chatGPT is based on, was released a year ahead of chatGPT, and many of us were seeing the power it can provide, and what it can do. I don't know if Ed agrees with that. >> Yeah, Ed? >> I do. Although I would acknowledge that the possibilities now, because of what we've hit from a maturity standpoint, have just opened up in an incredible way, that just wasn't tenable even three years ago. And that's what makes it, it's true that it developed incrementally, in the same way that, you know, the possibilities of a mobile handheld device, you know, in 2006 were there, but when the iPhone came out, the possibilities just exploded. And that's the moment we're in. >> Well, I've had many conversations over the past couple months around this area with chatGPT. John Markoff told me the other day, that he calls it, "The five dollar toy," because it's not that big of a deal, in context to what AI's doing behind the scenes, and all the work that's done on ethics, that's happened over the years, but it has woken up the mainstream, so everyone immediately jumps to ethics. "Does it work? "It's not factual," And everyone who's inside the industry is like, "This is amazing." 'Cause you have two schools of thought there. One's like, people that think this is now the beginning of next gen, this is now we're here, this ain't your grandfather's chatbot, okay?" With NLP, it's got reasoning, it's got other things. >> I'm in that camp for sure. >> Yeah. Well I mean, everyone who knows what's going on is in that camp. And as the naysayers start to get through this, and they go, "Wow, it's not just plagiarizing homework, "it's helping me be better. "Like it could rewrite my memo, "bring the lead to the top." It's so the format of the user interface is interesting, but it's still a data-driven app. >> Absolutely. >> So where does it go from here? 'Cause I'm not even calling this the first ending. This is like pregame, in my opinion. What do you guys see this going, in terms of scratching the surface to what happens next? >> I mean, I'll start with, I just don't see how an application is going to look the same in the next three years. Who's going to want to input data manually, in a form field? Who is going to want, or expect, to have to put in some text in a search box, and then read through 15 different possibilities, and try to figure out which one of them actually most closely resembles the question they asked? You know, I don't see that happening. Who's going to start with an absolute blank sheet of paper, and expect no help? That is not how an application will work in the next three years, and it's going to fundamentally change how people interact and spend time with opening any element on their mobile phone, or on their computer, to get something done. >> Yes. I agree with that. Like every single application, over the next five years, will be rewritten, to fit within this model. So imagine an HR application, I don't want to name companies, but imagine an HR application, and you go into application and you clicking on buttons, because you want to take two weeks of vacation, and menus, and clicking here and there, reasons and managers, versus just telling the system, "I'm taking two weeks of vacation, going to Las Vegas," book it, done. >> Yeah. >> And the system just does it for you. If you weren't completing in your input, in your description, for what you want, then the system asks you back, "Did you mean this? "Did you mean that? "Were you trying to also do this as well?" >> Yeah. >> "What was the reason?" And that will fit it for you, and just do it for you. So I think the user interface that we have with apps, is going to change to be very similar to the user interface that we have with each other. And that's why all these apps will need to evolve. >> I know we don't have a lot of time, 'cause you guys are very busy, but I want to definitely have multiple segments with you guys, on this topic, because there's so much to talk about. There's a lot of parallels going on here. I was talking again with Swami who runs all the AI database at AWS, and I asked him, I go, "This feels a lot like the original AWS. "You don't have to provision a data center." A lot of this heavy lifting on the back end, is these large language models, with these foundational models. So the bottleneck in the past, was the energy, and cost to actually do it. Now you're seeing it being stood up faster. So there's definitely going to be a tsunami of apps. I would see that clearly. What is it? We don't know yet. But also people who are going to leverage the fact that I can get started building value. So I see a startup boom coming, and I see an application tsunami of refactoring things. >> Yes. >> So the replatforming is already kind of happening. >> Yes, >> OpenAI, chatGPT, whatever. So that's going to be a developer environment. I mean if Amazon turns this into an API, or a Microsoft, what you guys are doing. >> We're turning it into API as well. That's part of what we're doing as well, yes. >> This is why this is exciting. Amr, you've lived the big data dream, and and we used to talk, if you didn't have a big data problem, if you weren't full of data, you weren't really getting it. Now people have all the data, and they got to stand this up. >> Yeah. >> So the analogy is again, the mobile, I like the mobile movement, and using mobile as an analogy, most companies were not building for a mobile environment, right? They were just building for the web, and legacy way of doing apps. And as soon as the user expectations shifted, that my expectation now, I need to be able to do my job on this small screen, on the mobile device with a touchscreen. Everybody had to invest in re-architecting, and re-implementing every single app, to fit within that model, and that model of interaction. And we are seeing the exact same thing happen now. And one of the core things we're focused on at Vectara, is how to simplify that for organizations, because a lot of them are overwhelmed by large language models, and ML. >> They don't have the staff. >> Yeah, yeah, yeah. They're understaffed, they don't have the skills. >> But they got developers, they've got DevOps, right? >> Yes. >> So they have the DevSecOps going on. >> Exactly, yes. >> So our goal is to simplify it enough for them that they can start leveraging this technology effectively, within their applications. >> Ed, you're the COO of the company, obviously a startup. You guys are growing. You got great backup, and good team. You've also done a lot of business development, and technical business development in this area. If you look at the landscape right now, and I agree the apps are coming, every company I talk to, that has that jet chatGPT of, you know, epiphany, "Oh my God, look how cool this is. "Like magic." Like okay, it's code, settle down. >> Mm hmm. >> But everyone I talk to is using it in a very horizontal way. I talk to a very senior person, very tech alpha geek, very senior person in the industry, technically. they're using it for log data, they're using it for configuration of routers. And in other areas, they're using it for, every vertical has a use case. So this is horizontally scalable from a use case standpoint. When you hear horizontally scalable, first thing I chose in my mind is cloud, right? >> Mm hmm. >> So cloud, and scalability that way. And the data is very specialized. So now you have this vertical specialization, horizontally scalable, everyone will be refactoring. What do you see, and what are you seeing from customers, that you talk to, and prospects? >> Yeah, I mean put yourself in the shoes of an application developer, who is actually trying to make their application a bit more like magic. And to have that soon-to-be, honestly, expected experience. They've got to think about things like performance, and how efficiently that they can actually execute a query, or a question. They've got to think about cost. Generative isn't cheap, like the inference of it. And so you've got to be thoughtful about how and when you take advantage of it, you can't use it as a, you know, everything looks like a nail, and I've got a hammer, and I'm going to hit everything with it, because that will be wasteful. Developers also need to think about how they're going to take advantage of, but not lose their own data. So there has to be some controls around what they feed into the large language model, if anything. Like, should they fine tune a large language model with their own data? Can they keep it logically separated, but still take advantage of the powers of a large language model? And they've also got to take advantage, and be aware of the fact that when data is generated, that it is a different class of data. It might not fully be their own. >> Yeah. >> And it may not even be fully verified. And so when the logical cycle starts, of someone making a request, the relationship between that request, and the output, those things have to be stored safely, logically, and identified as such. >> Yeah. >> And taken advantage of in an ongoing fashion. So these are mega problems, each one of them independently, that, you know, you can think of it as middleware companies need to take advantage of, and think about, to help the next wave of application development be logical, sensible, and effective. It's not just calling some raw API on the cloud, like openAI, and then just, you know, you get your answer and you're done, because that is a very brute force approach. >> Well also I will point, first of all, I agree with your statement about the apps experience, that's going to be expected, form filling. Great point. The interesting about chatGPT. >> Sorry, it's not just form filling, it's any action you would like to take. >> Yeah. >> Instead of clicking, and dragging, and dropping, and doing it on a menu, or on a touch screen, you just say it, and it's and it happens perfectly. >> Yeah. It's a different interface. And that's why I love that UIUX experiences, that's the people falling out of their chair moment with chatGPT, right? But a lot of the things with chatGPT, if you feed it right, it works great. If you feed it wrong and it goes off the rails, it goes off the rails big. >> Yes, yes. >> So the the Bing catastrophes. >> Yeah. >> And that's an example of garbage in, garbage out, classic old school kind of comp-side phrase that we all use. >> Yep. >> Yes. >> This is about data in injection, right? It reminds me the old SQL days, if you had to, if you can sling some SQL, you were a magician, you know, to get the right answer, it's pretty much there. So you got to feed the AI. >> You do, Some people call this, the early word to describe this as prompt engineering. You know, old school, you know, search, or, you know, engagement with data would be, I'm going to, I have a question or I have a query. New school is, I have, I have to issue it a prompt, because I'm trying to get, you know, an action or a reaction, from the system. And the active engineering, there are a lot of different ways you could do it, all the way from, you know, raw, just I'm going to send you whatever I'm thinking. >> Yeah. >> And you get the unintended outcomes, to more constrained, where I'm going to just use my own data, and I'm going to constrain the initial inputs, the data I already know that's first party, and I trust, to, you know, hyper constrain, where the application is actually, it's looking for certain elements to respond to. >> It's interesting Amr, this is why I love this, because one we are in the media, we're recording this video now, we'll stream it. But we got all your linguistics, we're talking. >> Yes. >> This is data. >> Yep. >> So the data quality becomes now the new intellectual property, because, if you have that prompt source data, it makes data or content, in our case, the original content, intellectual property. >> Absolutely. >> Because that's the value. And that's where you see chatGPT fall down, is because they're trying to scroll the web, and people think it's search. It's not necessarily search, it's giving you something that you wanted. It is a lot of that, I remember in Cloudera, you said, "Ask the right questions." Remember that phrase you guys had, that slogan? >> Mm hmm. And that's prompt engineering. So that's exactly, that's the reinvention of "Ask the right question," is prompt engineering is, if you don't give these models the question in the right way, and very few people know how to frame it in the right way with the right context, then you will get garbage out. Right? That is the garbage in, garbage out. But if you specify the question correctly, and you provide with it the metadata that constrain what that question is going to be acted upon or answered upon, then you'll get much better answers. And that's exactly what we solved Vectara. >> Okay. So before we get into the last couple minutes we have left, I want to make sure we get a plug in for the opportunity, and the profile of Vectara, your new company. Can you guys both share with me what you think the current situation is? So for the folks who are now having those moments of, "Ah, AI's bullshit," or, "It's not real, it's a lot of stuff," from, "Oh my god, this is magic," to, "Okay, this is the future." >> Yes. >> What would you say to that person, if you're at a cocktail party, or in the elevator say, "Calm down, this is the first inning." How do you explain the dynamics going on right now, to someone who's either in the industry, but not in the ropes? How would you explain like, what this wave's about? How would you describe it, and how would you prepare them for how to change their life around this? >> Yeah, so I'll go first and then I'll let Ed go. Efficiency, efficiency is the description. So we figured that a way to be a lot more efficient, a way where you can write a lot more emails, create way more content, create way more presentations. Developers can develop 10 times faster than they normally would. And that is very similar to what happened during the Industrial Revolution. I always like to look at examples from the past, to read what will happen now, and what will happen in the future. So during the Industrial Revolution, it was about efficiency with our hands, right? So I had to make a piece of cloth, like this piece of cloth for this shirt I'm wearing. Our ancestors, they had to spend month taking the cotton, making it into threads, taking the threads, making them into pieces of cloth, and then cutting it. And now a machine makes it just like that, right? And the ancestors now turned from the people that do the thing, to manage the machines that do the thing. And I think the same thing is going to happen now, is our efficiency will be multiplied extremely, as human beings, and we'll be able to do a lot more. And many of us will be able to do things they couldn't do before. So another great example I always like to use is the example of Google Maps, and GPS. Very few of us knew how to drive a car from one location to another, and read a map, and get there correctly. But once that efficiency of an AI, by the way, behind these things is very, very complex AI, that figures out how to do that for us. All of us now became amazing navigators that can go from any point to any point. So that's kind of how I look at the future. >> And that's a great real example of impact. Ed, your take on how you would talk to a friend, or colleague, or anyone who asks like, "How do I make sense of the current situation? "Is it real? "What's in it for me, and what do I do?" I mean every company's rethinking their business right now, around this. What would you say to them? >> You know, I usually like to show, rather than describe. And so, you know, the other day I just got access, I've been using an application for a long time, called Notion, and it's super popular. There's like 30 or 40 million users. And the new version of Notion came out, which has AI embedded within it. And it's AI that allows you primarily to create. So if you could break down the world of AI into find and create, for a minute, just kind of logically separate those two things, find is certainly going to be massively impacted in our experiences as consumers on, you know, Google and Bing, and I can't believe I just said the word Bing in the same sentence as Google, but that's what's happening now (all laughing), because it's a good example of change. >> Yes. >> But also inside the business. But on the crate side, you know, Notion is a wiki product, where you try to, you know, note down things that you are thinking about, or you want to share and memorialize. But sometimes you do need help to get it down fast. And just in the first day of using this new product, like my experience has really fundamentally changed. And I think that anybody who would, you know, anybody say for example, that is using an existing app, I would show them, open up the app. Now imagine the possibility of getting a starting point right off the bat, in five seconds of, instead of having to whole cloth draft this thing, imagine getting a starting point then you can modify and edit, or just dispose of and retry again. And that's the potential for me. I can't imagine a scenario where, in a few years from now, I'm going to be satisfied if I don't have a little bit of help, in the same way that I don't manually spell check every email that I send. I automatically spell check it. I love when I'm getting type ahead support inside of Google, or anything. Doesn't mean I always take it, or when texting. >> That's efficiency too. I mean the cloud was about developers getting stuff up quick. >> Exactly. >> All that heavy lifting is there for you, so you don't have to do it. >> Right? >> And you get to the value faster. >> Exactly. I mean, if history taught us one thing, it's, you have to always embrace efficiency, and if you don't fast enough, you will fall behind. Again, looking at the industrial revolution, the companies that embraced the industrial revolution, they became the leaders in the world, and the ones who did not, they all like. >> Well the AI thing that we got to watch out for, is watching how it goes off the rails. If it doesn't have the right prompt engineering, or data architecture, infrastructure. >> Yes. >> It's a big part. So this comes back down to your startup, real quick, I know we got a couple minutes left. Talk about the company, the motivation, and we'll do a deeper dive on on the company. But what's the motivation? What are you targeting for the market, business model? The tech, let's go. >> Actually, I would like Ed to go first. Go ahead. >> Sure, I mean, we're a developer-first, API-first platform. So the product is oriented around allowing developers who may not be superstars, in being able to either leverage, or choose, or select their own large language models for appropriate use cases. But they that want to be able to instantly add the power of large language models into their application set. We started with search, because we think it's going to be one of the first places that people try to take advantage of large language models, to help find information within an application context. And we've built our own large language models, focused on making it very efficient, and elegant, to find information more quickly. So what a developer can do is, within minutes, go up, register for an account, and get access to a set of APIs, that allow them to send data, to be converted into a format that's easy to understand for large language models, vectors. And then secondarily, they can issue queries, ask questions. And they can ask them very, the questions that can be asked, are very natural language questions. So we're talking about long form sentences, you know, drill down types of questions, and they can get answers that either come back in depending upon the form factor of the user interface, in list form, or summarized form, where summarized equals the opportunity to kind of see a condensed, singular answer. >> All right. I have a. >> Oh okay, go ahead, you go. >> I was just going to say, I'm going to be a customer for you, because I want, my dream was to have a hologram of theCUBE host, me and Dave, and have questions be generated in the metaverse. So you know. (all laughing) >> There'll be no longer any guests here. They'll all be talking to you guys. >> Give a couple bullets, I'll spit out 10 good questions. Publish a story. This brings the automation, I'm sorry to interrupt you. >> No, no. No, no, I was just going to follow on on the same. So another way to look at exactly what Ed described is, we want to offer you chatGPT for your own data, right? So imagine taking all of the recordings of all of the interviews you have done, and having all of the content of that being ingested by a system, where you can now have a conversation with your own data and say, "Oh, last time when I met Amr, "which video games did we talk about? "Which movie or book did we use as an analogy "for how we should be embracing data science, "and big data, which is moneyball," I know you use moneyball all the time. And you start having that conversation. So, now the data doesn't become a passive asset that you just have in your organization. No. It's an active participant that's sitting with you, on the table, helping you make decisions. >> One of my favorite things to do with customers, is to go to their site or application, and show them me using it. So for example, one of the customers I talked to was one of the biggest property management companies in the world, that lets people go and rent homes, and houses, and things like that. And you know, I went and I showed them me searching through reviews, looking for information, and trying different words, and trying to find out like, you know, is this place quiet? Is it comfortable? And then I put all the same data into our platform, and I showed them the world of difference you can have when you start asking that question wholeheartedly, and getting real information that doesn't have anything to do with the words you asked, but is really focused on the meaning. You know, when I asked like, "Is it quiet?" You know, answers would come back like, "The wind whispered through the trees peacefully," and you know, it's like nothing to do with quiet in the literal word sense, but in the meaning sense, everything to do with it. And that that was magical even for them, to see that. >> Well you guys are the front end of this big wave. Congratulations on the startup, Amr. I know you guys got great pedigree in big data, and you've got a great team, and congratulations. Vectara is the name of the company, check 'em out. Again, the startup boom is coming. This will be one of the major waves, generative AI is here. I think we'll look back, and it will be pointed out as a major inflection point in the industry. >> Absolutely. >> There's not a lot of hype behind that. People are are seeing it, experts are. So it's going to be fun, thanks for watching. >> Thanks John. (soft music)

(upbeat music) >> Hello, friends. Welcome back to The Cube's live coverage of AWS re:Invent 2022. We're in Vegas. Lovely Las Vegas. Beautiful outside, although I have only seen outside today once, but very excited to be at re:Invent. We're hearing between 50,000 and 70,000 attendees and it's insane, but people are ready to be back. This morning's keynote by CEO Adam Selipsky was full of great messages, big focus on data, customers, partners, the ecosystem. So excited. And I'm very pleased to welcome back one of our alumni to the program, David Shacochis, VP Enterprise Portfolio Strategy Product Management at Lumen. David, welcome back. >> Lisa, good to be here. The Five Timers Club. >> You are in the Five Timers Club. This is David's fifth appearance on the show. And we were talking before we went live- >> Do we do the jacket now and do we do the jacket later? >> Yeah, the jacket will come later. >> Okay. >> The Five Timers Club, like on SNL. We're going to have that for The Cube. We'll get you measured up and get that all fitted for you. >> That'd be better. >> So talk a little bit about Lumen. 'Cause last time you hear it wasn't Lumen. >> We weren't Lumen last time. So this is the first time... last time we were here on The Cube at re:Invent. This was probably 2019 or so. We were a different company. The company was called CenturyLink back then. We rebranded in 2020 to really represent our identity as a delivery of...as a solutions provider over our fiber network. So Lumen is the corporate brand, the company name. It represents basically a lot of the fiber that's been laid throughout the world and in North America and in enterprise metropolitan areas over the past 10 to 15 years. You know, companies like CenturyLink and Quest and Level 3, all those companies have really rolled up into building that core asset of the network. So Lumen is really the brand for the 21st century for the company, really focused on delivering services for the enterprise and then delivering a lot of value added services around that core network asset. >> So rebranding during the pandemic, what's been the customer feedback and sentiment? >> Yeah, I think customers have really actually appreciated it as certainly a more technology oriented brand, right? Sort of shifting away a little bit from some of the communications and telecom background of the company and the heritage. And while those assets that were built up during that period of time have been substantial, and we still build off of those assets going forward, really what a lot of the customer feedback has been is that it puts us in a posture to be a little bit more of a business solutions provider for customers, right? So there's a lot of things that we can do with that core network asset, the fiber networking a lot of the services that we launch on that in terms of public IP, you know, public internet capacity, private networking, private VPNs, VoIP and voice services. These are services that you'd expect from a company like that. But there's a lot of services inside the Lumen brand that you might surprise you, right? There's an edge computing capability that can deliver five milliseconds of latency within 95% of North American enterprise. >> Wow. >> There's a threat detection lab that goes and takes all of the traffic flowing over the public side of our network and analyzes it in a data lake and turns it into threat intelligence that we then offer off to our customers on a subscription basis. There's a production house that goes and, you know, does production networking for major sports arenas and sports events. There's a wide range of services inside of Lumen that really what the Lumen brand allows us to do is start talking about what those services can do and what networking can do for our customers in the enterprise in a more comprehensive way. >> So good changes, big brand changes for Lumen in the last couple of years. Also, I mean, during a time of such turmoil in the world, we've seen work change dramatically. You know, everybody...companies had to pivot massively quickly a couple years ago. >> Yep. >> Almost approaching three years ago, which is crazy amazing to be digital because they had to be able to survive. >> They did >> Now they're looking at being able to thrive, but now we're also in this hybrid work environment. The future of work has changed. >> Totally. >> Almost permanently. >> Yep. >> How is Lumen positioned to address some of the permanent changes to the work environments? Like the last time we were at re:Invented- >> Yeah. >> In person. This didn't exist. >> That's right. So really, it's one of the things we talk to our customers almost the most about is this idea of the future of work. And, you know, we really think about the future of work as about, you know, workers and workloads and the networks that connect them. You think about how much all of those demands are shifting and changing, right? What we were talking about, and it's very easy for all of us to conceptualize what the changing face of the worker looks like, whether those are knowledge workers or frontline workers the venues in which people are working the environments and that connectivity, predictability of those work desk environments changes so significantly. But workloads are changing and, you know we're sitting here at a trade show that does nothing but celebrate the transformation of workloads. Workloads running in ways in business logic and capturing of data and analysis of data. The changing methodologies and the changing formats of workloads, and then the changing venues for workloads. So workloads are running in places that never used to be data centers before. Workloads are running in interesting places and in different and challenging locations for what didn't used to be the data center. And so, you know, the workloads and the workloads are in a very dynamic situation. And the networks that connect them have to be dynamic, and they have to be flexible. And that's really why a lot of what Lumen invests in is working on the networks that connect workers and workloads both from a visibility and a managed services perspective to make sure that we're removing blind spots and then removing potential choke points and capacity issues, but then also being adaptable and dynamic enough to be able to go and reconfigure that network to reach all of the different places that, you know, workers and workloads are going to evolve into. What you'll find in a lot of cases, you know, the workers...a common scenario in the enterprise. A 500 person company with, you know, five offices and maybe one major facility. You know, that's now a 505 office company. >> Right. >> Right? The challenge of the network and the challenge of connecting workers and workloads is really one of the main conversations we have with our customers heading into this 21st century. >> What are some of the things that they're looking forward to in terms of embracing the future of work knowing this is probably how it's going to remain? >> Yeah, I think companies are really starting to experiment carefully and start to think about what they can do and certainly think about what they can do in the cloud with things like what the AWS platform allows them to do with some of the AWS abstractions and the AWS services allow them to start writing software for, and they're starting to really carefully, but very creatively and reach out into their you know, their base of enterprise data, their base of enterprise value to start running some experiments. We actually had a really interesting example of that in a session that Lumen shared here at re:Invent yesterday. You know, for the few hundred people that were there. You know, I think we got a lot of great feedback. It was really interesting session about the...really gets at this issue of the future of work and the changing ways that people are working. It actually was a really cool use case we worked on with Major League Baseball, Fox Sports, and AWS with the... using the Lumen network to essentially virtualize the production truck. Right? So you've all heard that, you know, the sports metaphor of, you know, the folks in the booth were sitting there started looking down and they're saying, oh great job by the guys or the gals in the truck. >> Yep. >> Right? That are, you know, that bring in that replay or great camera angle. They're always talking about the team and their production truck. Well, that production truck is literally a truck sitting outside the stadium. >> Yep. >> Full of electronics and software and gear. We were able to go and for a Major League Baseball game in...back in August, we were able to go and work with AWS, using the Lumen network, working with our partners and our customers at Fox Sports and virtualize all of that gear inside the truck. >> Wow. That's outstanding. >> Yep. So it was a live game. You know, they simulcast it, right? So, you know, we did our part of the broadcast and many hundreds of people, you know, saw that live broadcast was the first time they tried doing it. But, you know, to your point, what are enterprises doing? They're really starting to experiment, sort to push the envelope, right? They're kind of running things in new ways, you know, obviously hedging their bets, right? And sort of moving their way and sort of blue-green testing their way into the future by trying things out. But, you know, this is a massive revenue opportunity for a Major League Baseball game. You know, a premier, you know, Sunday night baseball contest between the Yankees and the Cardinals. We were able to go and take the entire truck, virtualize it down to a small rack of connectivity gear. Basically have that production network run over redundant fiber paths on the Lumen network up into AWS. And AWS is where all that software worked. The technical director of the show sitting in his office in North Carolina. >> Wow. >> The sound engineer is sitting in, you know, on his porch in Connecticut. Right? They were able to go and do the work of production anywhere while connected to AWS and then using the Lumen network, right? You know, the high powered capabilities of Lumens network underlay to be able to, you know, go and design a network topology and a worked topology that really wasn't possible before. >> Right. It's nice to hear, to your point, that customers are really embracing experimentation. >> Right. >> That's challenging to, obviously there was a big massive forcing function a couple of years ago where they didn't have a choice if they wanted to survive and eventually succeed and grow. >> Yeah. >> But the mindset of experimentation requires cultural change and that's a hard thing to do especially for I would think legacy organizations like Major League Baseball, but it sounds like they have the appetite. >> Yeah. They have the interest. >> They've been a fairly innovative organization for some time. But, you know, you're right. That idea of experimenting and that idea of trying out new things. Many people have observed, right? It's that forcing function of the pandemic that really drove a lot of organizations to go and make a lot of moves really quickly. And then they realized, oh, wait a minute. You know... I guess there's some sort of storytelling metaphor in there at some point of people realizing, oh wait, I can swim in these waters, right? I can do this. And so now they're starting to experiment and push the envelope even more using platforms like AWS, but then using a lot of the folks in the AWS partner network like Lumen, who are designing and sort of similarly inspired to deliver, you know, on demand and virtualized and dynamic capabilities within the core of our network and then within the services that our network can and the ways that our network connects to AWS. All of that experimentation now is possible because a lot of the things you need to do to try out the experiment are things you can get on demand and you can kind of pat, you can move back, you can learn. You can try new things and you can evolve. >> Right. >> Yep. >> Right. Absolutely. What are some of the things that you're excited about as, you know, here was this forcing function a couple years ago, we're coming out of that now, but the world has changed. The future of work as you are so brilliantly articulated has changed permanently. What are you excited about in terms of Lumen and AWS going forward? As we saw a lot of announcements this morning, big focus on data, vision of AWS is really that flywheel with Adams Selipsky is really, really going. What are you excited about going forward into 2023? >> Yeah, I mean we've been working with AWS for so long and have been critical partners for so long that, you know, I think a lot of it is continuation of a lot of the great work we've been doing. We've been investing in our own capabilities around the AWS partner network. You know, we're actually in a fairly unique position, you know, and we like to think that we're that unique position around the future of work where between workers, workloads and the networks that connect them. Our fingers are on a lot of those pulse points, right? Our fingers are on at really at the nexus of a lot of those dynamics. And our investment with AWS even puts us even more so in a position to go where a lot of the workloads are being transformed, right? So that's why, you know, we've invested in being one of the few network operators that is in the AWS partner network at the advanced tier that have the managed services competency, that have the migration competency and the network competency. You can count on one hand the number of network operators that have actually invested at that level with AWS. And there's an even smaller number that is, you know, based here in the United States. So, you know, I think that investment with AWS, investment in their partner programs and then investment co-innovation with AWS on things like that MLB use case really puts us in a position to keep on doing these kinds of things within the AWS partner network. And that's one of the biggest things we could possibly be excited about. >> So what does the go to market look like? Is it Lumen goes in, brings in AWS, vice versa? Both? >> Yeah, so a lot of being a member of the AWS partner network you have a lot of flexibility. You know, we have a lot of customers that are, you know, directly working with AWS. We have a lot of customers that would basically look to us to deliver the solution and, you know, and buy it all as a complete turnkey capability. So we have customers that do both. We have customers that, you know, just look to Lumen for the Lumen adjacent services and then pay, you know, pay a separate bill with AWS. So there's a lot of flexibility in the partner network in terms of what Lumen can deliver as a service, Lumen can deliver as a complete solution and then what parts of its with AWS and their platform factors into on an on-demand usage basis. >> And that would all be determined I imagine by what the customer really needs in their environment? >> Yeah, and sort of their own cloud strategy. There's a lot of customers who are all in on AWS and are really trying to driving and innovating and using some of the higher level services inside the AWS platform. And then there are customers who kind of looked at AWS as one of a few cloud platforms that they want to work with. The Lumen network is compatible and connected to all of them and our services teams are, you know, have the ability to go and let customers sort of take on whatever cloud posture they need. But if they are all in on AWS, there's, you know. Not many networks better to be on than Lumen in order to enable that. >> With that said, last question for you is if you had a bumper sticker or a billboard. Lumen's rebranded since we last saw you. What would that tagline or that phrase of impact be on that bumper sticker? >> Yeah, I'd get in a lot of trouble with our marketing team if I didn't give the actual bumper sticker for the company. But we really think of ourselves as the platform for amazing things. The fourth industrial revolution, everything going on in terms of the future of work, in terms of the future of industrial innovation, in terms of all the data that's being gathered. You know, Adam in the keynote this morning really went into a lot of detail on, you know, the depth of data and the mystery of data and how to harness it all and wrangle it all. It requires a lot of networking and a lot of connectivity. You know, for us to acquire, analyze and act on all that data and Lumen's platform for amazing things really helps forge that path forward to that fourth industrial revolution along with great partners like AWS. >> Outstanding. David, it's been such a pleasure having you back on The Cube. We'll get you fitted for that five timers club jacket. >> It sounds good. (Lisa laughs) >> I'll be back. >> Thanks so much for your insights and your time and well done with what you guys are doing at Lumen and AWS. >> Thanks Lisa. >> For David Shacochis, I'm Lisa Martin. You've been watching The Cube hopefully all day. This is our first full day of coverage at AWS re:Invent '22. Stick around. We'll be back tomorrow, and we know we're going to see you then. Have a great night. (upbeat music)

>>Hello everyone and welcome back to Motor City, Michigan. We're live from the Cube and my name is Savannah Peterson. Joined this afternoon with my co-host John Ferer. John, how you doing? Doing >>Great. This next segment's gonna be awesome about application modernization, scaling pluses. This is what's gonna, how are the next generation software revolution? It's gonna be >>Fun. You know, it's kind of been a theme of our day today is scale. And when we think about the complex orchestration platform that is Kubernetes, everyone wants to scale faster, quicker, more efficiently, and our guests are here to tell us all about that. Please welcome to Char and Andy, thank you so much for being here with us. You were on the Red Hat OpenShift team. Yeah. I suspect most of our audience is familiar, but just in case, let's give 'em a quick one-liner pitch so everyone's on the same page. Tell us about OpenShift. >>I, I'll take that one. OpenShift is our ES platform is our ES distribution. You can consume it as a self-managed platform or you can consume it as a managed service on on public clouds. And so we just call it all OpenShift. So it's basically Kubernetes, but you know, with a CNCF ecosystem around it to make things more easier. So maybe there's two >>Lights. So what does being at coupon mean for you? How does it feel to be here? What's your initial takes? >>Exciting. I'm having a fantastic time. I haven't been to coupon since San Diego, so it's great to be back in person and see old friends, make new friends, have hallway conversations. It's, it's great as an engineer trying to work in this ecosystem, just being able to, to be in the same place with these folks. >>And you gotta ask, before we came on camera, you're like, this is like my sixth co con. We were like, we're seven, you know, But that's a lot of co coupons. It >>Is, yes. I mean, so what, >>Yes. >>Take us status >>For sure. Where we are now. Compare and contrast co. Your first co con, just scope it out. What's the magnitude of change? If you had to put a pin on that, because there's a lot of new people coming in, they might not have seen where it's come from and how we got here is maybe not how we're gonna get to the next >>Level. I've seen it grow tremendously since the first one I went to, which I think was Austin several years ago. And what's great is seeing lots of new people interested in contributing and also seeing end users who are trying to figure out the best way to take advantage of this great ecosystem that we have. >>Awesome. And the project management side, you get the keys to the Kingdom with Red Hat OpenShift, which has been successful. Congratulations by the way. Thank you. We watched that grow and really position right on the wave. It's going great. What's the update on on the product? Kind of, you're in a good, good position right now. Yeah, >>No, we we're feeling good about it. It's all about our customers. Obviously the fact that, you know, we have thousands of customers using OpenShift as the cloud native platform, the container platform. We're very excited. The great thing about them is that, I mean you can go to like OpenShift Commons is kind of a user group that we run on the first day, like on Tuesday we ran. I mean you should see the number of just case studies that our customers went through there, you know? And it is fantastic to see that. I mean it's across so many different industries, across so many different use cases, which is very exciting. >>One of the things we've been reporting here in the Qla scene before, but here more important is just that if you take digital transformation to the, to its conclusion, the IT department and developers, they're not a department to serve the business. They are the business. Yes. That means that the developers are deciding things. Yeah. And running the business. Prove their code. Yeah. Okay. If that's, if that takes place, you gonna have scale. And we also said on many cubes, certainly at Red Hat Summit and other ones, the clouds are distributed computer, it's distributed computing. So you guys are focusing on this project, Andy, that you're working on kcp. >>Yes. >>Which is, I won't platform Kubernetes platform for >>Control >>Planes. Control planes. Yes. Take us through, what's the focus on why is that important and why is that relate to the mission of developers being in charge and large scale? >>Sure. So a lot of times when people are interested in developing on Kubernetes and running workloads, they need a cluster of course. And those are not cheap. It takes time, it takes money, it takes resources to get them. And so we're trying to make that faster and easier for, for end users and everybody involved. So with kcp, we've been able to take what looks like one normal Kubernetes and partition it. And so everybody gets a slice of it. You're an administrator in your little slice and you don't have to ask for permission to install new APIs and they don't conflict with anybody else's APIs. So we're really just trying to make it super fast and make it super flexible. So everybody is their own admin. >>So the developer basically looks at it as a resource blob. They can do whatever they want, but it's shared and provisioned. >>Yes. One option. It's like, it's like they have their own cluster, but you don't have to go through the process of actually provisioning a full >>Cluster. And what's the alternative? What's the what's, what's the, what's the benefit and what was the alternative to >>This? So the alternative, you spin up a full cluster, which you know, maybe that's three control plane nodes, you've got multiple workers, you've got a bunch of virtual machines or bare metal, or maybe you take, >>How much time does that take? Just ballpark. >>Anywhere from five minutes to an hour you can use cloud services. Yeah. Gke, E Ks and so on. >>Keep banging away. You're configuring. Yeah. >>Those are faster. Yeah. But it's still like, you still have to wait for that to happen and it costs money to do all of that too. >>Absolutely. And it's complex. Why do something that's been done, if there's a tool that can get you a couple steps down the path, which makes a ton of sense. Something that we think a lot when we're talking about scale. You mentioned earlier, Tohar, when we were chatting before the cams were alive, scale means a lot of different things. Can you dig in there a little bit? >>Yeah, I >>Mean, so when, when >>We talk about scale, >>We are talking about from a user perspective, we are talking about, you know, there are more users, there are more applications, there are more workloads, there are more services being run on Kubernetes now, right? So, and OpenShift. So, so that's one dimension of this scale. The other dimension of the scale is how do you manage all the underlying infrastructure, the clusters, the name spaces, and all the observability data, et cetera. So that's at least two levels of scale. And then obviously there's a third level of scale, which is, you know, there is scale across not just different clouds, but also from cloud to the edge. So there is that dimension of scale. So there are several dimensions of this scale. And the one that again, we are focused on here really is about, you know, this, the first one that I talk about is a user. And when I say user, it could be a developer, it could be an application architect, or it could be an application owner who wants to develop Kubernetes applications for Kubernetes and wants to publish those APIs, if you will, and make it discoverable and then somebody consumes it. So that's the scale we are talking about >>Here. What are some of the enterprise, you guys have a lot of customers, we've talked to you guys before many, many times and other subjects, Red Hat, I mean you guys have all the customers. Yeah. Enterprise, they've been there, done that. And you know, they're, they're savvy. Yeah. But the cloud is a whole nother ballgame. What are they thinking about? What's the psychology of the customer right now? Because now they have a lot of choices. Okay, we get it, we're gonna re-platform refactor apps, we'll keep some legacy on premises for whatever reasons. But cloud pretty much is gonna be the game. What's the mindset right now of the customer base? Where are they in their, in their psych? Not the executive, but more of the the operators or the developers? >>Yeah, so I mean, first of all, different customers are at different levels of maturity, I would say in this. They're all on a journey how I like to describe it. And in this journey, I mean, I see a customers who are really tip of the sphere. You know, they have containerized everything. They're cloud native, you know, they use best of tools, I mean automation, you know, complete automation, you know, quick deployment of applications and all, and life cycle of applications, et cetera. So that, that's kind of one end of this spectrum >>Advanced. Then >>The advances, you know, and, and I, you know, I don't, I don't have any specific numbers here, but I'd say there are quite a few of them. And we see that. And then there is kind of the middle who are, I would say, who are familiar with containers. They know what app modernization, what a cloud application means. They might have tried a few. So they are in the journey. They are kind of, they want to get there. They have some other kind of other issues, organizational or talent and so, so on and so forth. Kinds of issues to get there. And then there are definitely the quota, what I would call the lag arts still. And there's lots of them. But I think, you know, Covid has certainly accelerated a lot of that. I hear that. And there is definitely, you know, more, the psychology is definitely more towards what I would say public cloud. But I think where we are early also in the other trend that I see is kind of okay, public cloud great, right? So people are going there, but then there is the so-called edge also. Yeah. That is for various regions. You, you gotta have a kind of a regional presence, a edge presence. And that's kind of the next kind of thing taking off here. And we can talk more >>About it. Yeah, let's talk about that a little bit because I, as you know, as we know, we're very excited about Edge here at the Cube. Yeah. What types of trends are you seeing? Is that space emerges a little bit more firmly? >>Yeah, so I mean it's, I mean, so we, when we talk about Edge, you're talking about, you could talk about Edge as a, as a retail, I mean locations, right? >>Could be so many things edges everywhere. Everywhere, right? It's all around us. Quite literally. Even on the >>Scale. Exactly. In space too. You could, I mean, in fact you mentioned space. I was, I was going to >>Kinda, it's this world, >>My space actually Kubernetes and OpenShift running in space, believe it or not, you know, So, so that's the edge, right? So we have Industrial Edge, we have Telco Edge, we have a 5g, then we have, you know, automotive edge now and, and, and retail edge and, and more, right? So, and space, you know, So it's very exciting there. So the reason I tag back to that question that you asked earlier is that that's where customers are. So cloud is one thing, but now they gotta also think about how do I, whatever I do in the cloud, how do I bring it to the edge? Because that's where my end users are, my customers are, and my data is, right? So that's the, >>And I think Kubernetes has brought that attention to the laggards. We had the Laed Martin on yesterday, which is an incredible real example of Kubernetes at the edge. It's just incredible story. We covered it also wrote a story about it. So compelling. Cuz it makes it real. Yes. And Kubernetes is real. So then the question is developer productivity, okay, Things are starting to settle in. We've got KCP scaling clusters, things are happening. What about the tool chains? And how do I develop now I got scale of development, more code coming in. I mean, we are speculating that in the future there's so much code in open source that no one has to write code anymore. Yeah. At some point it's like this gluing things together. So the developers need to be productive. How are we gonna scale the developer equation and eliminate the, the complexity of tool chains and environments. Web assembly is super hyped up at this show. I don't know why, but sounds good. No one, no one can tell me why, but I can kind of connect the dots. But this is a big thing. >>Yeah. And it's fitting that you ask about like no code. So we've been working with our friends at Cross Plain and have integrated with kcp the ability to no code, take a whole bunch of configuration and say, I want a database. I want to be a, a provider of databases. I'm in an IT department, there's a bunch of developers, they don't wanna have to write code to create databases. So I can just take, take my configuration and make it available to them. And through some super cool new easy to use tools that we have as a developer, you can just say, please give me a database and you don't have to write any code. I don't have to write any code to maintain that database. I'm actually using community tooling out there to get that spun up. So there's a lot of opportunities out there. So >>That's ease of use check. What about a large enterprise that's got multiple tool chains and you start having security issues. Does that disrupt the tool chain capability? Like there's all those now weird examples emerging, not weird, but like real plumbing challenges. How do you guys see that evolving with Red >>Hat and Yeah, I mean, I mean, talking about that, right? The software, secure software supply chain is a huge concern for everyone after, especially some of the things that have happened in the past few >>Years. Massive team here at the show. Yeah. And just within the community, we're all a little more aware, I think, even than we were before. >>Before. Yeah. Yeah. And, and I think the, so to step back, I mean from, so, so it's not just even about, you know, run time vulnerability scanning, Oh, that's important, but that's not enough, right? So we are talking about, okay, how did that container, or how did that workload get there? What is that workload? What's the prominence of this workload? How did it get created? What is in it? You know, and what, what are, how do I make, make sure that there are no unsafe attack s there. And so that's the software supply chain. And where Red Hat is very heavily invested. And as you know, with re we kind of have roots in secure operating system. And rel one of the reasons why Rel, which is the foundation of everything we do at Red Hat, is because of security. So an OpenShift has always been secure out of the box with things like scc, rollbacks access control, we, which we added very early in the product. >>And now if you kind of bring that forward, you know, now we are talking about the complete software supply chain security. And this is really about right how from the moment the, the, the developer rights code and checks it into a gateway repository from there on, how do you build it? How do you secure it at each step of the process, how do you sign it? And we are investing and contributing to the community with things like cosign and six store, which is six store project. And so that secures the supply chain. And then you can use things like algo cd and then finally we can do it, deploy it onto the cluster itself. And then we have things like acs, which can do vulnerability scanning, which is a container security platform. >>I wanna thank you guys for coming on. I know Savannah's probably got a last question, but my last question is, could you guys each take a minute to answer why has Kubernetes been so successful today? What, what was the magic of Kubernetes that made it successful? Was it because no one forced it? Yes. Was it lightweight? Was it good timing, right place at the right time community? What's the main reason that Kubernetes is enabling all this, all this shift and goodness that's coming together, kind of defacto unifies people, the stacks, almost middleware markets coming around. Again, not to use that term middleware, but it feels like it's just about to explode. Yeah. Why is this so successful? I, >>I think, I mean, the shortest answer that I can give there really is, you know, as you heard the term, I think Satya Nala from Microsoft has used it. I don't know if he was the original person who pointed, but every company wants to be a software company or is a software company now. And that means that they want to develop stuff fast. They want to develop stuff at scale and develop at, in a cloud native way, right? You know, with the cloud. So that's, and, and Kubernetes came at the right time to address the cloud problem, especially across not just one public cloud or two public clouds, but across a whole bunch of public clouds and infrastructure as, and what we call the hybrid clouds. I think the ES is really exploded because of hybrid cloud, the need for hybrid cloud. >>And what's your take on the, the magic Kubernetes? What made it, what's making it so successful? >>I would agree also that it came about at the right time, but I would add that it has great extensibility and as developers we take it advantage of that every single day. And I think that the, the patterns that we use for developing are very consistent. And I think that consistency that came with Kubernetes, just, you have so many people who are familiar with it and so they can follow the same patterns, implement things similarly, and it's just a good fit for the way that we want to get our software out there and have, and have things operate. >>Keep it simple, stupid almost is that acronym, but the consistency and the de facto alignment Yes. Behind it just created a community. So, so then the question is, are the developers now setting the standards? That seems like that's the new way, right? I mean, >>I'd like to think so. >>So I mean hybrid, you, you're touching everything at scale and you also have mini shift as well, right? Which is taking a super macro micro shift. You ma micro shift. Oh yeah, yeah, exactly. It is a micro shift. That is, that is fantastic. There isn't a base you don't cover. You've spoken a lot about community and both of you have, and serving the community as well as your engagement with them from a, I mean, it's given that you're both leaders stepping back, how, how Community First is Red Hat and OpenShift as an organization when it comes to building the next products and, and developing. >>I'll take and, and I'm sure Andy is actually the community, so I'm sure he'll want to a lot of it. But I mean, right from the start, we have roots in open source. I'll keep it, you know, and, and, and certainly with es we were one of the original contributors to Kubernetes other than Google. So in some ways we think about as co-creators of es, they love that. And then, yeah, then we have added a lot of things in conjunction with the, I I talk about like SCC for Secure, which has become part security right now, which the community, we added things like our back and other what we thought were enterprise features needed because we actually wanted to build a product out of it and sell it to customers where our customers are enterprises. So we have worked with the community. Sometimes we have been ahead of the community and we have convinced the community. Sometimes the community has been ahead of us for other reasons. So it's been a great collaboration, which is I think the right thing to do. But Andy, as I said, >>Is the community well set too? Are well said. >>Yes, I agree with all of that. I spend most of my days thinking about how to interact with the community and engage with them. So the work that we're doing on kcp, we want it to be a community project and we want to involve as many people as we can. So it is a heavy focus for me and my team. And yeah, we we do >>It all the time. How's it going? How's the project going? You feel good >>About it? I do. It is, it started as an experiment or set of prototypes and has grown leaps and bounds from it's roots and it's, it's fantastic. Yeah. >>Controlled planes are hot data planes control planes. >>I >>Know, I love it. Making things work together horizontally scalable. Yeah. Sounds like cloud cloud native. >>Yeah. I mean, just to add to it, there are a couple of talks that on KCP at Con that our colleagues s Stephan Schemanski has, and I, I, I would urge people who have listening, if they have, just Google it, if you will, and you'll get them. And those are really awesome talks to get more about >>It. Oh yeah, no, and you can tell on GitHub that KCP really is a community project and how many people are participating. It's always fun to watch the action live to. Sure. Andy, thank you so much for being here with us, John. Wonderful questions this afternoon. And thank all of you for tuning in and listening to us here on the Cube Live from Detroit. I'm Savannah Peterson. Look forward to seeing you again very soon.

foreign welcome back to thecube's coverage of VMware Explorer 2022 Lisa Martin and Dave Nicholson here at Moscone West we're with about seven to ten thousand folks here so really good attendance at this first event since 2019 and the First with the new name Dave and I are pleased to welcome Jeff seraki the senior director of product management at Lumen as our next guest Jeff great to have you thank you for having me welcome so looked at the website I always love to see what taglines are and and lumen's website says welcome to the platform for amazing things talk to the audience a little bit about Lumen it's Mission Vision value prop would love to so much like a lot of the Enterprises that are out there today in the market lumens in the process of transforming we're transforming to a technology company from our Network routes but we also have roots in the I.T infrastructure business so we're bringing those together and creating that platform for amazing things uh we believe that our purpose is if you further human progress through technology and how we do that is we're enabling the fourth Industrial Revolution so moving in to the digital age where everything is it's all about data it's about real-time use of that data you machine learning artificial intelligence autonomous Cars Smart cities so the key tenet that we have around the fourth Industrial Revolution is data you need to acquire it and once you acquire it you need to analyze it then you need to act upon it because when you think about it data is just growing and growing and growing from the phones in your pocket to the devices that are sitting in front of us it's not going to stop and information that data is critical to driving business value and outcomes for customers so um so with that the I totally lost my train of thought sorry um uh the ability to to leverage that is critical um you know driving driving the revenue from that so for example like machine learning you can't have machine learning without data to feed the machine so they can start learning so they can look at pictures like oh look this is a picture of a dog this is a picture of a kangaroo so that's what our platform enables and that's what we're building we're building it brand new sitting on top of the Lumen networking capabilities of Global Network one of the largest IP backbone providers so we're super excited about what we have so these days every company has to be a data company to be competitive to you know well even to survive talk a little bit about enabling lumens customers to become data companies while enabling the fourth Industrial Revolution those two seem to be hand in hand yes so with the services that we provide particularly with our partnership with VMware we provide private cloud services that we can deploy on the customer premises or so whether it's a corporate office manufacturing facility a you know logistical facility so we can provide compute there or we can provide it in one of our plus 60 Edge data centers that are located in plus 60 metros so you don't have to put equipment on premises that's all connected by the Lumen Network Dynamic networking capabilities that connect from a customer Prem to Edge data center third party data center all the way into the public Cloud so we can stitch all of that together so I know you mentioned that you know you're you're you know based on your history you're moving further up the value chain with your customers but I'm still fascinated by kind of the history of lumen and when you when you refer to this Lumen Network um tell us a little more about that because that that's kind of a secret sauce ingredient to what you're doing yes so roots and Telecom roots and fiber and we have one of the largest fiber networks in the world and with that comes not only breath but also capillarity going to the markets we have over a hundred and eighty thousand fiber fed Enterprise buildings so with that imagine if your compute's there or if it's in a one of our Edge data centers how quickly you can transmit information from that Prem to the compute all the way into the cloud to acquire analyze and act on that data so that's really kind of the secret sauce we have that as you mentioned is that is that fiber backbone so I'm going to use the word capillarity at least once a day for the next week that's one of my favorite words awesome awesome word in it because and it actually it's evocative of exactly what I know you're what you're referencing but so you you guys are experts in latency bandwidth throughput those underpinnings of making sure that you can get data where it needs to be you can communicate between between environments um you've got that you've got that down so that's a very very strong Foundation to build off of is I guess the point that I wanted to see if I was correct definitely understanding and um just with that capability it really it comes down to outside the data is the user experience and with application performance you know one of the levers you can pull to drive application performance is is network but also location so you can put more bandwidth at it you can take put it on a network with less hops that's one of the advantages of our large backbone or you move the compo compute closer to the point of digital interaction which is what we're doing with our Edge platform so whether it's an edge data center on-prem yeah one thing one thing at the cube that we like to do is we we dive into those things that sometimes people think are inane and banal because we know how important they are we have a whole series on the question of does Hardware matter and so so we understand that you're delivering higher value to your customers but we also want to acknowledge just how important it is for you to have that Foundation yes underneath yeah and we're I mean the customers that in the marketplace they're expecting more and more services up this stack they don't want to have to worry about speeds and feeds well the way we're looking at it is the network has compute endpoints on it and everything has compute customers want to run their applications they don't want to worry about everything underneath it so that's why we're moving up so we want to be able to create that platform you worry about your applications you worry about development and execution of your applications and we'll take care of everything else talk a little bit about the VMware partnership I see Lumen Edge private Cloud on VCF talk a little bit about that how you guys are working together and some of the value of what's in it for me as a customer okay we've been working with for VMware for decades they're one of our best partners and our Flagship private Cloud product is based upon the cloud Foundation and it's a tried and true platform that the market understands and they have confidence in so it's something that they can relate to and they already have experience in so they're not trying to learn something new like trying to go out and find resources that can manage kubernetes like that's probably one of the hottest jobs out there probably took the wrong career path but anyways it's it's new it's emerging whereas VMware people know it there's a lot of people that know it so why spend time as an Enterprise retooling and learning and going to a different platform so with that VMware brings that foundation and the security of that that cohesive ecosystem that comes with VCF so we can provide that dedicated solution to our customers that they know and they Trust trust is critical right I mean it's it's table Stakes for businesses and their vendors and suppliers you know here we are at the VMware explore event that called uh the center of the multi-cloud universe which just sounds like a Marvel movie to me haven't seen any superheroes yet but there's got to be somebody around here in a costume in any event talk about how Lumen and VMware are enabling customers to navigate the the multi-cloud world that they're in by default and really turn it into a strategic advantage uh sure it's tied to the network um as much as I'm trying to say we opsificate it but it's um network is the critical part to it because you do have to physically connect things and the cloud is their computer somewhere so there is a physical behind everything but with the connectivity that we have and the partnership with VMware and the ability to take that platform and either from on-prem Edge data center third party data center or we can also provide that service with uh vmc and AWS we can provide it in the cloud so you have a ubiquitous platform that looks and feels the same no matter where it is and then that's critical to our customers again that the switching costs of learning it's it's a great product VMware is a great partnership to help bring that all together so what is a delighted customer sound like you're interacting with a delighted customer they're not gonna they're not going to pick up the phone and tell you you know what I love your network what what are they going to be what are they going to tell you they're happy about a delighty customer wouldn't talk about our infrastructure at all our virtual machines work our applications work our software Engineers they can develop against it our costs are optimized that's what they're going to care about if they start talking about oh our virtual machines or servers and that means there's probably something wrong so we need to make sure that platform that we're providing as a service and managing works so it's really if your application if you want to talk to me about your application that's what's top of mind for you we're doing our job now you share that love with the folks in your organization responsible for making sure that that infrastructure works right yes you let them know it's like look no no one is no one is touting what you do but it really still is important it is very you want to make sure keep those folks happy yes very important talk a little bit Jeff about how your customer conversations have evolved over the last couple of years as we saw you know two and a half years ago businesses in every industry scrambling to go digital have you seen priorities shift up the c-suite stock over to the board in terms of the infrastructure and the network that powers these organizations yeah I mean over the past couple years with the proliferation of public cloud you know the edicts of got to go to the cloud we got to go Cloud go to the go to the cloud so everything goes to the cloud it's great it's good for a lot of applications but not for all applications and the customer conversations were having a lot of it are okay what what comes back because with Cloud cream and costs it just yeah if you're looking at a permanent VM basis you know public Cloud works but when you have an entire ecosystem of virtual machines and applications to support entire Enterprise that cost can get out of hand pretty quickly are you saying that we we yeah we hear the term repatriation yes used are you saying a fair fair amount of that yes we're seeing that then the other part that we're seeing is getting out of the data center business that's expensive especially if an Enterprise has their own like that's you're talking about 10 million dollars per megawatt just of capital cost there so and then if they're in a third party you still have physical space and power you have servers there you have to assume someone's optimizing those servers and even if you have a hypervisor sitting on top of it that's a lot of work that's a lot of resources and human capital that our private Cloud solution with VMware takes away so that they can again they can worry about their applications providing business value providing customer experience versus is there anything on this server or not does somebody need this virtual machine what are all these public Cloud spend items we have how's this out of control it allows them to focus so that's kind of how things have have evolved and changed over the years one of the things that VMware talked about this morning in terms of the journey the cloud journey is going from cloud chaos which is where a lot of businesses are now to Cloud smart how does Lumen facilitate that transition of a business from cloud chaos to Cloud smart what is a cloud smart strategy from lumen's lens look like first of all you have to have a strategy as an Enterprise you'd be surprised how many of those that are out there that they don't know what to do and part of not knowing what to do is do we even have the right people looking at this and so what Lumen what we bring is that consultative capability to start breaking down some of those issues so maybe they do have a hybrid Cloud strategy okay have you implemented it no why not we don't have enough people okay those are resources we can bring in because not only you provide network and infrastructure but we also have managed surface capabilities managed Services capabilities we can sit on top of that we have Cloud migration practices we have centers of excellence around sap and other services so let us help dissect your problem let's take a let's look at the landscape you have out there find out where everything's buried and dig it up and then we figure out okay how do we move from one place the other you don't just lift and shift and so that those are the other services that Lumen brings in and that's how we help them and our private Cloud product we have it sitting on our Edge right in those 60 metros they can spin up a private Cloud instance tomorrow and they can start moving virtual machines from their data center to that cloud as a staging point to either keep it there you know move it to another place or move it into the public Cloud if that's where the application needs to live I'm curious about lumen's go to market strategy customers have a finite number of strategic seats at the table when it comes time to planning things out like what you just were referencing you know what what do we do next uh what's lumen's path to a seat at that table are you are you generally seeking to directly engage separately with that end user customer or are you going in partnering with others what does that look like in the real world in the real world it's Partners working together no one single entity can provide everything we have to work together and with our infrastructure layer we want to find the right partners that can help provide vertical specific Solutions that then you know they can be Hardware Partners they can be software Partners but then we can collectively go talk to the market talk to our customers about what we can help them with and then with our managed Services capabilities that's how we can kind of glue it all together so that's the direction we're going in so be very focused we're focused on manufacturing you're focused on retail because we see the largest opportunities there that's where we have a strong customer base strong customer relationships and that's how we're doing it we don't want to have an infrastructure conversation we want to outcome and application conversation that's what every customer is talking about it's all about outcomes is there Jeff a favorite customer story in manufacturing or retail that you think really articulates the value of what Lumen and VMware are delivering together yeah it's a yeah we kind of use this one a lot but it's it's uh it's a really good one um and we've seen um uh clones of this and and other opportunities manufacturing smart manufacturing you need to have the equipment that takes that information again that data from all the iot devices analyze it operate your manufacturing facility because most of it's all automated now so you can run that facility at optimal production with that compute you don't necessarily want that compute you know a thousand miles away you want it as close as possible particularly if you look at what if there's a fiber cut your network goes down okay then your factory goes down that's millions of dollars so with that compute there we allow that smart manufacturing capabilities and that's running on Lumen private cloud based upon VMware on vcloud foundation and it's working great and it's it's an opportunity for us to continue to expand I've seen similar use cases in logistics it's yeah I mean it's phenomenal what we can do when you're in conversations with prospects what's the why what's the pitch that you give them about why they should be working with Lumen to help them really maximize the value of their Edge Solutions it's really the resources we bring to bear like you know we we keep talking a lot about Network and uh trying to get away from the sniper that's my cousin the network is is key to the value proposition but it's not what people look at first but it's those other resources the ability to to manage I.T infrastructure which have been doing for decades a lot of people don't know that but we've been doing this a very long time and then with those areas of expertise managed Services it's providing that all together and with lumen's history the Partnerships we have I mean we have a lot of Partnerships so we have the ability to bring all these resources to provide the best solution for the customer and we like to use the term best execution venue so each application has an optimal place to live and we'll help help customers find that out and it's really I mean it's that simple we just need to sit down and have a conversation we can figure out where we can help you and we can get started as soon as the customer is ready so obviously some some changes coming up for VMware in the next few months or so what are you excited about as you continue this long-standing partnership and evolving it forward I'm most excited about us working together even more because we have not only do we have our private Cloud products uh we're leveraging them for kubernetes but also our sassy product we're partnered with VMware on that so we're really tight at the hip with these Cutting Edge Products that we're taking to Market to help customers solve those problems that we were just talking about so I'm just looking forward us coming together more and just getting out there and helping people threatening of the partnership excellent Jeff thank you for joining Dave and me on the program talking about what's going on with Lumen how you're enabling the fourth Industrial Revolution enabling customers to really become data companies we appreciate your time on your insights thank you for Jeff saraki and Dave Nicholson I'm Lisa Martin you're watching thecube live from VMware Explorer 2022. you're watching thecube the leader in Live tech coverage [Music]

(upbeat music) >> Okay, welcome back everyone in live coverage here at theCUBE, Boston, Massachusetts, for AWS re:inforce 22 security conference for Amazon Web Services. Obviously reinvent the end of the years' the big celebration, "re:Mars" is the new show that we've covered as well. The res are here with theCUBE. I'm John Furrier, host with a great guest, Ameesh Divatia, co-founder, and CEO of a company called "Baffle." Ameesh, thanks for joining us on theCUBE today, congratulations. >> Thank you. It's good to be here. >> And we got the custom encrypted socks. >> Yup, limited edition >> 64 bitter 128. >> Base 64 encoding. >> Okay.(chuckles) >> Secret message in there. >> Okay.(chuckles) Secret message.(chuckles) We'll have to put a little meme on the internet, figure it out. Well, thanks for comin' on. You guys are goin' hot right now. You guys a hot startup, but you're in an area that's going to explode, we believe. >> Yeah. >> The SuperCloud is here, we've been covering that on theCUBE that people are building on top of the Amazon Hyperscalers. And without the capex, they're building platforms. The application tsunami has come and still coming, it's not stopping. Modern applications are faster, they're better, and they're driving a lot of change under the covers. >> Absolutely. Yeah. >> And you're seeing structural change happening in real time, in ops, the network. You guys got something going on in the encryption area. >> Yes >> Data. Talk about what you guys do. >> Yeah. So we believe very strongly that the next frontier in security is data. We've had multiple waves in security. The next one is data, because data is really where the threats will persist. If the data shows up in the wrong place, you get into a lot of trouble with compliance. So we believe in protecting the data all the way down at the field, or record level. That's what we do. >> And you guys doing all kinds of encryption, or other things? >> Yes. So we do data transformation, which encompasses three different things. It can be tokenization, which is format preserving. We do real encryption with counter mode, or we can do masked views. So tokenization, encryption, and masking, all with the same platform. >> So pretty wide ranging capabilities with respect to having that kind of safety. >> Yes. Because it all depends on how the data is used down the road. Data is created all the time. Data flows through pipelines all the time. You want to make sure that you protect the data, but don't lose the utility of the data. That's where we provide all that flexibility. >> So Kurt was on stage today on one of the keynotes. He's the VP of the platform at AWS. >> Yes. >> He was talking about encrypts, everything. He said it needs, we need to rethink encryption. Okay, okay, good job. We like that. But then he said, "We have encryption at rest." >> Yes. >> That's kind of been there, done that. >> Yes. >> And, in-flight? >> Yeah. That's been there. >> But what about in-use? >> So that's exactly what we plug. What happens right now is that data at rest is protected because of discs that are already self-encrypting, or you have transparent data encryption that comes native with the database. You have data in-flight that is protected because of SSL. But when the data is actually being processed, it's in the memory of the database or datastore, it is exposed. So the threat is, if the credentials of the database are compromised, as happened back then with Starwood, or if the cloud infrastructure is compromised with some sort of an insider threat like a Capital One, that data is exposed. That's precisely what we solve by making sure that the data is protected as soon as it's created. We use standard encryption algorithms, AES, and we either do format preserving, or true encryption with counter mode. And that data, it doesn't really matter where it ends up, >> Yeah. >> because it's always protected. >> Well, that's awesome. And I think this brings up the point that we want been covering on SiliconAngle in theCUBE, is that there's been structural change that's happened, >> Yes. >> called cloud computing, >> Yes. >> and then hybrid. Okay. Scale, role of data, higher level abstraction of services, developers are in charge, value creations, startups, and big companies. That success is causing now, a new structural change happening now. >> Yes. >> This is one of them. What areas do you see that are happening right now that are structurally changing, that's right in front of us? One is, more cloud native. So the success has become now the problem to solve - >> Yes. >> to get to the next level. >> Yeah. >> What are those, some of those? >> What we see is that instead of security being an afterthought, something that you use as a watchdog, you create ways of monitoring where data is being exposed, or data is being exfiltrated, you want to build security into the data pipeline itself. As soon as data is created, you identify what is sensitive data, and you encrypt it, or tokenize it as it flows into the pipeline using things like Kafka plugins, or what we are very clearly differentiating ourselves with is, proxy architectures so that it's completely transparent. You think you're writing to the datastore, but you're actually writing to the proxy, which in turn encrypts the data before its stored. >> Do you think that's an efficient way to do it, or is the only way to do it? >> It is a much more efficient way of doing it because of the fact that you don't need any app-dev resources. There are many other ways of doing it. In fact, the cloud vendors provide development kits where you can just go do it yourself. So that is actually something that we completely avoid. And what makes it really, really interesting is that once the data is encrypted in the data store, or database, we can do what is known as "Privacy Enhanced Computation." >> Mm. >> So we can actually process that data without decrypting it. >> Yeah. And so proxies then, with cloud computing, can be very fast, not a bottleneck that could be. >> In fact, the cloud makes it so. It's very hard to - >> You believe that? >> do these things in static infrastructure. In the cloud, there's infinite amount of processing available, and there's containerization. >> And you have good network. >> You have very good network, you have load balancers, you have ways of creating redundancy. >> Mm. So the cloud is actually enabling solutions like this. >> And the old way, proxies were seen as an architectural fail, in the old antiquated static web. >> And this is where startups don't have the baggage, right? We didn't have that baggage. (John laughs) We looked at the problem and said, of course we're going to use a proxy because this is the best way to do this in an efficient way. >> Well, you bring up something that's happening right now that I hear a lot of CSOs and CIOs and executives say, CXOs say all the time, "Our", I won't say the word, "Our stuff has gotten complicated." >> Yes. >> So now I have tool sprawl, >> Yeah. >> I have skill gaps, and on the rise, all these new managed services coming at me from the vendors who have never experienced my problem. And their reaction is, they don't get my problem, and they don't have the right solutions, it's more complexity. They solve the complexity by adding more complexity. >> Yes. I think we, again, the proxy approach is a very simple. >> That you're solving that with that approach. >> Exactly. It's very simple. And again, we don't get in the way. That's really the the biggest differentiator. The forcing function really here is compliance, right? Because compliance is forcing these CSOs to actually adopt these solutions. >> All right, so love the compliance angle, love the proxy as an ease of use, take the heavy lifting away, no operational problems, and deviations. Now let's talk about workloads. >> Yeah. >> 'Cause this is where the use is. So you got, or workloads being run large scale, lot a data moving around, computin' as well. What's the challenge there? >> I think it's the volume of the data. Traditional solutions that we're relying on legacy tokenizations, I think would replicate the entire storage because it would create a token wall, for example. You cannot do that at this scale. You have to do something that's a lot more efficient, which is where you have to do it with a cryptography approach. So the workloads are diverse, lots of large files in the workloads as well as structured workloads. What we have is a solution that actually goes across the board. We can do unstructured data with HTTP proxies, we can do structured data with SQL proxies. And that's how we are able to provide a complete solution for the pipeline. >> So, I mean, show about the on-premise versus the cloud workload dynamic right now. Hybrid is a steady state right now. >> Yeah. >> Multi-cloud is a consequence of having multiple vendors, not true multi-cloud but like, okay, they have Azure there, AWS here, I get that. But hybrid really is the steady state. >> Yes. >> Cloud operations. How are the workloads and the analytics the data being managed on-prem, and in the cloud, what's their relationship? What's the trend? What are you seeing happening there? >> I think the biggest trend we see is pipelining, right? The new ETL is streaming. You have these Kafka and Kinesis capabilities that are coming into the picture where data is being ingested all the time. It is not a one time migration. It's a stream. >> Yeah. >> So plugging into that stream is very important from an ingestion perspective. >> So it's not just a watchdog. >> No. >> It's the pipelining. >> It's built in. It's built-in, it's real time, that's where the streaming gets another diverse access to data. >> Exactly. >> Data lakes. You got data lakes, you have pipeline, you got streaming, you mentioned that. So talk about the old school OLTP, the old BI world. I think Power BI's like a $30 billion product. >> Yeah. >> And you got Tableau built on OLTP building cubes. Aren't we just building cubes in a new way, or, >> Well. >> is there any relevance to the old school? >> I think there, there is some relevance and in fact that's again, another place where the proxy architecture really helps, because it doesn't matter when your application was built. You can use Tableau, which nobody has any control over, and still process encrypted data. And so can with Power BI, any Sequel application can be used. And that's actually exactly what we like to. >> So we were, I was talking to your team, I knew you were coming on, and they gave me a sound bite that I'm going to read to the audience and I want to get your reaction to. >> Sure. >> 'Cause I love this. I fell out of my chair when I first read this. "Data is the new oil." In 2010 that was mentioned here on theCUBE, of course. "Data is the new oil, but we have to ensure that it does not become the next asbestos." Okay. That is really clever. So we all know about asbestos. I add to the Dave Vellante, "Lead paint too." Remember lead paint? (Ameesh laughs) You got to scrape it out and repaint the house. Asbestos obviously causes a lot of cancer. You know, joking aside, the point is, it's problematic. >> It's the asset. >> Explain why that sentence is relevant. >> Sure. It's the assets and liabilities argument, right? You have an asset which is data, but thanks to compliance regulations and Gartner says 75% of the world will be subject to privacy regulations by 2023. It's a liability. So if you don't store your data well, if you don't process your data responsibly, you are going to be liable. So while it might be the oil and you're going to get lots of value out of it, be careful about the, the flip side. >> And the point is, there could be the "Grim Reaper" waiting for you if you don't do it right, the consequences that are quantified would be being out of business. >> Yes. But here's something that we just discovered actually from our survey that we did. While 93% of respondents said that they have had lots of compliance related effects on their budgets. 75% actually thought that it makes them better. They can use the security postures as a competitive differentiator. That's very heartening to us. We don't like to sell the fear aspect of this. >> Yeah. We like to sell the fact that you look better compared to your neighbor, if you have better data hygiene, back to the. >> There's the fear of missing out, or as they say, "Keeping up with the Joneses", making sure that your yard looks better than the next one. I get the vanity of that, but you're solving real problems. And this is interesting. And I want to get your thoughts on this. I found, I read that you guys protect more than a 100 billion records across highly regulated industries. Financial services, healthcare, industrial IOT, retail, and government. Is that true? >> Absolutely. Because what we are doing is enabling SaaS vendors to actually allow their customers to control their data. So we've had the SaaS vendor who has been working with us for over three years now. They store confidential data from 30 different banks in the country. >> That's a lot of records. >> That's where the record, and. >> How many customers do you have? >> Well, I think. >> The next round of funding's (Ameesh laughs) probably they're linin' up to put money into you guys. >> Well, again, this is a very important problem, and there are, people's businesses are dependent on this. We're just happy to provide the best tool out there that can do this. >> Okay, so what's your business model behind? I love the success, by the way, I wanted to quote that stat to one verify it. What's the business model service, software? >> The business model is software. We don't want anybody to send us their confidential data. We embed our software into our customers environments. In case of SaaS, we are not even visible, we are completely embedded. We are doing other relationships like that right now. >> And they pay you how? >> They pay us based on the volume of the data that they're protecting. >> Got it. >> That in that case which is a large customers, large enterprise customers. >> Pay as you go. >> It is pay as you go, everything is annual licenses. Although, multi-year licenses are very common because once you adopt the solution, it is very sticky. And then for smaller customers, we do base our pricing also just on databases. >> Got it. >> The number of databases. >> And the technology just reviewed low-code, no-code implementation kind of thing, right? >> It is by definition, no code when it comes to proxy. >> Yeah. >> When it comes to API integration, it could be low code. Yeah, it's all cloud-friendly, cloud-native. >> No disruption to operations. >> Exactly. >> That's the culprit. >> Well, yeah. >> Well somethin' like non-disruptive operations.(laughs) >> No, actually I'll give an example of a migration, right? We can do live migrations. So while the databases are still alive, as you write your. >> Live secure migrations. >> Exactly. You're securing - >> That's the one that manifests. >> your data as it migrates. >> Awright, so how much funding have you guys raised so far? >> We raised 36 and a half, series A, and B now. We raised that late last year. >> Congratulations. >> Thank you. >> Who's the venture funders? >> True Ventures is our largest investor, followed by Celesta Capital, National Grid Partners is an investor, and so is Engineering Capital and Clear Vision Ventures. >> And the seed and it was from Engineering? >> Seed was from Engineering. >> Engineering Capital. >> And then True came in very early on. >> Okay. >> Greenspring is also an investor in us, so is Industrial Ventures. >> Well, privacy has a big concern, big application for you guys. Privacy, secure migrations. >> Very much so. So what we are believe very strongly in the security's personal, security is yours and my data. Privacy is what the data collector is responsible for. (John laughs) So the enterprise better be making sure that they've complied with privacy regulations because they don't tell you how to protect the data. They just fine you. >> Well, you're not, you're technically long, six year old start company. Six, seven years old. >> Yeah. >> Roughly. So yeah, startups can go on long like this, still startup, privately held, you're growing, got big records under management there, congratulations. What's next? >> I think scaling the business. We are seeing lots of applications for this particular solution. It's going beyond just regulated industries. Like I said, it's a differentiating factor now. >> Yeah >> So retail, and a lot of other IOT related industrial customers - >> Yeah. >> are also coming. >> Ameesh, talk about the show here. We're at re:inforce, actually we're live here on the ground, the show floor buzzing. What's your takeaway? What's the vibe this year? What if you had to share what your opinion the top story here at the show, what would be the two top things, or three things? >> I think it's two things. First of all, it feels like we are back. (both laugh) It's amazing to see people on the show floor. >> Yeah. >> People coming in and asking questions and getting to see the product. The second thing that I think is very gratifying is, people come in and say, "Oh, I've heard of you guys." So thanks to digital media, and digital marketing. >> They weren't baffled. They want baffled. >> Exactly. >> They use baffled. >> Looks like, our outreach has helped, >> Yeah. >> and has kept the continuity, which is a big deal. >> Yeah, and now you're a CUBE alumni, welcome to the fold. >> Thank you. >> Appreciate you coming on. And we're looking forward to profiling you some day in our startup showcase, and certainly, we'll see you in the Palo Alto studios. Love to have you come in for a deeper dive. >> Sounds great. Looking forward to it. >> Congratulations on all your success, and thanks for coming on theCUBE, here at re:inforce. >> Thank you, John. >> Okay, we're here in, on the ground live coverage, Boston, Massachusetts for AWS re:inforce 22. I'm John Furrier, your host of theCUBE with Dave Vellante, who's in an analyst session, right? He'll be right back with us on the next interview, coming up shortly. Thanks for watching. (gentle music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> After a two-year epic run in tech, 2022 has been an epically bad year. Through yesterday, The NASDAQ composite is down 30%. The S$P 500 is off 21%. And the Dow Jones Industrial average 16% down. And the poor holders at Bitcoin have had to endure a nearly 60% decline year to date. But judging by the attendance and enthusiasm, in major in-person tech events this spring. You'd never know that tech was in the tank. Moreover, walking around the streets of Las Vegas, where most tech conferences are held these days. One can't help but notice that the good folks of Main Street, don't seem the least bit concerned that the economy is headed for a recession. Hello, and welcome to this weeks Wiki Bond Cube Insights powered by ETR. In this Breaking Analysis we'll share our main takeaways from the first half of 2022. And talk about the outlook for tech going forward, and why despite some pretty concerning headwinds we remain sanguine about tech generally, but especially enterprise tech. Look, here's the bumper sticker on why many folks are really bearish at the moment. Of course, inflation is high, other than last year, the previous inflation high this century was in July of 2008, it was 5.6%. Inflation has proven to be very, very hard to tame. You got gas at $7 dollars a gallon. Energy prices they're not going to suddenly drop. Interest rates are climbing, which will eventually damage housing. Going to have that ripple effect, no doubt. We're seeing layoffs at companies like Tesla and the crypto names are also trimming staff. Workers, however are still in short supply. So wages are going up. Companies in retail are really struggling with the right inventory, and they can't even accurately guide on their earnings. We've seen a version of this movie before. Now, as it pertains to tech, Crawford Del Prete, who's the CEO of IDC explained this on theCUBE this very week. And I thought he did a really good job. He said the following, >> Matt, you have a great statistic that 80% of companies used COVID as their point to pivot into digital transformation. And to invest in a different way. And so what we saw now is that tech is now where I think companies need to focus. They need to invest in tech. They need to make people more productive with tech and it played out in the numbers. Now so this year what's fascinating is we're looking at two vastly different markets. We got gasoline at $7 a gallon. We've got that affecting food prices. Interesting fun fact recently it now costs over $1,000 to fill an 18 wheeler. All right, based on, I mean, this just kind of can't continue. So you think about it. >> Don't put the boat in the water. >> Yeah, yeah, yeah. Good luck if ya, yeah exactly. So a family has kind of this bag of money, and that bag of money goes up by maybe three, 4% every year, depending upon earnings. So that is sort of sloshing around. So if food and fuel and rent is taking up more, gadgets and consumer tech are not, you're going to use that iPhone a little longer. You're going to use that Android phone a little longer. You're going to use that TV a little longer. So consumer tech is getting crushed, really it's very, very, and you saw it immediately in ad spending. You've seen it in Meta, you've seen it in Facebook. Consumer tech is doing very, very, it is tough. Enterprise tech, we haven't been in the office for two and a half years. We haven't upgraded whether that be campus wifi, whether that be servers, whether that be commercial PCs as much as we would have. So enterprise tech, we're seeing double digit order rates. We're seeing strong, strong demand. We have combined that with a component shortage, and you're seeing some enterprise companies with a quarter of backlog, I mean that's really unheard of. >> And higher prices, which also profit. >> And therefore that drives up the prices. >> And this is a theme that we've heard this year at major tech events, they've really come roaring back. Last year, theCUBE had a huge presence at AWS Reinvent. The first Reinvent since 2019, it was really well attended. Now this was before the effects of the omicron variant, before they were really well understood. And in the first quarter of 2022, things were pretty quiet as far as tech events go But theCUBE'a been really busy this spring and early into the summer. We did 12 physical events as we're showing here in the slide. Coupa, did Women in Data Science at Stanford, Coupa Inspire was in Las Vegas. Now these are both smaller events, but they were well attended and beat expectations. San Francisco Summit, the AWS San Francisco Summit was a bit off, frankly 'cause of the COVID concerns. They were on the rise, then we hit Dell Tech World which was packed, it had probably around 7,000 attendees. Now Dockercon was virtual, but we decided to include it here because it was a huge global event with watch parties and many, many tens of thousands of people attending. Now the Red Hat Summit was really interesting. The choice that Red Hat made this year. It was purposefully scaled down and turned into a smaller VIP event in Boston at the Western, a couple thousand people only. It was very intimate with a much larger virtual presence. VeeamON was very well attended, not as large as previous VeeamON events, but again beat expectations. KubeCon and Cloud Native Con was really successful in Spain, Valencia, Spain. PagerDuty Summit was again a smaller intimate event in San Francisco. And then MongoDB World was at the new Javits Center and really well attended over the three day period. There were lots of developers there, lots of business people, lots of ecosystem partners. And then the Snowflake summit in Las Vegas, it was the most vibrant from the standpoint of the ecosystem with nearly 10,000 attendees. And I'll come back to that in a moment. Amazon re:Mars is the Amazon AI robotic event, it's smaller but very, very cool, a lot of innovation. And just last week we were at HPE Discover. They had around 8,000 people attending which was really good. Now I've been to over a dozen HPE or HPE Discover events, within Europe and the United States over the past decade. And this was by far the most vibrant, lot of action. HPE had a little spring in its step because the company's much more focused now but people was really well attended and people were excited to be there, not only to be back at physical events, but also to hear about some of the new innovations that are coming and HPE has a long way to go in terms of building out that ecosystem, but it's starting to form. So we saw that last week. So tech events are back, but they are smaller. And of course now a virtual overlay, they're hybrid. And just to give you some context, theCUBE did, as I said 12 physical events in the first half of 2022. Just to compare that in 2019, through June of that year we had done 35 physical events. Yeah, 35. And what's perhaps more interesting is we had our largest first half ever in our 12 year history because we're doing so much hybrid and virtual to compliment the physical. So that's the new format is CUBE plus digital or sometimes just digital but that's really what's happening in our business. So I think it's a reflection of what's happening in the broader tech community. So everyone's still trying to figure that out but it's clear that events are back and there's no replacing face to face. Or as I like to say, belly to belly, because deals are done at physical events. All these events we've been to, the sales people are so excited. They're saying we're closing business. Pipelines coming out of these events are much stronger, than they are out of the virtual events but the post virtual event continues to deliver that long tail effect. So that's not going to go away. The bottom line is hybrid is the new model. Okay let's look at some of the big themes that we've taken away from the first half of 2022. Now of course, this is all happening under the umbrella of digital transformation. I'm not going to talk about that too much, you've had plenty of DX Kool-Aid injected into your veins over the last 27 months. But one of the first observations I'll share is that the so-called big data ecosystem that was forming during the hoop and around, the hadoop infrastructure days and years. then remember it dispersed, right when the cloud came in and kind of you know, not wiped out but definitely dampened the hadoop enthusiasm for on-prem, the ecosystem dispersed, but now it's reforming. There are large pockets that are obviously seen in the various clouds. And we definitely see a ecosystem forming around MongoDB and the open source community gathering in the data bricks ecosystem. But the most notable momentum is within the Snowflake ecosystem. Snowflake is moving fast to win the day in the data ecosystem. They're providing a single platform that's bringing different data types together. Live data from systems of record, systems of engagement together with so-called systems of insight. These are converging and while others notably, Oracle are architecting for this new reality, Snowflake is leading with the ecosystem momentum and a new stack is emerging that comprises cloud infrastructure at the bottom layer. Data PaaS layer for app dev and is enabling an ecosystem of partners to build data products and data services that can be monetized. That's the key, that's the top of the stack. So let's dig into that further in a moment but you're seeing machine intelligence and data being driven into applications and the data and application stacks they're coming together to support the acceleration of physical into digital. It's happening right before our eyes in every industry. We're also seeing the evolution of cloud. It started with the SaaS-ification of the enterprise where organizations realized that they didn't have to run their own software on-prem and it made sense to move to SaaS for CRM or HR, certainly email and collaboration and certain parts of ERP and early IS was really about getting out of the data center infrastructure management business called that cloud 1.0, and then 2.0 was really about changing the operating model. And now we're seeing that operating model spill into on-prem workloads finally. We're talking about here about initiatives like HPE's Green Lake, which we heard a lot about last week at Discover and Dell's Apex, which we heard about in May, in Las Vegas. John Furrier had a really interesting observation that basically this is HPE's and Dell's version of outposts. And I found that interesting because outpost was kind of a wake up call in 2018 and a shot across the bow at the legacy enterprise infrastructure players. And they initially responded with these flexible financial schemes, but finally we're seeing real platforms emerge. Again, we saw this at Discover and at Dell Tech World, early implementations of the cloud operating model on-prem. I mean, honestly, you're seeing things like consoles and billing, similar to AWS circa 2014, but players like Dell and HPE they have a distinct advantage with respect to their customer bases, their service organizations, their very large portfolios, especially in the case of Dell and the fact that they have more mature stacks and knowhow to run mission critical enterprise applications on-prem. So John's comment was quite interesting that these firms are basically building their own version of outposts. Outposts obviously came into their wheelhouse and now they've finally responded. And this is setting up cloud 3.0 or Supercloud, as we like to call it, an abstraction layer, that sits above the clouds that serves as a unifying experience across a continuum of on-prem across clouds, whether it's AWS, Azure, or Google. And out to both the near and far edge, near edge being a Lowes or a Home Depot, but far edge could be space. And that edge again is fragmented. You've got the examples like the retail stores at the near edge. Outer space maybe is the far edge and IOT devices is perhaps the tiny edge. No one really knows how the tiny edge is going to play out but it's pretty clear that it's not going to comprise traditional X86 systems with a cool name tossed out to the edge. Rather, it's likely going to require a new low cost, low power, high performance architecture, most likely RM based that will enable things like realtime AI inferencing at that edge. Now we've talked about this a lot on Breaking Analysis, so I'm not going to double click on it. But suffice to say that it's very possible that new innovations are going to emerge from the tiny edge that could really disrupt the enterprise in terms of price performance. Okay, two other quick observations. One is that data protection is becoming a much closer cohort to the security stack where data immutability and air gaps and fast recovery are increasingly becoming a fundamental component of the security strategy to combat ransomware and recover from other potential hacks or disasters. And I got to say from our observation, Veeam is leading the pack here. It's now claiming the number one revenue spot in a statistical dead heat with the Dell's data protection business. That's according to Veeam, according to IDC. And so that space continues to be of interest. And finally, Broadcom's acquisition of Dell. It's going to have ripple effects throughout the enterprise technology business. And there of course, there are a lot of questions that remain, but the one other thing that John Furrier and I were discussing last night John looked at me and said, "Dave imagine if VMware runs better on Broadcom components and OEMs that use Broadcom run VMware better, maybe Broadcom doesn't even have to raise prices on on VMware licenses. Maybe they'll just raise prices on the OEMs and let them raise prices to the end customer." Interesting thought, I think because Broadcom is so P&L focused that it's probably not going to be the prevailing model but we'll see what happens to some of the strategic projects rather like Monterey and Capitola and Thunder. We've talked a lot about project Monterey, the others we'll see if they can make the cut. That's one of the big concerns because it's how OEMs like the ones that are building their versions of outposts are going to compete with the cloud vendors, namely AWS in the future. I want to come back to the comment on the data stack for a moment that we were talking about earlier, we talked about how the big data ecosystem that was once coalescing around hadoop dispersed. Well, the data value chain is reforming and we think it looks something like this picture, where cloud infrastructure lives at the bottom. We've said many times the cloud is expanding and evolving. And if companies like Dell and HPE can truly build a super cloud infrastructure experience then they will be in a position to capture more of the data value. If not, then it's going to go to the cloud players. And there's a live data layer that is increasingly being converged into platforms that not only simplify the movement in ELTing of data but also allow organizations to compress the time to value. Now there's a layer above that, we sometimes call it the super PaaS layer if you will, that must comprise open source tooling, partners are going to write applications and leverage platform APIs and build data products and services that can be monetized at the top of the stack. So when you observe the battle for the data future it's unlikely that any one company is going to be able to do this all on their own, which is why I often joke that the 2020s version of a sweaty Steve Bomber running around the stage, screaming, developers, developers developers, and getting the whole audience into it is now about ecosystem ecosystem ecosystem. Because when you need to fill gaps and accelerate features and provide optionality a list of capabilities on the left hand side of this chart, that's going to come from a variety of different companies and places, we're talking about catalogs and AI tools and data science capabilities, data quality, governance tools and it should be of no surprise to followers of Breaking Analysis that on the right hand side of this chart we're including the four principles of data mesh, which of course were popularized by Zhamak Dehghani. So decentralized data ownership, data as products, self-serve platform and automated or computational governance. Now whether this vision becomes a reality via a proprietary platform like Snowflake or somehow is replicated by an open source remains to be seen but history generally shows that a defacto standard for more complex problems like this is often going to emerge prior to an open source alternative. And that would be where I would place my bets. Although even that proprietary platform has to include open source optionality. But it's not a winner take all market. It's plenty of room for multiple players and ecosystem innovators, but winner will definitely take more in my opinion. Okay, let's close with some ETR data that looks at some of those major platform plays who talk a lot about digital transformation and world changing impactful missions. And they have the resources really to compete. This is an XY graphic. It's a view that we often show, it's got net score on the vertical access. That's a measure of spending momentum, and overlap or presence in the ETR survey. That red, that's the horizontal access. The red dotted line at 40% indicates that the platform is among the highest in terms of spending velocity. Which is why I always point out how impressive that makes AWS and Azure because not only are they large on the horizontal axis, the spending momentum on those two platforms rivals even that of Snowflake which continues to lead all on the vertical access. Now, while Google has momentum, given its goals and resources, it's well behind the two leaders. We've added Service Now and Salesforce, two platform names that have become the next great software companies. Joining likes of Oracle, which we show here and SAP not shown along with IBM, you can see them on this chart. We've also plotted MongoDB, which we think has real momentum as a company generally but also with Atlas, it's managed cloud database as a service specifically and Red Hat with trying to become the standard for app dev in Kubernetes environments, which is the hottest trend right now in application development and application modernization. Everybody's doing something with Kubernetes and of course, Red Hat with OpenShift wants to make that a better experience than do it yourself. The DYI brings a lot more complexity. And finally, we've got HPE and Dell both of which we've talked about pretty extensively here and VMware and Cisco. Now Cisco is executing on its portfolio strategy. It's got a lot of diverse components to its company. And it's coming at the cloud of course from a networking and security perspective. And that's their position of strength. And VMware is a staple of the enterprise. Yes, there's some uncertainty with regards to the Broadcom acquisition, but one thing is clear vSphere isn't going anywhere. It's entrenched and will continue to run lots of IT for years to come because it's the best platform on the planet. Now, of course, these are just some of the players in the mix. We expect that numerous non-traditional technology companies this is important to emerge as new cloud players. We've put a lot of emphasis on the data ecosystem because to us that's really going to be the main spring of digital, i.e., a digital company is a data company and that means an ecosystem of data partners that can advance outcomes like better healthcare, faster drug discovery, less fraud, cleaner energy, autonomous vehicles that are safer, smarter, more efficient grids and factories, better government and virtually endless litany of societal improvements that can be addressed. And these companies will be building innovations on top of cloud platforms creating their own super clouds, if you will. And they'll come from non-traditional places, industries, finance that take their data, their software, their tooling bring them to their customers and run them on various clouds. Okay, that's it for today. Thanks to Alex Myerson, who is on production and does the podcast for Breaking Analysis, Kristin Martin and Cheryl Knight, they help get the word out. And Rob Hoofe is our editor and chief over at Silicon Angle who helps edit our posts. Remember all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me at dvellante, or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE's Insights powered by ETR. Thanks for watching be well. And we'll see you next time on Breaking Analysis. (upbeat music)

>>we'll go back to the cubes. Coverage of the age of US Executive Summit at Davis. Reinvent made possible by Accenture My name is Dave Volunteer. We're gonna talk about the arm nation advantage, embraced the future of productivity, improve speed quality and customer experience through artificial intelligence. And we herewith Bhaskar goes, Who's the chief strategy Officer X censure in Rajendra RP Prasad is the senior managing director in Global Automation. The Accenture guys walk into the Cube. Get to seal. >>Thank you. >>Hey, congratulations on the new book. I know it's like giving birth, but it's a mini version. If the well, the automation advantage embraced a future of productivity, improve speed, quality and customer experience to artificial intelligence. What inspired you to write this book? Can you tell us a little bit more about it and how businesses are going to be able to take advantage of the information that's in there? Maybe you could start, >>so I think you know, if we say that what inspired as primarily the two things really style, you know, over inspired have to start this project in first of all is the technology change step change in the technology. Second is the mile maturity of the buyer maturity of the market when it's a little more, you know, when I talk about the technology change, automation is nothing new in the industry. In the starting from the Industrial Revolution, always, industry adopted the automation. But last few years would happen. That there is a significant change in the technology in terms of not of new technologies are coming together like cloud data, artificial intelligence, machine learning and they are gearing match you, and that created a huge opportunity in the industry. So that is number one second if fighting the maturity of the buyer. So buyers are always buying automation, adopting the automation. So when I talked to this different by a different industrial wire, suddenly we realise they're not asking about workings automation, how that will help. But primarily they're talking about how they can scaling. They have all have done the pilot, the prototype, how they can take the full advantage in their enterprise through scheme and talking to few client few of our clients, and he realised that it's best to write this boat and film all our clients to take advantage of this new technologies to skill up their business. If I give a little more than inside that one, exactly we are trying to do in this boat primarily, we dealt with three things. One is the individual automation which deals with the human efficiency. Second is the industrial automation who visited a group efficiency. And third is the intelligent automation. We deal city business, official efficiency while business value. So we believe that this is what will really change their business and help our client help the automation. It users to really make clear an impact in their business. >>Yeah, And so you talked about that? The maturity of the customer. And and I like the way you should describe that spectrum ending with intelligent automation. So the point is you not just paving the cow path, if you will, automating processes that maybe were invented decades ago. You're really trying to rethink the best approach. And that's where you going to get the most business value, our peace In thinking about the maturity, I think the a pre pandemic people were maybe a little reluctant s Bhaskar was saying maybe needed some education. But But how? If things change me, obviously the penned Emmick has had a huge impact. It's accelerated things, but but what's changed in the business environment? In terms of the need to implement automation? R. P >>thank you Well, that is an excellent question. As even through the pandemic, most of the enterprises accelerated what I call as the digital transformation, technology transformation and the war all time that it takes to do. The transformation is compressed in our most land prices. Now do compress transformation. The core of it is innovation and innovation, led technology and technology based solutions. To drive this transformation automation. Artificial intelligence becomes hot of what we do while we are implementing this accelerators. Innovation enablers within the enterprises, most of the enterprises prior to the pandemic we're looking automation and I as a solution for cost efficiency. Saving cost in DePina deriving capacity efficiency does if they do the transformation when we press the fast forward but draw the transformation journey liberating automation. What happens is most of the enterprises which the focus from cost efficiency to speed to market application availability and system resiliency at the core. When I speaking to most of the sea woes Corrine Wall in the tech transformation they have now embrace automation and air as a Conan able to bribe this journeys towards, you know, growth, innovation, lead application, availability and transformation and sustainability of the applications through the are A book addresses all of these aspects, including the most important element of which is compute storeys and the enablement that it can accomplish through cloud transformation, cloud computing services and how I I and Michelle learning take log technologies can in a benefit from transformation to the block. In addition, we also heard person talk about automation in the cloud zero automation taking journey towards the cloud on automation Once you're in the clouds, water the philosophy and principles he should be following to drive the motivation. We also provide holy holistic approach to dry automation by focusing process technology that includes talent and change management and also addressing automation culture for the organisations in the way they work as they go forward. >>You mentioned a couple things computing, storage and when we look at our surveys, guys is it is interesting to see em, especially since the pandemic, four items have popped up where all the spending momentum is cloud province reasons scale and in resource and, you know, be able the report to remotely containers because a lot of people have work loads on Prem that they just can automatically move in the company, want to do development in the cloud and maybe connect to some of those on from work clothes. R P A. Which is underscores automation in, of course, and R. P. You mentioned a computing storage and, of course, the other pieces. Data's We have always data, but so my question is, how has the cloud and eight of us specifically influenced changes in automation? In a >>brilliant question and brilliant point, I say no winner. I talked to my clients. One of the things that I always says, Yeah, I I is nothing but y for the data that is the of the data. So that date of place underlying a very critical part of applying intelligence, artificial intelligence and I in the organization's right as the organisation move along their automation journey. Like you said, promoting process automation to contain a realisation to establishing data, building the data cubes and managing the massive data leveraging cloud and how Yebda please can help in a significant way to help the data stratification Dana Enablement data analysis and not data clustering classification All aspects of the what we need to do within the between the data space that helps for the Lord scale automation effort, the cloud and and ablest place a significant role to help accelerate and enable the data part. Once you do that, building mission learning models on the top of it liberating containers clusters develops techniques to drive, you know the principles on the top of it is very makes it easier to drive that on foster enablement advancement through cloud technologists. Alternatively, using automation itself to come enable the cloud transformation data transformation data migration aspects to manage the complexity, speed and scale is very important. The book stresses the very importance of fuelling the motion of the entire organisation to agility, embracing new development methods like automation in the cloud develops Davis a cop's and the importance of oral cloud adoptions that bills the foundational elements of, you know, making sure you're automation and air capabilities are established in a way that it is scalable and sustainable within the organisations as they move forward, >>Right? Thank you for that r p vast crime want to come back to this notion of maturity and and just quite automation. So Andy Jossy made the phrase undifferentiated, heavy lifting popular. But that was largely last decade. Apply to it. And now we're talking about deeper business integration. And so you know, automation certainly is solves the problem of Okay, I can take Monday and cast like provisioning storage in compute and automate that great. But what is some of the business problems, that deeper business integration that we're solving through things? And I want to use the phrase they used earlier intelligent automation? What is that? Can you give an example? >>Let's a very good question as we said, that the automation is a journey, you know, if we talk to any blind, so everybody wants to use data and artificial intelligence to transform their business, so that is very simple. But the point is that you cannot reach their anti unless you follow the steps. So in our book, we have explained that the process that means you know, we defined in a five steps. We said that everybody has to follow the foundation, which is primarily tools driven optimise, which is process drivel. An official see improvement, which is primarily are driven. Then comes predictive capability, the organisation, which is data driven, and then intelligence, which is primarily artificial intelligence driven. Now, when I talked about the use of artificial intelligence and this new intelligent in the business, what the what I mean is basically improved decision making in every level in the organisation and give the example. We have given multiple example in this, both in a very simple example, if I take suppose, a financial secretary organisation, they're selling wealth management product to the client, so they have a number of management product, and they have number of their number of clients a different profile. But now what is happening? This artificial intelligence is helping their agents to target the night product for the night customers. So then, at the success rate is very high. So that is a change that is a change in the way they do business. Now some of the platform companies like Amazon on Netflix. He will see that this this killed is a very native skill for them. They used the artificial intelligence try to use everywhere, but there a lot of other companies who are trying to adopt this killed today. Their fundamental problem is they do not have the right data. They do not have the capability. They do not have all the processes so that they can inject the decision making artificial intelligence capability in every decision making to empower their workforce. And that is what we have written in this book. To provide the guidance to this in this book. How they can use the better business decision improved the create, the more business value using artificial intelligence and intelligent automation. >>Interesting. Bhaskar are gonna stay with you, you know, in their book in the middle of last decade, Erik Brynjolfsson and Andy McAfee wrote the second Machine Age, and they made a point in the book that machines have always replaced humans in instead of various tasks. But for the first time ever, we're seeing machines replacing human in cognitive task that scares a lot of people so hardy you inspire employees to embrace the change that automation can bring. What what are you seeing is the best ways to do that? >>This is a very good question. The intelligent automation implementation is not, Iet Project is primarily change management. It's primarily change in the culture, the people in the organisation into embrace this change and how they will get empowered with the machine. It is not about the replacing people by machine, which has happened historically into the earlier stages of automation, which I explained. But in this intelligent automation, it is basically empowering people to do the better. Dwelled the example. That is the thing we have written in the book about about a newspaper, 100 years old newspaper in Italy. And you know, this industry has gone through multiple automation and changes black and white printing, printing to digital. Everything happened. And now what is happening? They're using artificial intelligence, so they're writers are using those technologies to write faster. So when they are writing immediately, they're getting supported with the later they're supporting with the related article they are supporting with this script, even they're supported to the heading of this article. So the question is that it is not replacing the news, you know, the content writer, but is basically empowering them so that they can produce the better quality of product they can, better writing in a faster time. So is very different approach and that is why is, um, needs a change management and it's a cultural change. >>Garden R P What's it for me? Why should we read the automation advantage? Maybe you can talk about some of the key takeaways and, you know, maybe the best places to start on an automation journey. >>Very will cut the fastest MP, Newer automation journey and Claude Adoption Journey is to start simple and start right if you know what's have free one of the process, Guru says, If you don't know where you are on a map, a map won't help you, so to start right, a company needs to know where they are on a map today, identify the right focus areas, create a clear roadmap and then move forward with the structured approach for successful our option. The other important element is if you automate an inefficient process, we are going to make your inefficiency run more efficiently. So it is very important to baseline, and then I established the baseline and know very or on the journey map. This is one of the key teams we discuss in the Automation Advantis book, with principles and tips and real world examples on how to approach each of these stages. We also stress the importance of building the right architecture is for intelligent automation, cloud enablement, security at the core of automation and the platform centric approach. Leading enterprises can fade out adopters and Iraq, whether they are in the early stages of the automation, journey or surrender advanced stage the formation journey. They can look at the automation advantage book and build and take the best practises and and what is provided as a practical tips within the book to drive there. Automation journey. This also includes importance of having right partners in the cloud space, like a loveliest who can accelerate automation, journey and making sure accompanies cloud migration. Strategy includes automation, automation, lead, yea and data as part of their journey. Management. >>That's great. Good advice there. Bring us home. Maybe you can wrap it up with the final final world. >>So, lefty, keep it very simple. This book will help you to create difference in your business with the power of automation and artificial intelligence. >>That's a simple message and will governor what industry you're in? There is a disruptions scenario for your industry and that disruption scenarios going to involve automation, so you better get ahead of editor game. They're The book is available, of course, at amazon dot com. You can get more information. X censure dot com slash automation advantage. Gosh, thanks so much for coming in the Cube. Really appreciate your time. >>Thank you. Thank >>you. >>Eh? Thank you for watching this episode of the eight of US Executive Summit of reinvent made possible by Accenture. Keep it right there for more discussions that educating spy inspire You're watching the queue.

>>Hello. And we're here at the cube startup showcase made possible by a Ws. Thanks so much for joining us today. You know when Jim McDaid Ghani was formulating her ideas around data mesh, She wasn't the only one thinking about decentralized data architecture. Hello, Fresh was going into hyper growth mode and realized that in order to support its scale, it needed to rethink how it thought about data. Like many companies that started in the early part of last decade, Hello Fresh relied on a monolithic data architecture and the internal team. It had concerns about its ability to support continued innovation at high velocity. The company's data team began to think about the future and work backwards from a target architecture which possessed many principles of so called data mesh even though they didn't use that term. Specifically, the company is a strong example of an early but practical pioneer of data mission. Now there are many practitioners and stakeholders involved in evolving the company's data architecture, many of whom are listed here on this on the slide to are highlighted in red are joining us today, we're really excited to welcome into the cube Clements cheese, the Global Senior Director for Data at Hello Fresh and christoph Nevada who's the Global Senior Director of data also, of course. Hello Fresh folks. Welcome. Thanks so much for making some time today and sharing your story. >>Thank you very much. Hey >>steve. All right, let's start with Hello Fresh. You guys are number one in the world in your field, you deliver hundreds of millions of meals each year to many, many millions of people around the globe. You're scaling christoph. Tell us a little bit more about your company and its vision. >>Yeah. Should I start or Clements maybe maybe take over the first piece because Clements has actually been a longer trajectory yet have a fresh. >>Yeah go ahead. Climate change. I mean yes about approximately six years ago I joined handle fresh and I didn't think about the startup I was joining would eventually I. P. O. And just two years later and the freshman public and approximately three years and 10 months after. Hello fresh was listed on the German stock exchange which was just last week. Hello Fresh was included in the Ducks Germany's leading stock market index and debt to mind a great great milestone and I'm really looking forward and I'm very excited for the future for the future for head of fashion. All our data. Um the vision that we have is to become the world's leading food solution group and there's a lot of attractive opportunities. So recently we did lounge and expand Norway. This was in july and earlier this year we launched the U. S. Brand green >>chef in the U. K. As >>well. We're committed to launch continuously different geographies in the next coming years and have a strong pipe ahead of us with the acquisition of ready to eat companies like factor in the U. S. And the planned acquisition of you foods in Australia. We're diversifying our offer now reaching even more and more untapped customer segments and increase our total addressable market. So by offering customers and growing range of different alternatives to shop food and consumer meals. We are charging towards this vision and the school to become the world's leading integrated food solutions group. >>Love it. You guys are on a rocket ship, you're really transforming the industry and as you expand your tam it brings us to sort of the data as a as a core part of that strategy. So maybe you guys could talk a little bit about your journey as a company specifically as it relates to your data journey. You began as a start up. You had a basic architecture like everyone. You made extensive use of spreadsheets. You built a Hadoop based system that started to grow and when the company I. P. O. You really started to explode. So maybe describe that journey from a data perspective. >>Yes they saw Hello fresh by 2015 approximately had evolved what amount of classical centralized management set up. So we grew very organically over the years and there were a lot of very smart people around the globe. Really building the company and building our infrastructure. Um This also means that there were a small number of internal and external sources. Data sources and a centralized the I team with a number of people producing different reports, different dashboards and products for our executives for example of our different operations teams, christian company's performance and knowledge was transferred um just via talking to each other face to face conversations and the people in the data where's team were considered as the data wizard or as the E. T. L. Wizard. Very classical challenges. And those et al. Reserves indicated the kind of like a silent knowledge of data management. Right? Um so a central data whereas team then was responsible for different type of verticals and different domains, different geographies and all this setup gave us to the beginning the flexibility to grow fast as a company in 2015 >>christoph anything that might add to that. >>Yes. Um Not expected to that one but as as clement says it right, this was kind of set up that actually work for us quite a while. And then in 2017 when L. A. Freshman public, the company also grew rapidly and just to give you an idea how that looked like. As was that the tech department self actually increased from about 40 people to almost 300 engineers And the same way as a business units as Clemens has described, also grew sustainable, sustainably. So we continue to launch hello fresh and new countries launching brands like every plate and also acquired other brands like much of a factor and with that grows also from a data perspective the number of data requests that centrally we're getting become more and more and more and also more and more complex. So that for the team meant that they had a fairly high mental load. So they had to achieve a very or basically get a very deep understanding about the business. And also suffered a lot from this context switching back and forth, essentially there to prioritize across our product request from our physical product, digital product from the physical from sorry, from the marketing perspective and also from the central reporting uh teams. And in a nutshell this was very hard for these people. And this that also to a situation that, let's say the solution that we have became not really optimal. So in a nutshell, the central function became a bottleneck and slowdown of all the innovation of the company. >>It's a classic case, isn't it? I mean Clements, you see you see the central team becomes a bottleneck and so the lines of business, the marketing team salesman's okay, we're going to take things into our own hands. And then of course I I. T. And the technical team is called in later to clean up the mess. Uh maybe, I mean was that maybe I'm overstating it, but that's a common situation, isn't it? >>Yeah. Uh This is what exactly happened. Right. So um we had a bottleneck, we have the central teams, there was always a little of tension um analytics teams then started in this business domains like marketing, trade chain, finance, HR and so on. Started really to build their own data solutions at some point you have to get the ball rolling right and then continue the trajectory um which means then that the data pipelines didn't meet the engineering standards. And um there was an increased need for maintenance and support from central teams. Hence over time the knowledge about those pipelines and how to maintain a particular uh infrastructure for example left the company such that most of those data assets and data sets are turned into a huge step with decreasing data quality um also decrease the lack of trust, decreasing transparency. And this was increasing challenge where majority of time was spent in meeting rooms to align on on data quality for example. >>Yeah. And and the point you were making christoph about context switching and this is this is a point that Jemaah makes quite often is we've we've we've contextualized are operational systems like our sales systems, our marketing system but not our our data system. So you're asking the data team, Okay. Be an expert in sales, be an expert in marketing, be an expert in logistics, be an expert in supply chain and it start stop, start, stop, it's a paper cut environment and it's just not as productive. But but on the flip side of that is when you think about a centralized organization you think, hey this is going to be a very efficient way, a cross functional team to support the organization but it's not necessarily the highest velocity, most effective organizational structure. >>Yeah, so so I agree with that. Is that up to a certain scale, a centralized function has a lot of advantages, right? That's clear for everyone which would go to some kind of expert team. However, if you see that you actually would like to accelerate that and specific and this hyper growth, right, you wanna actually have autonomy and certain teams and move the teams or let's say the data to the experts in these teams and this, as you have mentioned, right, that increases mental load and you can either internally start splitting your team into a different kind of sub teams focusing on different areas. However, that is then again, just adding another peace where actually collaboration needs to happen busy external sees, so why not bridging that gap immediately and actually move these teams and to end into into the function themselves. So maybe just to continue what, what was Clements was saying and this is actually where over. So Clements, my journey started to become one joint journey. So Clements was coming actually from one of these teams to build their own solutions. I was basically having the platform team called database housed in these days and in 2019 where basically the situation become more and more serious, I would say so more and more people have recognized that this model doesn't really scale In 2019, basically the leadership of the company came together and I identified data as a key strategic asset and what we mean by that, that if we leverage data in a proper way, it gives us a unique competitive advantage which could help us to, to support and actually fully automated our decision making process across the entire value chain. So what we're, what we're trying to do now or what we should be aiming for is that Hello, Fresh is able to build data products that have a purpose. We're moving away from the idea. Data is just a by problem products, we have a purpose why we would like to collect this data. There's a clear business need behind that. And because it's so important to for the company as a business, we also want to provide them as a trust versi asset to the rest of the organization. We say there's the best customer experience, but at least in a way that users can easily discover, understand and security access high quality data. >>Yeah, so and and and Clements, when you c J Maxx writing, you see, you know, she has the four pillars and and the principles as practitioners you look at that say, okay, hey, that's pretty good thinking and then now we have to apply it and that's and that's where the devil meets the details. So it's the four, you know, the decentralized data ownership data as a product, which we'll talk about a little bit self serve, which you guys have spent a lot of time on inclement your wheelhouse which is which is governance and a Federated governance model. And it's almost like if you if you achieve the first two then you have to solve for the second to it almost creates a new challenges but maybe you could talk about that a little bit as to how it relates to Hello fresh. >>Yes. So christophe mentioned that we identified economic challenge beforehand and for how can we actually decentralized and actually empower the different colleagues of ours. This was more a we realized that it was more an organizational or a cultural change and this is something that somebody also mentioned I think thought words mentioned one of the white papers, it's more of a organizational or cultural impact and we kicked off a um faced reorganization or different phases we're currently and um in the middle of still but we kicked off different phases of organizational reconstruct oring reorganization, try unlock this data at scale. And the idea was really moving away from um ever growing complex matrix organizations or matrix setups and split between two different things. One is the value creation. So basically when people ask the question, what can we actually do, what shall we do? This is value creation and how, which is capability building and both are equal in authority. This actually then creates a high urge and collaboration and this collaboration breaks up the different silos that were built and of course this also includes different needs of stuffing forward teams stuffing with more, let's say data scientists or data engineers, data professionals into those business domains and hence also more capability building. Um Okay, >>go ahead. Sorry. >>So back to Tzemach did johnny. So we the idea also Then crossed over when she published her papers in May 2019 and we thought well The four colors that she described um we're around decentralized data ownership, product data as a product mindset, we have a self service infrastructure and as you mentioned, Federated confidential governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then leads to a not only organisational restructure but also in completely new approach of how we need to manage data, show data. >>Got it. Okay, so your business is is exploding. Your data team will have to become domain experts in too many areas, constantly contact switching as we said, people started to take things into their own hands. So again we said classic story but but you didn't let it get out of control and that's important. So we actually have a picture of kind of where you're going today and it's evolved into this Pat, if you could bring up the picture with the the elephant here we go. So I would talk a little bit about the architecture, doesn't show it here, the spreadsheet era but christoph maybe you can talk about that. It does show the Hadoop monolith which exists today. I think that's in a managed managed hosting service, but but you you preserve that piece of it, but if I understand it correctly, everything is evolving to the cloud, I think you're running a lot of this or all of it in A W. S. Uh you've got everybody's got their own data sources, uh you've got a data hub which I think is enabled by a master catalog for discovery and all this underlying technical infrastructure. That is really not the focus of this conversation today. But the key here, if I understand it correctly is these domains are autonomous and not only that this required technical thinking, but really supportive organizational mindset, which we're gonna talk about today. But christoph maybe you could address, you know, at a high level some of the architectural evolution that you guys went through. >>Yeah, sure. Yeah, maybe it's also a good summary about the entire history. So as you have mentioned, right, we started in the very beginning with the model is on the operation of playing right? Actually, it wasn't just one model is both to one for the back end and one for the for the front and and or analytical plane was essentially a couple of spreadsheets and I think there's nothing wrong with spreadsheets, right, allows you to store information, it allows you to transform data allows you to share this information. It allows you to visualize this data, but all the kind of that's not actually separating concern right? Everything in one tool. And this means that obviously not scalable, right? You reach the point where this kind of management set up in or data management of isn't one tool reached elements. So what we have started is we've created our data lake as we have seen here on Youtube. And this at the very beginning actually reflected very much our operational populace on top of that. We used impala is a data warehouse, but there was not really a distinction between borders, our data warehouse and borders our data like the impala was used as a kind of those as the kind of engine to create a warehouse and data like construct itself and this organic growth actually led to a situation as I think it's it's clear now that we had to centralized model is for all the domains that will really lose kimball modeling standards. There was no uniformity used actually build in house uh ways of building materialized use abuse that we have used for the presentation layer, there was a lot of duplication of effort and in the end essentially they were missing feedbacks, food, which helped us to to improve of what we are filled. So in the end, in the natural, as we have said, the lack of trust and that's basically what the starting point for us to understand. Okay, how can we move away and there are a lot of different things that you can discuss of apart from this organizational structure that we have said, okay, we have these three or four pillars from from Denmark. However, there's also the next extra question around how do we implement our talking about actual right, what are the implications on that level? And I think that is there's something that we are that we are currently still in progress. >>Got it. Okay, so I wonder if we could talk about switch gears a little bit and talk about the organizational and cultural challenges that you faced. What were those conversations like? Uh let's dig into that a little bit. I want to get into governance as well. >>The conversations on the cultural change. I mean yes, we went through a hyper growth for the last year since obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company which then results that collaboration uh >>got a bit more difficult. Of course >>there are times and changes, you have different different artifacts that you were created um and documentation that were flying around. Um so we were we had to build the company from scratch right? Um Of course this then resulted always this tension which I described before, but the most important part here is that data has always been a very important factor at l a fresh and we collected >>more of this >>data and continued to improve use data to improve the different key areas of our business. >>Um even >>when organizational struggles, the central organizational struggles data somehow always helped us to go through this this kind of change. Right? Um in the end those decentralized teams in our local geography ease started with solutions that serve the business which was very very important otherwise wouldn't be at the place where we are today but they did by all late best practices and standards and I always used sport analogy Dave So like any sport, there are different rules and regulations that need to be followed. These rules are defined by calling the sports association and this is what you can think about data governance and compliance team. Now we add the players to it who need to follow those rules and bite by them. This is what we then called data management. Now we have the different players and professionals, they need to be trained and understand the strategy and it rules before they can play. And this is what I then called data literacy. So we realized that we need to focus on helping our teams to develop those capabilities and teach the standards for how work is being done to truly drive functional excellence in a different domains. And one of our mission of our data literacy program for example is to really empower >>every employee at hello >>fresh everyone to make the right data informs decisions by providing data education that scaled by royal Entry team. Then this can be different things, different things like including data capabilities, um, with the learning paths for example. Right? So help them to create and deploy data products connecting data producers and data consumers and create a common sense and more understanding of each other's dependencies, which is important, for example, S. S. L. O. State of contracts and etcetera. Um, people getting more of a sense of ownership and responsibility. Of course, we have to define what it means, what does ownership means? But the responsibility means. But we're teaching this to our colleagues via individual learning patterns and help them up skill to use. Also, there's shared infrastructure and those self self service applications and overall to summarize, we're still in this progress of of, of learning, we are still learning as well. So learning never stops the tele fish, but we are really trying this um, to make it as much fun as possible. And in the end we all know user behavior has changed through positive experience. Uh, so instead of having massive training programs over endless courses of workshops, um, leaving our new journalists and colleagues confused and overwhelmed. >>We're applying um, >>game ification, right? So split different levels of certification where our colleagues can access, have had access points, they can earn badges along the way, which then simplifies the process of learning and engagement of the users and this is what we see in surveys, for example, where our employees that your justification approach a lot and are even competing to collect Those learning path batteries to become the # one on the leader board. >>I love the game ification, we've seen it work so well and so many different industries, not the least of which is crypto so you've identified some of the process gaps uh that you, you saw it is gloss over them. Sometimes I say paved the cow path. You didn't try to force, in other words, a new architecture into the legacy processes. You really have to rethink your approach to data management. So what what did that entail? >>Um, to rethink the way of data management. 100%. So if I take the example of Revolution, Industrial Revolution or classical supply chain revolution, but just imagine that you have been riding a horse, for example, your whole life and suddenly you can operate a car or you suddenly receive just a complete new way of transporting assets from A to B. Um, so we needed to establish a new set of cross functional business processes to run faster, dry faster, um, more robustly and deliver data products which can be trusted and used by downstream processes and systems. Hence we had a subset of new standards and new procedures that would fall into the internal data governance and compliance sector with internal, I'm always referring to the data operations around new things like data catalog, how to identify >>ownership, >>how to change ownership, how to certify data assets, everything around classical software development, which we know apply to data. This this is similar to a new thinking, right? Um deployment, versioning, QA all the different things, ingestion policies, policing procedures, all the things that suffer. Development has been doing. We do it now with data as well. And in simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes and as a creation as management and as a consumption. >>So data has become kind of the new development kit. If you will um I want to shift gears and talk about the notion of data product and, and we have a slide uh that we pulled from your deck and I'd like to unpack it a little bit. Uh I'll just, if you can bring that up, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems where customers, both internal and external. So pretty straightforward. I know you've gone much deeper and you're thinking and into your organization, but how do you think about that And how do you determine for instance who owns what? How did you get everybody to agree? >>I can take that one. Um, maybe let me start with the data product. So I think um that's an ongoing debate. Right? And I think the debate itself is an important piece here, right? That visit the debate, you clarify what we actually mean by that product and what is actually the mindset. So I think just from a definition perspective, right? I think we find the common denominator that we say okay that our product is something which is important for the company has come to its value what you mean by that. Okay, it's it's a solution to a customer problem that delivers ideally maximum value to the business. And yes, it leverages the power of data and we have a couple of examples but it had a fresh year, the historical and classical ones around dashboards for example, to monitor or error rates but also more sophisticated ways for example to incorporate machine learning algorithms in our recipe recommendations. However, I think the important aspects of the data product is a there is an owner, right? There's someone accountable for making sure that the product that we are providing is actually served and is maintained and there are, there is someone who is making sure that this actually keeps the value of that problem thing combined with the idea of the proper documentation, like a product description, right that people understand how to use their bodies is about and related to that peace is the idea of it is a purpose. Right? You need to understand or ask ourselves, Okay, why does this thing exist does it provide the value that you think it does. That leads into a good understanding about the life cycle of the data product and life cycle what we mean? Okay from the beginning from the creation you need to have a good understanding, we need to collect feedback, we need to learn about that. We need to rework and actually finally also to think about okay benefits time to decommission piece. So overall, I think the core of the data product is product thinking 11 right that we start the point is the starting point needs to be the problem and not the solution and this is essentially what we have seen what was missing but brought us to this kind of data spaghetti that we have built there in in Russia, essentially we built at certain data assets, develop in isolation and continuously patch the solution just to fulfill these articles that we got and actually these aren't really understanding of the stakeholder needs and the interesting piece as a result in duplication of work and this is not just frustrating and probably not the most efficient way how the company should work. But also if I build the same that assets but slightly different assumption across the company and multiple teams that leads to data inconsistency and imagine the following too narrow you as a management for management perspective, you're asking basically a specific question and you get essentially from a couple of different teams, different kind of grass, different kind of data and numbers and in the end you do not know which ones to trust. So there's actually much more ambiguity and you do not know actually is a noise for times of observing or is it just actually is there actually a signal that I'm looking for? And the same is if I'm running in a B test right, I have a new future, I would like to understand what has it been the business impact of this feature. I run that specific source in an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you've seen in a B test is actually not what you see then in production typical thing then is you're asking some analytics tend to actually do a deep dive to understand where the discrepancies are coming from. The worst case scenario. Again, there's a different kind of source. So in the end it's a pretty frustrating scenario and that's actually based of time of people that have to identify the root cause of this divergence. So in a nutshell, the highest degree of consistency is actually achieved that people are just reusing Dallas assets and also in the media talk that we have given right, we we start trying to establish this approach for a B testing. So we have a team but just providing or is kind of owning their target metric associated business teams and they're providing that as a product also to other services including the A B testing team, they'll be testing team can use this information defines an interface is okay I'm joining this information that the metadata of an experiment and in the end after the assignment after this data collection face, they can easily add a graph to the dashboard. Just group by the >>Beatles Hungarian. >>And we have seen that also in other companies. So it's not just a nice dream that we have right. I have actually worked in other companies where we worked on search and we established a complete KPI pipeline that was computing all this information. And this information was hosted by the team and it was used for everything A B test and deep dives and and regular reporting. So uh just one of the second the important piece now, why I'm coming back to that is that requires that we are treating this data as a product right? If you want to have multiple people using the things that I am owning and building, we have to provide this as a trust mercy asset and in a way that it's easy for people to discover and actually work with. >>Yeah. And coming back to that. So this is to me this is why I get so excited about data mesh because I really do think it's the right direction for organizations. When people hear data product they say well, what does that mean? Uh but then when you start to sort of define it as you did, it's it's using data to add value, that could be cutting costs, that could be generating revenue, it could be actually directly you're creating a product that you monetize, So it's sort of in the eyes of the beholder. But I think the other point that we've made is you made it earlier on to and again, context. So when you have a centralized data team and you have all these P NL managers a lot of times they'll question the data because they don't own it. They're like wait a minute. If they don't, if it doesn't agree with their agenda, they'll attack the data. But if they own the data then they're responsible for defending that and that is a mindset change, that's really important. Um And I'm curious uh is how you got to, you know, that ownership? Was it a was it a top down with somebody providing leadership? Was it more organic bottom up? Was it a sort of a combination? How do you decide who owned what in other words, you know, did you get, how did you get the business to take ownership of the data and what is owning? You know, the data actually mean? >>That's a very good question. Dave I think this is one of the pieces where I think we have a lot of learnings and basically if you ask me how we could start the feeling. I think that would be the first piece. Maybe we need to start to really think about how that should be approached if it stopped his ownership. Right? It means somehow that the team has a responsibility to host and self the data efforts to minimum acceptable standards. This minimum dependencies up and down string. The interesting piece has been looking backwards. What what's happening is that under that definition has actually process that we have to go through is not actually transferring ownership from the central team to the distributor teams. But actually most cases to establish ownership, I make this difference because saying we have to transfer ownership actually would erroneously suggests that the data set was owned before. But this platform team, yes, they had the capability to make the changes on data pipelines, but actually the analytics team, they're always the ones who had the business understands, you use cases and but no one actually, but it's actually expensive expected. So we had to go through this very lengthy process and establishing ownership. We have done that, as in the beginning, very naively. They have started, here's a document here, all the data assets, what is probably the nearest neighbor who can actually take care of that and then we we moved it over. But the problem here is that all these things is kind of technical debt, right? It's not really properly documented, pretty unstable. It was built in a very inconsistent over years and these people who have built this thing have already left the company. So there's actually not a nice thing that is that you want to see and people build up a certain resistance, e even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, but what needs to happen as first, the company needs to really understand what our core business concept that they have, they need to have this mapping from. These are the core business concept that we have. These are the domain teams who are owning this concept and then actually link that to the to the assets and integrated better with both understanding how we can evolve actually, the data assets and new data build things new in the in this piece in the domain. But also how can we address reduction of technical death and stabilizing what we have already. >>Thank you for that christoph. So I want to turn a direction here and talk about governance and I know that's an area that's passionate, you're passionate about. Uh I pulled this slide from your deck, which I kind of messed up a little bit sorry for that, but but by the way, we're going to publish a link to the full video that you guys did. So we'll share that with folks. But it's one of the most challenging aspects of data mesh, if you're going to decentralize you, you quickly realize this could be the Wild West as we talked about all over again. So how are you approaching governance? There's a lot of items on this slide that are, you know, underscore the complexity, whether it's privacy, compliance etcetera. So, so how did you approach this? >>It's yeah, it's about connecting those dots. Right. So the aim of the data governance program is about the autonomy of every team was still ensuring that everybody has the right interoperability. So when we want to move from the Wild West riding horses to a civilised way of transport, um you can take the example of modern street traffic, like when all participants can manoeuvre independently and as long as they follow the same rules and standards, everybody can remain compatible with each other and understand and learn from each other so we can avoid car crashes. So when I go from country to country, I do understand what the street infrastructure means. How do I drive my car? I can also read the traffic lights in the different signals. Um, so likewise as a business and Hello Fresh, we do operate autonomously and consequently need to follow those external and internal rules and standards to set forth by the redistribution in which we operate so in order to prevent a car crash, we need to at least ensure compliance with regulations to account for society's and our customers increasing concern with data protection and privacy. So teaching and advocating this advantage, realizing this to everyone in the company um was a key community communication strategy and of course, I mean I mentioned data privacy external factors, the same goes for internal regulations and processes to help our colleagues to adapt to this very new environment. So when I mentioned before the new way of thinking the new way of um dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. Um in a nutshell then this means the data governance provides a framework for managing our people the processes and technology and culture around our data traffic. And those components must come together in order to have this effective program providing at least a common denominator, especially critical for shared dataset, which we have across our different geographies managed and shared applications on shared infrastructure and applications and is then consumed by centralized processes um for example, master data, everything and all the metrics and KPI s which are also used for a central steering. Um it's a big change day. Right. And our ultimate goal is to have this noninvasive, Federated um ultimatum and computational governance and for that we can't just talk about it. We actually have to go deep and use case by use case and Qc buy PVC and generate learnings and learnings with the different teams. And this would be a classical approach of identifying the target structure, the target status, match it with the current status by identifying together with the business teams with the different domains have a risk assessment for example, to increase transparency because a lot of teams, they might not even know what kind of situation they might be. And this is where this training and this piece of illiteracy comes into place where we go in and trade based on the findings based on the most valuable use case um and based on that help our teams to do this change to increase um their capability just a little bit more and once they hand holding. But a lot of guidance >>can I kind of kind of trying to quickly David will allow me I mean there's there's a lot of governance piece but I think um that is important. And if you're talking about documentation for example, yes, we can go from team to team and tell these people how you have to document your data and data catalog or you have to establish data contracts and so on the force. But if you would like to build data products at scale following actual governance, we need to think about automation right. We need to think about a lot of things that we can learn from engineering before. And that starts with simple things like if we would like to build up trust in our data products, right, and actually want to apply the same rigor and the best practices that we know from engineering. There are things that we can do and we should probably think about what we can copy and one example might be. So the level of service level agreements, service level objectives. So that level indicators right, that represent on on an engineering level, right? If we're providing services there representing the promises we made to our customers or consumers, these are the internal objectives that help us to keep those promises. And actually these are the way of how we are tracking ourselves, how we are doing. And this is just one example of that thing. The Federated Governor governance comes into play right. In an ideal world, we should not just talk about data as a product but also data product. That's code that we say, okay, as most as much as possible. Right? Give the engineers the tool that they are familiar basis and actually not ask the product managers for example to document their data assets in the data catalog but make it part of the configuration. Have this as a, as a C D C I, a continuous delivery pipeline as we typically see another engineering task through and services we say, okay, there is configuration, we can think about pr I can think about data quality monitoring, we can think about um the ingestion data catalog and so on and forest, I think ideally in the data product will become of a certain templates that can be deployed and are actually rejected or verified at build time before we actually make them deploy them to production. >>Yeah, So it's like devoPS for data product um so I'm envisioning almost a three phase approach to governance and you kind of, it sounds like you're in early phases called phase zero where there's there's learning, there's literacy, there's training, education, there's kind of self governance and then there's some kind of oversight, some a lot of manual stuff going on and then you you're trying to process builders at this phase and then you codify it and then you can automate it. Is that fair? >>Yeah, I would rather think think about automation as early as possible in the way and yes, there needs to be certain rules but then actually start actually use case by use case. Is there anything that small piece that we can already automate? It's as possible. Roll that out and then actually extended step by step, >>is there a role though that adjudicates that? Is there a central Chief state officer who is responsible for making sure people are complying or is it how do you handle that? >>I mean from a from a from a platform perspective, yes, we have a centralized team to uh implement certain pieces they'll be saying are important and actually would like to implement. However, that is actually working very closely with the governance department. So it's Clements piece to understand and defy the policies that needs to be implemented. >>So Clements essentially it's it's your responsibility to make sure that the policy is being followed. And then as you were saying, christoph trying to compress the time to automation as fast as possible percent. >>So >>it's really it's uh >>what needs to be really clear that it's always a split effort, Right? So you can't just do one thing or the other thing, but everything really goes hand in hand because for the right automation for the right engineering tooling, we need to have the transparency first. Uh I mean code needs to be coded so we kind of need to operate on the same level with the right understanding. So there's actually two things that are important which is one its policies and guidelines, but not only that because more importantly or even well equally important to align with the end user and tech teams and engineering and really bridge between business value business teams and the engineering teams. >>Got it. So just a couple more questions because we gotta wrap I want to talk a little bit about the business outcome. I know it's hard to quantify and I'll talk about that in a moment but but major learnings, we've got some of the challenges that you cited. I'll just put them up here. We don't have to go detailed into this, but I just wanted to share with some folks. But my question, I mean this is the advice for your peers question if you had to do it differently if you had a do over or a Mulligan as we like to say for you golfers, what would you do differently? Yeah, >>I mean can we start with from a from the transformational challenge that understanding that it's also high load of cultural change. I think this is this is important that a particular communication strategy needs to be put into place and people really need to be um supported. Right? So it's not that we go in and say well we have to change towards data mesh but naturally it's in human nature, you know, we're kind of resistance to to change right? Her speech uncomfortable. So we need to take that away by training and by communicating um chris we're gonna add something to that >>and definitely I think the point that I have also made before right we need to acknowledge that data mesh is an architecture of scale, right? You're looking for something which is necessary by huge companies who are vulnerable, data productive scale. I mean Dave you mentioned it right, there are a lot of advantages to have a centralized team but at some point it may make sense to actually decentralized here and at this point right? If you think about data Mash, you have to recognize that you're not building something on a green field. And I think there's a big learning which is also reflected here on the slide is don't underestimate your baggage. It's typically you come to a point where the old model doesn't doesn't broke anymore and has had a fresh right? We lost our trust in our data and actually we have seen certain risks that we're slowing down our innovation so we triggered that this was triggering the need to actually change something. So this transition implies that you typically have a lot of technical debt accumulated over years and I think what we have learned is that potentially we have decentralized some assets to earlier, this is not actually taking into account the maturity of the team where we are actually distributed to and now we actually in the face of correcting pieces of that one. Right? But I think if you if you if you start from scratch you have to understand, okay, is are my team is actually ready for taking on this new uh, this news capabilities and you have to make sure that business decentralization, you build up these >>capabilities and the >>teams and as Clements has mentioned, right, make sure that you take the people on your journey. I think these are the pieces that also here, it comes with this knowledge gap, right? That we need to think about hiring and literacy the technical depth I just talked about and I think the last piece that I would add now which is not here on the flight deck is also from our perspective, we started on the analytical layer because that's kind of where things are exploding, right, this is the thing that people feel the pain but I think a lot of the efforts that we have started to actually modernize the current state uh, towards data product towards data Mash. We've understood that it always comes down basically to a proper shape of our operational plane and I think what needs to happen is is I think we got through a lot of pains but the learning here is this need to really be a commitment from the company that needs to happen and to act. >>I think that point that last point you made it so critical because I I hear a lot from the vendor community about how they're gonna make analytics better and that's that's not unimportant, but but through data product thinking and decentralized data organizations really have to operationalize in order to scale. So these decisions around data architecture an organization, their fundamental and lasting, it's not necessarily about an individual project are why they're gonna be project sub projects within this architecture. But the architectural decision itself is an organizational, its cultural and what's the best approach to support your business at scale. It really speaks to to to what you are, who you are as a company, how you operate and getting that right, as we've seen in the success of data driven driven companies is yields tremendous results. So I'll ask each of you to give give us your final thoughts and then we'll wrap maybe >>maybe it quickly, please. Yeah, maybe just just jumping on this piece that you have mentioned, right, the target architecture. If we talk about these pieces right, people often have this picture of mind like OK, there are different kind of stages, we have sources, we have actually ingestion layer, we have historical transformation presentation layer and then we're basically putting a lot of technology on top of that kind of our target architecture. However, I think what we really need to make sure is that we have these different kind of viewers, right? We need to understand what are actually the capabilities that we need in our new goals. How does it look and feel from the different kind of personas and experience view? And then finally, that should actually go to the to the target architecture from a technical perspective um maybe just to give an outlook but what we're what we're planning to do, how we want to move that forward. We have actually based on our strategy in the in the sense of we would like to increase that to maturity as a whole across the entire company and this is kind of a framework around the business strategy and it's breaking down into four pillars as well. People meaning the data, cultural, data literacy, data organizational structure and so on that. We're talking about governance as Clements has actually mentioned that, right, compliance, governance, data management and so on. You talk about technology and I think we could talk for hours for that one. It's around data platform, better science platform and then finally also about enablement through data, meaning we need to understand that a quality data accessibility and the science and data monetization. >>Great, thank you christophe clement. Once you bring us home give us your final thoughts. >>Can't can just agree with christoph that uh important is to understand what kind of maturity people have to understand what the maturity level, where the company where where people organization is and really understand what does kind of some kind of a change replies to that those four pillars for example, um what needs to be taken first and this is not very clear from the very first beginning of course them it's kind of like Greenfield you come up with must wins to come up with things that we really want to do out of theory and out of different white papers. Um only if you really start conducting the first initiatives you do understand. Okay, where we have to put the starts together and where do I missed out on one of those four different pillars? People, process technology and governance. Right? And then that kind of an integration. Doing step by step, small steps by small steps not boiling the ocean where you're capable ready to identify the gaps and see where either you can fill um the gaps are where you have to increase maturity first and train people or increase your text text, >>you know Hello Fresh is an excellent example of a company that is innovating. It was not born in Silicon Valley which I love. It's a global company. Uh and I gotta ask you guys, it seems like this is an amazing place to work you guys hiring? >>Yes, >>definitely. We do >>uh as many rights as was one of these aspects distributing. And actually we are hiring as an entire company specifically for data. I think there are a lot of open roles serious. Please visit or our page from better engineering, data, product management and Clemens has a lot of rules that you can speak about. But yes >>guys, thanks so much for sharing with the cube audience, your, your pioneers and we look forward to collaborations in the future to track progress and really want to thank you for your time. >>Thank you very much. Thank you very much. Dave >>thank you for watching the cubes startup showcase made possible by A W. S. This is Dave Volonte. We'll see you next time. >>Yeah.

>>Hey, we're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, it's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great >>To see you take it away. >>All right, guys. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing and flute and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution, things got interesting, right? You started to see automation, but that automation was done essentially programmed your robot to do something and did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different, right? >>Cause now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue, there we'll issue that, but it's important. Not for technology's sake, right? It's important because it actually drives very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, uh, companies and manufacturers moving to improve while its quality prompts still accounts for 20% of sales, right? So every fifth of what you meant are manufactured from a revenue perspective, do back quality issues that are costing you a lot planned downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of new spaces, we're not doing it just merely to implement technology. We're doing it to move these from members, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life with what like, right, but this is actually the business. The cloud area is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I say, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things are taking about time, but this, the ability to take these real-time actions or, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into an enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you could start to think about, you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we can put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one history sets data, you can build out those machine learning models. >>I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. Once you understand that you can actually then build out the smiles, you could deploy the models after the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, but schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. >>So, >>You know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is for SIA for ECA is the, um, is the, was, is the, um, the, uh, a supplier associated with Pooja central line out of France. They are huge, right? This is a multinational automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, um, they connected 2000 machines, right. Um, and they once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor the data firms coming in, you know, monitor the process. >>That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, fibrations pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision, wilding inspection. So let's take pictures of parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections beer. And so they both have those machine learning models. So they took that data. All this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case, a great example of how you can start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you wanted to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turn in the morning sessions and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're gonna, they're gonna hit? >>You know, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right. So, and it's unsafe, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. >>Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world for a long time, the silos, um, uh, you know, the silos, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid theme and you've kind of got this world, that's going toward an equilibrium. You've got the OT side, you know, pretty hardcore engineers. And we know, we know it. Uh, a lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space. And when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to it earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims, kick kickoff. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots by about warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning where simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start something with monitoring, get a lot of value, start, then bring together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases there's value to be had throughout. I >>Remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question that it kind of, um, goes back to one of the things I alluded earlier, we've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they've built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Patera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of industry 4.0, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to lead this discussion on the technology advances. I'd love to talk tech here, uh, are the key technology enablers, and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space, sorry, manufacturing in >>A factory space. Yeah. I knew what you meant in know in the manufacturing space. There's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can w we're finally being able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got back way capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, very much more quickly. Yep. We got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, for everybody who joined us. Thanks. Thanks for joining.

>>Welcome to our industry. Drill-downs from manufacturing. I'm here with Michael Gerber, who is the managing director for automotive and manufacturing solutions at cloud era. And in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data connected trucks are fundamental to optimizing fleet performance costs and delivering new services to fleet operators. And what's going to happen here is Michael's going to present some data and information, and we're gonna come back and have a little conversation about what we just heard. Michael, great to see you over to you. >>Oh, thank you, Dave. And I appreciate having this conversation today. Hey, um, you know, this is actually an area connected trucks. You know, this is an area that we have seen a lot of action here at Cloudera. And I think the reason is kind of important, right? Because, you know, first of all, you can see that, you know, this change is happening very, very quickly, right? 150% growth is forecast by 2022. Um, and the reasons, and I think this is why we're seeing a lot of action and a lot of growth is that there are a lot of benefits, right? We're talking about a B2B type of situation here. So this is truck made truck makers providing benefits to fleet operators. And if you look at the F the top fleet operator, uh, the top benefits that fleet operators expect, you see this in the graph over here. >>Now almost 80% of them expect improved productivity, things like improved routing rates. So route efficiencies and improve customer service decrease in fuel consumption, but better technology. This isn't technology for technology sake, these connected trucks are coming onto the marketplace because Hey, it can provide for Mendez value to the business. And in this case, we're talking about fleet operators and fleet efficiencies. So, you know, one of the things that's really important to be able to enable this right, um, trucks are becoming connected because at the end of the day, um, we want to be able to provide fleet deficiencies through connected truck, um, analytics and machine learning. Let me explain to you a little bit about what we mean by that, because what, you know, how this happens is by creating a connected vehicle analytics machine learning life cycle, and to do that, you need to do a few different things, right? >>You start off of course, with connected trucks in the field. And, you know, you can have many of these trucks cause typically you're dealing at a truck level and at a fleet level, right? You want to be able to do analytics and machine learning to improve performance. So you start off with these trucks. And the first you need to be able to do is connect to those products, right? You have to have an intelligent edge where you can collect that information from the trucks. And by the way, once you conducted the, um, this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now what I'm going to show you the ability to take this real-time action is actually the result of your machine learning license. Let me explain to you what I mean by that. >>So we have this trucks, we start to collect data from it right at the end of the day. Well we'd like to be able to do is pull that data into either your data center or into the cloud where we can start to do more advanced analytics. And we start with being able to ingest that data into the cloud, into that enterprise data lake. We store that data. We want to enrich it with other data sources. So for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've connected collected from those trucks. And you want to augment that with your dealership, say service information. Now you have, you know, you have sensor data and there was salting repair orders. You're now equipped to do things like predict one day maintenance will work correctly for all the data sets that you need to be able to do that. >>So what do you do here? Like I said, you adjusted your storage, you're enriching it with data, right? You're processing that data. You're aligning say the sensor data to that transactional system data from your, uh, from your, your pair maintenance systems, you know, you're bringing it together so that you can do two things you can do. First of all, you could do self-service BI on that date, right? You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to do create machine learning models. So if you have the sensor right values and the need, for example, for, for a dealership repair, or as you could start to correlate, which sensor values predicted the need for maintenance, and you could build out those machine learning models. And then as I mentioned to you, you could push those machine learning models back out to the edge, which is how you would then take those real-time action. >>I mentioned earlier as that data that then comes through in real-time, you're running it against that model, and you can take some real time actions. This is what we are, this, this, this, this analytics and machine learning model, um, machine learning life cycle is exactly what Cloudera enables this end-to-end ability to ingest, um, stroke, you know, store it, um, put a query, lay over it, um, machine learning models, and then run those machine learning models. Real-time now that's what we, that's what we do as a business. Now when such customer, and I just wanted to give you one example, um, a customer that we have worked with to provide these types of results is Navistar and Navistar was kind of an early, early adopter of connected truck analytics. And they provided these capabilities to their fleet operators, right? And they started off, uh, by, um, by, you know, connecting 475,000 trucks to up to well over a million now. >>And you know, the point here is with that, they were centralizing data from their telematics service providers, from their trucks, from telematics service providers. They're bringing in things like weather data and all those types of things. Um, and what they started to do was to build out machine learning models, aimed at predictive maintenance. And what's really interesting is that you see that Navistar, um, made tremendous strides in reducing the need or the expense associated with maintenance, right? So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring and service it before it broke down so that you could do that in a much more cost-effective manner. And if you see the benefits, right, they, they reduced maintenance costs 3 cents a mile, um, from the, you know, down from the industry average of 15 cents a mile down to 12 cents cents a mile. >>So this was a tremendous success for Navistar. And we're seeing this across many of our, um, um, you know, um, uh, truck manufacturers. We were working with many of the truck OEMs and they are all working to achieve, um, you know, very, very similar types of, um, benefits to their customers. So just a little bit about Navistar. Um, now we're gonna turn to Q and a, Dave's got some questions for me in a second, but before we do that, if you want to learn more about our, how we work with connected vehicles and autonomous vehicles, please go to our lives or to our website, what you see up, uh, up on the screen, there's the URLs cloudera.com for slash solutions for slash manufacturing. And you'll see a whole slew of, um, um, lateral and information, uh, in much more detail in terms of how we connect, um, trucks to fleet operators who provide analytics, use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >>Thank you. Uh, Michael, that's a great example. You've got, I love the life cycle. You can visualize that very well. You've got an edge use case you do in both real time inference, really at the edge. And then you're blending that sensor data with other data sources to enrich your models. And you can push that back to the edge. That's that lifecycle. So really appreciate that, that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into. >>Yeah, that's really, that's a great question. They, you know, cause you know, everybody always thinks about machine learning. Like this is the first thing you go, well, actually it's not right for the first thing you really want to be able to go around. Many of our customers are doing slow. Let's simply connect our trucks or our vehicles or whatever our IOT asset is. And then you can do very simple things like just performance monitoring of the, of the piece of equipment in the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how has the, how has the driver performing? Is there a lot of idle time spent, um, you know, what's, what's route efficiencies looking like, you know, by connecting the vehicles, right? You get insights, as I said into the truck and into the driver and that's not machine learning. >>Right. But that, that, that monitoring piece is really, really important. The first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, uh, what I call the machine learning and AI models, where you're using inference on the edge. And then you start to see things like, uh, predictive maintenance happening, um, kind of route real-time, route optimization and things like that. And you start to see that evolution again, to those smarter, more intelligent dynamic types of decision-making, but let's not, let's not minimize the value of good old fashioned monitoring that site to give you that kind of visibility first, then moving to smarter use cases as you, as you go forward. >>You know, it's interesting. I'm, I'm envisioning when you talked about the monitoring, I'm envisioning a, you see the bumper sticker, you know, how am I driving this all the time? If somebody ever probably causes when they get cut off it's snow and you know, many people might think, oh, it's about big brother, but it's not. I mean, that's yeah. Okay, fine. But it's really about improvement and training and continuous improvement. And then of course the, the route optimization, I mean, that's, that's bottom line business value. So, so that's, I love those, uh, those examples. Um, I wonder, I mean, one of the big hurdles that people should think about when they want to jump into those use cases that you just talked about, what are they going to run into, uh, you know, the blind spots they're, they're going to, they're going to get hit with, >>There's a few different things, right? So first of all, a lot of times your it folks aren't familiar with the kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set, right? That's very specialized hardware in the car and things like that. And protocols that's number one, that that's the classic, it OT kind of conundrum that, um, you know, uh, many of our customers struggle with, but then more fundamentally is, you know, if you look at the way these types of connected truck or IOT solutions started, you know, oftentimes they were, the first generation were very custom built, right? So they were brittle, right? They were kind of hardwired. And as you move towards, um, more commercial solutions, you had what I call the silo, right? You had fragmentation in terms of this capability from this vendor, this capability from another vendor, you get the idea, you know, one of the things that we really think that we need with that, that needs to be brought to the table is first of all, having an end to end data management platform, that's kind of integrated, it's all tested together. >>You have the data lineage across the entire stack, but then also importantly, to be realistic, we have to be able to integrate to, um, industry kind of best practices as well in terms of, um, solution components in the car, how the hardware and all those types things. So I think there's, you know, it's just stepping back for a second. I think that there is, has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of art, um, offerings. Um, our job as a software maker is to make that easier and connect those dots. So customers don't have to do it all on all on their own. >>And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about, you know, new types of hardware coming in, you guys are optimizing for that. We see the it and the OT worlds blending together, no question. And then that end to end management piece, you know, this is different from your right, from it, normally everything's controlled or the data center, and this is a metadata, you know, rethinking kind of how you manage metadata. Um, so in the spirit of, of what we talked about earlier today, uh, uh, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >>Yeah, I'm really glad you're asking that because we actually embarked on a product on a project called project fusion, which really was about integrating with, you know, when you look at that connected vehicle life cycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Cloudera is Peter piece of this was ingesting data and all the things I talked about being storing and the machine learning, right? And so we provide that end to end. But what we wanted to do is we wanted to partner with some key partners and the partners that we did with, um, integrate with or NXP NXP provides the service oriented gateways in the car. So that's a hardware in the car when river provides an in-car operating system, that's Linux, right? >>That's hardened and tested. We then ran ours, our, uh, Apache magnify, which is part of flood era data flow in the vehicle, right on that operating system. On that hardware, we pump the data over into the cloud where we did them, all the data analytics and machine learning and, and builds out these very specialized models. And then we used a company called Arabic equity. Once we both those models to do, you know, they specialize in automotive over the air updates, right? So they can then take those models and update those models back to the vehicle very rapidly. So what we said is, look, there's, there's an established, um, you know, uh, ecosystem, if you will, of leaders in this space, what we wanted to do is make sure that our, there was part and parcel of this ecosystem. And by the way, you mentioned Nvidia as well. We're working closely with Nvidia now. So when we're doing the machine learning, we can leverage some of their hardware to get some further acceleration in the machine learning side of things. So, uh, yeah, you know, one of the things I always say about this types of use cases, it does take a village. And what we've really tried to do is build out that, that, uh, an ecosystem that provides that village so that we can speed that analytics and machine learning, um, lifecycle just as fast as it can be. This >>Is again another great example of, of data intensive workloads. It's not your, it's not your grandfather's ERP. That's running on, you know, traditional, you know, systems it's, these are really purpose-built, maybe they're customizable for certain edge use cases. They're low cost, low, low power. They can't be bloated, uh, ended you're right. It does take an ecosystem. You've got to have, you know, API APIs that connect and, and that's that, that takes a lot of work and a lot of thoughts. So that, that leads me to the technologies that are sort of underpinning this we've talked we've we talked a lot in the cube about semiconductor technology, and now that's changing and the advancements we're seeing there, what do you see as the, some of the key technical technology areas that are advancing this connected vehicle machine learning? >>You know, it's interesting, I'm seeing it in a few places, just a few notable ones. I think, first of all, you know, we see that the vehicle itself is getting smarter, right? So when you look at, we look at that NXP type of gateway that we talked about that used to be kind of a, a dumb gateway. That was really all it was doing was pushing data up and down and provided isolation, um, as a gateway down to the, uh, down from the lower level subsistence. So it was really security and just basic, um, you know, basic communication that gateway now is becoming what they call a service oriented gate. So it can run. It's not that it's bad desk. It's got memories that always, so now you could run serious compute in the car, right? So now all of these things like running machine learning, inference models, you have a lot more power in the corner at the same time. >>5g is making it so that you can push data fast enough, making low latency computing available, even on the cloud. So now you now you've got credible compute both at the edge in the vehicle and on the cloud. Right. And, um, you know, and then on the, you know, on the cloud, you've got partners like Nvidia who are accelerating, it's still further through better GPU based compute. So I mean the whole stack, if you look at it, that that machine learning life cycle we talked about, no, David seems like there's improvements and EV every step along the way, we're starting to see technology, um, optimum optimization, um, just pervasive throughout the cycle. >>And then real quick, it's not a quick topic, but you mentioned security. If it was seeing a whole new security model emerge, there is no perimeter anymore in this use case like this is there. >>No there isn't. And one of the things that we're, you know, remember where the data management platform platform and the thing we have to provide is provide end-to-end link, you know, end end-to-end lineage of where that data came from, who can see it, you know, how it changed, right? And that's something that we have integrated into from the beginning of when that data is ingested through, when it's stored through, when it's kind of processed and people are doing machine learning, we provide, we will provide that lineage so that, um, you know, that security and governance is a short throughout the, throughout the data learning life cycle, it >>Federated across in this example, across the fleet. So, all right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it, >>Dave. Thank you. And thank you. Thanks for the audience for listening in today. Yes. Thank you for watching. >>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime. And look, when you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces loss opportunities. Michael. Great to see you >>Take it away. All right. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right. And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, massive assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done, essentially programmed a robot to do something. It did the same thing over and over and over irrespective about it, of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfast. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adaptive right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives and very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, of, uh, companies, um, and manufacturers moving to improve while its quality promise still accounted to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. >>Plant downtime, cost companies, $50 billion a year. So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just merely to implement technology. We're doing it to move these from drivers, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle, what like, right, because this is actually the business that cloud era is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI, this, this analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors have connected over the internet. So suddenly we can collect all this data from your, um, ma manufacturing plants. What do we want to be able to do? >>You know, we want to be able to collect it. We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking the time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You've got, you're going to ingest that data. >>You're going to store it. You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. >>But as I mentioned, you, and what's really important here is the fact that once you've stored long histories that say that you can build out those machine learning models I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need, a correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for Maples. Once you understand that you can actually then build out those models for deploy the models out the edge, where they will then work in that inference mode that we talked about, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that PR that predicted the need for maintenance? If so, let's take real-time action, right? >>Let's schedule a work order or an equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connecting connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is bought for Russia, for SIA, for ACA is the, um, is the, was, is the, um, the, uh, a supplier associated with Peugeot central line out of France. They are huge, right? This is a multi-national automotive parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. >>Um, and then once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor data firms coming in, you know, monitor the process. That was the first step, right. Uh, and, you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models or compute. And what they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad Bali outcome. Then you teach the machine to make that decision on its own. >>So now, now the machine, the camera is doing the inspections. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you can start with monitoring, moved to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing, a lot more detail, and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the cost, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit, >>You know, there's, there's, there, there's a few of the, but I think, you know, one of the ones, uh, w one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant, are running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietorial pro protocols. That information can be very, very difficult to get to. Right. So, and it's, it's a much more unstructured than from your OT. So th the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world. And for a long time, the silos, um, uh, the silos a, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge, >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So, Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right. And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, but just talking about simple monitoring next level down, and we're seeing is something we would call quality event forensic analysis. >>And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims kick up. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots. What about warranty issues? What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning, we're simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole slew of machine learning, use dates, you know, and that ranges from things like Wally or say yield optimization. >>We start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. And you're certain start to say, which, um, you know, which on a sensor values or factors drove good or bad yield outcomes, and you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with, with monitoring, get a lot of value, start then bringing together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases, there's this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and the new player would come in and he'd be perfectly white uniform, and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so I question it relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. And it kind of goes back to one of the things I alluded to alluded upon earlier. We've had some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of, um, industry for porno, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and, and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to li lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry, manufacturing. Yeah. >>Yeah. I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can, we've finally been able to get to the OT data, right? That's that's number one, you know, numb number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, the super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed a book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to, to your equipment. All of those things are making this, um, there's, you know, the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, uh, very much more quickly. Yeah, we got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined us. Thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

>>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great to see you, >>Dave. All right, guys. Thank you so much. So I'll tell you, we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done essentially programmed a robot to do something. It did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfasts. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives very important business outcomes. First of all, falling, right? If you look at the cost of quality, even despite decades of, of, uh, companies and manufacturers moving to improve while its quality prompts still account to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. Plant downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just narrowly to implement technology. We're doing it to move these from adverse, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle with what like, right. But so this is actually the business that cloud areas is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics, life something, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking that time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right? And that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you bring these datasets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one histories that say that you can build out those machine learning models I talked to you about earlier. >>So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And then you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. And once you understand that you can actually then build out those models, you deploy the models out to the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, right? Let's schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that a piece of equipment fails and allows us to be very, very proactive. >>So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different, um, manufacturers around the world. I want to just highlight one of them. Cause I thought it's really interesting. This company is bought for Russia. And for SIA for ACA is the, um, is the, is the, um, the, uh, a supplier associated with out of France. They are huge, right? This is a multi-national automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. Um, I mean at once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? >>To be able to just monitor the data from coming in, you know, monitor the process. That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things, just start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections for you. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go, then you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of where the data is, you've got to be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit? >>No, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES system, Freightos your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right? So, and it's uncertain, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own. And for a long time, the silos, the silos, a bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. This is Chris now is getting, you know, instrumented and captured. Uh, and so you've got that, that cultural challenge and, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a great, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about this, a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards the internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I'm got warranty plans in the, in the field, right? So I'm starting to see warranty claims kicked off on them. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots I've got, I've got warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of a car. So, and that, again, also not machine learning is simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day so that you could take corrective actions, but then you get into a whole slew of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with monitoring, get a lot of value, start, then bring together more diverse datasets to do things like connect the.analytics then all and all the way then to, to, to the more advanced machine learning use cases this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. I kind of, um, goes back to one of the things I alluded a little bit about earlier. We've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to get to practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to implement those types of, um, industry 4.0, uh, analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, uh, barrier that we've always had and, and bring together those data sets that really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to Lee lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry. Manufacturing in >>Factor space. Yeah, I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and it had become ubiquitous that number one, we can w we're finally been able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, um, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book, bullet, uh, GP, you know, GPU level, machine learning, all that, those models, and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processes are getting much smarter, uh, very much more quickly. >>Yep. We've got a lot of data and we have way lower costs, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined. Uh, thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

>> (soft music) >> Presenter: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Welcome back everyone to the CUBE's coverage of IBM Think 2021. I'm your host, John furry with theCUBE. Our next guest Manish Chawla who's the industry general manager of energy, resources and manufacturing, a great guest to break down this next generation of infrastructure modern applications and changing the business in the super important areas he's regulated verticals. Manish, it's great to see you. Thank you for coming back on theCUBE. >> Thank you John. Good to meet you. >> You know, this is the area where I've been saying for years the cloud brings great scale horizontally scalable data, but at the end of the day, AI and automation really has to be specialized in the verticals. In this we're going to see the action the ecosystems for connecting. This is a big deal here I think this year, transformation is the innovation, innovation at scale. This seems to be the underlying theme that we've been reporting on. So I'd love to get your thoughts on how you see this Fourth Industrial Revolution as you say, coming about. Can you define for us what that means? And when you say that, what does it mean for customers? >> Yeah, sure, sure. So, you know, in sort of simple terms all the technologies that we see around us, whether it's AI we talk about AI, we talk about 5G, we talk about Edge Cloud Robotics. So the application of those to the physical world in some sense in the industrial world is what we define as the Fourth Industrial Revolution. Essentially, it's the convergence between the humans, the physical aspect, like the machines and the cyber either digital aspects, bringing that together. So companies can unlock the value from the terabytes and petabytes of data that our connected world is now able to produce. >> How does the IOT world come in? We've been again, I did a panel I think two years ago called you know the industrial IOT Armageddon. And it was really kind of pointing. It was kind of provocative title, but the point was you know, the industrial connections are all devices now and they're connected to the network security super important. This industrial revolution includes this new edge. >> It's got to be smarter and intelligent. What's your take on that? >> Yeah, absolutely. It is about the edge. It's about devices. It's about delivering capturing the data from the umpteen devices. You know, we've recently heard about the chip shortage which gives you an idea that there is so much utilization of compute power everywhere in the world. And the world is becoming very software defined. So whether it's software defined machines software defined products, the washing machines that we use at home, the cars we use home, everything is gradually becoming, not gradually I'd say rapidly becoming intelligent. And so that edge or IOT is the foundation stone of everything we're talking about. >> Well, you mentioned software on a chip SOC that's a huge mega wave coming. That's going to bring so much more compute into smaller form factors which leads me to my next question, which kind of, I'm kind of answering for myself but I'm not a manufacturing company but why should they care about this trend from a business perspective besides the obvious new connection points? What's really in it for them? >> Yeah. So big topic right now is this topic of resilience, right? So that's one aspect. This, the pandemic has taught us that resilience is a core objective. The second objective, which is front and center of all CEOs or CEOs is out-performance. And so what we're seeing is out-performance are investing in technology for many goals, right? So it's either sustainability which is a big topic these days, and a huge priority. It's about efficiency. It's about productivity. It's also now more and more about delivering a much stronger customer experience, right? Making your products easier to use much easily consumable as well. So if you, when you pull it all together it's an end to end thinking about using data to drive those objectives of out-performance as well as resilience. >> What's the progress being made so far on the manufacturing industry on this front? I mean, is it moving faster or you mentioned accelerating but where is the progress bar right now? >> So I think as we came into 2020, I would have described it as we were starting to enter the chapter two where companies were moving from experimentation to really thinking of scaling this. And what we found is the pandemic really caused a big focus on these, as Winston Churchill has been attributed the quote "Never waste a good crisis." A lot of CEOs, a lot of executives and leadership really put their energy into accelerating digital transformation. I think we really, two thirds have been able to accelerate their digital transformation. So the good news is, you know companies don't have to be convinced about this anymore. They're really, their focus is on where should I start? Where should I focus? And what should I do next? Right, is really the focus. And they are investing in sort of two types of technologies is the way we see it. What I would call foundational technologies because there's a recognition that to apply the differentiating technologies like AI and capturing and taking value of the data you need a strong architectural foundation. So whether it's cybersecurity, it's what we call ITOT integration connecting the devices back to the mothership. And it's also applying cloud but cloud in this context is not about typically what we think as public cloud or a central spot. It's really bringing cloud-like technologies also to the edge or to the plant or to the device itself whether it's a mobile device or a physical device. And that foundation is that recognition that you've got to have the foundation that you can build your capabilities on top. Whether it's for customers or clients or colleagues. >> That's a great insight on the architecture. I think that's a successful playbook. It sounds so easy. I do agree with you. I think people have said this is a standard now hybrid cloud, the edge pretty clear visibility on the architecture of what to do or what needs to be done, how to do it, all other story. So I have to ask you, we hear of these barriers. There's always blockers. I think COVID's released some of those relieved some of those blockers because people have to force their way into the transformation but what are those barriers that are stopping the acceleration for customers to achieve the benefits that they need to see? >> Yeah. So I think one or one key barrier is a recognition that most of our plants or manufacturing facilities or supply chains really run in a brownfield manner. I, there's so many machines so many facilities that have been built over decades. So there's a proliferation of different ages of devices, machines, et cetera. So making sure that there is a focus on laying out a foundation, that's a key barrier. There is also a concern that, you know the companies have around cybersecurity. The more you connect the more you increase the attack surface. And we know that that hacks and so on are, are a dominant issue now whether it's for ransomware or for other malicious reasons. And so modernizing the foundation and making sure you're doing it in a secure way those are the key concerns that executives have. And then another key barrier I see is making sure that you have a key, key core objective and not making too many different varied experimentation beds. So keeping a focus on what's the core use case of benefit you're after and then what's the foundation to make sure that you're going after it. Like I said, whether it's quality or productivity or such like. >> So the keys to success, if I get this right is you have the right framework for this as you say, industry 4.0 you got to understand the collaborative dynamics and then have an ecosystem. >> Yeah. Can you unpack those three things? Because take me through that. You got to the framework, the collaboration and the ecosystem. What does that mean specifically? >> So the way I take the simplest way to think of it is the amount of work and effort that all companies have to put in, is so great in front of them. The opportunities are so great as well that nobody can hire all the smart people that are needed to achieve the goals. Everybody has their own specific I would say focus and capabilities they bring to bear. So the collaboration between manufacturers the collaboration between operational technology companies like the Siemens, ABB, Schlumberger, et cetera and IT technology companies like ourselves that three-part collaboration is sort of the heart of what I see as ecosystems coming together. The other dimensionality of ecosystems is also looking at it from a supply chain or a value chain perspective cause how something becomes more intelligent or smarter or more effective is also being able to work across the supply chain or value chain. So those are our key focus areas make sure we are collaborating across value chains and supply chains, as well as collaborating with manufacturers and OT, operational technology companies to be able to bring these digital capabilities with the right capabilities of operational technology companies into the manufacturers. >> If I asked you, how are you doing that? What specifically would you say? I mean, how are you collaborating? What's some examples give some examples of this enaction. >> Certainly. So we recently announced over the last say, nine months or so three strategic, very transformative partnerships. The first one I'll share with you is with Schlumberger. Schlumberger is the world's largest oil field services company. And now also the world's largest distill technology company for the oil and gas industry. So we've collaborated with them to bring hybrid cloud to the digital platform so they now can deploy their capabilities to any customer regardless of whether they want it in country or on a public cloud. Another example is we've established a data platform with Schlumberger for the oil and gas industry, to be able to bring again that data platform to any location around the world. The advantage of hybrid, the advantage of AI. With EVB, what we've done is we've taken our smart sync IT security connected with their products and capabilities for operational systems. And now are delivering an end to end solution that you can get cyber alerts or issues coming from manufacturing systems dry down to right up to an IT command center where you're seeing all the events and alerts so that they can be acted upon right away. So that's a great example of collaborating with IT from a security point of view. The third one is industrial IOT with Siemens and we've partnered with Siemens to deliver the MindSphere private cloud edition. Delivered on our red hat hybrid cloud. So this is an example where we are able to take our horizontal technologies, apply it with their verticals smarts and deep industry context put our services capabilities on top of it so they can deliver their innovations anywhere >> Manish is such an expert on this such a great leader on this area and I have to ask you you know, you've been in this mode of evangelizing and leading teams and building solutions around digital re platforming or whatever you want to call it, renovations. >> Manish: Right >> What's the big deal now, if you had to, I mean, it seems like it's all coming together with red hat under the covers, you get distributed networks with the Edge. It's all kind of coming together now for the verticals because you got the best of both worlds. Programmable scalable infrastructure with modern software applications on top. I mean, you've been even in the industry for many many waves, why is this wave so big and important? >> So I think there is no longer the big reason why it's important is I think there's no reason why companies have to be convinced now that the clarity is there that this needs to happen so that's one. The second is, I think there's a high degree of expectation among consumers, among employees and among customers as well, that everything that we touch will be intelligent. So these technologies really unlock the value unlock the value, and they can be deployed at scale that's really, I think what we're seeing as the focus now. And being able to deliver the innovation anywhere whether someone wants it at the Edge next to a machine that's operating, or be able to look at how a manufacturing facility or different product portfolio is doing in the boardroom. It's all available. And so that shop floor, the top floor connection is what everybody's aiming for but we also now call it Edge to enterprise. >> And everything works better, the employees are happy people are happy, stakeholders are happy. Manish great insight. Thank you for sharing here on theCUBE for Think 2021. Thanks for coming on theCUBE. >> Absolutely thanks John for having me. >> Okay. I'm John Furry host theCUBE for IBM Think 2021. Thanks for watching. (soft music)

>> Announcer: From around the globe, It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back to IBM Think 2021. This is theCUBE's ongoing coverage where we go out to the events, in this case virtually to extract the signal from the noise. Now we're going to talk about one of the deepest customer relationships in the tech business with Piet Bill who is the IBM managing director for American Express. Piet, great to see you. Thanks for coming on. >> Thanks for having me, Dave. >> So as I said, this is one of the deepest vendor-client relationships. I mean, it's more than that. It's just, you're not a vendor. You're a partner, a very deep relationship many many decades, plus executives know each other. There's been some senior executives from American Express, as I recall came over to IBM of course, famously Lou Gerstner. But, talk about the, just give us the overview of the evolution of that partnership. >> Yeah well, as you rightly mentioned, the relationship is long and deep. It's over a hundred years. I mean the original deal was probably around the combine clocks and scales and all that kind of stuff, and it evolved over time. But what it does indeed create is a long, deep, lasting relationship as a fundament for doing business. And yeah, that business has gone through a lot of cycles over the last decade. So as you say, from buying stuff but I would say over time evolving really into a partnership around services, mutual business back and forth, exchanging executives on board level. American Express executives on the board of IBM and vice versa. So yeah, it's a very, very deep long relationship of two iconic companies in Manhattan. >> Yeah, well so it's got to be more than just buying stuff. Obviously, there's a lot of business being transacted, but you've got an intimate, I mean your title has American Express in it. So you've got to intimately understand your client's business. I mean, I guess that's always the case but we're taking it to another level here, aren't we? >> Yeah, yeah, absolutely. I mean, so what you really are often what we do as IBM is really get into the shoes basically of American Express trying to support their business to their clients. So American Express is very focused on small and medium businesses. So, we tap into how can we help the small and medium business as part of the American Express customer set. And how can we help evolve their business models, their technology, their services, to serve their clients better because in the old days, indeed, to your point, it was like, oh we wanted to buy the right stuff. And then we use that to do our thing but that the technology today, the area in which we operate is completely different. If you don't understand the client of American Express, we cannot serve American Express as a company. So it is indeed very important and it is therefore deeper and it requires way more focus on the clients of American Express than in the old days, I would say. >> Well, the pandemic must've been a challenging environment. Of course, I mean, you know people aren't out shopping as much, although people are waiting, they can't wait to get back out. They say, it's going to be like Woodstock here with their American Express cards. But so, maybe talk a little bit about how you worked together during the pandemic. >> Yeah, so well, first of all, like anybody we all work from home, but American Express really, I would say almost re-engaged on what is core in their strategy, is the support to small and medium business. So, American Express started this Stand For Small Initiative led by Steve Squeri himself, about how can we enable the small enterprises in doing business in the COVID period? What do they need? I mean, yeah, they need money, but they also need help. Like how to deal with your financials with your people. Can we use the spare time to do more education? And so IBM was one of the partners that jumped on board immediately to say, okay let us help in that platform, support you when necessary with the platform, but definitely help you in that platform to reach out to the small and medium enterprises, specifically in the New York area And like many other partners, we all got on board. And I think it got another focus again, I mean small and medium business has always been a focus but it's different when so many companies are struggling right now. And so we got on board and I think that is really a very clear partnership expression, I would say. >> How do you measure success with American Express? What are some of the key things that you guys look at? How have you evolved that over time? >> Well, ultimately I would say it's client satisfaction in the end. It sounds like an open door, but it really is. I mean, the real measurement, I mean there's always money measurements back and forth. And you can argue that of course you need to do solid business. There's no discussion there but I would say it's where do we align on the strategic intent from both companies? And let me elaborate a second on that one. If American Express is really transforming its business to become way more, I would say cloud enabled, hybrid technologies enabled. We provide a lot of that material. So we are really working together on trying to leverage each other in building that hybrid platform that will enable that future. And why do you need that? Well, because American Express needs to be dynamic and getting fintechs on board, getting exchanges with new companies are going way faster. It's not the traditional old style anymore where you could go for transformations for years. No, it needs to be on the spot. So we felt our strategies are really well aligned. And I would say the real measurement of success is how can we now make that to the benefit of American Express and on the back of that, we will do good business. So client satisfaction should be the primary one, strategic alignment important, and then of course doing the sound business on the back of that for both sides >> Financial services firms have always been pretty savvy when it comes to applying technology to business. Some of the most demanding customers and more advanced. And so, American Express was likely already on a digital transformation prior to the COVID hitting. At the same time, if he talks about it being accelerated. But, I think what people miss is that it wasn't, well they don't miss it, but to think about it and this way it wasn't planned, it was like forced. And, so you just, you had no choice. You couldn't think about it. You just had to do an act. And so on the one hand, okay, that's good. It was a forcing function. It also served as a Petri dish, but on the other hand, I'm sure a lot of mistakes were made. Now, as we exit the pandemic, we step back and say, okay, wow, we learned a lot. Now we can make a more planful approach and really go deeper and lean in over the next several years. What are your thoughts on that? And how does it relate to what you guys are doing with American Express? >> I think that's a very good point, I agree. It's what you see is that this indeed has forced us in a lot of things. I think the good news is American Express was already enabled for a lot of that new technology. They have invested. They have a lot of very skilled, good people and a very clear strategy and what they were after. This indeed put more pressure on it. I think what you will see happening in the foreseeable future after we get out of all of this, let's say the the urgency to complete the transformation on the cloud and data will become even more crucial. And so the priority will become higher and it will not be just higher because of the techies wanting it to do it, but because the business needs it. So they need it from a risk perspective, they need it from an agility perspective, go to market of new products. They need to really move fast. It's a fast moving market. You get a lot of it. I mean, the competition is there. So to enable that the move to get new technologies in faster is becoming pivotal and crucial. And I think for now, it's more of an almost like a survival statement. We need to get through this bubble of COVID. As soon as that's done, we need to think way more on the structural elements of data and how we enable a hybrid strategy going forward. >> So in the spirit of, you know you need to understand your customer. In this case, American Express and understand their business. And American Express is, I'll make you laugh. Anytime I call American Express, if I have to work out a problem or whatever, and I got to talk to customer service, they always thank me for my loyalty. Because I've been a customer for a long time. Back when probably when Ronald Reagan was president it was my first Amex card. And so they're like, "Oh, thank you, Mr. Vellante. We really appreciate your loyalty." So loyalty is a big thing for American Express with its customers. So what about IBM and American Express? How are you breeding? You know, what's that loyalty factor look like for you guys? >> Yeah, I think it's a very important element. I mean, to your point, I have the same experience. It's a crucial element. The whole, I mean, American express is famous for its loyalty schemes for loyalty as a company. I think loyalty, like the business has evolved. I think the loyalty evolves in the same style. And I would say in the old days, I would say the argument was you need to have the best product. You know, you need to be, and then we'll buy the product. In the current environment, I would argue it's way more about skills. Do we have the right people? Do we have the right technology, strategy kind of stuff? I would say for the future, it's way more about do we have the right trust, commitment, and loyalty of the people that work with us going forward to serve the client needs? And I think that evolution, it's almost like you have an Industrial Revolution. There was an Information Revolution. I think there's more of a Loyalty Revolution coming up where the real differentiating factors is because we can study this and argue this for ages but a lot of parties will deliver a lot of good technology to the market. They will deliver a lot of good people. They will have good price points. So what's the real differentiating factor? It's like, do we really trust these people? And then I think relationship loyalty will really come in play and it will not become in play just between an IBM and an American Express. But I would argue it will come in play in the whole business cycle of American Express to their clients. I mean, if the credit card swipe of your American Express card in a shop fails, it needs to be my problem. If I deliver the service to American Express, it cannot be that, oh, American Express has a problem. And you know what, it's eight o'clock in the evening. Yeah, we have reduced services. No, we never had that. We will never have that. But we need to get even deeper in understanding what the effects are of these business issues. >> Yeah. you're right. The nature of loyalty, I mean, certainly the products have changed. I remember, you used to travel overseas with American Express Travelers Checks. That was a staple of every overseas trip that I ever took. No matter where I was going, whether it was the Asia Pacific or Europe, I had to have that packet. And there were times when one time in particular I had a problem, boom, they were right there. It solved that problem. Now of course, many young people in the audience don't even know what America's Express Travelers Check is. They probably don't know what cash is. Carrying around crypto in their wallet. But, that's an example and that's about trust. I trust that product. I trust that company behind the product. And again, that has to extend to your relationship, doesn't it? >> Absolutely, so the technology that American Express uses, whether they do it themselves, or whether it's provided by partners like IBM. It needs to be seamless because, let's face it. Dave, you will not be interested to know who provides you the security on your credit card. If you have an American Express card, you expect it expect American Express to deliver you the security that you need. And whether American Express delivers that or IBM, you couldn't care less and you shouldn't care less. But what it does require is that, in the old school I would say it was more like, okay, we'll give some services and some products to American Express and guys, good luck! Now, we need to think ahead. And I think that's where the power of IBM comes in whether we really are tuned by industry to the industry issues like compliance, security, stability, services to the end clients, to you. So you need to feel if I cannot explain what I do to American Express in your terms as an end-user of an Amex credit card, you can argue what's the real value at? And definitely if there's like three, four, or five parties playing exactly the same game, it needs to be differentiating. And I think a company like IBM we have differentiating value, but we need to make it very clear. And that's, I think where you see companies like American Express really work together and that's where loyalty and trust really comes into play. >> Last question and we've got to go is, you have American Express in your title. Are other companies jealous? (laughs) We want that too. >> They should. They should be. I must say, we deal with a ton of financial institutions as you know around the globe, including the other credit cards. But yeah, I think when these deep relationships come in place and two, they're so old. So deep, so entrenched, and it really started. There's different dimensions to it. And it's not always that hard-coded anymore. It's the subtlety of really relying on each other. I mean, when something happens in the middle of the night with American Express, all of IBM is on board as of the second. And it's not driven by contracts or by anything. It's by people that have an American Express logo on the forehead and worked for an IBM. >> Yeah, right. That's awesome. Piet, Piet Bill's great story. Thanks so much for coming to theCUBE. It was great to have you. >> Thanks for having me. >> All right. And keep it right there. This is Dave Vellante, ongoing coverage of Think 2021. You're watching theCUBE.

>>from around the >>globe. It's the cube with digital >>coverage of IBM >>Think 2021 >>brought to you by IBM. Welcome back everyone to the cubes coverage of IBM Think 2021 I'm your host john ferry with the cube. Our next guest is Michelle well who's the industry General manager of Energy resources manufacturing. Great guest to break down this next generation of infrastructure, modern applications and changing the business and the super important areas is regulated verticals. Great to see you. Thank you for coming back on the queue. >>Thank you john good to meet you. >>You know this is the area where I've been saying for years the cloud brings great scale, horizontally scalable data but at the end of the day AI and automation really has to be specialized in in the verticals and this. We're going to see the action ecosystems for connecting. This is a big deal here think this year transformation is the innovation innovation at scale. It seems to be the underlying theme that we've been reporting on. So I'd love to get your thoughts on how you see this fourth industrial revolution as you say, coming about. Can you define for us what that means and when you say that, what does it mean for customers? >>Yeah, sure, sure. So you know, in in sort of simple terms, all the technologies that we see around us whether it's a I we talk about a I we talked about five G. We talk about edge cloud, robotics. So the application of those to the physical world in some sense, in the industrial world is what we define as uh as the fourth industrial revolution. Essentially it's the convergence between the humans, the physical aspect by the machines and the cyber at the digital aspects, bringing that together so companies can unlock the value from the terabytes and petabytes of data that's that are connected world is now able to produce, >>How does the IOT world come in? We've been again, I did a panel I think two years ago called you know the industrial IOT Armageddon. And it was really kind of point, it was kind of provocative title but the point was you know, the industrial connections are all devices now and they're connected to the network security. Super important, this industrial revolution includes this new edge, it's gotta be smarter and intelligent. What's your take on that? >>Absolutely, it is about the edge, it's about devices, it's about delivering capturing the data from the emptying devices. We've recently heard about the chip shortage which gives you an idea that there is so much utilization of compute power everywhere in the world and the world is becoming very software defined. So whether it's software defined machines, software defined products, the washing machines that he that we use at home, the cars we use at home, there everything is gradually becoming, not gradually, I'd say rapidly becoming intelligent and so that edge or IOT is the foundation stone also everything we're talking about. >>Well you mentioned software on a chip, S. O. C. Um, that's a huge mega wave coming. That's gonna bring so much more compute into smaller form factors. Which leads me to my next question, which kind of, I'm kind of answering for myself, but I'm not a manufacturing company, but why should they care about this trend from a business perspective? Besides the obvious new connection points? What's really in it for them? >>Yes, it's a big topic right now, is, is this topic of resilience? Right, So that's one aspect uh, this the pandemic has taught us that resilience is a core objective. The second objective which which is front and center of all CEOS, or CEOS, is out performance. And so what we're seeing is is out performance, are investing in technology for many goals, right? So it's either sustainability which is a big topic these days and huge priority. Uh it's about efficiency, it's about productivity, it's also now more and more about delivering a much stronger customer experience, right? Making your products easier to use much easily consumable as well. So, if you, when you pull it all together, it's it's an end to end thinking about using data to drive those objectives of out performance, as well as resilience. >>What's the progress being made so far in the manufacturing industry on this front? I mean, is it moving faster? Are you mentioned accelerating? But where is the progress bar? Right now? >>So, I think as we came into 2020, I would have described it as we were starting to enter the Chapter. Two companies were moving from experimentation to really thinking of scaling this and and what we found is the pandemic really caused a big focus on these. As Winston Churchill has been attributed the court never waste a good crisis. So a lot of ceos, a lot of executives and leadership really put their What their energy into accelerate industrial transformation. I think we relieve 2/3 southwell have been able to accelerate the industrial transformation. So the good news is, you know, companies don't have to be convinced about this anymore. They're really they're focuses on what's where should I start? Where should I focus on what should I do next? Right is really the focus and they're investing instead of two types of technologies is the way we see it, what I would call foundational technologies because there's a recognition that to apply the differentiating technologies like Ai and captured and taking value of the data, you need a strong architectural foundation. So whether it's it's cybersecurity, it's what we call it, the integration, connecting the devices back to to the mother ship and it's also applying cloud. But cloud in this context is not about typically what we think is public cloud or or or central spot. It's really bringing cloud like technology is also to the edge I. E. To the plant or to the device itself, whether it's a mobile device or a physical device. And that foundation is the recognition that you've got to have the foundation, that you can build your your capabilities on top, whether it's for customers or clients and colleagues >>as a great insight on the architecture, I think that's a successful playbook. Um It sounds so easy, I do agree with you. I think people have said this is a standard now, Hybrid cloud the edge, pretty clear visibility on the architecture of what to do or what needs to be done, how to do it almost story. So I have to ask you, we hear this barriers, there's always blockers. I think Covid released some of those, relieved some of those blockers because people have to force their way into into the transformation. But what are those barriers um that that are stopping the acceleration for customers to achieve the benefits that they need to see. >>Yes. So I think 11 key barrier is is a recognition that most of our plants or manufacturing facilities that supply chains really run run in a brownfield manner. I there's so many machines, so many facilities that have been built over decades. So there's a there's a proliferation of different ages of devices, machines, etcetera. So making sure that there is a focus on laying out the foundation. That's a key key barrier. Uh There is also a concern that uh you know, the companies have around cybersecurity, the more you connect, the more you increase the attack surface and we know that that acts and so on are the dominant issue. Now, whether it's for ransom, fair or for or for other malicious reasons, uh and so modernizing the foundation and making sure you're doing it in a secure way. Those are the key concerns that executives have. And then another key barrier I see is making sure that you have a key key core objective and not making sure making too many different varied experimentation bets. So keeping a focus on what's the call? Use case of benefit your after and then what's the foundation to make sure that you're going after it? Like I said, whether it's quality or productivity or such, like >>So the keys to success that I get this right is gonna have the right framework for this, as you say, industry 4.0, you got to understand the collaborative dynamics and then have an ecosystem. Yeah, can you unpack those three things? Because take me through that, you got to the framework, the collaboration and the ecosystem. What does that mean? Specifically? >>So uh the way, I think the simplest way to think of it as the amount of work and effort that all companies have been put in is so great in front of them, the opportunities are so great as well uh that nobody can hire all the smart people that are needed to achieve the goals. Everybody has their own specific I would say focus and capabilities they bring to bear. So the collaboration between manufacturers, the collaboration between operational technology companies like the Seaman's, A B B, Schlumberger's, etcetera. And and it technology companies like ourselves that three part collaboration is sort of the heart of what I see as ecosystems coming together. The other dimensionality of ecosystems is also looking at it from a supply chain or value chain perspective because how something becomes more intelligent or smarter or more effective is also being able to work across the supply chain or value chain. So those, those are our key focus areas, make sure we are collaborating across value chains and supply chains as well as collaborating with manufacturers and oT operational technology companies to be able to bring these digital capabilities with the right capabilities of operational technology companies into the manufacturers. >>If I asked you, how is you doing that? What specifically would you say? I mean, how are you collaborating? What's some examples, give some examples of of this in action? >>Certainly. So we recently announced uh over the last say nine months or so, three strategic very translated partnerships. The first one I'll share with you is uh is which number number two is the world's largest oil field services company and now also the world's largest distal technology company for the oil and gas industry. So we've collaborated with them to bring hybrid cloud to the digital platforms so they now can deploy the capabilities to any customer regardless of whether they want it in country or on a public cloud. Another example is we've we've established a data platform which number J for the oil and gas industry to be able to bring again that data platform to any location around the world. The advantage of hybrid, the advantage of A. I with the B. B. What we've done is we've taken our smarts in I. T. Security connected with their products and capabilities for operational systems and now are delivering an into institution that you can get cyber alerts or issues coming from from manufacturing systems right down to right up to an I. T. Command center where you're seeing all the events and alerts so that they can be acted upon right away. So that's a great example of collaborating with from a security point of view. The 3rd 1 is industrial iot with ceilings and we've partnered with Siemens to deliver their minds Fear Private cloud edition delivered on our red hat Hybrid cloud. So this is an example where we are able to take our horizontal technologies, apply it with their vertical smarts and deep industry cause of context put our services capabilities on top of it so they can deliver their innovations anymore. >>It is such an expert on this, such a great leader on this area. And I have to ask you, you know, you've been in this um mode of evangelizing and leading teams and building solutions around digital re platform or whatever you wanna call her innovation. Um what's the big deal now? If you had to? I mean, it seems like it's all coming together with red hat under the covers, get distributed networks with the edge, it's all kind of coming together now for the verticals because you get the best of both worlds programmable scalable infrastructure with modern software applications on top. I mean you've been even even in the industry for many, many waves, why is this wave so big and important? >>So I think there is no longer uh big reason why it's important. I think there's no no reason why companies have to be convinced now the clarity is there, that this needs to happen. So that's one. The second is I think there is a high degree of expectation among consumers, among employees and among among customers as well that everything that we touch will be intelligent. So these technologies really unlock the value, uh unlock the value and they can be deployed at scale. That's really, I think what we're seeing as the focus now and being able to deliver the innovation anywhere, whether someone wants it at the edge next to a machine that's operating or be able to look at how a manufacturing facility or different product portfolio is doing in the boardroom, it's all available and so that shop floor, the top floor connection is what everybody is aiming for. We also now called edge to enterprise >>And everything works better. The employees are happy, people are happy to, stakeholders are happy finish. Great insight. Thank you for sharing here on the Cube for think 2021. Thanks for coming on the Cube. >>Absolutely. Thanks for having me. >>Okay. I'm John Kerry hosted the queue for IBM think 2021. Thanks for watching. Yeah. Mm. Yeah.

>>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome to cube three 60 fives coverage of the Accenture executive summit. Part of AWS reinvent. I'm your host Rebecca Knight. Today we are joined by a cube alum Karthik NurAin. He is Accenture senior managing director and lead Accenture cloud. First, welcome back to the show Karthik. >>Thank you. Thanks for having me here. >>Always a pleasure. So I want to talk to you. You are an industry veteran, you've been in Silicon Valley for decades. Um, I want to hear from your perspective what the impact of the COVID-19 pandemic has been, what are you hearing from clients? What are they struggling with? What are their challenges that they're facing day to day? >>I think, um, COVID-19 is being a eye-opener from, you know, various facets, you know, um, first and foremost, it's a, it's a head, um, situation that everybody's facing, which is not just, uh, highest economic bearings to it. It has enterprise, um, an organization with bedding to it. And most importantly, it's very personal to people, um, because they themselves and their friends, family near and dear ones are going to this challenge, uh, from various different dimension. But putting that aside, when you come to it from an organization enterprise standpoint, it has changed everything well, the behavior of organizations coming together, working in their campuses, working with each other as friends, family, and, uh, um, near and dear colleagues, all of them are operating differently. So that's what big change to get things done in a completely different way, from how they used to get things done. >>Number two, a lot of things that were planned for normal scenarios, like their global supply chain, how they interact with their client customers, how they coordinate with their partners on how that employees contribute to the success of an organization at all changed. And there are no data models that give them a hint of something like this for them to be prepared for this. So we are seeing organizations, um, that have adapted to this reasonably okay, and are, you know, launching to innovate faster in this. And there are organizations that have started with struggling, but are continuing to struggle. And the gap, uh, between the leaders and legs are widening. So this is creating opportunities in a different way for the leaders, um, with a lot of pivot their business, but it's also creating significant challenge for the lag guides, uh, as we defined in our future systems research that we did a year ago, uh, and those organizations are struggling further. So the gap is actually whitening. >>So you've just talked about the widening gap. I've talked about the tremendous uncertainty that so many companies, even the ones who have adapted reasonably well, uh, in this, in this time, talk a little bit about Accenture cloud first and why, why now? >>I think it's a great question. Um, we believe that for many of our clients COVID-19 has turned, uh, cloud from an experimentation aspiration to an origin mandate. What I mean by that is everybody has been doing something on the other end cloud. There's no company that says we don't believe in cloud. Uh, our, we don't want to do cloud. It was how much they did in cloud. And they were experimenting. They were doing the new things in cloud. Um, but they were operating a lot of their core business outside the cloud or not in the cloud. Those organizations have struggled to operate in this new normal, in a remote fashion as with us, uh, that ability to pivot to all the changes the pandemic has brought to them. But on the other hand, the organizations that had a solid foundation in cloud were able to collect faster and not actually gone into the stage of innovating faster and driving a new behavior in the market, new behavior within their organization. >>So we are seeing that spend to make is actually fast-forwarded something that we always believed was going to happen. This, uh, uh, moving to cloud over the next decade is fast, forwarded it to, uh, happen in the next three to five years. And it's created this moment where it's a once in an era, really replatforming of businesses in the cloud that we are going to see. And we see this moment as a cloud first moment where organizations will use cloud as the, the canvas and the foundation with which they're going to reimagine their business after they were born in the cloud. Uh, and this requires a whole new strategy. Uh, and as Accenture, we are getting a lot in cloud, but we thought that this is the moment where we bring all of that capabilities together because we need a strategy for addressing, moving to cloud are embracing cloud in a holistic fashion. And that's what Accenture cloud first brings together a holistic strategy, a team that's 70,000 plus people that's coming together with rich cloud skills, but investing to tie in all the various capabilities of cloud to Delaware, that holistic strategy to our clients. So I want you to >>Delve into a little bit more about what this strategy actually entails. I mean, it's clearly about embracing change and being willing to experiment and, and having capabilities to innovate. Can you tell us a little bit more about what this strategy entails? >>Yeah. The reason why we say that there's a need for the strategy is, like I said, COVID is not new. There's almost every customer client is doing something with the cloud, but all of them have taken different approaches to cloud and different boundaries to cloud. Some organizations say, I just need to consolidate my multiple data centers to a small data center footprint and move the nest to cloud. Certain other organizations say that well, I'm going to move certain workloads to cloud. Certain other organizations said, well, I'm going to build this Greenfield application or workload in cloud. Certain other said, um, I'm going to use the power of AI ML in the cloud to analyze my data and drive insights. But a cloud first strategy is all of this tied with the corporate strategy of the organization with an industry specific cloud journey to say, if in this current industry, if I were to be reborn in the cloud, would I do it in the exact same passion that I did in the past, which means that the products and services that they offer need to be the matching, how they interact with that customers and partners need to be revisited, how they bird and operate their IP systems need to be the, imagine how they unearthed the data from all the systems under which they attract need to be liberated so that you could drive insights of cloud. >>First strategy. Hans is a corporate wide strategy, and it's a C-suite responsibility. It doesn't take the ownership away from the CIO or CIO, but the CIO is, and CDI was felt that it was just their problem and they were to solve it. And everyone as being a customer, now, the center of gravity is elevated to it becoming a C-suite agenda on everybody's agenda, where probably the CDI is the instrument to execute that that's a holistic cloud-first strategy >>And it, and it's a strategy, but the way you're describing it, it sounds like it's also a mindset and an approach, as you were saying, this idea of being reborn in the cloud. So now how do I think about things? How do I communicate? How do I collaborate? How do I get done? What I need to get done. Talk a little bit about how this has changed, the way you support your clients and how Accenture cloud first is changing your approach to cloud services. >>Wonderful. Um, you know, I did not color one very important aspect in my previous question, but that's exactly what you just asked me now, which is to do all of this. I talked about all of the vehicles, uh, an organization or an enterprise is going to go to, but the good part is they have one constant. And what is that? That is their employees, uh, because you do, the employees are able to embrace this change. If they are able to, uh, change them, says, pivot them says retool and train themselves to be able to operate in this new cloud. First one, the ability to reimagine every function of the business would be happening at speed. And cloud first approach is to do all of this at speed, because innovation is deadly proposed there, do the rate of probability on experimentation. You need to experiment a lot for any kind of experimentation. >>There's a probability of success. Organizations need to have an ability and a mechanism for them to be able to innovate faster for which they need to experiment a lot. The more the experiment and the lower cost at which they experiment is going to help them experiment a lot and experiment demic speed, fail fast, succeed more. And hence, they're going to be able to operate this at speed. So the cloud-first mindset is all about speed. I'm helping the clients fast track that innovation journey, and this is going to happen. Like I said, across the enterprise and every function across every department, I'm the agent of this change is going to be the employee's weapon, race, this change through new skills and new grueling and new mindset that they need to adapt to. >>So Karthik what you're describing it, it sounds so exciting. And yet for a pandemic wary workforce, that's been working remotely that may be dealing with uncertainty if for their kid's school and for so many other aspects of their life, it sounds hard. So how are you helping your clients, employees get onboard with this? And because the change management is, is often the hardest part. >>Yeah, I think it's, again, a great question. A bottle has only so much capacity. Something got to come off for something else to go in. That's what you're saying is absolutely right. And that is again, the power of cloud. The reason why cloud is such a fundamental breakthrough technology and capability for us to succeed in this era, because it helps in various forms. What we talked so far is the power of innovation that could create, but cloud can also simplify the life of the employees in an enterprise. There are several activities and tasks that people do in managing their complex infrastructure, complex ID landscape. They used to do certain jobs and activities in a very difficult, uh, underground about with cloud has simplified. And democratised a lot of these activities. So that things which had to be done in the past, like managing the complexity of the infrastructure, keeping them up all the time, managing the, um, the obsolescence of the capabilities and technologies and infrastructure, all of that could be offloaded to the cloud. >>So that the time that is available for all of these employees can be used to further innovate. Every organization is good to spend almost the same amount of money, but rather than spending activities, by looking at the rear view mirror on keeping the lights on, they're going to spend more money, more time, more energy, and spend their skills on things that are going to add value to their organization. Because you, every innovation that an enterprise can give to their end customer need not come from that enterprise. The word of platform economy is about democratising innovation. And the power of cloud is to get all of these capabilities from outside the four walls of the enterprise, >>It will add value to the organization, but I would imagine also add value to that employee's life because that employee, the employee will be more engaged in his or her job and therefore bring more excitement and energy into her, his or her day-to-day activities too. >>Absolutely. Absolutely. And this is, this is a normal evolution we would have seen everybody would have seen in their lives, that they keep moving up the value chain of what activities that, uh, gets performed buying by those individuals. And there's this, um, you know, no more true than how the United States, uh, as an economy has operated where, um, this is the power of a powerhouse of innovation, where the work that's done inside the country keeps moving up to that. You change. And, um, us leverages the global economy for a lot of things that is required to power the United States and that global economic, uh, phenomenon is very proof for an enterprise as well. There are things that an enterprise needs to do them soon. There are things an employee needs to do themselves. Um, but there are things that they could leverage from the external innovation and the power of innovation that is coming from technologies like cloud. >>So at Accenture, you have long, long, deep Stan, sorry, you have deep and long standing relationships with many cloud service providers, including AWS. How does the Accenture cloud first strategy, how does it affect your relationships with those providers? >>Yeah, we have great relationships with cloud providers like AWS. And in fact, in the cloud world, it was one of the first, um, capability that we started about years ago, uh, when we started developing these capabilities. But five years ago, we hit a very important milestone where the two organizations came together and said that we are forging a pharma partnership with joint investments to build this partnership. And we named that as a Accenture, AWS business group ABG, uh, where we co-invest and brought skills together and develop solutions. And we will continue to do that. And through that investment, we've also made several acquisitions that you would have seen in the recent times, like, uh, an invoice and gecko that we made acquisitions in in Europe. But now we're taking this to the next level. What we are saying is two cloud first and the $3 billion investment that we are bringing in, uh, through cloud first, we are going to make specific investment to create unique joint solution and landing zones foundation, um, cloud packs with which clients can accelerate their innovation or their journey to cloud first. >>And one great example is what we are doing with Takeda, uh, billable, pharmaceutical giant, um, between we've signed a five-year partnership. And it was out in the media just a month ago or so, where we are, the two organizations are coming together. We have created a partnership as a power of three partnership where the three organizations are jointly hoarding hats and taking responsibility for the innovation and the leadership position that Decatur wants to get to with this. We are going to simplify their operating model and organization by providing it flexibility. We're going to provide a lot more insights. Tequila has a 230 year old organization. Imagine the amount of trapped data and intelligence that is there. How about bringing all of that together with the power of AWS and Accenture and Takeda to drive more customer insights, um, come up with breakthrough, uh, R and D uh, accelerate clinical trials and improve the patient experience using AI ML and edge technologies. So all of these things that we will do through this partnership with joint investment from Accenture cloud first, as well as partner like AWS, so that Takeda can realize their gain. And, uh, they're seeing you actually made a statement that five years from now, every ticket an employee will have an AI assistant. That's going to make that beginner employee move up the value chain on how they contribute and add value to the future of tequila with the AI assistant, making them even more equipped and smarter than what they could be otherwise. >>So, one last question to close this out here. What is your future vision for, for Accenture cloud first? What are we going to be talking about at next year's Accenture executive summit? Yeah, the future >>Is going to be, um, evolving, but the part that is exciting to me, and this is, uh, uh, a fundamental belief that we are entering a new era of industrial revolution from industry first, second, and third industry. The third happened probably 20 years ago with the advent of Silicon and computers and all of that stuff that happened here in the Silicon Valley. I think the fourth industrial revolution is going to be in the cross section of, uh, physical, digital and biological boundaries. And there's a great article, um, in what economic forum that, that people, uh, your audience can Google and read about it. Uh, but the reason why this is very, very important is we are seeing a disturbing phenomenon that over the last 10 years, they are seeing a Blackwing of the, um, labor productivity and innovation, which has dropped to about 2.1%. When you see that kind of phenomenon over that longer period of time, there has to be breakthrough innovation that needs to happen to come out of this barrier and get to the next base camp, as I would call it to further this productivity, um, lack that we are seeing, and that is going to happen in the intersection of the physical, digital and biological boundaries. >>And I think cloud is going to be the connective tissue between all of these three, to be able to provide that where it's the edge, especially is going to come closer to the human lives. It's going to come from cloud pick totally in your mind, you can think about cloud as central, either in a private cloud, in a data center or in a public cloud, you know, everywhere. But when you think about edge, it's going to be far reaching and coming close to where we live and maybe work and very, um, get entertained and so on and so forth. And there's going to be, uh, intervention in a positive way in the field of medicine, in the field of entertainment, in the field of, um, manufacturing in the field of, um, uh, you know, mobility. When I say mobility, human mobility, people, transportation, and so on and so forth with all of this stuff, cloud is going to be the connective tissue and the vision of cloud first is going to be, uh, you know, blowing through this big change that is going to happen. And the evolution that is going to happen where, you know, the human grace of mankind, um, our person kind of being very gender neutral in today's world. Um, go first needs to be that beacon of, uh, creating the next generation vision for enterprises to take advantage of that kind of an exciting future. And that's why it, Accenture. We say, let there be change as our, as a purpose. >>I genuinely believe that cloud first is going to be in the forefront of that change agenda, both for Accenture as well as for the rest of the world. Excellent. Let there be change, indeed. Thank you so much for joining us Karthik. A pleasure I'm Rebecca night's stay tuned for more of Q3 60 fives coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS >>Welcome everyone to the Q virtual and our coverage of the Accenture executive summit, which is part of AWS reinvent 2020. I'm your host Rebecca Knight. Today, we are talking about the green, the cloud and joining me is Kishor Dirk. He is Accenture senior managing director cloud first global services lead. Thank you so much for coming on the show. Kishor nice to meet you. So I want to start by asking you what it is that we mean when we say green cloud, we know that sustainability is a business imperative. So many organizations around the world are committing to responsible innovation, lowering carbon emissions, but what's this, what is it? What does it mean when they talk about cloud from a sustainability perspective? I think it's about responsible innovation being cloud is a cloud first approach that has profits and benefit the clients by helping reduce carbon emissions. >>Think about it this way. You have a large number of data centers. Each of these data centers are increasing by 14% every year. And this double digit growth. What you're seeing is these data centers and the consumption is nearly coolant to the kind of them should have a country like Spain. So the magnitude of the problem that is out there and how do we pursue a green approach. If you look at this, our Accenture analysis, in terms of the migration to public cloud, we've seen that we can reduce that by 59 million tons of CO2 per year with just the 5.9% reduction in total ID emissions and equates this to 22 million cars off the road. And the magnitude of reduction can go a long way in meeting climate change commitments, particularly for data sensitive. >>Wow, that's incredible. What the numbers that you're putting forward are, are absolutely mind blowing. So how does it work? Is it a simple cloud migration? So, you know, when companies begin their cloud journey and then they confront, uh, with them a lot of questions, the decision to make, uh, this particular, uh, element sustainable in the solution and benefits they drive and they have to make wise choices, and then they will be unprecedented level of innovation leading to both a greener planet, as well as, uh, a greener balance sheet, I would say, uh, so effectively it's all about ambition data, the ambition, greater the reduction in carbon emissions. So from a cloud migration perspective, we look at it as a, as a simple solution with approaches and sustainability benefits, uh, that vary based on things it's about selecting the right cloud provider, a very carbon thoughtful provider and the first step towards a sustainable cloud journey. >>And here we're looking at cloud operators, obviously they have different corporate commitments towards sustainability, and that determines how they plan, how they build, uh, their, uh, uh, the data centers, how they are consumed and assumptions that operate there and how they, or they retire their data centers. Then, uh, the next element that you want to do is how do you build it ambition, you know, for some of the companies, uh, and average on-prem, uh, drives about 65% energy reduction and the carbon emissions and reduction number was 84%, which is kind of good, I would say. But then if you could go up to 98% by configuring applications to the cloud, that is significant benefit for, uh, for the board. And obviously it's a, a greener cloud that we're talking about. And then the question is, how far can you go? And, uh, you know, the, obviously the companies have to unlock greater financial societal environmental benefits, and Accenture has this cloud based circular operations and sustainable products and services that we bring into play. So it's a, it's a very thoughtful, broader approach that w bringing in, in terms of, uh, just a simple concept of cloud migration, >>We know that in the COVID era, shifting to the cloud has really become a business imperative. How is Accenture working with its clients at a time when all of this movement has been accelerated? How do you partner and what is your approach in terms of helping them with their migration? >>Yeah, I mean, let, let me talk a little bit about the pandemic and the crisis that is there today. And if you really look at that in terms of how we partnered with a lot of our clients in terms of the cloud first approach, I'll give you a couple of examples. We worked with rolls Royce, McLaren, DHL, and others, as part of the ventilator challenge consortium, again, to, uh, coordinate production of medical ventilator surgically needed for the UK health service. Many of these farms I've taken similar initiatives in, in terms of, uh, you know, from a few manufacturers hand sanitizers and to hand sanitizers, and again, leading passionate labels, making PPE, and again, at the UN general assembly, we launched the end-to-end integration guide that helps company essentially to have a sustainable development goals. And that's how we have parking at a very large scale. >>Uh, and, and if you really look at how we work with our clients and what is Accenture's role there, uh, you know, from, in terms of our clients, you know, there are multiple steps that we look at. One is about, uh, planning, building, deploying, and managing an optimal green cloud solution. And Accenture has this concept of, uh, helping clients with a platform to kind of achieve that goal. And here we are having, we are having a platform or a mine app, which has a module called BGR advisor. And this is a capability that helps you provide optimal green cloud, uh, you know, a business case, and obviously a blueprint for each of our clients and right from the start in terms of how do we complete cloud migration recommendation to an improved solution, accurate accuracy to obviously bringing in the end to end perspective, uh, you know, with this green card advisor capability, we're helping our clients capture what we call as a carbon footprint for existing data centers and provide, uh, I would say the current cloud CO2 emission score that, you know, obviously helps them, uh, with carbon credits that can further that green agenda. >>So essentially this is about recommending a green index score, reducing carbon footprint for migration migrating for green cloud. And if we look at how Accenture itself is practicing what we preach, 95% of our applications are in the cloud. And this migration has helped us, uh, to lead to about $14.5 million in benefit. And in the third year and another 3 million analytics costs that are saved through right-sizing a service consumption. So it's a very broad umbrella and a footprint in terms of how we engage societaly with the UN or our clients. And what is it that we exactly bring to our clients in solving a specific problem? >>Accenture isn't is walking the walk, as you say yes. >>So that's that instead of it, we practice what we preach, and that is something that we take it to heart. We want to have a responsible business and we want to practice it. And we want to advise our clients around that >>You are your own use case. And so they can, they know they can take your advice. So talk a little bit about, um, the global, the cooperation that's needed. We know that conquering this pandemic is going to take a coordinated global effort and talk a little bit about the great reset initiative. First of all, what is that? Why don't we, why don't we start there and then we can delve into it a little bit more. >>Okay. So before we get to how we are cooperating, the great reset, uh, initiative is about improving the state of the world. And it's about a group of global stakeholders cooperating to simultaneously manage the direct consequences of their COVID-19 crisis. Uh, and in spirit of this cooperation that we're seeing during COVID-19, uh, which will obviously either to post pandemic, to tackle the world's pressing issues. As I say, uh, we are increasing companies to realize a combined potential of technology and sustainable impact to use enterprise solutions, to address with urgency and scale, and, um, obviously, uh, multiple challenges that are facing our world. One of the ways that you're increasing, uh, companies to reach their readiness cloud with Accenture's cloud core strategy is to build a solid foundation that is resilient and will be able to faster to the current, as well as future times. Now, when you think of cloud as the foundation, uh, that drives the digital transformation, it's about scale speed, streamlining your operations, and obviously reducing costs. >>And as these businesses seize the construct of cloud first, they must remain obviously responsible and trusted. Now think about this, right, as part of our analysis, uh, that profitability can co-exist with responsible and sustainable practices. Let's say that all the data centers, uh, migrated from on-prem to cloud based, we estimate that would reduce carbon emissions globally by 60 million tons per year. Uh, and think about it this way, right? Easier metric would be taking out 22 million cars off the road. Um, the other examples that you've seen, right, in terms of the NHS work that they're doing, uh, in, in UK to build, uh, uh, you know, uh, Microsoft teams in based integration. And, uh, the platform rolled out for 1.2 million in interest users, uh, and got 16,000 users that we were able to secure, uh, instant messages, obviously complete audio video calls and host virtual meetings across India. So, uh, this, this work that we did with NHS is something that we have are collaborating with a lot of tools and powering businesses. >>Well, you're vividly describing the business case for sustainability. What do you see as the future of cloud when thinking about it from this lens of sustainability, and also going back to what you were talking about in terms of how you are helping your, your fostering cooperation within these organizations. >>Yeah, that's a very good question. So if you look at today, right, businesses are obviously environmentally aware and they are expanding efforts to decrease power consumption, carbon emissions, and they want to run a sustainable operational efficiency across all elements of their business. And this is an increasing trend, and there is that option of energy efficient infrastructure in the global market. And this trend is the cloud first thinking. And with the right cloud migration that we've been discussing is about unlocking new opportunity, like clean energy foundations enable enabled by cloud based geographic analysis, material, waste reductions, and better data insights. And this is something that, uh, uh, we'll we'll drive, uh, with obviously faster analytics platform that is out there. Now, the sustainability is actually the future of business, which is companies that are historically different, the financial security or agility benefits to cloud. Now sustainability becomes an imperative for them. And I would on expedience Accenture's experience with cloud migrations, we have seen 30 to 40% total cost of ownership savings. And it's driving a greater workload, flexibility, better service, your obligation, and obviously more energy efficient, uh, public clouds that cost we'll see that, that drive a lot of these enterprise own data centers. So in our view, what we are seeing is that this, this, uh, sustainable cloud position helps, uh, helps companies to, uh, drive a lot of the goals in addition to their financial and other goods. >>So what should organizations who are, who are watching this interview and saying, Hey, I need to know more, what, what do you recommend to them? And what, where should they go to get more information on Greenplum? >>No, if you you're, if you are a business leader and you're thinking about which cloud provider is good, or how, how should applications be modernized to meet our day-to-day needs, which cloud driven innovations should be priorities. Uh, you know, that's why Accenture, uh, formed up the cloud first organization and essentially to provide the full stack of cloud services to help our clients become a cloud first business. Um, you know, it's all about excavation, uh, the digital transformation innovating faster, creating differentiated, uh, and sustainable value for our clients. And we're powering it up at 70,000 cloud professionals, $3 billion investment, and, uh, bringing together and services for our clients in terms of cloud solutions. And obviously the ecosystem partnership that we have that we are seeing today, uh, and the assets that help our clients realize their goals. Um, and again, to do reach out to us, uh, we can help them determine obviously, an optimal, sustainable cloud for solution that meets the business needs and being unprecedented levels of innovation. Our experience will be our advantage. And now more than ever, Rebecca, >>Just closing us out here. Do you have any advice for these companies who are navigating a great deal of uncertainty? We, what, what do you think the next 12 to 24 months? What do you think that should be on the minds of CEOs as they go through? >>So, as CEO's are thinking about rapidly leveraging cloud, migrating to cloud, uh, one of the elements that we want them to be thoughtful about is can they do that, uh, with unprecedent level of innovation, but also build a greener planet and a greener balance sheet, if we can achieve this balance and kind of, uh, have a, have a world which is greener, I think the world will win. And we all along with Accenture clients will win. That's what I would say, uh, >>Optimistic outlook. And I will take it. Thank you so much. Kishor for coming on the show >>That was >>Accenture's Kishor Dirk, I'm Rebecca Knight stay tuned for more of the cube virtuals coverage of the Accenture executive summit >>Around the globe. >>It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cube virtual and our coverage of the Accenture executive summit. Part of AWS reinvent 2020. I'm your host Rebecca Knight. Today, we are talking about the power of three. And what happens when you bring together the scientific know-how of a global bias biopharmaceutical powerhouse in Takeda, a leading cloud services provider in AWS, and Accenture's ability to innovate, execute, and deliver innovation. Joining me to talk about these things. We have Aaron, sorry, Arjun, baby. He is the senior managing director and chairman of Accenture's diamond leadership council. Welcome Arjun Karl hick. He is the chief digital and information officer at Takeda. >>What is your bigger, thank you, Rebecca >>And Brian bowhead, global director, and head of the Accenture AWS business group at Amazon web services. Thanks so much for coming on. Thank you. So, as I said, we're talking today about this relationship between, uh, your three organizations. Carl, I want to talk with you. I know you're at the beginning of your cloud journey. What was the compelling reason? What, what, why, why move to the cloud and why now? >>Yeah, no, thank you for the question. So, you know, as a biopharmaceutical leader, we're committed to bringing better health and a brighter future to our patients. We're doing that by translating science into some really innovative and life transporting therapies, but throughout, you know, we believe that there's a responsible use of technology, of data and of innovation. And those three ingredients are really key to helping us deliver on that promise. And so, you know, while I think, uh, I'll call it, this cloud journey is already always been a part of our strategy. Um, and we've made some pretty steady progress over the last years with a number of I'll call it diverse approaches to the digital and AI. We just weren't seeing the impact at scale that we wanted to see. Um, and I think that, you know, there's a, there's a need ultimately to, you know, accelerate and, uh, broaden that shift. >>And, you know, we were commenting on this earlier, but there's, you know, it's been highlighted by a number of factors. One of those has been certainly a number of the large acquisitions we've made Shire, uh, being the most pressing example, uh, but also the global pandemic, both of those highlight the need for us to move faster, um, at the speed of cloud, ultimately. Uh, and so we started thinking outside of the box because it was taking us too long and we decided to leverage this strategic partner model. Uh, and it's giving us a chance to think about our challenges very differently. We call this the power of three, uh, and ultimately our focus is singularly on our patients. I mean, they're waiting for us. We need to get there faster. It can take years. And so I think that there is a focus on innovation, um, at a rapid speed, so we can move ultimately from treating conditions to keeping people healthy. >>So as you are embarking on this journey, what are some of the insights you want to share about, about what you're seeing so far? >>Yeah, no, it's a great question. So, I mean, look, maybe right before I highlight some of the key insights, uh, I would say that, you know, with cloud now as the, as the launchpad for innovation, you know, our vision all along has been that in less than 10 years, we want every single to kid, uh, associate we're employed to be empowered by an AI assistant. And I think that, you know, that's going to help us make faster, better decisions. That'll help us, uh, fundamentally deliver transformative therapies and better experiences to, to that ecosystem, to our patients, to physicians, to payers, et cetera, much faster than we previously thought possible. Um, and I think that technologies like cloud and edge computing together with a very powerful I'll call it data fabric is going to help us to create this, this real-time, uh, I'll call it the digital ecosystem. >>The data has to flow ultimately seamlessly between our patients and providers or partners or researchers, et cetera. Uh, and so we've been thinking about this, uh, I'll call it legal, hold up, sort of this pyramid, um, that helps us describe our vision. Uh, and a lot of it has to do with ultimately modernizing the foundation, modernizing and rearchitecting, the platforms that drive the company, uh, heightening our focus on data, which means that there's an accelerated shift towards enterprise data platforms and digital products. And then ultimately, uh, uh, P you know, really an engine for innovation sitting at the very top. Um, and so I think with that, you know, there's a few different, uh, I'll call it insights that, you know, are quickly kind of come zooming into focus. I would say one is this need to collaborate very differently. Um, you know, not only internally, but you know, how do we define ultimately, and build a connected digital ecosystem with the right partners and technologies externally? >>I think the second, uh, component that maybe people don't think as much about, but, you know, I find critically important is for us to find ways of really transforming our culture. We have to unlock talent and shift the culture certainly as a large biopharmaceutical very differently. And then lastly, you've touched on it already, which is, you know, innovation at the speed of cloud. How do we re-imagine that, you know, how do ideas go from getting tested and months to kind of getting tested in days? You know, how do we collaborate very differently? Uh, and so I think those are three, uh, perhaps of the larger I'll call it, uh, insights that, you know, the three of us are spending a lot of time thinking about right now. >>So Arjun, I want to bring you into this conversation a little bit. Let's, let's delve into those a bit. Talk first about the collaboration, uh, that Carl was referencing there. How, how have you seen that it is enabling, uh, colleagues and teams to communicate differently and interact in new and different ways? Uh, both internally and externally, as Carl said, >>No, th thank you for that. And, um, I've got to give call a lot of credit, because as we started to think about this journey, it was clear, it was a bold ambition. It was, uh, something that, you know, we had all to do differently. And so the, the concept of the power of three that Carl has constructed has become a label for us as a way to think about what are we going to do to collectively drive this journey forward. And to me, the unique ways of collaboration means three things. The first one is that, um, what is expected is that the three parties are going to come together and it's more than just the sum of our resources. And by that, I mean that we have to bring all of ourselves, all of our collective capabilities, as an example, Amazon has amazing supply chain capabilities. >>They're one of the best at supply chain. So in addition to resources, when we have supply chain innovations, uh, that's something that they're bringing in addition to just, uh, talent and assets, similarly for Accenture, right? We do a lot, uh, in the talent space. So how do we bring our thinking as to how we apply best practices for talent to this partnership? So, um, as we think about this, so that's, that's the first one, the second one is about shared success very early on in this partnership, we started to build some foundations and actually develop seven principles that all of us would look at as the basis for this success shared success model. And we continue to hold that sort of in the forefront, as we think about this collaboration. And maybe the third thing I would say is this one team mindset. So whether it's the three of our CEOs that get together every couple of months to think about, uh, this partnership, or it is the governance model that Carl has put together, which has all three parties in the governance and every level of leadership, we always think about this as a collective group, so that we can keep that front and center. >>And what I think ultimately has enabled us to do is it allowed us to move at speed, be more flexible. And ultimately all we're looking at the target the same way, the North side, the same way. >>Brian, what about you? What have you observed and what are you thinking about in terms of how this is helping teams collaborate differently? >>Yeah, absolutely. And RJ made some, some great points there. And I think if you really think about what he's talking about, it's that, that diversity of talent, diversity of skill and viewpoint and even culture, right? And so we see that in the power of three. And then I think if we drill down into what we see at Takeda, and frankly, Takeda was, was really, I think, pretty visionary and on their way here, right. And taking this kind of cross-functional approach and applying it to how they operate day to day. So moving from a more functional view of the world to more of a product oriented view of the world, right? So when you think about we're going to be organized around a product or a service or a capability that we're going to provide to our customers or our patients or donors in this case, it implies a different structure, although altogether, and a different way of thinking, right? >>Because now you've got technical people and business experts and marketing experts, all working together in this is sort of cross collaboration. And what's great about that is it's really the only way to succeed with cloud, right? Because the old ways of thinking where you've got application people and infrastructure, people in business, people is suboptimal, right? Because we can all access this tool was, and these capabilities and the best way to do that, isn't across kind of a cross collaborative way. And so this is product oriented mindset. It's a keto was already on. I think it's allowed us to move faster in those areas. >>Carl, I want to go back to this idea of unlocking talent and culture. And this is something that both Brian and Arjun have talked about too. People are, are an essential part of their, at the heart of your organization. How will their experience of work change and how are you helping re-imagine and reinforce a strong organizational culture, particularly at this time when so many people are working remotely. >>Yeah. It's a great question. And it's something that, you know, I think we all have to think a lot about, I mean, I think, um, you know, driving this, this call it, this, this digital and data kind of capability building, uh, takes a lot of, a lot of thinking. So, I mean, there's a few different elements in terms of how we're tackling this one is we're recognizing, and it's not just for the technology organization or for those actors that, that we're innovating with, but it's really across all of the Cato where we're working through ways of raising what I'll call the overall digital leaders literacy of the organization, you know, what are the, you know, what are the skills that are needed almost at a baseline level, even for a global bio-pharmaceutical company and how do we deploy, I'll call it those learning resources very broadly. >>And then secondly, I think that, you know, we're, we're very clear that there's a number of areas where there are very specialized skills that are needed. Uh, my organization is one of those. And so, you know, we're fostering ways in which, you know, we're very kind of quickly kind of creating, uh, avenues excitement for, for associates in that space. So one example specifically, as we use, you know, during these very much sort of remote, uh, sort of days, we, we use what we call global it days, and we set a day aside every single month and this last Friday, um, you know, we, we create during that time, it's time for personal development. Um, and we provide active seminars and training on things like, you know, robotic process automation, data analytics cloud, uh, in this last month we've been doing this for months and months now, but in his last month, more than 50% of my organization participated, and there's this huge positive shift, both in terms of access and excitement about really harnessing those new skills and being able to apply them. >>Uh, and so I think that that's, you know, one, one element that, uh, can be considered. And then thirdly, um, of course, every organization to work on, how do you prioritize talent, acquisition and management and competencies that you can't rescale? I mean, there are just some new capabilities that we don't have. And so there's a large focus that I have with our executive team and our CEO and thinking through those critical roles that we need to activate in order to kind of, to, to build on this, uh, this business led cloud transformation. And lastly, probably the hardest one, but the one that I'm most jazzed about is really this focus on changing the mindsets and behaviors. Um, and I think there, you know, this is where the power of three is, is really, uh, kind of coming together nicely. I mean, we're working on things like, you know, how do we create this patient obsessed curiosity, um, and really kind of unlock innovation with a real, kind of a growth mindset. >>Uh, and the level of curiosity that's needed, not to just continue to do the same things, but to really challenge the status quo. So that's one big area of focus we're having the agility to act just faster. I mean, to worry less, I guess I would say about kind of the standard chain of command, but how do you make more speedy, more courageous decisions? And this is places where we can emulate the way that a partner like AWS works, or how do we collaborate across the number of boundaries, you know, and I think, uh, Arjun spoke eloquently to a number of partnerships that we can build. So we can break down some of these barriers and use these networks, um, whether it's within our own internal ecosystem or externally to help, to create value faster. So a lot of energy around ways of working and we'll have to check back in, but I mean, we're early in on this mindset and behavioral shift, um, but a lot of good early momentum. >>Carl you've given me a good segue to talk to Brian about innovation, because you said a lot of the things that I was the customer obsession and this idea of innovating much more quickly. Obviously now the world has its eyes on drug development, and we've all learned a lot about it, uh, in the past few months and accelerating drug development is all, uh, is of great interest to all of us. Brian, how does a transformation like this help a company's, uh, ability to become more agile and more innovative and at a quicker speed to, >>Yeah, no, absolutely. And I think some of the things that Carl talked about just now are critical to that, right? I think where sometimes folks fall short is they think, you know, we're going to roll out the technology and the technology is going to be the silver bullet where we're, in fact it is the culture. It is, is the talent. And it's the focus on that. That's going to be, you know, the determinant of success. And I will say, you know, in this power of three arrangement and Carl talked a little bit about the pyramid, um, talent and culture and that change, and the kind of thinking about that has been a first-class citizen since the very beginning, right. That absolutely is critical for, for being there. Um, and, and so that's been, that's been key. And so we think about innovation at Amazon and AWS, and Carl mentioned some of the things that, you know, partner like AWS can bring to the table is we talk a lot about builders, right? >>So kind of obsessive about builders. Um, and, and we meet what we mean by that is we at Amazon, we hire for builders, we cultivate builders and we like to talk to our customers about it as well. And it also implies a different mindset, right? When you're a builder, you have that, that curiosity, you have that ownership, you have that stake in whatever I'm creating, I'm going to be a co-owner of this product or this service, right. Getting back to that kind of product oriented mindset. And it's not just the technical people or the it people who are builders. It is also the business people as, as Carl talked about. Right. So when we start thinking about, um, innovation again, where we see folks kind of get into a little bit of a innovation pilot paralysis, is that you can focus on the technology, but if you're not focusing on the talent and the culture and the processes and the mechanisms, you're going to be putting out technology, but you're not going to have an organization that's ready to take it and scale it and accelerate it. >>Right. And so that's, that's been absolutely critical. So just a couple of things we've been doing with, with Takeda and Decatur has really been leading the way is, think about a mechanism and a process. And it's really been working backward from the customer, right? In this case, again, the patient and the donor. And that was an easy one because the key value of Decatur is to be a patient focused bio-pharmaceutical right. So that was embedded in their DNA. So that working back from that, that patient, that donor was a key part of that process. And that's really deep in our DNA as well. And Accenture's, and so we were able to bring that together. The other one is, is, is getting used to experimenting and even perhaps failing, right. And being able to iterate and fail fast and experiment and understanding that, you know, some decisions, what we call it at Amazon or two-way doors, meaning you can go through that door, not like what you see and turn around and go back. And cloud really helps there because the costs of experimenting and the cost of failure is so much lower than it's ever been. You can do it much faster and the implications are so much less. So just a couple of things that we've been really driving, uh, with the cadence around innovation, that's been really critical. Carl, where are you already seeing signs of success? >>Yeah, no, it's a great question. And so we chose, you know, uh, with our focus on innovation to try to unleash maybe the power of data digital in, uh, in focusing on what I call sort of a Maven. And so we chose our, our, our plasma derived therapy business, um, and you know, the plasma-derived therapy business unit, it develops critical life-saving therapies for patients with rare and complex diseases. Um, but what we're doing is by bringing kind of our energy together, we're focusing on creating, I'll call it state of the art digitally connected donation centers. And we're really modernizing, you know, the, the, the donor experience right now, we're trying to, uh, improve also I'll call it the overall plasma collection process. And so we've, uh, selected a number of alcohol at a very high speed pilots that we're working through right now, specifically in this, in this area. And we're seeing >>Really great results already. Um, and so that's, that's one specific area of focus are Jen, I want you to close this out here. Any ideas, any best practices advice you would have for other pharmaceutical companies that are, that are at the early stage of their cloud journey? Yes. Sorry. Arjun. >>Yeah, no, I was breaking up a bit. No, I think they, um, the key is what what's sort of been great for me to see is that when people think about cloud, you know, you always think about infrastructure technology. The reality is that the cloud is really the true enabler for innovation and innovating at scale. And, and if you think about that, right, in all the components that you need, uh, ultimately that's where the value is for the company, right? Because yes, you're going to get some cost synergies and that's great, but the true value is in how do we transform the organization in the case of the Qaeda and the life sciences clients, right. We're trying to take a 14 year process of research and development that takes billions of dollars and compress that right. Tremendous amounts of innovation opportunity. You think about the commercial aspect, lots of innovation can come there. The plasma derived therapy is a great example of how we're going to really innovate to change the trajectory of that business. So I think innovation is at the heart of what most organizations need to do. And the formula, the cocktail that Takeda has constructed with this Fuji program really has all the ingredients, um, that are required for that success. >>Great. Well, thank you so much. Arjun, Brian and Carl was really an enlightening conversation. >>Thank you. Yeah, it's been fun. Thanks Rebecca. >>And thank you for tuning into the cube. Virtual is coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cubes coverage of Accenture executive summit here at AWS reinvent. I'm your host Rebecca Knight for this segment? We have two guests. First. We have Helen Davis. She is the senior director of cloud platform services, assistant director for it and digital for the West Midlands police. Thanks so much for coming on the show, Helen, and we also have Matthew lb. He is Accenture health and public service associate director and West Midlands police account lead. Thanks so much for coming on the show. Matthew, thank you for joining us. So we are going to be talking about delivering data-driven insights to the West Midlands police force. Helen, I want to start with >>You. Can you tell us a little bit about the West Midlands police force? How big is the force and also what were some of the challenges that you were grappling with prior to this initiative? >>Yeah, certainly. So Westerners police is the second largest police force in the UK, outside of the metropolitan police in London. Um, we have an excessive, um, 11,000 people work at Westman ins police serving communities, um, through, across the Midlands region. So geographically, we're quite a big area as well, as well as, um, being population, um, density, having that as a, at a high level. Um, so the reason we sort of embarked on the data-driven insights platform and it, which was a huge change for us was for a number of reasons. Um, namely we had a lot of disparate data, um, which was spread across a range of legacy systems that were many, many years old, um, with some duplication of what was being captured and no single view for offices or, um, support staff. Um, some of the access was limited. You have to be in a, in an actual police building on a desktop computer to access it. Um, other information could only reach the offices on the front line, through a telephone call back to one of our enabling services where they would do a manual checkup, um, look at the information, then call the offices back, um, and tell them what they needed to know. So it was a very long laborious, um, process and not very efficient. Um, and we certainly weren't exploiting the data that we had in a very productive way. >>So it sounds like as you're describing, and I'm old clunky system that needed a technological, uh, reimagination. So what was the main motivation for, for doing, for making this shift? >>It was really, um, about making us more efficient and more effective in how we do how we do business. So, um, you know, certainly as a, as an it leader and some of my operational colleagues, we recognize the benefits, um, that data analytics could bring in, uh, in a policing environment, not something that was, um, really done in the UK at the time. You know, we have a lot of data, so we're very data rich and the information that we have, but we needed to turn it into information that was actionable. So that's where we started looking for, um, technology partners and suppliers to help us and sort of help us really with what's the art of the possible, you know, this hasn't been done before. So what could we do in this space? That's appropriate, >>Helen. I love that idea. What is the art of the possible, can you tell us a little bit about why you chose AWS? >>I think really, you know, as with all things and when we're procuring a partner in the public sector that, you know, there are many rules and regulations quite rightly as you would expect that to be because we're spending public money. So we have to be very, very careful and, um, it's, it's a long process and we have to be open to public scrutiny. So, um, we sort of look to everything, everything that was available as part of that process, but we recognize the benefits that Clyde would provide in this space because, you know, we're like moving to a cloud environment. We would literally be replacing something that was legacy with something that was a bit more modern. Um, that's not what we wanted to do. Our ambition was far greater than that. So I think, um, in terms of AWS, really, it was around scalability, interoperability, you know, just us things like the disaster recovery service, the fact that we can scale up and down quickly, we call it dialing up and dialing back. Um, you know, it's it's page go. So it just sort of ticked all the boxes for us. And then we went through the full procurement process, fortunately, um, it came out on top for us. So we were, we were able to move forward, but it just sort of had everything that we were looking for in that space. >>Matthew, I want to bring you into the conversation a little bit here. How are you working with a wet with the West Midlands police, sorry. And helping them implement this cloud-first >>Yeah, so I guess, um, by January the West Midlands police started, um, favorite five years ago now. So, um, we set up a partnership with the fools. I wanted to operate in a way that was very different to a traditional supplier relationship. Um, secretary that the data difference insights program is, is one of many that we've been working with last on, um, over the last five years, um, as having said already, um, cloud gave a number of, uh, advantages certainly from a big data perspective and things that, that enabled us today. Um, I'm from an Accenture perspective that allowed us to bring in a number of the different teams that we have say, cloud teams, security teams, um, and drafted from an insurance perspective, as well as the more traditional services that people would associate with the country. >>I mean, so much of this is about embracing comprehensive change to experiment and innovate and try different things. Matthew, how, how do you help, uh, an entity like West Midlands police think differently when they are, there are these ways of doing things that people are used to, how do you help them think about what is the art of the possible, as Helen said, >>There's a few things to that enable those being critical is trying to co-create solutions together. Yeah. There's no point just turning up with, um, what we think is the right answer, try and say, um, collectively work three, um, the issues that the fullest is seeing and the outcomes they're looking to achieve rather than simply focusing on a long list of requirements, I think was critical and then being really open to working together to create the right solution. Um, rather than just, you know, trying to pick something off the shelf that maybe doesn't fit the forces requirements in the way that it should too, >>Right. It's not always a one size fits all. >>Obviously, you know, today what we believe is critical is making sure that we're creating something that met the forces needs, um, in terms of the outcomes they're looking to achieve the financial envelopes that were available, um, and how we can deliver those in a, uh, iterative agile way, um, rather than spending years and years, um, working towards an outcome, um, that is gonna update before you even get that. >>So Helen, how, how are things different? What kinds of business functions and processes have been re-imagined in, in light of this change and this shift >>It's, it's actually unrecognizable now, um, in certain areas of the business as it was before. So to give you a little bit of, of context, when we, um, started working with essentially an AWS on the data driven insights program, it was very much around providing, um, what was called locally, a wizzy tool for our intelligence analyst to interrogate data, look at data, you know, decide whether they could do anything predictive with it. And it was very much sort of a back office function to sort of tidy things up for us and make us a bit better in that, in that area or a lot better in that area. And it was rolled out to a number of offices, a small number on the front line. Um, and really it was, um, in line with a mobility strategy that we, hardware officers were getting new smartphones for the first time, um, to do sort of a lot of things on, on, um, policing apps and things like that to again, to avoid them, having to keep driving back to police stations, et cetera. >>And the pilot was so successful. Every officer now has access to this data, um, on their mobile devices. So it literally went from a handful of people in an office somewhere using it to do sort of clever whizzbang things to, um, every officer in the force, being able to access that level of data at their fingertips. Literally. So what they were touched we've done before is if they needed to check and address or check details of an individual, um, just as one example, they would either have to, in many cases, go back to a police station to look it up themselves on a desktop computer. Well, they would have to make a call back to a centralized function and speak to an operator, relay the questions, either, wait for the answer or wait for a call back with the answer when those people are doing the data interrogation manually. >>So the biggest change for us is the self-service nature of the data we now have available. So officers can do it themselves on their phone, wherever they might be. So the efficiency savings from that point of view are immense. And I think just parallel to that is the quality of our, because we had a lot of data, but just because you've got a lot of data and a lot of information doesn't mean it's big data and it's valuable necessarily. Um, so again, it was having the single source of truth as we, as we call it. So you know that when you are completing those safe searches and getting the responses back, that it is the most accurate information we hold. And also you're getting it back within minutes, as opposed to, you know, half an hour, an hour or a drive back to a station. So it's making officers more efficient and it's also making them safer. The more efficient they are, the more time they have to spend out with the public doing what they, you know, we all should be doing, >>Seen that kind of return on investment, because what you were just describing with all the steps that we needed to be taken in prior to this, to verify an address say, and those are precious seconds when someone's life is on the line in, in sort of in the course of everyday police work. >>Absolutely. Yeah, absolutely. It's difficult to put a price on it. It's difficult to quantify. Um, but all the, you know, the minutes here and that certainly add up to a significant amount of efficiency savings, and we've certainly been able to demonstrate the officers are spending less time up police stations as a result or more time out on the front frontline also they're safer because they can get information about what may or may not be and address what may or may not have occurred in an area before very, very quickly without having to wait. >>Thank you. I want to hear your observations of working so closely with this West Midlands police. Have you noticed anything about changes in its culture and its operating model in how police officers interact with one another? Have you seen any changes since this technology change? >>What's unique about the Western new misplaces, the buy-in from the top down, the chief and his exact team and Helen as the leader from an IOT perspective, um, the entire force is bought in. So what is a significant change program? Uh, I'm not trickles three. Um, everyone in the organization, um, change is difficult. Um, and there's a lot of time effort. That's been put into both the technical delivery and the business change and adoption aspects around each of the projects. Um, but you can see the step change that is making in each aspect to the organization, uh, and where that's putting West Midlands police as a leader in, um, technology I'm policing in the UK. And I think globally, >>And this is a question for both of you because Matthew, as you said, change is difficult and there is always a certain intransigence in workplaces about this is just the way we've always done things and we're used to this and don't try us to get us. Don't try to get us to do anything new here. It works. How do you get the buy-in that you need to do this kind of digital transformation? >>I think it, it would be wrong to say it was easy. Um, um, we also have to bear in mind that this was one program in a five-year program. So there was a lot of change going on, um, both internally for some of our back office functions, as well as front Tai, uh, frontline offices. So with DDI in particular, I think the stat change occurred when people could see what it could do for them. You know, we had lots of workshops and seminars where we all talk about, you know, big data and it's going to be great and it's data analytics and it's transformational, you know, and quite rightly people that are very busy doing a day job that not necessarily technologists in the main and, you know, are particularly interested quite rightly so in what we are not dealing with the cloud, you know? >>And it was like, yeah, okay. It's one more thing. And then when they started to see on that, on their phones and what teams could do, that's when it started to sell itself. And I think that's when we started to see, you know, to see the stat change, you know, and, and if we, if we have any issues now it's literally, you know, our help desks in meltdown. Cause everyone's like, well, we call it manage without this anymore. And I think that speaks for itself. So it doesn't happen overnight. It's sort of incremental changes and then that's a step change in attitude. And when they see it working and they see the benefits, they want to use it more. And that's how it's become fundamental to all policing by itself, really, without much selling >>You, Helen just made a compelling case for how to get buy in. Have you discovered any other best practices when you are trying to get everyone on board for this kind of thing? >>We've um, we've used a lot of the traditional techniques, things around comms and engagement. We've also used things like, um, the 30 day challenge and nudge theory around how can we gradually encourage people to use things? Um, I think there's a point where all of this around, how do we just keep it simple and keep it user centric from an end user perspective? I think DDI is a great example of where the, the technology is incredibly complex. The solution itself is, um, you know, extremely large and, um, has been very difficult to, um, get delivered. But at the heart of it is a very simple front end for the user to encourage it and take that complexity away from them. Uh, I think that's been critical through the whole piece of DDR. >>One final word from Helen. I want to hear, where do you go from here? What is the longterm vision? I know that this has made productivity, um, productivity savings equivalent to 154 full-time officers. Uh, what's next, >>I think really it's around, um, exploiting what we've got. Um, I use the phrase quite a lot, dialing it up, which drives my technical architects crazy. But so, because it's apparently not that simple, but, um, you know, we've, we've been through significant change in the last five years and we are still continuing to batch all of those changes into everyday, um, operational policing. But what we need to see is we need to exploit and build on the investments that we've made in terms of data and claims specifically, the next step really is about expanding our pool of data and our functions. Um, so that, you know, we keep getting better and better at this. And the more we do, the more data we have, the more refined we can be, the more precise we are with all of our actions. Um, you know, we're always being expected to, again, look after the public purse and do more for less. >>And I think this is certainly an and our cloud journey and, and cloud first by design, which is where we are now, um, is helping us to be future-proofed. So for us, it's very much an investment. And I see now that we have good at embedded in operational policing for me, this is the start of our journey, not the end. So it's really exciting to see where we can go from here. Exciting times. Indeed. Thank you so much. Lily, Helen and Matthew for joining us. I really appreciate it. Thank you. And you are watching the cube stay tuned for more of the cubes coverage of the AWS reinvent Accenture executive summit. I'm Rebecca Knight from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome to the cube virtual coverage of the executive summit at AWS reinvent 2020 virtual. This is the cube virtual. We can't be there in person like we are every year we have to be remote. This executive summit is with special programming supported by Accenture where the cube virtual I'm your host John for a year, we had a great panel here called uncloud first digital transformation from some experts, Stuart driver, the director of it and infrastructure and operates at lion Australia, Douglas Regan, managing director, client account lead at lion for Accenture as a deep Islam associate director application development lead for Centure gentlemen, thanks for coming on the cube virtual that's a mouthful, all that digital, but the bottom line it's cloud transformation. This is a journey that you guys have been on together for over 10 years to be really a digital company. Now, some things have happened in the past year that kind of brings all this together. This is about the next generation organization. So I want to ask Stuart you first, if you can talk about this transformation at lion has undertaken some of the challenges and opportunities and how this year in particular has brought it together because you know, COVID has been the accelerant of digital transformation. Well, if you're 10 years in, I'm sure you're there. You're in the, uh, on that wave right now. Take a minute to explain this transformation journey. >>Yeah, sure. So a number of years back, we, we looked at kind of our infrastructure in our landscape trying to figure out where we >>Wanted to go next. And we were very analog based and stuck in the old it groove of, you know, Capitol reef rash, um, struggling to transform, struggling to get to a digital platform and we needed to change it up so that we could become very different business to the one that we were back then obviously cloud is an accelerant to that. And we had a number of initiatives that needed a platform to build on. And a cloud infrastructure was the way that we started to do that. So we went through a number of transformation programs that we didn't want to do that in the old world. We wanted to do it in a new world. So for us, it was partnering up with a dried organizations that can take you on the journey and, uh, you know, start to deliver bit by bit incremental progress, uh, to get to the, uh, I guess the promise land. >>Um, we're not, not all the way there, but to where we're on the way along. And then when you get to some of the challenges like we've had this year, um, it makes all of the hard work worthwhile because you can actually change pretty quickly, um, provide capacity and, uh, and increase your environments and, you know, do the things that you need to do in a much more dynamic way than we would have been able to previously where we might've been waiting for the hardware vendors, et cetera, to deliver capacity. So for us this year, it's been a pretty strong year from an it perspective and delivering for the business needs >>Before I hit the Douglas. I want to just real quick, a redirect to you and say, you know, if all the people said, Oh yeah, you got to jump on cloud, get in early, you know, a lot of naysayers like, well, wait till to mature a little bit, really, if you got in early and you, you know, paying your dues, if you will taking that medicine with the cloud, you're really kind of peaking at the right time. Is that true? Is that one of the benefits that comes out of this getting in the cloud? Yeah, >>John, this has been an unprecedented year, right. And, um, you know, Australia, we had to live through Bush fires and then we had covert and, and then we actually had to deliver a, um, a project on very nice transformational project, completely remote. And then we also had had some, some cyber challenges, which is public as well. And I don't think if we weren't moved into and enabled through the cloud, we would have been able to achieve that this year. It would have been much different and would have been very difficult to do the backing. We're able to work and partner with Amazon through this year, which is unprecedented and actually come out the other end and we've delivered a brand new digital capability across the entire business. Um, in many, you know, wouldn't have been impossible if we could, I guess, stayed in the old world. The fact that we were moved into the new Naval by the new allowed us to work in this unprecedented year. >>Just quilt. What's your personal view on this? Because I've been saying on the Cuban reporting necessity is the mother of all invention and the word agility has been kicked around as kind of a cliche, Oh, it'd be agile. You know, we're going to get the city, you get a minute on specifically, but from your perspective, uh, Douglas, what does that mean to you? Because there is benefits there for being agile. And >>I mean, I think as Stuart mentioned, right, in a lot of these things we try to do and, you know, typically, you know, hardware and, uh, the last >>To be told and, and, and always on the critical path to be done, we really didn't have that in this case, what we were doing with our projects in our deployments, right. We were able to move quickly able to make decisions in line with the business and really get things going. Right. So you see a lot of times in a traditional world, you have these inhibitors, you have these critical path, it takes weeks and months to get things done as opposed to hours and days, and, and truly allowed us to, we had to, you know, VJ things, move things. And, you know, we were able to do that in this environment with AWS to support and the fact that they can kind of turn things off and on as quickly as we needed. >>Yeah. Cloud-scale is great for speed. So DECA, Gardez get your thoughts on this cloud first mission, you know, it, you know, the dev ops world, they saw this early, that jumping in there, they saw the, the, the agility. Now the theme this year is modern applications with the COVID pandemic pressure, there's real business pressure to make that happen. How did you guys learn to get there fast? And what specifically did you guys do at Accenture and how did it all come together? Can you take us inside kind of how it played out? >>Right. So, yeah, we started off with, as we do in most cases with a much more bigger group, and we worked with lions functional experts and, uh, the lost knowledge that allowed the infrastructure had. Um, we then applied our journey to cloud strategy, which basically revolves around the seminars and, and, uh, you know, the deep three steps from our perspective, uh, assessing the current and bottom and setting up the new cloud environment. And as we go modernizing and, and migrating these applications to the cloud now, you know, one of the key things that, uh, you know, we learned along this journey was that, you know, you can have the best plans, but bottom line that we were dealing with, we often than not have to make changes, uh, what a lot of agility and also work with a lot of collaboration with the, uh, lion team, as well as, uh, uh, AWS. I think the key thing for me was being able to really bring it all together. It's not just, uh, you know, we want to hear it's all of us working together to make this happen. >>What were some of the learnings real quick journey there? >>So I think perspective, the key learnings were that, you know, uh, you know, work, when you look back at, uh, the, the infrastructure that was that we were trying to migrate over to the cloud. A lot of the documentation, et cetera, was not, uh, available. We were having to, uh, figure out a lot of things on the fly. Now that really required us to have, uh, uh, people with deep expertise who could go into those environments and, and work out, uh, you know, the best ways to, to migrate the workloads to the cloud. Uh, I think, you know, the, the biggest thing for me was making sure all the had on that real SMEs across the board globally, that we could leverage across the various technologies, uh, uh, and, and, and, you know, that would really work in our collaborative and agile environment with line. >>Let's do what I got to ask you. How did you address your approach to the cloud and what was your experience? >>Yeah, for me, it's around getting the foundations right. To start with and then building on them. Um, so, you know, you've got to have your, your, your process and you've got to have your, your kind of your infrastructure there and your blueprints ready. Um, AWS do a great job of that, right. Getting the foundations right. And then building upon it, and then, you know, partnering with Accenture allows you to do that very successfully. Um, I think, um, you know, the one thing that was probably surprising to us when we started down this journey and kind of after we got a long way down the track and looking backwards is actually how much you can just turn off. Right? So a lot of stuff that you, uh, you get electric with a legacy in your environment, and when you start to work through it with the types of people that civic just mentioned, you know, the technical expertise working with the business, um, you can really rationalize your environment and, uh, you know, cloud is a good opportunity to do that, to drive that legacy out. >>Um, so you know, a few things there, the other thing is, um, you've got to try and figure out the benefits that you're going to get out of moving here. So there's no point in just taking something that is not delivering a huge amount of value in the traditional world, moving it into the cloud, and guess what is going to deliver the same limited amount of value. So you've got to transform it, and you've got to make sure that you build it for the future and understand exactly what you're trying to gain out of it. So again, you need a strong collaboration. You need a good partners to work with, and you need good engagement from the business as well, because the kind of, uh, you know, digital transformation, cloud transformation, isn't really an it project, I guess, fundamentally it is at the core, but it's a business project that you've got to get the whole business aligned on. You've got to make sure that your investment streams are appropriate and that's, uh, you're able to understand the benefits and the value that say, you're going to drive back towards the business. >>Let's do it. If you don't mind me asking, what was some of the obstacles you encountered or learnings, um, that might different from the expectation we all been there, Hey, you know, we're going to change the world. Here's the sales pitch, here's the outcome. And then obviously things happen, you know, you learn legacy, okay. Let's put some containerization around that cloud native, um, all that rational. You're talking about what are, and you're going to have obstacles. That's how you learn. That's how perfection has developed. How, what obstacles did you come up with and how are they different from your expectations going in? >>Yeah, they're probably no different from other people that have gone down the same journey. If I'm totally honest, the, you know, 70 or 80% of what you do is relatively easy of the known quantity. It's relatively modern architectures and infrastructures, and you can upgrade, migrate, move them into the cloud, whatever it is, rehost, replatform, rearchitect, whatever it is you want to do, it's the other stuff, right? It's the stuff that always gets left behind. And that's the challenge. It's, it's getting that last bit over the line and making sure that you haven't been invested in the future while still carrying all of your legacy costs and complexity within your environment. So, um, to be quite honest, that's probably taken longer and has been more of a challenge than we thought it would be. Um, the other piece I touched on earlier on in terms of what was surprising was actually how much of, uh, your environment is actually not needed anymore. >>When you start to put a critical eye across it and understand, um, uh, ask the tough questions and start to understand exactly what, what it is you're trying to achieve. So if you ask a part of a business, do they still need this application or this service a hundred percent of the time, they will say yes until you start to lay out to them, okay, now I'm going to cost you this to migrate it or this, to run it in the future. And, you know, here's your ongoing costs and, you know, et cetera, et cetera. And then, uh, for a significant amount of those answers, you get a different response when you start to layer on the true value of it. So you start to flush out those hidden costs within the business, and you start to make some critical decisions as a company based on, uh, based on that. So that was a little tougher than we first thought and probably broader than we thought there was more of that than we anticipated, um, which actually results in a much cleaner environment, post post migration, >>You know, the old expression, if it moves automated, you know, it's kind of a joke on government, how they want to tax everything, you know, you want to automate, that's a key thing in cloud, and you've got to discover those opportunities to create value Stuart and Siddique. Mainly if you can weigh in on this love to know the percentage of total cloud that you have now, versus when you started, because as you start to uncover whether it's by design for purpose, or you discover opportunity to innovate, like you guys have, I'm sure it kind of, you took on some territory inside Lyon, what percentage of cloud now versus start? >>Yeah. And at the start it was minimal, right. You know, close to zero, right. Single and single digits. Right. It was mainly SAS environments that we had, uh, sitting in clouds when we, uh, when we started, um, Doug mentioned earlier on a really significant transformation project, um, that we've undertaken and recently gone live on a multi-year one. Um, you know, that's all stood up on AWS and is a significant portion of our environment, um, in terms of what we can move to cloud. Uh, we're probably at about 80 or 90% now. And the balance bit is, um, legacy infrastructure that is just going to retire as we go through the cycle rather than migrate to the cloud. Um, so we are significantly cloud-based and, uh, you know, we're reaping the benefits of it in a year, like 2020, and makes you glad that you did all of the hard yards in the previous years when you started that business challenges thrown out as, >>So do you any common reaction still the cloud percentage penetration? >>Sorry, I didn't, I didn't guys don't, but I, I was going to say it was, I think it's like the 80 20 rule, right? We, we, we worked really hard in the, you know, I think 2018, 19 to get any person off, uh, after getting onto the cloud and, or the last year is the 20% that we have been migrating. And Stuart said like a non-athlete that is also, that's going to be the diet. And I think our next big step is going to be obviously, you know, the icing on the cake, which is to decommission all these apps as well. Right. So, you know, to get the real benefits out of, uh, the whole conservation program from a, uh, from a >>Douglas and Stewart, can you guys talk about the decision around the cloud because you guys have had success with AWS, why AWS how's that decision made? Can you guys give some insight into some of those thoughts? >>I can, I can start, start off. I think back when the decision was made and it was, Oh, it was a while back, um, you know, there's some clear advantages of moving relay, Ws, a lot of alignment with some of the significant projects and, uh, the trend, that particular one big transformation project that we've alluded to as well. Um, you know, we needed some, um, some very robust and, um, just future proof and, um, proven technology. And AWS gave that to us. We needed a lot of those blueprints to help us move down the path. We didn't want to reinvent everything. So, um, you know, having a lot of that legwork done for us and an AWS gives you that, right. And particularly when you partner up with, uh, with a company like Accenture as well, you get combinations of the technology and the skills and the knowledge to, to move you forward in that direction. >>So, um, you know, for us, it was a, uh, uh, it was a decision based on, you know, best of breed, um, you know, looking forward and, and trying to predict the future needs and, and, and kind of the environmental that we might need. Um, and, you know, partnering up with organizations that can take you on the journey. Yeah. And just to build on it. So obviously, you know, lion's like an NWS, but, you know, we knew it was a very good choice given that, um, uh, the skills and the capability that we had, as well as the assets and tools we had to get the most out of, um, out of AWS. And obviously our, our CEO globally is just spending, you know, announcement about a huge investment that we're making in cloud. Um, but you know, we've, we've worked very well. AWS, we've done some joint workshops and joint investments, um, some joint POC. So yeah, w we have a very good working relationship, AWS, and I think, um, one incident to reflect upon whether it's cyber it's and again, where we actually jointly, you know, dove in with, um, with Amazon and some of their security experts and our experts. And we're able to actually work through that with mine quite successful. So, um, you know, really good behaviors as an organization, but also really good capabilities. >>Yeah. As you guys, you're essential cloud outcomes, research shown, it's the cycle of innovation with the cloud. That's creating a lot of benefits, knowing what you guys know now, looking back certainly COVID is impacted a lot of people kind of going through the same process, knowing what you guys know now, would you advocate people to jump on this transformation journey? If so, how, and what tweaks they make, which changes, what would you advise? >>Uh, I might take that one to start with. Um, I hate to think where we would have been when, uh, COVID kicked off here in Australia and, you know, we were all sent home, literally were at work on the Friday, and then over the weekend. And then Monday, we were told not to come back into the office and all of a sudden, um, our capacity in terms of remote access and I quadrupled, or more four, five X, what we had on the Friday we needed on the Monday. And we were able to stand that up during the day Monday into Tuesday, because we were cloud-based and, uh, you know, we just spun up your instances and, uh, you know, sort of our licensing, et cetera. And we had all of our people working remotely, um, within, uh, you know, effectively one business day. Um, I know peers of mine in other organizations and industries that are relying on kind of a traditional wise and getting hardware, et cetera, that were weeks and months before they could get there the right hardware to be able to deliver to their user base. >>So, um, you know, one example where you're able to scale and, uh, um, get, uh, get value out of this platform beyond probably what was anticipated at the time you talk about, um, you know, less the, in all of these kinds of things. And you can also think of a few scenarios, but real world ones where you're getting your business back up and running in that period of time is, is just phenomenal. There's other stuff, right? There's these programs that we've rolled out, you do your sizing, um, and in the traditional world, you would just go out and buy more servers than you need. And, you know, probably never realize the full value of those, you know, the capability of those servers over the life cycle of them. Whereas, you know, in a cloud world, you put in what you think is right. And if it's not right, you pump it up a little bit when, when all of your metrics and so on, tell you that you need to bump it up. And conversely you scale it down at the same rate. So for us, with the types of challenges and programs and, uh, uh, and just business need, that's come at as this year, uh, we wouldn't have been able to do it without a strong cloud base, uh, to, uh, to move forward. >>You know, Douglas, one of the things I talked to, a lot of people on the right side of history who have been on the right wave with cloud, with the pandemic, and they're happy, they're like, and they're humble. Like, well, we're just lucky, you know, luck is preparation meets opportunity. And this is really about you guys getting in early and being prepared and readiness. This is kind of important as people realize, then you gotta be ready. I mean, it's not just, you don't get lucky by being in the right place, the right time. And there were a lot of companies were on the wrong side of history here who might get washed away. This is a super important, I think, >>To echo and kind of building on what Stewart said. I think that the reason that we've had success and I guess the momentum is we didn't just do it in isolation within it and technology. It was actually linked to broader business changes, you know, creating basically a digital platform for the entire business, moving the business, where are they going to be able to come back stronger after COVID, when they're actually set up for growth, um, and actually allows, you know, a line to achievements growth objectives, and also its ambitions as far as what it wants to do, uh, with growth in whatever they make, do with acquiring other companies and moving into different markets and launching new products. So we've actually done it in a way that is, you know, real and direct business benefit, uh, that actually enables line to grow >>General. I really appreciate you coming. I have one final question. If you can wrap up here, uh, Stuart and Douglas, you don't mind weighing in what's the priorities for the future. What's next for lion in a century >>Christmas holidays, I'll start Christmas holidays. I spent a good year and then a, and then a reset, obviously, right? So, um, you know, it's, it's figuring out, uh, transform what we've already transformed, if that makes sense. So God, a huge proportion of our services sitting in the cloud. Um, but we know we're not done even with the stuff that is in there. We need to take those next steps. We need more and more automation and orchestration. We need to, um, our environment is more future proof. We need to be able to work with the business and understand what's coming at them so that we can, um, you know, build that into, into our environment. So again, it's really transformation on top of transformation is the way that I'll describe it. And it's really an open book, right? Once you get it in and you've got the capabilities and the evolving tool sets that AWS continue to bring to the market based, um, you know, working with the partners to, to figure out how we unlock that value, um, you know, drive our costs down efficiency, uh, all of those kind of, you know, standard metrics. >>Um, but you know, we're looking for the next things to transform and showed value back out to our customer base, um, that, uh, that we continue to, you know, sell our products to and work with and understand how we can better meet their needs. Yeah, I think just to echo that, I think it's really leveraging this and then did you capability they have and getting the most out of that investment. And then I think it's also moving to, uh, and adopting more new ways of working as far as, you know, the speed of the business, um, is getting up to speed in the market is changing. So being able to launch and do things quickly and also, um, competitive and efficient operating costs, uh, now that they're in the cloud, right? So I think it's really leveraging the most out of the platform and then, you know, being efficient in launching things. So putting them with >>Siddique, any word from you on your priorities by you see this year in folding, >>There's got to say like e-learning squares, right, for me around, you know, just journey. This is a journey to the cloud, right? >>And, uh, you know, as well dug into sort of Saturday, it's getting all, you know, different parts of the organization along the journey business to it, to your, uh, product lenders, et cetera. Right. And it takes time. It is tough, but, uh, uh, you know, you got to get started on it. And, you know, once we, once we finish off, uh, it's the realization of the benefits now that, you know, looking forward, I think for, from Alliance perspective, it is, uh, you know, once we migrate all the workloads to the cloud, it is leveraging, uh, all stack drive. And as I think Stewart said earlier, uh, with, uh, you know, the latest and greatest stuff that AWS it's basically working to see how we can really, uh, achieve more better operational excellence, uh, from a, uh, from a cloud perspective. >>Well, Stewart, thanks for coming on with a and sharing your environment and what's going on and your journey you're on the right wave. Did the work you're in, it's all coming together with faster, congratulations for your success, and, uh, really appreciate Douglas with Steve for coming on as well from essential. Thank you for coming on. Thanks, John. Okay. Just the cubes coverage of executive summit at AWS reinvent. This is where all the thought leaders share their best practices, their journeys, and of course, special programming with Accenture and the cube. I'm Sean ferry, your host, thanks for watching from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cube virtuals coverage of the Accenture executive summit. Part of AWS reinvent 2020. I'm your host Rebecca Knight. We are talking today about reinventing the energy data platform. We have two guests joining us. First. We have Johan Krebbers. He is the GM digital emerging technologies and VP of it. Innovation at shell. Thank you so much for coming on the show, Johan you're welcome. And next we have Liz Dennett. She is the lead solution architect for O S D U on AWS. Thank you so much, Liz, maybe here. So I want to start our conversation by talking about OSD. You like so many great innovations. It started with a problem. Johann, what was the problem you were trying to solve at shell? We go back a couple of years, we started summer 2017, where we had a meeting with the guys from exploration in shell, and the main problem they had, of course, they got lots of lots of data, but are unable to find the right data. They need to work from all over the place and told him >>To, and we'll probably try to solve is how that person working exploration could find their proper date, not just a day, but also the date you really needed that we did probably talked about is summer 2017. And we said, okay, the only way ABC is moving forward is to start pulling that data into a single data platform. And that, that was at the time that we called it as the, you, the subsurface data universe in there was about the shell name was so in, in January, 2018, we started a project with Amazon to start grating a co fricking that building, that Stu environment, that the, the universe, so that single data level to put all your exploration and Wells data into that single environment that was intent. And every cent, um, already in March of that same year, we said, well, from Michele point of view, we will be far better off if we could make this an industry solution and not just a shelf solution, because Shelby, Shelby, if you can make an industry solution, but people are developing applications for it. >>It also is far better than for shell to say we haven't shell special solution because we don't make money out of how we start a day that we can make money out of it. We have access to the data, we can explore the data. So storing the data we should do as efficiently possibly can. So we monitor, we reach out to about eight or nine other last, uh, or I guess operators like the economics, like the tutorials, like the shepherds of this world and say, Hey, we inshallah doing this. Do you want to join this effort? And to our surprise, they all said, yes. And then in September, 2018, we had our kickoff meeting with your open group where we said, we said, okay, if you want to work together and lots of other companies, we also need to look at, okay, how, how we organize that. >>Or if you started working with lots of large companies, you need to have some legal framework around some framework around it. So that's why we went to the open group and say, okay, let's, let's form the old forum as we call it at the time. So it's September, 2080, where I did a Galleria in Houston, but the kickoff meeting for the OT four with about 10 members at the time. So that's just over two years ago, we started an exercise for me called ODU. They kicked it off. Uh, and so that's really them will be coming from and how we've got there. Also >>The origin story. Um, what, so what digging a little deeper there? What were some of the things you were trying to achieve with the OSU? >>Well, a couple of things we've tried to achieve with you, um, first is really separating data from applications for what is, what is the biggest problem we have in the subsurface space that the data and applications are all interlinked or tied together. And if, if you have them and a new company coming along and say, I have this new application and he's access to the data that is not possible because the data often interlinked with the application. So the first thing we did is really breaking the link between the application, the data as those levels, the first thing we did, secondly, put all the data to a single data platform, take the silos out what was happening in the sub-service space. They got all the data in what we call silos in small little islands out there. So what we're trying to do is first break the link to great, great. >>They put the data single day, the bathroom, and the third part, put a standard layer on top of that, it's an API layer on top to equate a platform. So we could create an ecosystem out of companies to start a valving Schoff application on top of dev data platform across you might have a data platform, but you're only successful if have a rich ecosystem of people start developing applications on top of that. And then you can export the data like small companies, last company, university, you name it, we're getting after create an ecosystem out here. So the three things were first break the link between application data, just break it and put data at the center and also make sure that data, this data structure would not be managed by one company, but it would only be met. It would be managed the data structures by the ODI forum. Secondly, then put a, the data, a single data platform certainly then has an API layer on top and then create an ecosystem. Really go for people, say, please start developing applications, because now you had access to the data. I've got the data no longer linked to somebody whose application was all freely available, but an API layer that was, that was all September, 2018, more or less. >>And hear a little bit. Can you talk a little bit about some of the imperatives from the AWS standpoint in terms of what you were trying to achieve with this? Yeah, absolutely. And this whole thing is Johann said started with a challenge that was really brought out at shell. The challenges that geoscientists spend up to 70% of their time looking for data. I'm a geologist I've spent more than 70% of my time trying to find data in these silos. And from there, instead of just figuring out how we could address that one problem, we worked together to really understand the root cause of these challenges and working backwards from that use case OSU and OSU on AWS has really enabled customers to create solutions that span, not just this in particular problem, but can really scale to be inclusive of the entire energy value chain and deliver value from these use cases to the energy industry and beyond. Thank you, Lee, uh, Johann. So talk a little bit about Accenture's cloud first approach and how it has, uh, helped shell work faster and better with speed. >>Well, of course, access a cloud first approach only works together. It's been an Amazon environment, AWS environment. So we're really looking at, uh, at, at Accenture and others altogether helping shell in this space. Now the combination of the two is what we're really looking at, uh, where access of course can be recent knowledge student to that environment operates support knowledge, do an environment. And of course, Amazon will be doing that to today's environment that underpinning their services, et cetera. So, uh, we would expect a combination, a lot of goods when we started rolling out and put in production, the old you are three and bug because we are anus. Then when the release feed comes to the market in Q1, next year of ODU have already started going to Audi production inside shell. But as the first release, which is ready for prime time production across an enterprise will be released just before Christmas, last year when he's still in may of this year. But really three is the first release we want to use for full scale production deployment inside shell, and also the operators around the world. And there is one Amazon, sorry, at that one. Um, extensive can play a role in the ongoing, in the, in deployment building up, but also support environment. >>So one of the other things that we talk a lot about here on the cube is sustainability. And this is a big imperative at so many organizations around the world in particular energy companies. How does this move to OSD you, uh, help organizations become, how is this a greener solution for companies? >>Well, first we make it's a greatest solution because you start making a much more efficient use of your resources, which is already an important one. The second thing we're doing is also, we started ODU in framers, in the oil and gas space in the expert development space. We've grown, uh, OTU in our strategy of growth. I was, you know, also do an alternative energy sociology. We'll all start supporting next year. Things like solar farms, wind farms, uh, the, the dermatomal environment hydration. So it becomes an and an open energy data platform, not just what I want to get into sleep. That's what new industry, any type of energy industry. So our focus is to create, bring the data of all those various energy data sources to get me to a single data platform you can to use AI and other technologies on top of that, to exploit the data, to meet again into a single data platform. >>Liz, I want to ask you about security because security is, is, is such a big concern when it comes to data. How secure is the data on OSD? You, um, actually, can I talk, can I do a follow up on this sustainability talking? Oh, absolutely. By all means. I mean, I want to interject though security is absolutely our top priority. I don't mean to move away from that, but with sustainability, in addition to the benefits of the OSU data platform, when a company moves from on-prem to the cloud, they're also able to leverage the benefits of scale. Now, AWS is committed to running our business in the most environmentally friendly way possible. And our scale allows us to achieve higher resource utilization and energy efficiency than a typical data center. >>Now, a recent study by four 51 research found that AWS is infrastructure is 3.6 times more energy efficient than the median of surveyed enterprise data centers. Two thirds of that advantage is due to higher, um, server utilization and a more energy efficient server population. But when you factor in the carbon intensity of consumed electricity and renewable energy purchases for 51 found that AWS performs the same task with an 88% lower carbon footprint. Now that's just another way that AWS and OSU are working to support our customers is they seek to better understand their workflows and make their legacy businesses less carbon intensive. >>That's that's incorrect. Those are those statistics are incredible. Do you want to talk a little bit now about security? Absolutely. And security will always be AWS is top priority. In fact, AWS has been architected to be the most flexible and secure cloud computing environment available today. Our core infrastructure is built to satisfy. There are the security requirements for the military, local banks and other high sensitivity organizations. And in fact, AWS uses the same secure hardware and software to build and operate each of our regions. So that customers benefit from the only commercial cloud that's hat hits service offerings and associated supply chain vetted and deemed secure enough for top secret workloads. That's backed by a deep set of cloud security tools with more than 200 security compliance and governmental service and key features as well as an ecosystem of partners like Accenture, that can really help our customers to make sure that their environments for their data meet and or exceed their security requirements. Johann, I want you to talk a little bit about how OSD you can be used today. Does it only handle subsurface data? >>Uh, today it's Honda's subserves or Wells data, we go to add to that production around the middle of next year. That means that the whole upstate business. So we've got goes from exploration all the way to production. You've made it together into a single data platform. So production will be added around Q3 of next year. Then a principal. We have a difficult, the elder data that single environment, and we want to extend them to other data sources or energy sources like solar farms, wind farms, uh, hydrogen, hydro, et cetera. So we're going to add a whore, a whole list of audit day energy source to them and be all the data together into a single data club. So we move from a falling guest data platform to an aniseed data platform. That's really what our objective is because the whole industry, if you look it over, look at our companies are all moving in. That same two acts of quantity of course, are very strong in oil and gas, but also increased the, got into the other energy sources like, like solar, like wind, like th like highly attended, et cetera. So we would be moving exactly. But that same method that, that, that the whole OSU can't really support at home. And as a spectrum of energy sources, >>Of course, and Liz and Johan. I want you to close us out here by just giving us a look into your crystal balls and talking about the five and 10 year plan for OSD. You we'll start with you, Liz. What do you, what do you see as the future holding for this platform? Um, honestly, the incredibly cool thing about working at AWS is you never know where the innovation and the journey is going to take you. I personally am looking forward to work with our customers, wherever their OSU journeys, take them, whether it's enabling new energy solutions or continuing to expand, to support use cases throughout the energy value chain and beyond, but really looking forward to continuing to partner as we innovate to slay tomorrow's challenges, Johann first, nobody can look at any more nowadays, especially 10 years own objective is really in the next five years, you will become the key backbone for energy companies for storing your data. You are efficient intelligence and optimize the whole supply energy supply chain in this world down here, you'll uncovers Liz Dennett. Thank you so much for coming on the cube virtual I'm Rebecca Knight stay tuned for more of our coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cubes coverage of the Accenture executive summit. Part of AWS reinvent. I'm your host Rebecca Knight today we're welcoming back to Kubila. We have Kishor Dirk. He is the Accenture senior managing director cloud first global services lead. Welcome back to the show Kishore. Thank you very much. Nice to meet again. And, uh, Tristan moral horse set. He is the managing director, Accenture cloud first North America growth. Welcome back to you to trust and great to be back in grapes here again, Rebecca. Exactly. Even in this virtual format, it is good to see your faces. Um, today we're going to be talking about my nav and green cloud advisor capability. Kishor I want to start with you. So my nav is a platform that is really celebrating its first year in existence. Uh, November, 2019 is when Accenture introduced it. Uh, but it's, it has new relevance in light of this global pandemic that we are all enduring and suffering through. Tell us a little bit about the lineup platform, what it is that cloud platform to help our clients navigate the complexity of cloud and cloud decisions to make it faster. And obviously, you know, we have in the cloud, uh, you know, with >>The increased relevance and all the, especially over the last few months with the impact of COVID crisis and exhibition of digital transformation, you know, we are seeing the transformation or the acceleration to cloud much faster. This platform that you're talking about has enabled and 40 clients globally across different industries. You identify the right cloud solution, navigate the complexity, provide a cloud specific solution simulate for our clients to meet the strategy business needs, and the clients are loving it. >>I want to go to you now trust and tell us a little bit about how mine nav works and how it helps companies make good cloud choice. >>Yeah, so Rebecca, we we've talked about cloud is, is more than just infrastructure and that's what mine app tries to solve for it. It really looks at a variety of variables, including infrastructure operating model and fundamentally what client's business outcomes, um, uh, our clients are, are looking for and, and identifies the optimal solution for what they need. And we assign this to accelerate and we mentioned the pandemic. One of the big focus now is to accelerate. And so we worked through a three-step process. The first is scanning and assessing our client's infrastructure, their data landscape, their application. Second, we use our automated artificial intelligence engine to interact with. We have a wide variety and library of a collective plot expertise. And we look to recommend what is the enterprise architecture and solution. And then third, before we aligned with our clients, we look to simulate and test this scaled up model. And the simulation gives our clients a way to see what cloud is going to look like, feel like and how it's going to transform their business before they go there. >>Tell us a little bit about that in real life. Now as a company, so many of people are working remotely having to collaborate, uh, not in real life. How is that helping them right now? >>So, um, the, the pandemic has put a tremendous strain on systems, uh, because of the demand on those systems. And so we talk about resiliency. We also now need to collaborate across data across people. Um, I think all of us are calling from a variety of different places where our last year we were all at the VA cube itself. Um, and, and cloud technologies such as teams, zoom that we're we're leveraging now has fundamentally accelerated and clients are looking to onboard this for their capabilities. They're trying to accelerate their journey. They realize that now the cloud is what is going to become important for them to differentiate. Once we come out of the pandemic and the ability to collaborate with their employees, their partners, and their clients through these systems is becoming a true business differentiator for our clients. >>Keisha, I want to talk with you now about my navs multiple capabilities, um, and helping clients design and navigate their cloud journeys. Tell us a little bit about the green cloud advisor capability and its significance, particularly as so many companies are thinking more deeply and thoughtfully about sustainability. >>Yes. So since the launch of my lab, we continue to enhance, uh, capabilities for our clients. One of the significant, uh, capabilities that we have enabled is the being taught advisor today. You know, Rebecca, a lot of the businesses are more environmentally aware and are expanding efforts to decrease power consumption, uh, and obviously carbon emissions and, uh, and run a sustainable operations across every aspect of the enterprise. Uh, as a result, you're seeing an increasing trend in adoption of energy, efficient infrastructure in the global market. And one of the things that we did a lot of research we found out is that there's an ability to influence our client's carbon footprint through a better cloud solution. And that's what the internet brings to us, uh, in, in terms of a lot of the client connotation that you're seeing in Europe, North America and others, lot of our clients are accelerating to a green cloud strategy to unlock beta financial, societal and environmental benefit, uh, through obviously cloud-based circular, operational, sustainable products and services. That is something that we are enhancing my now, and we are having active client discussions at this point of time. >>So Tristan, tell us a little bit about how this capability helps clients make greener decisions. >>Yeah. Um, well, let's start about the investments from the cloud providers in renewable and sustainable energy. Um, they have most of the hyperscalers today, um, have been investing significantly on data centers that are run on renewable energy, some incredibly creative constructs on the how to do that. And sustainability is there for a key, um, key item of importance for the hyperscalers and also for our clients who now are looking for sustainable energy. And it turns out this marriage is now possible. I can, we marry the, the green capabilities of the comm providers with a sustainability agenda of our clients. And so what we look into the way the mine EF works is it looks at industry benchmarks and evaluates our current clients, um, capabilities and carpet footprint leveraging their existing data centers. We then look to model from an end-to-end perspective, how the, their journey to the cloud leveraging sustainable and, um, and data centers with renewable energy. We look at how their solution will look like and, and quantify carbon tax credits, um, improve a green index score and provide quantifiable, um, green cloud capabilities and measurable outcomes to our clients, shareholders, stakeholders, clients, and customers. Um, and our green plot advisers sustainability solutions already been implemented at three clients. And in many cases in two cases has helped them reduce the carbon footprint by up to 400% through migration from their existing data center to green cloud. Very, very, >>That is remarkable. Now tell us a little bit about the kinds of clients. Is this, is this more interesting to clients in Europe? Would you say that it's catching on in the United States? Where, what is the breakdown that you're seeing right now? >>Sustainability is becoming such a global agenda and we're seeing our clients, um, uh, tie this and put this at board level, um, uh, agenda and requirements across the globe. Um, Europe has specific constraints around data sovereignty, right, where they need their data in country, but from a green, a sustainability agenda, we see clients across all our markets, North America, Europe, and our growth markets adopt this. And we have seen case studies and all three months. >>Keisha, I want to bring you back into the conversation. Talk a little bit about how MindUP ties into Accenture's cloud first strategy, your Accenture's CEO, Julie Sweet has talked about post COVID leadership requiring every business to become a cloud first business. Tell us a little bit about how this ethos is in Accenture and how you're sort of looking outward with it too. >>So Rebecca mine is the launch pad, uh, to a cloud first transformation for our clients. Uh, Accenture, see your jewelry suite, uh, you know, shared the Accenture cloud first and our substantial investment demonstrate our commitment and is delivering greater value for our clients when they need it the most. And with the digital transformation requiring cloud at scale, you know, we're seeing that in the post COVID leadership, it requires that every business should become a cloud business. And my nap helps them get there by evaluating the cloud landscape, navigating the complexity, modeling architecting and simulating an optimal cloud solution for our clients. And as Justin was sharing a greener cloud. >>So Tristan, talk a little bit more about some of the real life use cases in terms of what are we, what are clients seeing? What are the results that they're having? >>Yes. Thank you, Rebecca. I would say two key things right around my neck. The first is the iterative process. Clients don't want to wait, um, until they get started, they want to get started and see what their journey is going to look like. And the second is fundamental acceleration, dependent make, as we talked about, has accelerated the need to move to cloud very quickly. And my nav is there to do that. So how do we do that? First is generating the business cases. Clients need to know in many cases that they have a business case by business case, we talk about the financial benefits, as well as the business outcomes, the green, green clot impact sustainability impacts with minus. We can build initial recommendations using a basic understanding of their environment and benchmarks in weeks versus months with indicative value savings in the millions of dollars arranges. >>So for example, very recently, we worked with a global oil and gas company, and in only two weeks, we're able to provide an indicative savings for $27 million over five years. This enabled the client to get started, knowing that there is a business case benefit and then iterate on it. And this iteration is, I would say the second point that is particularly important with my nav that we've seen in bank, the clients, which is, um, any journey starts with an understanding of what is the application landscape and what are we trying to do with those, these initial assessments that used to take six to eight weeks are now taking anywhere from two to four weeks. So we're seeing a 40 to 50% reduction in the initial assessment, which gets clients started in their journey. And then finally we've had discussions with all of the hyperscalers to help partner with Accenture and leverage mine after prepared their detailed business case module as they're going to clients. And as they're accelerating the client's journey, so real results, real acceleration. And is there a journey? Do I have a business case and furthermore accelerating the journey once we are by giving the ability to work in iterative approach. >>I mean, it sounds as though that the company that clients and and employees are sort of saying, this is an amazing time savings look at what I can do here in, in so much in a condensed amount of time, but in terms of getting everyone on board, one of the things we talked about last time we met, uh, Tristan was just how much, uh, how one of the obstacles is getting people to sign on and the new technologies and new platforms. Those are often the obstacles and struggles that companies face. Have you found that at all? Or what is sort of the feedback that you're getting from employers? >>Sorry. Yes. We clearly, there are always obstacles to a cloud journey. If there were an obstacles, all our clients would be, uh, already fully in the cloud. What man I gives the ability is to navigate through those, to start quickly. And then as we identify obstacles, we can simulate what things are going to look like. We can continue with certain parts of the journey while we deal with that obstacle. And it's a fundamental accelerator. Whereas in the past one, obstacle would prevent a class from starting. We can now start to address the obstacles one at a time while continuing and accelerating the contrary. That is the fundamental difference. >>Kishor I want to give you the final word here. Tell us a little bit about what is next for Accenture might have and what we'll be discussing next year at the Accenture executive summit >>Sort of echo, we are continuously evolving with our client needs and reinventing, reinventing for the future. For mine, as I've been taught advisor, our plan is to help our clients reduce carbon footprint and again, migrate to a green cloud. Uh, and additionally, we're looking at, you know, two capabilities, uh, which include sovereign cloud advisor, uh, with clients, especially in, in Europe and others are under pressure to meet, uh, stringent data norms that Kristen was talking about. And the sovereign cloud advisor health organization to create an architecture cloud architecture that complies with the green. Uh, I would say the data sovereignty norms that is out there. The other element is around data to cloud. We are seeing massive migration, uh, for, uh, for a lot of the data to cloud. And there's a lot of migration hurdles that come within that. Uh, we have expanded mine app to support assessment capabilities, uh, for, uh, assessing applications, infrastructure, but also covering the entire state, including data and the code level to determine the right cloud solution. So we are, we are pushing the boundaries on what mine app can do with mine. Have you created the ability to take the guesswork out of cloud navigate the complexity? We are roaring risks costs, and we are, you know, achieving client's static business objectives while building a sustainable alerts with being cloud >>Any platform that can take some of the guesswork out of the future. I'm I'm onboard with. Thank you so much, Tristin and Kishore. This has been a great conversation. >>Thank you. >>Stay tuned for more of the cubes coverage of the Accenture executive summit. I'm Rebecca Knight from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Hey, welcome back to the cubes coverage of 80 us reinvent 2020 virtual centric executive summit. The two great guests here to break down the analysis of the relationship with cloud and essential Brian bowhead director ahead of a century 80. It was business group at Amazon web services. And Andy T a B G the M is essentially Amazon business group lead managing director at Accenture. Uh, I'm sure you're super busy and dealing with all the action, Brian. Great to see you. Thanks for coming on. So thank you. You guys essentially has been in the spotlight this week and all through the conference around this whole digital transformation, essentially as business group is celebrating its fifth anniversary. What's new, obviously the emphasis of next gen post COVID generation, highly digital transformation, a lot happening. You got your five-year anniversary, what's new. >>Yeah, it, you know, so if you look back, it's exciting. Um, you know, so it was five years ago. Uh, it was actually October where we, where we launched the Accenture AWS business group. And if we think back five years, I think we're still at the point where a lot of customers were making that transition from, you know, should I move to cloud to how do I move to cloud? Right? And so that was one of the reasons why we launched the business group. And since, since then, certainly we've seen that transition, right? Our conversations today are very much around how do I move to cloud, help me move, help me figure out the business case and then pull together all the different pieces so I can move more quickly, uh, you know, with less risk and really achieve my business outcomes. And I would say, you know, one of the things too, that's, that's really changed over the five years. >>And what we're seeing now is when we started, right, we were focused on migration data and IOT as the big three pillars that we launched with. And those are still incredibly important to us, but just the breadth of capability and frankly, the, the, the breadth of need that we're seeing from customers. And obviously as AWS has matured over the years and launched our new capabilities, we're Eva with Accenture and in the business group, we've broadened our capabilities and deepened our capabilities over the, over the last five years as well. For instance, this year with, with COVID, especially, it's really forced our customers to think differently about their own customers or their citizens, and how do they service those citizens? So we've seen a huge acceleration around customer engagement, right? And we powered that with Accenture customer engagement platform powered by ADA, Amazon connect. And so that's been a really big trend this year. And then, you know, that broadens our capability from just a technical discussion to one where we're now really reaching out and, and, um, and helping transform and modernize that customer and citizen experience as well, which has been exciting to see. >>Yeah, Andy, I want to get your thoughts here. We've been reporting and covering essentially for years. It's not like it's new to you guys. I mean, five years is a great anniversary. You know, check is good relationship, but you guys have been doing the work you've been on the trend line. And then this hits and Andy said on his keynote and I thought he said it beautifully. And he even said it to me in my one-on-one interview with them was it's on full display right now, the whole digital transformation, everything about it is on full display and you're either were prepared for it or you kind of word, and you can see who's there. You guys have been prepared. This is not new. So give us the update from your perspective, how you're taking advantage of this, of this massive shift, highly accelerated digital transformation. >>Well, I think, I think you can be prepared, but you've also got to be prepared to always sort of, I think what we're seeing in, in, um, in, in, in, in recent times and particularly 20 w what is it I think today there are, um, full sense of the enterprise workloads, the cloud, um, you know, that leaves 96 percentile now for him. Um, and I, over the next four to >>Five years, um, we're going to see that sort of, uh, acceleration to the, to the cloud pick up, um, this year is, as Andy touched on, I think, uh, uh, on Tuesday in his, I think the pandemic is a forcing function, uh, for companies to, to really pause and think about everything from, from, you know, how they, um, manage that technology to infrastructure, to just to carotenoids where the data sets to what insights and intelligence that getting from that data. And then eventually even to, to the talent, the talent they have in the organization and how they can be competitive, um, their culture, their culture of innovation, of invention and reinvention. And so I think, I think, you know, when you, when you think of companies out there faced with these challenges, it, it forces us, it forces AWS, it forces AEG to come together and think through how can we help create value for them? How can we help help them move from sort of just causing and rethinking to having real plans in an action and that taking them, uh, into, into implementation. And so that's, that's what we're working on. Um, I think over the next five years, we're looking to just continue to come together and help these, these companies get to the cloud and get the value from the cloud because it's beyond just getting to the cloud attached to them and living in the cloud and, and getting the value from it. >>It's interesting. Andy was saying, don't just put your toe in the water. You got to go beyond the toe in the water kind of approach. Um, I want to get to that large scale cause that's the big pickup this week that I kind of walked away with was it's large scale. Acceleration's not just toe in the water experimentation. Can you guys share, what's causing this large scale end to end enterprise transformation? And what are some of the success criteria have you seen for the folks who have done that? >>Yeah. And I'll, I'll, I'll start. And at the end you can buy a lawn. So, you know, it's interesting if I look back a year ago at re-invent and when I did the cube interview, then we were talking about how the ABG, we were starting to see this shift of customers. You know, we've been working with customers for years on a single of what I'll call a single-threaded programs, right. We can do a migration, we could do SAP, we can do a data program. And then even last year, we were really starting to see customers ask. The question is like, what kind of synergies and what kind of economies of scale do I get when I start bringing these different threads together, and also realizing that it's, you know, to innovate for the business and build new applications, new capabilities. Well, that then is going to inform what data you need to, to hydrate those applications, right? Which then informs your data strategy while a lot of that data is then also embedded in your underlying applications that sit on premises. So you should be thinking through how do you get those applications into the cloud? So you need to draw that line through all of those layers. And that was already starting last year. And so last year we launched the joint transformation program with AEG. And then, so we were ready when this year happened and then it was just an acceleration. So things have been happening faster than we anticipated, >>But we knew this was going to be happening. And luckily we've been in a really good position to help some of our customers really think through all those different layers of kind of pyramid as we've been calling it along with the talent and change pieces, which are also so important as you make this transformation to cloud >>Andy, what's the success factors. Andy Jassy came on stage during the partner day, a surprise fireside chat with Doug Hume and talking about this is really an opportunity for partners to, to change the business landscape with enablement from Amazon. You guys are in a pole position to do that in the marketplace. What's the success factors that you see, >>Um, really from three, three fronts, I'd say, um, w one is the people. Um, and, and I, I, again, I think Andy touched on sort of eight, uh, success factors, uh, early in the week. And for me, it's these three areas that it sort of boils down to these three areas. Um, one is the, the, the, the people, uh, from the leaders that it's really important to set those big, bold visions point the way. And then, and then, you know, set top down goals. How are we going to measure Z almost do get what you measure, um, to be, you know, beyond the leaders, to, to the right people in the right position across the company. We we're finding a key success factor for these end to end transformations is not just the leaders, but you haven't poached across the company, working in a, in a collaborative, shared, shared success model, um, and people who are not afraid to, to invent and fail. >>And so that takes me to perhaps the second point, which is the culture, um, it's important, uh, with finding for the right conditions to be set in the company that enabled, uh, people to move at pace, move at speed, be able to fail fast, um, keep things very, very simple and just keep iterating and that sort of culture of iteration and improvement versus seeking perfection is, is super important for, for success. And then the third part of maybe touch on is, is partners. Um, I think, you know, as we move forward over the next five years, we're going to see an increasing number of players in the ecosystem in the enterprise and state. Um, you're going to see more and more SAS providers. And so it's important for companies and our joint clients out there to pick partners like, um, like AWS or, or Accenture or others, but to pick partners who have all worked together and you have built solutions together, and that allows them to get speed to value quicker. It allows them to bring in pre-assembled solutions, um, and really just drive that transformation in a quicker, it sorts of manner. >>Yeah, that's a great point worth calling out, having that partnership model that's additive and has synergy in the cloud, because one of the things that came out of this this week, this year is reinvented, is there's new things going on in the public cloud, even though hybrid is an operating model, outpost and super relevant. There, there are benefits for being in the cloud and you've got partners API, for instance, and have microservices working together. This is all new, but I got, I got to ask that on that thread, Andy, where did you see your customers going? Because I think, you know, as you work backwards from the customers, you guys do, what's their needs, how do you see them? W you know, where's the puck going? Where can they skate where the puck's going, because you can almost look forward and say, okay, I've got to build modern apps. I got to do the digital transformation. Everything is a service. I get that, but what are they, what solutions are you building for them right now to get there? >>Yeah. And, and of course, with, with, you know, industries blurring and multiple companies, it's always hard to boil down to the exact situations, but you could probably look at it from a sort of a thematic lens. And what we're seeing is as the cloud transformation journey picks up, um, from us perspective, we've seen a material shift in the solutions and problems that we're trying to address with clients that they are asking for us, uh, to, to help, uh, address is no longer just the back office, where you're sort of looking at cost and efficiency and, um, uh, driving gains from that perspective. It's beyond that, it's now materially the top line. It's, how'd you get the driving to the, you know, speed to insights, how'd you get them decomposing, uh, their application set in order to derive those insights. Um, how'd you get them, um, to, to, um, uh, sort of adopt leading edge industry solutions that give them that jump start, uh, and that accelerant to winning the customers, winning the eyeballs. >>Um, and then, and then how'd, you help drive the customer experience. We're seeing a lot of push from clients, um, or ask for help on how do I optimize my customer experience in order to retain my eyeballs. And then how do I make sure I've got a soft self-learning ecosystem of play, um, where, uh, you know, it's not just a practical experience that I can sort of keep learning and iterating, um, how I treat my, my customers, um, and a lot of that, um, that still self-learning, that comes from, you know, putting in intelligence into your, into your systems, getting an AI and ML in there. And so, as a result of that work, we're seeing a lot of push and a lot of what we're doing, uh, is pouring investment into those areas. And then finally, maybe beyond the bottom line, and the top line is how do you harden that and protect that with, um, security and resilience? So I'll probably say those are the three areas. John, >>You know, the business model side, obviously the enablement is what Amazon has. Um, we see things like SAS factory coming on board and the partner network, obviously a century is a big, huge partner of you guys. Um, the business models there, you've got I, as, as doing great with chips, you have this data modeling this data opportunity to enable these modern apps. We heard about the partner strategy for me and D um, talking to me now about how can partners within even Accenture, w w what's the business model, um, side on your side that you're enabling this. Can you just share your thoughts on that? >>Yeah, yeah. And so it's, it's interesting. I think I'm going to build it and then build a little bit on some of the things that Andy really talked about there, right? And that we, if you think of that from the partnership, we are absolutely helping our customers with kind of that it modernization piece. And we're investing a lot and there's hard work that needs to get done there. And we're investing a lot as a partnership around the tools, the assets and the methodology. So in AWS and Accenture show up together as AEG, we are executing office single blueprint with a single set of assets, so we can move fast. So we're going to continue to do that with all the hybrid announcements from this past week, those get baked into that, that migration modernization theme, but the other really important piece here as we go up the stack, Andy mentioned it, right? >>The data piece, like so much of what we're talking about here is around data and insights. Right? I did a cube interview last week with, uh, Carl hick. Um, who's the CIO from Takeda. And if you hear Christophe Weber from Takeda talk, he talks about Takeda being a data company, data and insights company. So how do we, as a partnership, again, build the capabilities and the platforms like with Accenture's applied insights platform so that we can bootstrap and really accelerate our client's journey. And then finally, on the innovation on the business front, and Andy was touching on some of these, we are investing in industry solutions and accelerators, right? Because we know that at the end of the day, a lot of these are very similar. We're talking about ingesting data, using machine learning to provide insights and then taking action. So for instance, the cognitive insurance platform that we're working together on with Accenture, if they give out property and casualty claims and think about how do we enable touchless claims using machine learning and computer vision that can assess based on an image damage, and then be able to triage that and process it accordingly, right? >>Using all the latest machine learning capabilities from AWS with that deep, um, AI machine learning data science capability from Accenture, who knows all those algorithms that need to get built and build that library by doing that, we can really help these insurance companies accelerate their transformation around how they think about claims and how they can speed those claims on behalf of their policy holder. So that's an example of a, kind of like a bottom to top, uh, view of what we're doing in the partnership to address these new needs. >>That's awesome. Andy, I want to get back to your point about culture. You mentioned it twice now. Um, talent is a big part of the game here. Andy Jassy referenced Lambda. The next generation developers were using Lambda. He talked about CIO stories around, they didn't move fast enough. They lost three years. A new person came in and made it go faster. This is a new, this is a time for a certain kind of, um, uh, professional and individual, um, to, to be part of, um, this next generation. What's the talent strategy you guys have to attract and attain the best and retain the people. How do you do it? >>Um, you know, it's, it's, um, it's an interesting one. It's, it's, it's oftentimes a, it's, it's a significant point and often overlooked. Um, you know, people, people really matter and getting the right people, um, in not just in AWS or it, but then in our customers is super important. We often find that much of our discussions with, with our clients is centered around that. And it's really a key ingredient. As you touched on, you need people who are willing to embrace change, but also people who are willing to create new, um, to invent new, to reinvent, um, and to, to keep it very simple. Um, w we're we're we're seeing increasingly that you need people that have a sort of deep learning and a deep, uh, or deep desire to keep learning and to be very curious as, as they go along. Most of all, though, I find that, um, having people who are not willing or not afraid to fail is critical, absolutely critical. Um, and I think that that's, that's, uh, a necessary ingredient that we're seeing, um, our clients needing more off, um, because if you can't start and, and, and you can't iterate, um, you know, for fear of failure, you're in trouble. And, and I think Andy touched on that you, you know, where that CIO, that you referred to last three years, um, and so you really do need people who are willing to start not afraid to start, uh, and, uh, and not afraid to lead >>Was a gut check there. I just say, you guys have a great team over there. Everyone at the center I've interviewed strong, talented, and not afraid to lean in and, and into the trends. Um, I got to ask on that front cloud first was something that was a big strategic focus for Accenture. How does that fit into your business group? That's an Amazon focused, obviously they're cloud, and now hybrid everywhere, as I say, um, how does that all work it out? >>We're super excited about our cloud first initiative, and I think it fits it, um, really, uh, perfectly it's it's, it's what we needed. It's, it's, it's a, it's another accelerant. Um, if you think of count first, what we're doing is we're, we're putting together, um, uh, you know, capability set that will help enable him to and transformations as Brian touched on, you know, help companies move from just, you know, migrating to, to, to modernizing, to driving insights, to bringing in change, um, and, and, and helping on that, on that talent side. So that's sort of component number one is how does Accenture bring the best, uh, end to end transformation capabilities to our clients? Number two is perhaps, you know, how do we, um, uh, bring together pre-assembled as Brian touched on pre-assembled industry offerings to help as an accelerant, uh, for our, for our customers three years, as we touched on earlier is, is that sort of partnership with the ecosystem. >>We're going to see an increasing number of SAS providers in an estate, in the enterprise of snakes out there. And so, you know, panto wild cloud first, and our ABG strategy is to increase our touch points in our integrations and our solutions and our offerings with the ecosystem partners out there, the ISP partners out, then the SAS providers out there. And then number four is really about, you know, how do we, um, extend the definition of the cloud? I think oftentimes people thought of the cloud just as sort of on-prem and prem. Um, but, but as Andy touched on earlier this week, you know, you've, you've got this concept of hybrid cloud and that in itself, um, uh, is, is, is, you know, being redefined as well. You know, when you've got the intelligent edge and you've got various forms of the edge. Um, so that's the fourth part of, of, uh, of occupied for strategy. And for us was super excited because all of that is highly relevant for ABG, as we look to build those capabilities as industry solutions and others, and as when to enable our customers, but also how we, you know, as we, as we look to extend how we go to market, I'll join tele PS, uh, in, uh, in our respective skews and products. >>Well, what's clear now is that people now realize that if you contain that complexity, the upside is massive. And that's great opportunity for you guys. We got to get to the final question for you guys to weigh in on, as we wrap up next five years, Brian, Andy weigh in, how do you see that playing out? What do you see this exciting, um, for the partnership and the cloud first cloud, everywhere cloud opportunities share some perspective. >>Yeah, I, I think, you know, just kinda building on that cloud first, right? What cloud first, and we were super excited when cloud first was announced and you know, what it signals to the market and what we're seeing in our customers, which has cloud really permeates everything that we're doing now. Um, and so all aspects of the business will get infused with cloud in some ways, you know, it, it touches on, on all pieces. And I think what we're going to see is just a continued acceleration and getting much more efficient about pulling together the disparate, what had been disparate pieces of these transformations, and then using automation using machine learning to go faster. Right? And so, as we started thinking about the stack, right, well, we're going to get, I know we are, as a partnership is we're already investing there and getting better and more efficient every day as the migration pieces and the moving the assets to the cloud are just going to continue to get more automated, more efficient. And those will become the economic engines that allow us to fund the differentiated, innovative activities up the stack. So I'm excited to see us kind of invest to make those, those, um, those bets accelerated for customers so that we can free up capital and resources to invest where it's going to drive the most outcome for their end customers. And I think that's going to be a big focus and that's going to have the industry, um, you know, focus. It's going to be making sure that we can >>Consume the latest and greatest of AWS as capabilities and, you know, in the areas of machine learning and analytics, but then Andy's also touched on it bringing in ecosystem partners, right? I mean, one of the most exciting wins we had this year, and this year of COVID is looking at the universe, looking at Massachusetts, the COVID track and trace solution that we put in place is a partnership between Accenture, AWS, and Salesforce, right? So again, bringing together three really leading partners who can deliver value for our customers. I think we're going to see a lot more of that as customers look to partnerships like this, to help them figure out how to bring together the best of the ecosystem to drive solutions. So I think we're going to see more of that as well. >>All right, Andy final word, your take >>Thinks of innovation is, is picking up, um, dismiss things are just going faster and faster. I'm just super excited and looking forward to the next five years as, as you know, the technology invention, um, comes out and continues to sort of set new standards from AWS. Um, and as we, as Accenture wringing, our industry capabilities, we marry the two. We, we go and help our customers super exciting time. >>Well, congratulations on the partnership. I want to say thank you to you guys, because I've reported a few times some stories around real successes around this COVID pandemic that you guys worked together on with Amazon that really changed people's lives. Uh, so congratulations on that too as well. I want to call that out. Thanks for coming >>Up. Thank you. Thanks for coming on. >>Okay. This is the cubes coverage, essentially. AWS partnership, part of a century executive summit at Atrius reinvent 2020 I'm John for your host. Thanks. >>You're watching from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Hello, and welcome back to the cubes coverage of AWS reinvent 2020. This is special programming for the century executive summit, where all the thought leaders going to extract the signal from the nose to share with you their perspective of this year's reinvent conference, as it respects the customers' digital transformation. Brian Bohan is the director and head of a center. ADA was business group at Amazon web services. Brian, great to see you. And Chris Wegman is the, uh, center, uh, Amazon business group technology lead at Accenture. Um, guys, this is about technology vision, this, this conversation, um, Chris, I want to start with you because you, Andy Jackson's keynote, you heard about the strategy of digital transformation, how you gotta lean into it. You gotta have the guts to go for it, and you got to decompose. He went everywhere. So what, what did you hear? What was striking about the keynote? Because he covered a lot of topics. Yeah. You know, it >>Was Epic, uh, as always for Mandy, a lot of topics, a lot to cover in the three hours. Uh, there was a couple of things that stood out for me, first of all, hybrid, uh, the concept, the new concept of hybrid and how Andy talked about it, you know, uh, bringing the compute and the power to all parts of the enterprise, uh, whether it be at the edge or are in the big public cloud, uh, whether it be in an outpost or wherever it might be right with containerization now, uh, you know, being able to do, uh, Amazon containerization in my data center and that that's, that's awesome. I think that's gonna make a big difference, all that being underneath the Amazon, uh, console and billing and things like that, which is great. Uh, I'll also say the, the chips, right. And I know compute is always something that, you know, we always kind of take for granted, but I think again, this year, uh, Amazon and Andy really focused on what they're doing with the chips and PR and compute, and the compute is still at the heart of everything in cloud. And that continued advancement is, is making an impact and will make a continue to make a big impact. >>Yeah, I would agree. I think one of the things that really, I mean, the container thing was, I think really kind of a nuanced point when you got Deepak sing on the opening day with Andy Jassy and he's, he runs a container group over there, you know, small little team he's on the front and front stage. That really is the key to the hybrid. And I think this showcases this new layer and taking advantage of the graviton two chips that, which I thought was huge. Brian, this is really a key part of the platform change, not change, but the continuation of AWS higher level servers building blocks that provide more capabilities, heavy lifting as they say, but the new services that are coming on top really speaks to hybrid and speaks to the edge. >>It does. Yeah. And it, it, you know, I think like Andy talks about, and we talk about, I, you know, we really want to provide choice to our customers, uh, first and foremost, and you can see that and they re uh, services. We have, we can see it in the, the hybrid options that Chris talked about, being able to run your containers through ECS or EKS anywhere I just get to the customer's choice. And one of the things that I'm excited about as you talk about going up the stack and on the edge are things will certainly outpost. Um, right. So now I'll post those launched last year, but then with the new form factors, uh, and then you look at services like Panorama, right? Being able to take computer vision and embed machine learning and computer vision, and do that as a managed capability at the edge, um, for customers. >>And so we see this across a number of industries. And so what we're really thinking about is customers no longer have to make trade-offs and have to think about those, those choices that they can really deploy, uh, natively in the cloud. And then they can take those capabilities, train those models, and then deploy them where they need to, whether that's on premises or at the edge, you know, whether it be in a factory or retail environment. When we start, I think we're really well positioned when, um, you know, hopefully next year we started seeing the travel industry rebound, um, and the, the need, you know, more than ever really to, uh, to kind of rethink about how we kind of monitor and make those environments safe. Having this kind of capability at the edge is really going to help our customers as, as we come out of this year and hopefully rebound next year. >>Yeah. Chris, I want to go back to you for a second. It's hard to hard to pick your favorite innovation from the keynote, because, you know, just reminded me that Brian just reminded me of some things I forgot happened. It was like a buffet of innovation. Some keynotes have one or two, it was like 20, you got the industrial piece that was huge. Computer vision machine learning. That's just a game changer. The connect thing came out of nowhere, in my opinion, I mean, it's a call center technology. This is boring as hell. What are you gonna do with that? It turns out it's a game changer. It's not about the calls with the contact and that's discern intermediating, um, in the stack as well. So again, a feature that looks old is actually new and relevant. What's your, what was your favorite, um, innovation? >>Uh, it it's, it's, it's hard to say. I will say my personal favorite was the, the maca last. I, I just, I think that is a phenomenal, um, uh, just addition, right? And the fact that AWS is, has worked with Apple to integrate the Nitra chip into, into, uh, you know, the iMac and offer that out. Um, you know, a lot of people are doing development, uh, on for ILS and that stuff. And that there's just gonna be a huge benefit, uh, for the development teams. But, you know, I will say, I'll come back to connect you. You mentioned it. Um, you know, but you're right. It was a, it's a boring area, but it's an area that we've seen huge success with since, since connect was launched and the additional features and the Amazon continues to bring, you know, um, obviously with, with the pandemic and now that, you know, customer engagement through the phone, uh, through omni-channel has just been critical for companies, right. >>And to be able to have those agents at home, working from home versus being in the office, it was a huge, huge advantage for, for several customers that are using connect. You know, we, we did some great stuff with some different customers, but the continue technology, like you said, the, you know, the call translation and during a call to be able to pop up those key words and have a, have a supervisor, listen is awesome. And a lot of that was some of that was already being done, but we were stitching multiple services together. Now that's right out of the box. Um, and that Google's location is only going to make that go faster and make us to be able to innovate faster for that piece of the business. >>It's interesting, you know, not to get all nerdy and, and business school life, but you've got systems of records, systems of engagement. If you look at the call center and the connect thing, what got my attention was not only the model of disintermediating, that part of the engagement in the stack, but what actually cloud does to something that's a feature or something that could be an element, like say, call center, you old days of, you know, calling an 800 number, getting some support you got in chip, you have machine learning, you actually have stuff in the, in the stack that actually makes that different now. So you w you know, the thing that impressed me was Andy was saying, you could have machine learning, detect pauses, voice inflections. So now you have technology making that more relevant and better and different. So a lot going on, this is just one example of many things that are happening from a disruption innovation standpoint. W what do you guys, what do you guys think about that? And is that like getting it right? Can you share it? >>I think, I think, I think you are right. And I think what's implied there and what you're saying, and even in the, you know, the macro S example is the ability if we're talking about features, right. Which by themselves, you're saying, Oh, wow, what's, what's so unique about that, but because it's on AWS and now, because whether you're a developer working on, you know, w with Mac iOS and you have access to the 175 plus services, that you can then weave into your new applications, talk about the connect scenario. Now we're embedding that kind of inference and machine learning to do what you say, but then your data Lake is also most likely running in AWS, right? And then the other channels, whether they be mobile channels or web channels, or in store physical channels, that data can be captured in that same machine learning could be applied there to get that full picture across the spectrum. Right? So that's the, that's the power of bringing together on AWS to access to all those different capabilities of services, and then also the where the data is, and pulling all that together, that for that end to end view, okay, >>You guys give some examples of work you've done together. I know this stuff we've reported on. Um, in the last session we talked about some of the connect stuff, but that kind of encapsulates where this, where this is all going with respect to the tech. >>Yeah. I think one of the, you know, it was called out on Doug's partner summit was, you know, is there a, uh, an SAP data Lake accelerator, right? Almost every enterprise has SAP, right. And SAP getting data out of SAP has always been a challenge, right. Um, whether it be through, you know, data warehouses and AWS, sorry, SAP BW, you know, what we've focused on is, is getting that data when you're on have SAP on AWS getting that data into the data Lake, right. And getting it into, into a model that you can pull the value out of the customers can pull the value out, use those AI models. Um, so that was one thing we worked on in the last 12 months, super excited about seeing great success with customers. Um, you know, a lot of customers had ideas. They want to do this. They had different models. What we've done is, is made it very, uh, simplified, uh, framework that allows customers to do it very quickly, get the data out there and start getting value out of it and iterating on that data. Um, we saw customers are spending way too much time trying to stitch it all together and trying to get it to work technically. Uh, and we've now cut all that out and they can immediately start getting down to, to the data and taking advantage of those, those different, um, services are out there by AWS. >>Brian, you want to weigh in as things you see as relevant, um, builds that you guys done together that kind of tease out the future and connect the dots to what's coming. >>Uh, I, you know, I'm going to use a customer example. Uh, we worked with, um, and it just came out with, with Unilever around their blue air connected, smart air purifier. And what I think is interesting about that, I think it touches on some of the themes we're talking about, as well as some of the themes we talked about in the last session, which is we started that program before the pandemic. Um, and, but, you know, Unilever recognized that they needed to differentiate their product in the marketplace, move to more of a services oriented business, which we're seeing as a trend. We, uh, we enabled this capability. So now it's a smart air purifier that can be remote manage. And now in the pandemic head, they are in a really good position, obviously with a very relevant product and capability, um, to be used. And so that data then, as we were talking about is going to reside on the cloud. And so the learning that can now happen about usage and about, you know, filter changes, et cetera, can find its way back into future iterations of that valve, that product. And I think that's, that's keeping with, you know, uh, Chris was talking about where we might be systems of record, like in SAP, how do we bring those in and then start learning from that data so that we can get better on our future iterations? >>Hey, Chris, on the last segment we did on the business mission, um, session, Andy Taylor from your team, uh, talked about partnerships within a century and working with other folks. I want to take that now on the technical side, because one of the things that we heard from, um, Doug's, um, keynote and that during the partner day was integrations and data were two big themes. When you're in the cloud, technically the integrations are different. You're going to get unique things in the public cloud that you're just not going to get on premise access to other cloud native technologies and companies. How has that, how do you see the partnering of Accenture with people within your ecosystem and how the data and the integration play together? What's your vision? >>Yeah, I think there's two parts of it. You know, one there's from a commercial standpoint, right? So marketplace, you know, you, you heard Dave talk about that in the, in the partner summit, right? That marketplace is now bringing together this ecosystem, uh, in a very easy way to consume by the customers, uh, and by the users and bringing multiple partners together. And we're working with our ecosystem to put more products out in the marketplace that are integrated together, uh, already. Um, you know, I think one from a technical perspective though, you know, if you look at Salesforce, you know, we talked a little earlier about connect another good example, technically underneath the covers, how we've integrated connect and Salesforce, some of it being prebuilt by AWS and Salesforce, other things that we've added on top of it, um, I think are good examples. And I think as these ecosystems, these IFCs put their products out there and start exposing more and more API APIs, uh, on the Amazon platform, make opening it up, having those, those prebuilt network connections there between, you know, the different VPCs and the different areas within, within a customer's network. >>Um, and having them, having that all opened up and connected and having all that networking done underneath the covers. You know, it's one thing to call the API APIs. It's one thing to have access to those. And that's been a big focus of a lot of, you know, ISBNs and customers to build those API APIs and expose them, but having that network infrastructure and being able to stay within the cloud within AWS to make those connections, the past that data, we always talk about scale, right? It's one thing if I just need to pass like a, you know, a simple user ID back and forth, right? That's, that's fine. We're not talking massive data sets, whether it be seismic data or whatever it be passing those of those large, those large data sets between customers across the Amazon network is going to, is going to open up the world. >>Yeah. I see huge possibilities there and love to keep on this story. I think it's going to be important and something to keep track of. I'm sure you guys will be on top of it. You know, one of the things I want to, um, dig into with you guys now is Andy had kind of this philosophy philosophical thing in his keynote, talk about societal change and how tough the pandemic is. Everything's on full display. Um, and this kind of brings out kind of like where we are and the truth. You look at the truth, it's a virtual event. I mean, it's a website and you got some sessions out there with doing remote best weekend. Um, and you've got software and you've got technology and, you know, the concept of a mechanism it's software, it does something, it does a purpose. Essentially. You guys have a concept called living systems where growth strategy powered by technology. How do you take the concept of a, of a living organism or a system and replace the mechanism, staleness of computing and software. And this is kind of an interesting, because we're on the cusp of a, of a major inflection point post COVID. I get the digital transformation being slow that's yes, that's happening. There's other things going on in society. What do you guys think about this living systems concept? >>Yeah, so I, you know, I'll start, but, you know, I think the living system concept, um, you know, it started out very much thinking about how do you rapidly change the system, right? And, and because of cloud, because of, of dev ops, because of, you know, all these software technologies and processes that we've created, you know, that's where it started it, making it much easier to make it a much faster being able to change rapidly, but you're right. I think as you now bring in more technologies, the AI technology self-healing technologies, again, you're hurting Indian in his keynote, talk about, you know, the, the systems and services they're building to the tech problems and, and, and, and give, uh, resolve those problems. Right. Obviously automation is a big part of that living systems, you know, being able to bring that all together and to be able to react in real time to either what a customer, you know, asks, um, you know, either through the AI models that have been generated and turning those AI models around much faster, um, and being able to get all the information that came came in in the last 20 minutes, right. >>You know, society's moving fast and changing fast. And, you know, even in one part of the world, if, um, something, you know, in 10 minutes can change and being able to have systems to react to that, learn from that and be able to pass that on to the next country, especially in this world with COVID and, you know, things changing very quickly with quickly and, and, and, um, diagnosis and, and, um, medical response, all that so quickly to be able to react to that and have systems pass that information learned from that information is going to be critical. >>That's awesome. Brian, one of the things that comes up every year is, Oh, the cloud scalable this year. I think, you know, we've, we've talked on the cube before, uh, years ago, certainly with the censure and Amazon, I think it was like three or four years ago. Yeah. The clouds horizontally scalable, but vertically specialized at the application layer. But if you look at the data Lake stuff that you guys have been doing, where you have machine learning, the data's horizontally scalable, and then you got the specialization in the app changes that changes the whole vertical thing. Like you don't need to have a whole vertical solution or do you, so how has this year's um, cloud news impacted vertical industries because it used to be, Oh, the oil and gas financial services. They've got a team for that. We've got a stack for that. Not anymore. Is it going away? What's changing. Wow. >>I, you know, I think it's a really good question. And I don't think, I think what we're saying, and I was just on a call this morning talking about banking and capital markets. And I do think the, you know, the, the challenges are still pretty sector specific. Um, but what we do see is the, the kind of commonality, when we start looking at the, and we talked about it as the industry solutions that we're building as a partnership, most of them follow the pattern of ingesting data, analyzing that data, and then being able to, uh, provide insights and an actions. Right. So if you think about creating that yeah. That kind of common chassis of that ingest the data Lake and then the machine learning, can you talk about, you know, the announces around SageMaker and being able to manage these models, what changes then really are the very specific industries algorithms that you're, you're, you're writing right within that framework. And so we're doing a lot in connect is a good example of this too, where you look at it. Yeah. Customer service is a horizontal capability that we're building out, but then when you stop it into insurance or retail banking or utilities, there are nuances then that we then extend and build so that we meet the unique needs of those, those industries. And that's usually around those, those models. >>Yeah. And I think this year was the first reinvented. I saw real products coming out that actually solve that problem. And that was their last year SageMaker was kinda moving up the stack, but now you have apps embedding machine learning directly in, and users don't even know it's in there. I mean, Christmas is kind of where it's going. Right. I mean, >>Yeah. Announcements. Right. How many, how many announcements where machine learning is just embedded in? I mean, so, you know, code guru, uh, dev ops guru Panorama, we talked about, it's just, it's just there. >>Yeah. I mean, having that knowledge about the linguistics and the metadata, knowing the, the business logic, those are important specific use cases for the vertical and you can get to it faster. Right. Chris, how is this changing on the tech side, your perspective? Yeah. >>You know, I keep coming back to, you know, AWS and cloud makes it easier, right? None of this stuff, you know, all of this stuff can be done, uh, and has some of it has been, but you know, what Amazon continues to do is make it easier to consume by the developer, by the, by the customer and to actually embedded into applications much easier than it would be if I had to go set up the stack and build it all on that and, and, and, uh, embed it. Right. So it's, shortcutting that process. And again, as these products continue to mature, right. And some of the stuff is embedded, um, it makes that process so much faster. Uh, it makes it reduces the amount of work required by the developers, uh, the engineers to get there. So I I'm expecting, you're going to see more of this. >>Right. I think you're going to see more and more of these multi connected services by AWS that has a lot of the AIML, um, pre-configured data lakes, all that kind of stuff embedded in those services. So you don't have to do it yourself and continue to go up the stack. And we was talking about, Amazon's built for builders, right. But, you know, builders, you know, um, have been super specialized in, or we're becoming, you know, as engineers, we're being asked to be bigger and bigger and to be, you know, uh, be able to do more stuff. And I think, you know, these kinds of integrated services are gonna help us do that >>And certainly needed more. Now, when you have hybrid edge that are going to be operating with microservices on a cloud model, and with all those advantages that are going to come around the corner for being in the cloud, I mean, there's going to be, I think there's going to be a whole clarity around benefits in the cloud with all these capabilities and benefits cloud guru. Thanks my favorite this year, because it just points to why that could happen. I mean, that happens because of the cloud data. If you're on premise, you may not have a little cloud guru, you got to got to get more data. So, but they're all different edge certainly will come into your vision on the edge. Chris, how do you see that evolving for customers? Because that could be complex new stuff. How is it going to get easier? >>Yeah. It's super complex now, right? I mean, you gotta design for, you know, all the different, uh, edge 5g, uh, protocols are out there and, and, and solutions. Right. You know, Amazon's simplifying that again, to come back to simplification. Right. I can, I can build an app that, that works on any 5g network that's been integrated with AWS. Right. I don't have to set up all the different layers to get back to my cloud or back to my, my bigger data side. And I was kind of choking. I don't even know where to call the cloud anymore, big cloud, which is a central and I go down and then I've got a cloud at the edge. Right. So what do I call that? >>Exactly. So, you know, again, I think it is this next generation of technology with the edge comes, right. And we put more and more data at the edge. We're asking for more and more compute at the edge, right? Whether it be industrial or, you know, for personal use or consumer use, um, you know, that processing is gonna get more and more intense, uh, to be able to manage and under a single console, under a single platform and be able to move the code that I develop across that entire platform, whether I have to go all the way down to the, you know, to the very edge, uh, at the, at the 5g level, right? Or all the way into the bigger cloud and how that process, isn't there be able to do that. Seamlessly is going to be allow the speed of development that's needed. >>Well, you guys done a great job and no better time to be a techie or interested in technology or computer science or social science for that matter. This is a really perfect storm, a lot of problems to solve a lot of things, a lot of change happening, positive change opportunities, a lot of great stuff. Uh, final question guys, five years working together now on this partnership with AWS and Accenture, um, congratulations, you guys are in pole position for the next wave coming. Um, what's exciting. You guys, Chris, what's on your mind, Brian. What's, what's getting you guys pumped up >>Again. I come back to G you know, Andy mentioned it in his keynote, right? We're seeing customers move now, right. We're seeing, you know, five years ago we knew customers were going to get a new, this. We built a partnership to enable these enterprise customers to make that, that journey. Right. But now, you know, even more, we're seeing them move at such great speed. Right. Which is super excites me. Right. Because I can see, you know, being in this for a long time, now I can see the value on the other end. And I really, we've been wanting to push our customers as fast as they can through the journey. And now they're moving out of, they're getting, they're getting the religion, they're getting there. They see, they need to do it to change your business. So that's what excites me is just the excites me. >>It's just the speed at which we're, we're in a single movement. Yeah, yeah. I'd agree with, yeah, I'd agree with that. I mean, so, you know, obviously getting, getting customers to the cloud is super important work, and we're obviously doing that and helping accelerate that, it's it, it's what we've been talking about when we're there, all the possibilities that become available right. Through the common data capabilities, the access to the 175 some-odd AWS services. And I also think, and this is, this is kind of permeated through this week at re-invent is the opportunity, especially in those industries that do have an industrial aspect, a manufacturing aspect, or a really strong physical aspect of bringing together it and operational technology and the business with all these capabilities, then I think edge and pushing machine learning down to the edge and analytics at the edge is really going to help us do that. And so I'm super excited by all that possibility is I feel like we're just scratching the surface there, >>Great time to be building out. And you know, this is the time for re reconstruction. Re-invention big themes. So many storylines in the keynote, in the events. It's going to keep us busy here. It's looking at angle in the cube for the next year. Gentlemen, thank you for coming out. I really appreciate it. Thanks. Thank you. All right. Great conversation. You're getting technical. We could've go on another 30 minutes. Lot to talk about a lot of storylines here at AWS. Reinvent 2020 at the Centure executive summit. I'm John furrier. Thanks for watching.