>>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.

>>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Okay. Welcome back to the cubes coverage of 80 us. Re-invent 2020 virtual ecentric executive summit. The two great guests here to break down the analysis of the relationship with cloud and essential. Brian bowhead director ahead of Accenture. 80 was a 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 50th anniversary. What's new, obviously the emphasis of next gen post COVID generation, highly accelerated 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. Um, 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 serve as 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 say, Andy want to get your thoughts here. We've >>Been reporting and covering essential 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, 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 in two 20, what, what is it I think today there are, um, 4% of the enterprise workloads sits at the cloud. Um, you know, that leaves 96% out there on prem. Um, and I think 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 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, to really pause and think about everything from, from, you know, how they, um, manage that technology, their infrastructure, to, to clarity to where that 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, that culture, that 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 forces us, it forces AWS's 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 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, and help these companies get to the cloud and get the value from the cloud. Cause it's, it's beyond just getting to the cloud attached to me and living in the cloud and getting the value from it. >>It's interesting. Andy was saying, don't just put your toe in the water. You've 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 start in the end. >>You can buy a lawn. So you, it's interesting if I look >>Back a year ago at reinvent and when I did the cube interview, then we were talking about how ABG we're >>Starting to see that shift of customers. You know, we've been working with customers for years on a single of what I call a single-threaded programs, right? We can do a migration, we can 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, that 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 the 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 you know, one is the, the people. Um, and, and I, I, again, I think Andy touched on sort of a, uh, success factors, uh, early in the week. And for me, it's these three areas that it sort of boils down to, 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 you 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'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 food for the right conditions to be set in the company, not enable 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, um, 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 enterprises 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 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, APIs, 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? You know, where's the puck going? Where can they skate to 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 do they, what, 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 can probably look at it from a sort of a thematic lens. And what we're seeing is as the cloud transformation journey picks up 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 at play, um, where I, you know, it's not just a practical experience, but I can sort of keep learning and iterating, um, how treat my, eat, my customers, um, and a lot of that, um, that's still self-learning that comes from, you know, putting in, uh, intelligence into your, into your systems, getting an AI and ML, uh, in that. And so as a result of that, where it was seeing a lot of push and a lot of what we're doing, uh, is pouring investments 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? Uh, so I'll probably say those are the three areas. John >>Brian on 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 I've see a, 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 from Andy. I'm talking about yesterday now about how can partners within even a center. What's the business model side on your side that you're enabling this. Can you just share your thoughts on that? >>Yeah. And so it's, it's interesting. And again, I'm kind of build it in a 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 that 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 off a 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 get about 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, what's an example of a, kind of like a bottom to top 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, challenge is a big part of the game here. Andy Jassy referenced Lambda. 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 best 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, but then on 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 keep it very simple. Um, w we're we're we're seeing increasingly that you need people who 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 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, um, and, uh, and not afraid to lead. Yeah. >>It takes a gut-check there. I just said, 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, uh, Amazon focus, obviously their 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 first, what we're doing is we're, we're putting together, um, a capability set that will help enable him to and translations as Brian touched on your help companies move, you know, 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. 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 preassembled industry offerings to help as an accelerant, uh, for our, for our customers three, as, 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 enterprises States out there. And so, you know, parts of our cloud first and our AEG strategy is to increase our touchpoints and our integrations and our solutions and our offerings where the ecosystem partners out there, the ISV partners out there, and 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, the concept of hybrid cloud and that in itself, um, uh, is, is, is, uh, you know, being redefined as well, you know, where you've got the intelligent edge and you've got various forms of the edge. Um, so that's the fourth part of, of our, of our cloud first strategy. And, and, 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 we look to enable our customers, but also how we, you know, as we, as we look to extend how we go to market, uh, I joined tally 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, they, 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 is cloud really permeates everything that we're doing now. Um, and, and so all aspects of the business will get infused with cloud in some ways, you know, it, it touches 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 start 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 assets, 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 app activities up the stack. >>So I'm excited to see us, you know, kind of invest to make those, those, um, those bits 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. Um, 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 has 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, uh, 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 >>Of innovation is, is picking up. Um, the split 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 bringing our industry capabilities, we marry the two, we, we go and help our customers super exciting times. >>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, Accenture AWS partnership, part of the center's executive summit at Avis reinvent 2020. I'm John for your host. Thanks for watching.

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> We're back with the Cuban active FiO Data driven day one day Volante with student a man you're watching The Cube. Steve Duplessis here is the, uh, let's see. Uh, I'm going to say benevolent. Dictator of Enterprise Strategy Group. Chief analyst, Founder Welcome. Welcome back to the Cube. >> Thanks. Nice friend. Nice to be here, you fellows, and we don't Great. Congratulations. Newly newly closed. That's awesome. I want Yeah, thank you very much. >> Great. Looking good. You're here for your honeymoon. >> He said this is it? After a few marriages. This is the honeymoon. >> Yeah. That's good to know that the honeymoon's not over. So let's talk data, Tio. It's happening. >> That is a terrible question, Dave. >> So yeah, Data. Okay, everybody talks. Data you here, bro. My data is the new oil. Fate is a competitive advantage. And >> you like that. >> You do like what Data's in oil. >> So it's funny because we're I think I'm way older than you. You look better. >> God, no. >> But if you go back in time as long as we were doing this, it's been kind of hilarious, really. In retrospect, when you watch way watch these massive industries get created like the AMC just created because all they were about building bigger buckets to put data, zeros and ones. But no context, completely useless, just big buckets. So we valued Wow, you built a big fast bucket. Then IBM and her tachy whoever was gonna leap frog your next built a faster, bigger bucket. And that was with the world considered valuable. And it's now fast forward to the modern day and oh, maybe with the thing that's really valuable with those zeros and he's in contact. Maybe it's not really the bucket. It's, uh so valuable anymore. So >> So, do you think the with the bucket builders still bucket builders air they actually becoming data Insite creators? Or is it just still build a better bucket? That's cheaper. Faster >> till it's a great question. I think >> that we're first of all, you You still have to have the buckets, right? It's a relative who's going to make a smarter bucket builder. I don't know. >> You need someplace to put it, so >> you're gonna have to put it some place and you're gonna have to deliver it in the good news, you know, storage and or infrastructural say is the most brilliant business ever. From a capacity demand perspective, no one ever needs less, right. You always need Mauritz justa matter what you're gonna do with it, how you're going to address that. So it's we've propagated for 50 years and infrastructure business that build a bigger, faster bucket. Build a bigger, faster processor, build a bigger, faster. And every time you you solve one of those particular problems as long as data doesn't abate and it never does, is only is there's more versus Les. It's just every time we fix one problem way, you stick your finger in the dike and another poll springs out. So right now we're at the we've got more processing capabilities that week, ever possible. Use not true, right? We'll figure out a way to use it so that the last five years of and for the >> next five years waiting talk about analytics, wouldn't talk about io ti. We didn't talk about any of those things that are all just precursors to folk crap. We could make a whole bunch more NATO and do stuff with >> so So computers. Kind of a similar dynamic. It's sort of sensational. But is the relatively crappy business compared to storage rights? Storage is 60% plus gross margin. Business servers. I don't know. You're lucky if you get in in a low twenty's. Um, why is that? >> Hello, Number one. It's essentially monogamous. So 20% is wonderful if your intel and you get it. All right. Well, it sells. Got great gross margins, right? Everybody else's does it. You go down the supply chain. That's where you're gonna add value. So that's difficult for anything. Hard to get gross margins out of like spending. She had a box. >> So, Steve Yes, she's now 20 years old. >> I know >> when I think back 20 years ago. You know, short. You know this capacity price per dollar price per gigabyte. You know, all that stuff has changed a lot. The other thing, You know, I think back 20 years talk about automation and intelligent infrastructure. We were using those terms back that sure, one of things that they did. That that's right. Well, that's what I wanted to ask you about is like, right back then when you talked about well, how intelligent wasn't what could it do? And automation was There was a lot of times, you know, I'm just building a little script. I'm doing something like that. At least you know, from what we see, it feels like, you know, today's automation and intelligence is light times away from what we were talking about. 20 years. Sure, and it's true. What do you see in that? Well, >> so remember where we came from When we were talking originally about automation and orchestration, we were talking about how to manage a box, how to expand a box, how to manage infrastructure. Now it's data operations. Right now it's that that's the whole point of activity. Right to be in with is all right, if you are good enough and smart enoughto have the data sort of everything. What kind of matters? There you've gotta have the data and what can you up? What can you automate an orchestra from a data out perspective? Not from a box, not from a Let's scale out or scale up or something like that again, that's just a bigger bucket. It's a better bucket, but to be able to actually take data and say, You know what? I don't even know necessarily what I'm going to want to use this for, but I know that I gotta have. It's gotta be You have to be able to go click, click, click and get it. If if and when I figure out who I want to find out how lowering the price of Sharman and Seattle at a Wal Mart is going to affect my revenue or my supply chain or whatever. >> So one of the things I've talked with you in the past about is the pace of change of the industry. And, you know, I've said, you know, we know things are changing rather fast, but the average company, how much were they? Actually are they good at adopting change? And you've called me on stupid enterprises slow getting any faster, you know? Are they Are they open to change? Mohr. You know, what do you see in 2019? Is is it any different than it was in, You know, two thousand nine? >> That's a great question. So thie answer is yes, they're getting better. We are finally getting better. Problem, though, is a CZ industry insider watcher or a Boyar is ur is you see it and know what should happen 10 years. It takes 10 years in general for the world to actually catch upto the stuff that we're talking about. So it's not really that helpful to the poor schlub that's running on operation that build sneakers in Kansas, right? That's not really that helpful that we're talking about. This is what you could be doing and should be doing. The pace of change is much faster now because and give the em where most of the credit. Because once that went into place, all of the sudden and that you gotta remember there, everyone thinks vm where was an instant home run? It was 10 years of the same cold sitting in the corner in a queue, a environment before. Finally, we ran out of room in the data center, and that's the only reason they were able to come out. But once it was there, and it enabled you to stop associating the physical to the to the logical once, we could just just dis aggregate that stuff that I think opened up a tidal wave of kind of what else can we do? And people have adopted now. Now it's pervasive. So VM where's everywhere? Now? We're moving in the next level of kind of woman. Why can't I just build a containerized app that I can execute anywhere? No matter of fact, I don't even want it in my data center on. No one has to know that necessarily. So as modernization exercises have started to take off, they just they pick up, they actually pick up steam. So what we know empirically is those that are are halfway down. Call it the transformation or the modernization curve are going three times faster than those just starting. And those guys are going three times faster than the ones that are sitting there in idle doing stuff. The same >> city with the inertia going on. What do you make of this Bubblicious Back up market. Let's talk about that a little bit. You got these big install bases? The veritas, Conmebol, Delhi emcee, IBM, Tivoli install base. Everybody wants a piece of that action. Well, I guess cohesive rubric also want a piece of each other. Sure, which is kind of, you know, they get that urinary Olympics going on. I'd like to say And then you got these guys, which is kind of, you know, playing. Uh, I said to Ashleigh kind of East Coast, West Coast, There's no no, it's not East Coast, West Coast, but there's definitely more conservativism on this side of the of the flyover states. What's your take on what's going on in the landscape right now? >> So back up is awesome from the again, still probably the single most consistently line item budget thing for five decades. It's a guaranteed money in and out, and by and large it still sucks. My general rule is still it's crazy that we haven't been able to solve that particular problem. But regardless, the reason that it's so important is, besides the obvious. Yeah, you need to protect stuff, case. Something goes away and something bad happened good. But really, it's That's the inn. Just point for everything you do, you create data today. I'm backing it up on our later so that backup becomes the injust engine and it also is kicking off point. So at tapioca it started as wow, this is a better backup, most trap for lack of a better term. But really what? It was is didn't matter what with was back up or something else. It's I need tohave the data in order to do other stuff with it, and back up is just a natural, easiest way to be able to do that. So I think what's finally happening is we're moving from Christophe Would would say it's really about intelligence intelligence more so than just capturing those bits and being able to assemble and put it back together. It's understanding the context of those bits so that I can say stew in test. Dev has a different use case than Dave in whatever analytics, etcetera, etcetera. But they both need a copy of the exact scene data, the exact same state at the exact same point in time, etcetera. So if lungs backup's going to be kind of a tip of the spear in terms of going from what I will say, production or live data to the first copy, there's almost always back up. It's gonna matter. >> Christoph, Christoph Bertrand want your analyst? And so we saw, uh, c'mon, Danni Allen put a slideshow $15,000,000,000 tam and back up being a big chunk of that, probably half of it um, how does that jibe with your gut feel in terms of the opportunity beyond backup Dev ops? You know, I don't know. Ransomware insights. So you think that's low? High? Makes sense. >> I think I could justify the number. And what history has taught me is that it's probably low because we we're only talking about a handful of use cases that we've all glommed onto. But there will be remembered, like 11 years ago, there was no iPhone. You know what? How bad that changed. Everything that we do over there. And when did you know at some point during that particular journey, the phone became Who gives a shit about the phone? Excuse. But it's a text machine and it's an instagram thing, and it's a video production facility and all these other things, and the phone's almost dead. I only use it when my mom calls me kind of thing. So, you know, really, it's difficult to imagine. I certainly don't have the mental capabilities to imagine what the next 10 things after Dev Ops and this that and the other. But it's still all predicated on the same you got Somebody's gonna have a copy of that data and you're gonna be able to access it. You've got to be able to put it where you need it for whatever the reason again, a disaster is an important thing to recover from. But so is being ableto farm That data for nuggets of gold. >> Well, I guess I asked the question because, you know, it's a logical question is, is the market big enough to support all these companies that are in, You know, that gardener thing that they do? And I hope so because we love competition. >> I think I >> can answer it >> this way. Everything. Even the oldest guard Veritas, for God's sakes, 1000 years old, t sm 1000 years old con vault code base, 1000 years old. You're all big companies, right? And they're not perishing anytime soon. And I don't run. Love the startup Love the active FiOS or the cohesive sees coming in. But what they're really trying to do is not, you know, they might have started, as in a common ground, backup is a common warzone, but because there's money there like this consistent money there go get. But they soon turn in Teo other value propositions. And that's not is true with the incumbent back up guys because of their own legacy, right? It's hard to turn 1,000,000 year 1,000,000 lines of code into something. It wasn't designed, innit? >> Yeah, and it's not trivial to disrupt that base. But I guess if you get, you know, raising I don't know how much the industry is raised, but it's well over $1,000,000,000 now. I mean, activity has raised 200,000,000 and that's like chump change. Compared to some of the other races that you've seen. Cody City was to 60 and their last rubric was even, you know, crazy, crazy, even >> count the private money that beam God is that, you know, that was half 1,000,000,000 >> right? Well, that's a That's an off camera discussion. All right, we gotta go. So, Steve, thanks so much for for coming. Thank you. Great to >> have you. All right. All right, everybody. We'll be back with our next guest. You wanted the Cube from active field data driven from Boston, right on the harbor. Right back

>> live from Boston, Massachusetts. It's the queue covering your red. Have some twenty nineteen brought to you by bread. >> Welcome back to the Cube. Continuing coverage here read. Had some twenty nineteen day three of our three days of covering some nine thousand attendees, great keynotes, great educational sessions and a couple of great guests for you to meddle. And John Walls were joined by Marco Bill Peter, who is the senior vice president of customer experience and engagement at Red Hat. Good to see you, Marco. Thanks for having the job on the keynote stage this morning. And Dr Christoph back, who was the head of infrastructure from Hilty and Christof. Thank you for being here is Well, thankyou. Hailing from from election Stein. And we think you're the first guest alum were to check our database, But But we've set a new record today. So thanks for adding to our having. First off, let's talk about Hilty. I'm sure people don't stay healthy. I've seen them, but this building probably wouldn't be here without you. Have imagined half the city wouldn't be here without you. But just tell folks at home a healthy a little bit about where you fit into the construction. >> Lt was founded in the nineteen forties in principality of a Lichtenstein as and is now today leading supplier for the construction industry. We provide tours, consumables, services and software solutions for professional construction companies. Daddy's from hammer drills, two anchors to calculation software and overall complete services for the industry. That's what hell is doing. >> So you did a very good job this morning on the keynote of painting that picture about about the scope of your work and the necessity of your work, the vitality of it. Because construction projects, as we all know, how very strict deadlines. Sometimes they have unique needs. They have immediate needs emergency needs, and you're in the center of all that. And so your technology is central to your general operation. >> Absolutely yes. I mean, with twenty five thousand or twenty nine thousand employees, twenty five thousand users in our system, basically, everybody's using everyday ASAP or the fast majority of users. We have ten thousand concurrent users every day on our system. That deal with customer requests with orders with quotes, but also, of course, with complaints with repair handling and so on. In >> just a few. Yeah, just >> so Marco, I hear ASAP and, you know, bring me back to when? Oh, well, you know, Lennox was that stuff that sat on little sidelines. We're well past that. You've got so many customers that run their business, you know, mission critical around the globe. Just give give, give us a little bit of background on the partnership with Hilty and Red Hat and Solutions like asap. >> Yes, sure. Yeah. The Department of Hilty goes back to, I think, two thousand seven for me. Personally, I started working with Hilton for another company in ninety three. So I know where the hell did Quite well, actually studied in the same town next to Lichtenstein, Son of the mail. And And it's it's amazing to see the journey kind of two thousand nine going all s ap mission critical on rail and now actually moved to Asa P s for Han. And yes, Hill is one ofthe declines. But it kind of talks that we can handle this mission. Critical applications are mission critical customers and built this good relationship to make sure they have these these courage to actually do this Bold jumps limited The last six months. >> Christoph, you know you've got a broad, you know, roll at the at the company way talked to so many companies on becoming a tech company on becoming a software company. Well, software is critical, but at the end of the day, you know, infrastructure and running your business is core. You know, you're not going to become a fully digital software. You have real stuff in the physical world that lots of people and lots of, you know, physical things that need to go to a little bit about that balance. And now the company has been changing over those last ten years. >> I was excited to be open with you. I was really excited when our executive board a couple of years ago, besides tools, consumables and services also added software into a strategic pillar for Hilty. Um, and while I believe that software will be an interesting pillar for us, well will generate additional revenue, will generate additional sales from early. Also in the consumables and tools and services piece software becomes more and more important when you look at the journey off building a building like this. As you mentioned John, I mean it starts with specifying it starts with the planning on CD, and it ends at the end with with Asset Management. Where are the tours and so on. So it's a complete life cycle through out the building off off throughout the construction of ah building. You >> know, Marco had mentioned that you made this decision to migrate Ohana last year right? Twenty eighteen or or where he might be rated last year? Isn't last year's decision made before that? Talk about that a little bit, if you would please and where Red Hat fit into that? Because that that's that's not a small decision, right? I mean, that's a That's a very calculated and I wouldn't not risky, but it's It's just a big move. Yeah, and so the confidence that you had a CZ well, that red hat was your partner to make that happen. >> Absolutely. I mean, the announcement of SAPI to support Hana as thie only database after twenty twenty five voice one off the factors to push us into that direction, that that was then clear for us that we want to go there. And it was also pretty clear for us that in our size it was not that easy to move in twenty twenty three or something like that in that direction, but that we have to be the first movers to be fully supported by ASAP and >> all >> these Parkins because later on, they will be busy with migrating all the big shots. So Wade took the decision to move first and soon, and that allowed us to be in the focus off all thes attached partners ASAP. But also read had also tell emcee for storage and HP for for servers. That meant that we had confidence that we have full attention from all these providers and partners to help us to migrate. On the other hand, it was clear the the the journey we started in two thousand nine has indicated by Marko that we moved to an open software that we move to commodity hardware. Intel based server hardware was a move that had paid off in the past, and we didn't want to go away from that and move again to a proprietary hardware or software solutions. So it was very clear that we want to do that jointly with red hat on commodity and until based service and That's how we went there, Right? >> So, Christophe, big theme, we hear not only at this show, but almost every show we go to is today customers. It's, you know, the hybrid and multi cloud world I see ASAP at all of the Big Cloud shows that that that we cover well, we're just cloud fit into your over discussion, you know, at your company. And then, you know, we can drill down to the specifics of that sapien red hat. But it's what do you have? A cloud strategy, as it were? >> Oh, yes, you know, we moved fairly soon to Amazon with all our customer facing workload. So when you go to hilton dot com or any of our Web pages, you typically land on a ws powered website because that one gave us the flexibility off operating systems off databases of whatever we needed. That was that was available there with our internal workload. However, So all the software we use Internet eternally toe run the company. We have a world that is split between ASAP, which runs entirely on Red hat, um, and the rest of the workload. Witches to a large degree, windows based workload so there. We decided a few years ago to Movinto Microsoft Azure platform to move the internal workload into Azure as it is mainly Windows based. >> So Marco actually want one a depart from healthy for a second. Just give us a little bit of a broad view. You know, we've talked to you many times. You talked about the stage. You know, the customer experiences a critical piece of red hats mission out there When I talk to customers today, One of the biggest changes they've seen the last few years is I'm managing a lot of stuff that's not in my environment. It's the stuff I'm responsible for it and something goes wrong. I'm absolutely getting a call, but you know, it's not my network. It's not my servers. It's not my piece there, but I have to do all of them, you know, got imagine. That's been a transformation for red Hat in the partnerships, and you're everywhere, so it just gets a little context. Yeah, I >> mean, you described it very well, right? I mean, I think the last two years before, I think it was just like some use cases in the public club. But today. The harder cloud is here, right? And everybody does it right. It's not like just one company from a customer experience to stand behind. Like I mentioned it on the state gets harder. Right? And you gonna have these partnerships, right? One partnership, right. We can talk about the azure. We have people in enrichment, right? Think about it today. And then everything changed with start having on stage here. But we have support people in micro for the last two or three years, right? Same diff ASAP as an example, right? We have people. We actually build a fairly large teeming, involved off to be close of us. Time together. I want to do that speed ASAP. A cloud bead on regular bear closes in general. Yes, That challenges. You mentioned networking, right? It gets tricky, right? And he shifted from, but it's unavoidable, right? It shifted from, like, okay, we own and control the stacked kind of too. Yes, you need to know you're open source after and to have really partnerships. Right? And I think the announcement Microsoft, too have this managed services offering that we do joint. It's That's what we're driving so that we can do this better together with partners. >> Marco is great to hear you that but Christoph, he's not listening. Tell us to reality. You've worked with Red Hat for ten years. You're going to cloud how they doing? How's the ecosystem, the vendors in general? They're all up on stage, holding hands. I mean, it's it's seamless and nobody ever point fingers. I'm sure >> to be very, very honest with you. I mean, I appreciate it last year, hearing that redhead will be offered in Azure. I mean, that was not possible to mention those two company names in one sentence in the past, at least for us as customers, and that that was a bold statement last year that those two parties will suddenly join. That fits very well in our strategy, because we believe internal workload for Hilton should run in in In Azure seeing on last Tuesday, Microsoft and Red Hat shaking hands and movie. Even beyond that one was, for me, them almost the most exciting event here, or the most exciting statement that I saw here during these few days because that reemphasized the close relationship that those two have, and that exactly fits our road map. That's exciting. >> And, you know, we heard that, you know, again from from both CEO Saying customers really kind of brought us together. They made this deal work because we kept hearing that they love us and they love you, and they like us together. So So we got that. We understand that. So? So Marco customers drove that to a certain degree. You've got a customer here who made this big Hana jump, which is you say small guy. You know, I would beg to differ little bit that you had him before the big guys did. But what, like an initiative like that? What is that doing for you? What? Red hat. In terms of carrying that over to other customers. Now, you've learned from one you've seen what they've gone through. What kind of confidence does that give you? What kind of interest does it give you about how to approach this game? >> Absolutely. You know what we learned from give you one example right? If you moved his heart ever closer Christopher Hilty uses systems have twelve terabytes memory. Think about it that fairly large systems and that foot train tried to actually test our softer with that footprint and then even think about the next. Next journey is in if you want to do this in the cloud. What does that mean? If you take a twelve terabyte image and running in a double? Yes. And so that is, since my team also does quality assurance and product security. That's for them as well as in. Okay, we've seen what tilted can do work. How do we actually make this more robust? How do we test you are there? And how do we do that in this journey? It's, I think I'm pretty proud of how we actually learn from these instances, and health is not the only one. It's just one the republic. But yet it's every time. I think that's the only survived is into industry. If you really learn continuously and also applied right. I mean our whole setup involved or we shifted completely and not just from the people. They have theirs. So we have people that do open. Chief. There were people do Lennox and performance, but also from structure. I really be sure that they were set up for success and know what the next they have customers is obviously every casting off. A message we will do will go through a journey license over the next ten years. >> Kristoff obviously being on stage, you know it is good for the company, but coming to Red Hat Summit one. Just give our audience that if they hadn't come to it. Some of the value is, too what you place in some, the activities that have excited you most here this week. >> I mean, one thing is, of course, hearing about latest technologies, new releases, off software, off new possibilities and opportunities for us as customers from Red Hat. But also, it's great to see how on the floor out there other partners customers on DH fingers mingle around the ecosystem that created that was created around open software about, ah, not only operating system, but also about containers about all these those different technologies, which I have an important role for all of us in nineteen the future. >> Sure. Well, good week, that's for sure. Very nice job you get on the Kino stage to both of you and good luck with the partnership on down the road. And again, I would make the difference that way. little guys got in early hilt. He's no small fry in inner world, that's for sure. Thanks for the time, Krystof. Marco. Thank you. Thank you very much. Back with more. We're live here in Boston and we're covering the red hat. Summer twenty nineteen on the

(upbeat techno music) >> Live, from Las Vegas, it's theCUBE covering VMworld 2018, brought to you by VMware and it's ecosystem partners. >> Okay, welcome back everyone, we're here live in Las Vegas for VMworld 2018 coverage. It's the final analysis, the final interview of three days, 94 interviews, two CUBE sets, amazing production, our ninth year covering VMworld. We've seen the evolution, we've seen the trials and tribulations of VMware and it's ecosystem and as it moves into the modern era, the dynamics are changing. We heard quotes like, "From playing tennis "to playing soccer," it's a lot of complicated things, the cloud certainly a big part of it. I'm John Furrier your host, Stu Miniman couldn't be here for the wrap, he had an appointment. I'm here with Dave Vallente and Jim Kobielus who's with Wikibon and SiliconANGLE and theCUBE team. >> Guys, great job, I want to say thanks to you guys and thanks to the crew on both sets. Amazing production, we're just going to have some fun here. We've analyzed this event, ten different ways from Sunday. >> So many people working so hard for such a steady clip as we have here the last three days, amazing. >> Just to give some perspective, I want to get, just lay out kind of what's going on with theCUBE. I've get a lot of people come up and ask me hey what's going on, you guys are amazing. It's gotten so much bigger, there's two sets. But every year, Dave, we always try to at VMworld, make VMworld our show to up our value. We always love to innovate, but we got a business to run. We have no outside finance, we have a great set of partners. I'm proud of the team, what Jeff Frick did and the team has done amazing work. Sonia's here's, the whole analyst team's here, our whole team's here. But we have an orchestrated system now, we have the blogging at SilconANGLE.com and Rob Hof leading the editorial. Working on a content immersion program. Jim you were involved in with Rob and Peter in the team, bringing content on the written word side, as fast as possible, the best quality, fast as possible, the analysts getting the pre-briefing and the NDAs, theCUBE team setting it up. Pretty unique formula at full stride right now, only going to get better. New photography, better pictures, better video, better guests, more content. Now with the video clipper tool and our video cloud service and we did a tech preview of our block chain, token economics, a lot of the insiders of VMworld, the senior executives and the community, all with great results, they all loved it, they want to do more. Opening up our platform, opening up the content's been a big success, I want to thank you guys for that. >> And I agree, I should point out that one of the things we have that say an agency doesn't offer, I used to be with a large multi national solutions provider doing kind of similar work but in a thought leadership market kind of, let me just state something here, what we've got is unique because we have analysts, market researchers, who know this stuff at the core of our business model, including, especially the content immersion program. Peter Boroughs did a bit, I did a fair amount on this one. You need subject matter experts to curate and really define the themes that the entire editorial team, and I'm including theCUBE people on the editorial team, are basically, so we're all aligned around we know what the industry is all about, the context, the vendor, and somebody's just curating making sure that the subject matter is on target was what the community wants to see. >> So I got to day, first of all, VMware set us up with two stages here, two sets, amazing. They've been unbelievable partners. They really put their money with their mouth is. They allow us to bring in the ecosystem, do our own thing, so that's phenomenal and our goal is to give back to the community. We had two sets, 94 guests this week, 70 interview segments, hundreds and hundreds of assets coming out, all free. >> It was amazing. >> SiliconANGLE.com, Wikibon.com, theCUBE.net, all free content was really incredible. >> It's good free content. >> It's great free content. >> We dropped a true private cloud report with market shares, that's all open and free. Floyer did a piece on VMware's hybrid cloud strategy, near to momentum, ice bergs ahead. Jim Kobelius, first of all, every day here you laid out here's what happened today with your analysis plus you had previews plus you have a trip report coming. >> Plus I had a Wikibon research note that had been in the pipeline for about a month and I held off on publishing until Monday at the show, the AI ready IT infrastructure because it's so aligned with what's going on. >> And then Paul Gillan and Rob Hof did a series in their team on the future of the data center. Paul Gillan, the walls are tumbling down, I mean that thing got amazing play, check that out. It's just a lot of detail in there. >> And more importantly, that's our content. We're linking, we're open, we're linking to other people's content, from Tech Field Day what Foskett's doing to vBrownBag to linking to stories, sharing, quoting other analysts, Patrick Moorehead for more insights. Anyone who has content that we can get it in fast, in real time, out to the marketplace, is our mission and we love doing it so I think the formula of open is working. >> Yeah Charles King, this morning I saw Charles, I thanked him for, he had great quotes. >> Yeah, great guy. >> He's like, "I love with Paul Gillan calls me." John, talk about the tech preview because the tech preview was an open community project that's all about bringing the community together, helping them and helping get content out into the marketplace. >> Well our goal for this event was to use the VMworld to preview some of our innovations and you're going to start to hear more from the siliconANGLE media, CUBE and siliconANGLE team around concepts like the CUBE cloud. We have technology we're going to start to surface and bring out to the marketplace and we want to make it free and open and allow people to use and share in what we do and make theCUBE a community brand and a community concept and continue this mission and treat theCUBE like an upstream project. Let's all co-create together because the downstream benefits in communities are significantly better when there's co-creation and self governance. Highest quality content, from highly reputable people, whether it's news, analysis, opinion, commentary, pontification, we love it all, let the content stand on it's own and let's the benefits come down so if you're a sponsor, if you're a thought leader, you're a news maker, you're an analyst, we love to do that and we love talking with the executives so that's great. The tech preview is about showcasing how we want to create a new network. As communities are growing and changing, VMware's community is robust, Dave, it's it's own subnet, but as the world grows in those multiple clouds, Azure has a community, Google has a community, and people have been trained to sit in these silos, okay? >> Mm-hmm. >> We go to so many events and we engage with so many communities, we want to connect them all through the CUBE coin concept of block chain where if someone's in a community, they can download the wallet and join theCUBE network. Today there's no mechanism to join theCUBE network. You can go to theCUBE.net and subscribe, you can go to YouTube and subscribe, you can get e-mail marketing but that's not acceptable to us we want a subscribe button that's going to add value to people who contribute value, they can capture it. That was the tech preview, it's a block chain based community. We're calling it the Open Community Project. >> Wow. >> Open Community Project is the first upstream content software model that's free to use, where if the community uses it, they can capture value that they create. It's a new concept and it's radical and revolutionary. >> In some ways were analogous to what VMware has evolved into where they bridge clouds and they say that, "We bridge clouds." We bridge communities all around thought leadership and to provide a forum for conversations that bridge the various siloed communities. >> Well Jim you and I talked about this, we've seen the movie and media. In the old school media days and search engine marketing and e-mail marketing and starting a blog, which we were part of, the blogging was the first generation of sharing economy where you linked to other bloggers and shared your traffic, because you were working together against the mainstream media. >> It's my major keyboard, by the way, I love blogs. >> And if you were funded you had to build an audience. Audience development, audience development. Not anymore, the audience is already there. They are now in networks so the new ethos, like blogging, is joining networks and not making it an ownership, lock in walled garden. So the new ethos is not link sharing, community sharing, co-creation and merging networks. This is something that we're seeing across all event communities and content is the nutrients and the glue for those communities. >> You got multi cloud, you got multi content networks. Making it together, it's exciting. I mean there were some people that I saw this week, I mean Alan Cohen as a guest host, amazingly articulate, super smart guy, plugged in to Silicon Valley. Christophe Bertrand, analyst at ESG, a great analysis today on theCUBE, bringing those guys, nominate them into the community for the Open Community Project. >> You know what I like, Dave, was also Jeff Frick, Sonia and Gabe were all at the front there, greeting the guests. We had great speakers, it all worked. The stages worked but it's for the community, by the community, this is the model, right? This is what we want to do and it was a lot of fun, I had a lot of great interviews from Andy Bechtolsheim, Michael Dell, Pat Gellsinger to practitioners and to the vendors and suppliers all co-creating here in real time, it was really a lot of fun. >> Oh yes, amen. >> Well Dave, thanks for everything. Thanks for the crew, great job everybody. >> Awesome. >> Jim, well done. >> Thanks to Stu Miniman, Peter Burris and all the guests, Justin Warren, John Troyer, guest host Alan Cohen, great community participation. This is theCUBE signing off from Las Vegas, this is VMworld 2018 final analysis, thanks for watching. (upbeat techno music)