(upbeat music) >> Hello everyone, welcome back to our special presentation with TheCUBE and Horizon3.ai. I'm John Ferrier host of TheCUBE here in Palo Alto with the CEO and co-founder of Horizon3 Snehal Antani who's here with me to talk about the big news, we've been talking about your global expansion, congratulations on the growth, and international, and just overall success of, what looks like to be a very high margin, relevant business in the security space. >> Yeah, thank you John. Very excited to be here and especially this focus on partners, because partners in cyber security have such an important role and we've built a company that enables partners to grow with us. >> We had a chance to talk to some of your staff and some of the people in the industry around the channel. I mean the old school technology vendors would go in build channels and distributed resellers, VARs value added resellers, value added businesses all kinds of different ways to serve customers, indirectly. And then you got the direct sales force. You guys seem to have a perfect product for a hard, profitable, market where channels are starved for solutions in the security space. What did you guys find as you guys launched this? What was some of the feedback? What was some of the reasoning behind- obviously indirect sales helps your margins, you enable MSPs to sell for you, but what's the, what was the epiphany? >> So when you think about the telecommunications industry back in the two thousands, we always talked about the last mile in Telco, right? It was easy to get fiber run to the neighborhood but the last mile from the neighborhood to the house was very difficult. So what we found during Covid was, this was especially true in cybersecurity because in Covid you've got individuals that need security capabilities whether they are IT directors, barely treading water or CSOs and so on. And they needed these trusted relationships to decide what security technologies to use, how to improve their posture. And they're not going to go to just some website to learn. They've got years of relationships built with those regional partners, those regional resellers MSSPs, MSPs, IT consulting shops. So what we did over the past two years was embrace this idea that regional partners are the last mile of cybersecurity. So how do we build a product and a business model that enables those last miles channel partners to make even more revenue using us to underpin their offerings and services and get them to take advantage of the trust that they've built over many hard years and use that trust to not only improve the posture of their customers but have Horizon3 become a force enabler along the way. >> Yeah it's interesting you have that pre-built channel makeup, but also new opportunities for people to bring security 'cause you guys have the node zero capability. 'Cause pen testing is only one of the things you guys are starting to do now. And everyone knows, we've talked about this on our previous interviews, it's hard. People have, y'know, all kinds of AppSec review, application reviews, all the time. And if you're doing cloud native you're constantly pushing new code. So the need for a pen test is kind of a continuous thing. Okay, So I get that. The other thing that I found out on the interviews was, and I want to get your reaction to this, is that there's an existing channel of pen testers that are high IQ, high paid services. So it almost feels like you guys have created kind of like a way to automate some of the basic stuff but still enable the existing folks out there doing this work. I won't say it was below their pay grade but a lot of it was kind of, y'know remedial things, explain and react to that. Because I think that's a key nuance point to this expansion. >> Yeah, so the key thing is how do you run a security test at scale? So if you are a human pen tester maybe in a couple of weeks you could pen test 5,000 hosts. If you're really good, maybe 10,000 hosts. But when you've got a large manufacturer or a bank that's got hundreds of thousands or millions of hosts, there's no way a human's going to be able to do that. So for the really large shops, what we've found is this idea of human machine teaming. Where you run us to run infrastructure testing at scale we'll conduct reconnaissance, we'll do exploitation at scale, we'll find all the juicy interesting stuff. And then that frees up the time for the human to focus on the stuff humans are gifted at. And there's this joke that "Let us focus on all the things that will test at scale, so the human can focus on the problems that get them to speak at DEFCON and let them focus on the really hard interesting juicy stuff while we are executing tests. And at a large scale that's important but also think about Europe. In Germany there are less than 600 certified pen testers for the entire country, in Norway I think there's less than 85, in Estonia there's less than 20. There's just not enough supply of certified testers to be able to effectively meet the demand. >> It's interesting, when you ever have to see these inflection points in industries there's always a 10x multiple or some multiple inflection point that kicks up the growth. Google pioneered site reliability engineers you're seeing it now in cloud native with containers and Kubernetes writing scripts is now going to be more about architecture operating large scale systems. So instead of being a pen tester they're now a pen architect. >> Yeah, well in many ways it's a security by design philosophy which is, I would rather verify my architecture up front, verify my security posture up front, and not wait for the bad guys to show up to poke holes in my environment. And then even economically, the way we design the product most of our users are not pen testers they're actually IT admins, network engineers, people with the CISSP type certification and we give them superpowers. And there are, in back to 10x, for every one certified ethical hacker there are 10 to 20 certified CISSPs. So even the entire experience was designed around those types of security practitioners and network engineers versus the very exquisite pen test types. >> Yeah, it's a great market opportunity. I think this is going to be a big kind of a, an example of how scale works So congratulations. Couple questions I had for you for this announcement was, what are some of the obstacles that you see organizations facing that the channel partners can participate in? 'Cause again, more feet on the street, I get the expansion, but what problems are they solving? >> Yeah, when you think about, back when I was a CIO, there was a very well defined journey I went through. Assess my security posture, I have to assess it at least once or twice a year, I want to assess it as often as possible. From there, as I find problems, the hardest part of my job was deciding what not to fix. And I didn't have enough people to remediate all the issues. So the natural next step is how do I get surge expertise to remediate all of the findings from those assessments. From there, the next thing is, okay while I'm fixing those problems, did my security team or outsourced MSSP detect and respond to those attacks? Not, and if so, great, if not what are the blind spots in my detection response? And then the final step is being that trusted advisor to the executive team, the board, and the regulators around that virtual CISO or strategic security advice. So that is the spectrum of requirements that any customer has. Assess, remediate, verify your detections, and then strategic advice and guidance. Every channel partner has some aspect of those businesses within their portfolio and we enable revenue to be generated for our partners across every one of those. Use us to do assessments at scale, automatically generate the statement of work for everything that we've found, and then our partners make money fixing the issues that we've identified. Use us to audit the blind spots of your security stack and then finally use our results over time to provide strategic advice to the CISO, the board, and their regulators. >> Yeah, it's great, great gap you fill for sure. And with the op, the scale you give other pen testers a lot of growth there. The question that comes up though, I have to ask you and this is what's on people's minds, probably, 'cause it would be, first thing that I would ask Well you guys are kind of new and I get this thing. So what will make you an ideal partner? Why Horizon3.ai as the partner? What do you bring to the table? >> Yeah, I think there's a few things. One is we're approaching our three year anniversary, we've scaled very quickly, we've built a great team. But what differentiates us is our authenticity at scale, our transparency of how we work as a partner, and the fact that we've built a company, that very specifically enables partners to make money, high quality money. In my previous companies I've worked at, partners are kind of relegated to doing low level professional services type work. And if I'm a services shop, that's not going to be very valuable for me. That's a one and done come in, install a product, tune, and so on. What I want, if I'm a partner, is working with technology companies that care deeply about my growth as a partner and then is creating an offering that allows me to white label it, to build my own high margin business above it, give me predictable cost of goods sold so I can build and staff a high functioning organization. That's what we did at Horizon3 is we built the entire company around enabling MSSPs, MSPs, consulting shops, and so on. >> From day one. This is- >> From day one, that was the goal. And so the entire company's been designed you can white label the product, the entire experience can look like yours if you want it to be. The entire company was built from day one to be channel friendly >> This is again, a key point again, I want to double click on that because y'know, at the end of the day, money making's pretty big important thing. Partners don't, channel partners, and resellers, and partners don't want to lose their customer. Want to add value and make high margins. So is it easy to use? How do I consume it? How do I deploy it? You feel comfortable that you guys can deliver on that. >> Yeah, and in fact, a big cultural aspect of Horizon3 is we let our results do the talking. So I don't need to convince people through PowerPoint. What partners will do is they'll show up, they will run us for themselves, they'll run us against some trusted customers of theirs. They get blown away by the results. They get a Horizon3 tattoo at the end. >> Yeah. >> And then they become our biggest champions and advocates. >> And ultimately when you have that land and you can show results and it's a white label, it's an instant money maker. Right? For the partner. That's great Snehal, thanks so much for coming on. Really appreciate it. That's a wrap here, big news and the big news announcement around Horizon3.ai global expansion, new opportunities new channel partners, great product, good for the channel, makes money, helps customers. Can't beat that. I'm John Ferrier with TheCUBE. Thanks for watching. (upbeat music)

>>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.

(upbeat music) >> Hello everyone. We're here at theCUBE startup showcase made possible by AWS. Thanks so much for joining us today. You know, when Zhamak Dehghani was formulating her ideas around data mesh, she wasn't the only one thinking about decentralized data architectures. HelloFresh 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 the last decade, HelloFresh 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 mesh. Now, there are many practitioners and stakeholders involved in evolving the company's data architecture many of whom are listed here on this slide. Two are highlighted in red and joining us today. We're really excited to welcome you to theCUBE, Clemence Chee, who is the global senior director for data at HelloFresh, and Christoph Sawade, who's the global senior director of data also of course at HelloFresh. Folks, welcome. Thanks so much for making some time today and sharing your story. >> Thank you very much. >> Thanks, Dave. >> All right, let's start with HelloFresh. 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 Clemence? Maybe take over the first piece because Clemence has actually been longer a director at HelloFresh. >> Yeah go ahead Clemence. >> I mean, yes, about approximately six years ago I joined and HelloFresh, and I didn't think about the startup I was joining would eventually IPO. And just two years later, HelloFresh went public. And approximately three years and 10 months after HelloFresh was listed on the German stock exchange which was just last week, HelloFresh was included in the DAX Germany's leading stock market index and that, to mind a great, great milestone, and I'm really looking forward and I'm very excited for the future for HelloFresh and also our data. The vision that we have is to become the world's leading food solution group. And there are a lot of attractive opportunities. So recently we did launch and expand in Norway. This was in July. And earlier this year, we launched the US brand, Green Chef, in the UK as well. We're committed to launch continuously different geographies in the next coming years and have a strong path ahead of us. With the acquisition of ready to eat companies like factor in the US and the plant acquisition of Youfoodz in Australia, we are diversifying our offer, now reaching even more and more untapped customer segments and increase our total address for the market. So by offering customers and growing range of different alternatives to shop food and to consume meals, we are charging towards this vision and this goal 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 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. I mean, you began as a startup, you had a basic architecture and like everyone, you've made extensive use of spreadsheets, you built a Hadoop based system that started to grow. And when the company IPO'd, you really started to explode. So maybe describe that journey from a data perspective. >> Yes, Dave. So HelloFresh by 2015, approximately had evolved what amount, a classical centralized data 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. This also means that there were a small number of internal and external sources, data sources, and a centralized BI team with a number of people producing different reports, different dashboards and, and products for our executives, for example, or for different operations teams to see a company's performance and knowledge was transferred just by our talking to each other face-to-face conversations. And the people in the data warehouse team were considered as the data wizard or as the ETL wizard. Very classical challenges. And it was ETL, who reserved, indicated the kind of like a style of knowledge of data management, right? So our central data warehouse team then was responsible for different type of verticals in different domains, different geographies. And all this setup gave us in the beginning, the flexibility to grow fast as a company in 2015. >> Christoph, anything to add to that? >> Yes, not explicitly to that one, but as, as Clemence said, right, this was kind of the setup that actually worked for us quite a while. And then in 2017, when HelloFresh went public, the company also grew rapidly. And just to give you an idea how that looked like as well, the tech departments have actually increased from about 40 people to almost 300 engineers. And in the same way as the business units, as there Clemence has described, also grew sustainably. So we continue to launch HelloFresh in new countries, launched new brands like Every Plate, and also acquired other brands like we have Factor. And that grows also from a data perspective, the number of data requests that the central (mumbles), 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, they had to prioritize across our product requests from our physical product, digital product, from a physical, from, sorry, from the marketing perspective, and also from the central reporting teams. And in a nutshell, this was very hard for these people, and that altered situations that let's say the solution that we have built. We can not really optimal. So in a, in a, in a, in a nutshell, the central function became a bottleneck and slow down of all the innovation of the company. >> It's a classic case. Isn't it? I mean, Clemence, you see, you see the central team becomes a bottleneck, and so the lines of business, the marketing team, sales teams say "Okay, we're going to take things into our own hands." And then of course IT and the technical team is called in later to clean up the mess. Maybe, maybe I'm overstating it, but, but that's a common situation. Isn't it? >> Yeah this is what exactly happened. Right. So we had a bottleneck, we had those central teams, there was always a bit of tension. Analytics teams then started in those business domains like marketing, supply 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, which means then that the data pipelines didn't meet the engineering standards. And 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 infrastructure, for example, left the company, such that most of those data assets and data sets that turned into a huge debt with decreasing data quality, also decreasing lack of trust, decreasing transparency. And this was an increasing challenge where a majority of time was spent in meeting rooms to align on, on data quality for example. >> Yeah. And the point you were making Christoph about context switching, and this is, this is a point that Zhamak makes quite often as we've, we've, we've contextualized our operational systems like our sales systems, our marketing systems, but not our, our data systems. 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's start, stop, start, stop. It's a paper cut environment, and it's just not as productive. But, but, and 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 across 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 piece, that's up to a certain scale. A centralized function has a lot of advantages, right? So it's a tool for everyone, which would go to a destined kind of expert team. However, if you see that you actually would like to accelerate that in specific as the type of growth. But you want to actually have autonomy on 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 different kinds of sub teams focusing on different areas, however, that is then again, just adding another piece where actually collaboration needs to happen because the external seized, so why not bridging that gap immediately and actually move these teams end to end into the, into the function themselves. So maybe just to continue what Clemence was saying, and this is actually where our, so, Clemence and my journey started to become one joint journey. So Clemence was coming actually from one of these teams who builds their own solutions. I was basically heading the platform team called data warehouse team these days. And in 2019, where (mumbles) become more and more serious, I would say, so more and more people have recognized that this model does not really scale, in 2019, basically the leadership of the company came together and identified data as a key strategic asset. And what we mean by that, that if he leveraged it in a, in a, an appropriate way, it gives us a unique, competitive advantage, which could help us to, to support and actually fully automate our decision making process across the entire value chain. So once we, what we're trying to do now, or what we would be aiming for is that HelloFresh is able to build data products that have a purpose. We're moving away from the idea that it's just a bi-product. 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 trustworthy asset to the rest of the organization. We'd say, this is the best customer experience, but at least in a way that users can easily discover, understand and securely access, high quality data. >> Yeah. So, and, and, and Clemence, when you see Zhamak's writing, you see, you know, she has the four pillars 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 where the devil meets the details. So it's the for, 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, and Clemence 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 two, it almost creates a new challenges, but maybe you could talk about that a little bit as to how it relates to HelloFresh. >> Yes. So Chris has mentioned that we identified kind of a challenge beforehand and said, how can we actually decentralized and actually empower the different colleagues of ours? And this was more a, we realized that it was more an organizational or a cultural change. And this is something that someone also mentioned. I think ThoughtWorks mentioned one of the white papers, it's more of an organizational or a cultural impact. And we kicked off a phased reorganization, or different phases we're currently on, in the middle of still, but we kicked off different phases of organizational restructuring or reorganization trying to lock this data at scale. And the idea was really moving away from 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 should we do? This is value creation and the how, which is capability building, and both are equal in authority. This actually then creates a high urge in collaboration and this collaboration breaks up the different silos that were built. And of course, this also includes different needs of staffing for teams staffing with more, let's say data scientists or data engineers, data professionals into those business domains, enhance, or some more capability building. >> Okay, go ahead. Sorry. >> So back to Zhamak Dehghani. So we, the idea also then crossed over when she published her papers in May, 2019. And we thought, well, the four pillars that she described were around decentralized data ownership, product, data as a product mindset, we have a self-service infrastructure. And as you mentioned, federated computational governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then that to not only organizational restructure, but also in completely new approach of how we need to manage data, through data. >> Got it. Okay. So your businesses is exploding. The data team was having to become domain experts to many areas, constantly context 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. And so we, 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 will talk a little bit about the architecture. It doesn't show it here, the spreadsheet era, but Christoph, maybe you could talk about that. It does show the Hadoop monolith, which exists today. I think that's in a 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 AWS. You've got, everybody's got their own data sources. 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, is really not the focus of this conversation today. But the key here, if I understand correctly is these domains are autonomous and that not only this required technical thinking, but really supportive organizational mindset, which we're going to talk about today. But, 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, it's a monolith on the operational plan, right? Actually it wasn't just one model it was two, one for the backend and one for the front end. And our analytical plan was essentially a couple of spreadsheets. And I think there's nothing wrong with spreadsheets, but it allows you to store information, it allows you to transform data, it allows you to share this information, it allows you to visualize this data, but all kind of, it's not actually separating concern, right? Every single one tool. And this means that it's obviously not scalable, right? You reach the point where this kind of management's set up in, or data management is in one tool, reached elements. So what we have started is we created our data lake, as we have seen here on our dupe. And just in the very beginning actually reflected very much our operation upon this. On top of that, we used Impala as a data warehouse, but there was not really a distinction between what is our data warehouse and what is our data lakes as the Impala was used as kind of both as a kind of engine to create a warehouse and data lake constructed itself. And this organic growth actually led to a situation. As I think it's clear now that we had the centralized model as, for all the domains that were really lose Kimball, the modeling standards and there's new uniformity we used to actually build, in-house, a base of building materialized use, of use that we have used for the presentation there. There was a lot of duplication of effort. And in the end, essentially the amendments and feedback tool, which helped us to, to improve of what we, have built during the end in a natural, as you said, the lack of trust. And this basically was a starting point for us to understand, okay, how can we move away? And there are a lot of different things that we can discuss of apart from this organizational structure that we have set up here, we have three or four pillars from Zhamak. However, there's also the next, extra question around, how do we implement product, right? What are the implications on that level and I think that is, that'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? And let's, 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 through the last year, and obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company, which then results that collaborations got a bit more difficult. Of course, the time zone changes. You have different, different artifacts that you had recreated in documentation that were flying around. So we were, we had to build the company from scratch, right? 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 HelloFresh, and we collected more of this data and continued to improve, use data to improve the different key areas of our business. Even when organizational struggles like the central (mumbles) struggles, data somehow always helped us to grow through this kind of change, right? In the end, those decentralized teams in our local geographies started with solutions that serve the business, which was very, very important. Otherwise, we wouldn't be at the place where we are today, but they did violate best practices and standards. And I always use the sports analogy, Dave. So like any sport, there are different rules and regulations that need to be followed. These routes are defined by, I'll call it, the sports association. And this is what you can think about other data governance and then our compliance team. Now we add the players to it who need to follow those rules and abide by them. This is what we then call data management. Now we have the different players, the professionals they also need to be trained and understand the strategy and the 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 the different domains. And one of our ambition of our data literacy program for example, is to really empower every employee at HelloFresh, everyone, to make the right data-informed decisions by providing data education that scales (mumbles), and that can be different things. Different things like including data capabilities with, in the learning path 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, SIS, SLO, state of contracts, et cetera, people get more of a sense of ownership and responsibility. Of course, we have to define what it means. What does ownership means? What does responsibility mean? But we are teaching this to our colleagues via individual learning patterns and help them upscale to use also their shared infrastructure, and those self-service data applications. And of all to summarize, we are still in this progress of learning. We're still learning as well. So learning never stops at Hello Fresh, but we are really trying this to make it as much fun as possible. And in the end, we all know user behavior is changed through positive experience. So instead of having massive training programs over endless courses of workshops, leaving our new joiners and colleagues confused and overwhelmed, we're applying gamification, 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 value this gamification approach a lot and are even competing to collect those learning pet badges, to become the number one on the leaderboard. >> I love the gamification. I mean, we've seen it work so well in so many different industries, not the least of which is crypto. So you've identified some of the process gaps that you, you saw, you just gloss over them. Sometimes I say, pave the cow path. You didn't try to force. In other words, a new architecture into the legacy processes, you really had to rethink your approach to data management. So what did that entail? >> 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. So we needed to establish a new set of cross-functional business processes to run faster, drive faster, 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 is software development, which we now apply to data. This, this is some old and new thinking, right? Deployment, versioning, QA, all the different things, ingestion policies, the deletion procedures, all the things that software development has been doing, we do it now with data as well. And it's simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes in asset creation, asset management and asset consumption. >> So data's become kind of the new development kit, if you will. I want to shift gears and talk about the notion of data product, and we have a slide that, that we pulled from your deck. And I'd like to unpack it a little bit. I'll just, if you can bring that up, I'll, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems, where customers are both internal and external. so pretty straightforward. I know you've, you've gone much deeper in your 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. Maybe let me start as a data product. So I think that's an ongoing debate, right? And I think the debate itself is the important piece here, right? You mentioned the debate, you've clarified what we actually mean by that, a 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 that comes with value. What do you mean by that? Okay. It's a solution to a customer problem that delivers ideally maximum value to the business. And yes, leverage is the power of data. And we have a couple of examples, and I'll hit refresh here, the historical and classical ones around dashboards, for example, to monitor our error rates, but also more sophisticated based for example, to incorporate machine learning algorithms in our recipe recommendation. However, I think the important aspects of a data product is A: there is an owner, right? There's someone accountable for making sure that the product that you're providing is actually served and has maintained. And there are, there's someone who's making sure that this actually keeps the value of what we are promising. Combined with the idea of the proper documentation, like a product description, right? The people understand how to use it. What is this about? And related to that piece is the idea of, there's a purpose, right? We need to understand or ask ourselves, okay, why does a thing exist? Does it provide the value that we think it does? Then it leads in to a good understanding of what the life cycle of the data product and product life cycle. What do we mean? Okay. From the beginning, from the creation, you need to have a good understanding. You need to collect feedback. We need to learn about that, you need to rework, and actually finally, also to think about, okay, when is it time to decommission that piece So overall I think the core of this data product is product thinking 101, 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, what brought us to this kind of data spaghetti that we have built there in Rush, essentially, we built it. Certain data assets develop in isolation and continuously patch the solution just to fulfill these ad hoc requests that we got and actually really understanding what the stakeholder needs. And the interesting piece as a results in duplication of (mumbled) 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 data assets, but slightly different assumption across the company and multiple teams that leads to data inconsistency. And imagine the following scenario. 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 kinds of graphs, different kinds of data and numbers. And in the end, you do not know which ones to trust. So there's actually much (mumbles) but good. You do not know what actually is it noise for times of observing or is it just actually, is there actually a signal that I'm looking for? And the same as if I'm running an AB test, right? I have a new feature, I would like to understand what is the business impact of this feature? I run that with a specific source and an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you have seen in the AB test is actually not what you see then in production, typical thing. Then as you asking some analytics team to actually do a deep dive, to understand where the discrepancies are coming from, worst case scenario again, there's a different kind of source. So in the end, it's a pretty frustrating scenario. And it's actually a waste of time of people that have to identify the root cause of this type of divergence. So in a nutshell, the highest degree of consistency is actually achieved if people are just reusing data assets. And also in the end, the meetup talk they've given, right? We start trying to establish this approach by AB 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 AB testing team. The AB testing team can use this information to find an interface say, okay, I'm drawing information for the metadata of an experiment. And in the end, after the assignment, after this data collection phase, they can easily add a graph to a dashboard just grouped by the AB testing barrier. And we have seen that also in other companies. So it's not just a nice dream that we have, right? I have actually looked at other companies maybe looked on search and we established a complete KPI pipeline that was computing all these information and this information both hosted by the team and those that (mumbles) AB testing, deep dives and, and regular reporting again. So just one last second, the, the important piece, Now, why I'm coming back to that is that it requires that we are treating this data as a product, right? If we want to have multiple people using the thing that I am owning and building, we have to provide this as a trust (mumbles) asset and in a way that it's easy for people to discover and to 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 think, "Well, what does that mean?" But then when you start to sort of define it as you did, it's using data to add value that could be cutting costs, that could be generating revenue, it could be actually directly 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 too, and again, context. So when you have a centralized data team and you have all these P&L managers, a lot of times they'll question the data 'cause they don't own it. They're like, "Well, wait a minute." 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. And I'm curious is how you got to that ownership. Was it a top-down or was 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 does owning the data actually mean? >> That's a very good question, Dave. I think that one of the pieces where I think we have a lot of learning and basically if you ask me how we could stop the filling, I think that would be the first piece that we need to start. Really think about how that should be approached. If it's staff has ownership, right? That means somehow that the team has the responsibility to host themselves the data assets to minimum acceptable standards. That's minimum dependencies up and down stream. The interesting piece has to be looking backwards. What was happening is that under that definition, this extra process that we have to go through is not actually transferring ownership from a central team to the other teams, but actually in most cases to establish ownership. I make this difference because saying we have to transfer ownership actually would erroneously suggest that the dataset was owned before, but this platform team, yes, they had the capability to make the change, but actually the analytics team, but always once we had the business understand the use cases and what no one actually bought, it's actually expensive, expected. So we had to go through this very lengthy process and establishing ownership, how we have done that as in the beginning, very naively started, here's a document, here are 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 way over years. And these people that built this thing have already left the company. So this is actually not a nice thing that you want to see and people build up a certain resistance, even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, what needs to happen is first, the company needs to really understand what our core business concept that we have the need to have this mapping from this other core business concept that we have. These are the domain teams who are owning this concept, and then actually linked that to the, the assets and integrate that better, but suppose understanding how we can evolve, actually the data assets and new data builds things new and the, in this piece and the domain, but also how can we address reduction of technical depth and stabilizing what we have already. >> Thank you for that Christoph. So I want to turn a direction here and talk Clemence about governance. And I know that's an area that's passionate, you're passionate about. I pulled this slide from your deck, which I kind of messed up a little bit, sorry for that. But, 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, et cetera. 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 to promote the autonomy of every team while still ensuring that everybody has the right interoperability. So when we want to move from the wild west, riding horses to a civilized way of transport, I can take the example of modern street traffic. Like when all participants can maneuver 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 and the different signals. So likewise, as a business in HelloFresh we do operate autonomously and consequently need to follow those external and internal rules and standards set forth by the tradition in which we operate. So in order to prevent a, a car crash, we need to at least ensure compliance with regulations, to account for societies and our customers' increasing concern with data protection and privacy. So teaching and advocating this imaging, evangelizing this to everyone in the company was a key community or 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 for this very new environment. So when I mentioned before, the new way of thinking, the new way of dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. In a nutshell, then this means that data governance provides a framework for managing our people, the processes and technology and culture around our data traffic. And that governance must come together in order to have this effective program providing at least a common denominator is especially critical for shared data sets, which we have across our different geographies managed, and shared applications on shared infrastructure and applications. And as then consumed by centralized processes, for example, master data, everything, and all the metrics and KPIs, which are also used for a central steering. It's a big change, right? And our ultimate goal is to have this non-invasive federated, automated 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 by PUC 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 and 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 data literacy comes into place, where we go in and trade based on the findings, based on the most valuable use case. And based on that, help our teams to do this change, to increase their capability. I just told a little bit more, I wouldn't say hand-holding, but a lot of guidance. >> Can I kind of kind of chime in quickly and (mumbled) below me, I mean, there's a lot of governance piece, but I think that is important. And if you're talking about documentation, for example, yes, we can go from team to team and tell these people, hey, you have to document your data assets and data catalog, or you have to establish a data contract and so on and forth. But if we 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 just starts as 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, so that level objectives. So the level of indicators, right, that represent on a, on an engineering level, right? Are we providing services? They're representing the promises we make to our customer and to our consumers. These are the internal objectives that help us to keep those promises. And actually these audits of, of how we are tracking ourselves, how we are doing. And this is just one example of where I think the federated governance, governance comes into play, right? In an ideal world, you should not just talk about data as a product, but also data product that's code. That'd be say, okay, as most, as much as possible, right? Give the engineers the tool that they are familiar with, and actually not ask the product managers, for example, to document the data assets in the data catalog, but make it part of the configuration has as, as a, as a CDCI continuous delivery pipeline, as we typically see in other engineering, tasks through it and services maybe say, okay, there is configuration, we can think about PII, we can think about data quality monitoring, we can think about the ingestion data catalog and so on and forth. But I think ideally in a data product goals become a sort of templates that can be deployed and are actually rejected or verified at build time before we actually make them and deploy them to production. >> Yeah so it's like DevOps for data product. So, so I'm envisioning almost a three-phase approach to governance. And you're kind of, it sounds like you're in the early phase of it, call it phase zero, where 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 a way, and yes, it needs to be separate rules, but then actually start actually use case by use case. Is there anything that small piece that we can already automate? If just possible roll that out at the next extended step-by-step. >> Is there a role though, that adjudicates that? Is there a central, you know, chief state officer who's responsible for making sure people are complying or is it, how do you handle it? >> I mean, from a, from a, from a platform perspective, yes. This applies in to, to implement certain pieces, that we are saying are important and actually would like to implement, however, that is actually working very closely with the governance department, So it's Clemence's piece to understand that defy the policies that needs to be implemented. >> So good. So Clemence essentially, it's, it's, it's your responsibility to make sure that the policy is being followed. And then as you were saying, Christoph, you want to compress the time to automation as fast as possible. Is that, is that-- >> Yeah, so it's a really, it's a, what needs to be really clear is that it's always a split effort, right? So you can't just do one or the other thing, but there is some that really goes hand in hand because for the right information, for the right engineering tooling, we need to have the transparency first. 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 it's policies and guidelines, but not only that, because more importantly or equally important is 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 got to wrap up, 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, 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, what would you do differently? >> I mean, I, can we start with, from, from the transformational challenge that understanding that it's also high load of cultural exchange. I think this is, this is important that a particular communication strategy needs to be put into place and people really need to be supported, right? So it's not that we go in and say, well, we have to change into, towards data mash, but naturally it's the human nature, nature, nature, we are kind of resistant to change, right? And (mumbles) uncomfortable. So we need to take that away by training and by communicating. Chris, you might want to add something to that. >> Definitely. I think the point that I've also made before, right? We need to acknowledge that data mesh it's an architectural scale, right? If you're looking for something which is necessary by huge companies who are vulnerable, that are product at scale. I mean, Dave, you mentioned that right, there are a lot of advantages to have a centralized team, but at some point it may make sense to actually decentralize here. And at this point, right, if you think about data mesh, 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 on the slide is, don't underestimate your baggage. It's typically is you come to a point where the old model doesn't work anymore. And as had a fresh write, we lost the trust in our data. And actually we have seen certain risks of slowing down our innovation. So we triggered that, this was triggering the need to actually change something. So at this transition applies that you took, we have a lot of technical depth accumulated over years. And I think what we have learned is that potentially we have, de-centralized some assets too early. This is not actually taking into account the maturity of the team. We are actually investigating too. And now we'll be 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 all my teams actually ready for taking on this new, this new capability? And you have to make sure that this is decentralization. You build up these capabilities and the teams, and as Clemence has mentioned, right? Make sure that you take the, 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 literacy, the technical depth I just talked about. And I think the, the last piece that I would add now, which is not here on the slide deck is also from our perspective, we started on the analytical layer because it was kind of where things are exploding, right? This is the bit where people feel the pain. But I think a lot of the efforts that we have started to actually modernize the current stage and data products, towards data mesh, we've understood that it always comes down basically to a proper shape of our operational plan. And I think what needs to happen is I think we got through a lot of pains, but the learning here is this needs to really be an, a commitment from the company. It needs to have an end to end. >> I think that point, that last point you made is so critical because I, I, I hear a lot from the vendor community about how they're going to make analytics better. And that's not, that's not unimportant, but, but true data product thinking and decentralized data organizations really have to operationalize in order to scale it. So these decisions around data architecture and organization, they're fundamental and lasting, it's not necessarily about an individual project ROI. They're going to be projects, sub projects, you know, within this architecture. But the architectural decision itself is organizational it's cultural and, 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 companies is, yields tremendous results. So I'll, I'll, I'll ask each of you to give, give us your final thoughts and then we'll wrap. Maybe. >> Just can I quickly, maybe just jumping on this piece, what you have mentioned, right, the target architecture. If you talk about these pieces, right, people often have this picture of (mumbled). Okay. There are different kinds of stages. We have (incomprehensible speech), we have actually a gesture layer, we have a storage layer, transformation layer, presentation data, and then we are basically putting a lot of technology on top of that. That's kind of our target architecture. However, I think what we really need to make sure is that we have these different kinds of views, right? We need to understand what are actually the capabilities that we need to know, what new goals, how does it look and feel from the different kinds of personas and experience view. And then finally that should actually go to the, to the target architecture from a technical perspective. Maybe just to give an outlook what we are planning to do, how we want to move that forward. Yes. Actually based on our strategy in the, in the sense of we would like to increase the 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 culture, data literacy, data organizational structure and so on. If you're talking about governance, as Clemence had actually mentioned that right, compliance, governance, data management, and so on, you're talking about technology. And I think we could talk for hours for that one it's around data platform, data science platform. And then finally also about enablements through data. Meaning we need to understand data quality, data accessibility and applied science and data monetization. >> Great. Thank you, Christoph. Clemence why don't you bring us home. Give us your final thoughts. >> Okay. I can just agree with Christoph that important is to understand what kind of maturity people have, but I understand we're at the maturity level, where a company, where people, our organization is, and really understand what does kind of, it's just kind of a change applies to that, those four pillars, for example, what needs to be tackled first. And this is not very clear from the very first beginning (mumbles). It's kind of like green field, you come up with must wins to come up with things that you really want to do out of theory and out of different white papers. Only if you really start conducting the first initiatives, you do understand that you are going to have to put those thoughts together. And where do I miss out on one of those four different pillars, people process technology and governance, but, and then that can often the integration like doing step by step, small steps, by small steps, not pulling the ocean where you're capable, really to identify the gaps and see where either you can fill the gaps or where you have to increase maturity first and train people or increase your tech stack. >> You know, HelloFresh 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. And, and I got to ask you guys, it seems like it's just an amazing place to work. Are you guys hiring? >> Yes, definitely. We do. As, as mentioned right as well as one of these aspects distributing and actually hiring as an entire company, specifically for data. I think there are a lot of open roles, so yes, please visit or our page from data engineering, data, product management, and Clemence has a lot of roles that you can speak to about. But yes. >> Guys, thanks so much for sharing with theCUBE audience, you're, you're 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. >> And thank you for watching theCUBE's startup showcase made possible by AWS. This is Dave Volante. We'll see you next time. (cheerful music)

(bright music) >> Hello and welcome to today's session of the 2021 AWS Global Public Sector Partner Awards for the award for Best Partner Transformation, Best Cybersecurity Solution. I'm now honored to welcome our next guests, General Keith Alexander, Founder, and Co-CEO of IronNet Cybersecurity, as well as Gil Quiniones, President and CEO of the New York Power Authority. Welcome to the program gentlemen, delighted to have you here. >> Good to be here. >> Terrific. Well, General Alexander, I'd like to start with you. Tell us about the collective defense program or platform and why is it winning awards? >> Well, great question and it's great to have Gil here because it actually started with the energy sector. And the issue that we had is how do we protect the grid? The energy sector CEOs came together with me and several others and said, how do we protect this grid together? Because we can't defend it each by ourselves. We've got to defend it together. And so the strategy that IronNet is using is to go beyond what the conventional way of sharing information known as signature-based solutions to behavioral-based so that we can see the events that are happening, the unknown unknowns, share those among companies and among both small and large in a way that helps us defend because we can anonymize that data. We can also share it with the government. The government can see a tax on our country. That's the future, we believe, of cybersecurity and that collective defense is critical for our energy sector and for all the companies within it. >> Terrific. Well, Gil, I'd like to shift to you. As the CEO of the largest state public power utility in the United States, why do you think it's so important now to have a collective defense approach for utility companies? >> Well, the utility sector lied with the financial sector as number one targets by our adversaries and you can't really solve cybersecurity in silos. We, NYPA, my company, New York Power Authority alone cannot be the only one and other companies doing this in silos. So what's really going to be able to be effective if all of the utilities and even other sectors, financial sectors, telecom sectors cooperate in this collective defense situation. And as we transform the grid, the grid is getting transformed and decentralized. We'll have more electric cars, smart appliances. The grid is going to be more distributed with solar and batteries charging stations. So the threat surface and the threat points will be expanding significantly and it is critical that we address that issue collectively. >> Terrific. Well, General Alexander, with collective defense, what industries and business models are you now disrupting? >> Well, we're doing the energy sector, obviously. Now the defense industrial base, the healthcare sector, as well as international partners along the way. And we have a group of what we call technical and other companies that we also deal with and a series of partner companies, because no company alone can solve this problem, no cybersecurity company alone. So partners like Amazon and others partner with us to help bring this vision to life. >> Terrific. Well, staying with you, what role does data and cloud scale now play in solving these security threats that face the businesses, but also nations? >> That's a great question. Because without the cloud, bringing collective security together is very difficult. But with the cloud, we can move all this information into the cloud. We can correlate and show attacks that are going on against different companies. They can see that company A, B, C or D, it's anonymized, is being hit with the same thing. And the government, we can share that with the government. They can see a tax on critical infrastructure, energy, finance, healthcare, the defense industrial base or the government. In doing that, what we quickly see is a radar picture for cyber. That's what we're trying to build. That's where everybody's coming together. Imagine a future where attacks are coming against our country can be seen at network speed and the same for our allies and sharing that between our nation and our allies begins to broaden that picture, broaden our defensive base and provide insights for companies like NYPA and others. >> Terrific. Well, now Gil, I'd like to move it back to you. If you could describe the utility landscape and the unique threats that both large ones and small ones are facing in terms of cybersecurity and the risks, the populous that live there. >> Well, the power grid is an amazing machine, but it is controlled electronically and more and more digitally. So as I mentioned before, as we transform this grid to be a cleaner grid, to be more of an integrated energy network with solar panels and electric vehicle charging stations and wind farms, the threat is going to be multiple from a cyber perspective. Now we have many smaller utilities. There are towns and cities and villages that own their poles and wires. They're called municipal utilities, rural cooperative systems, and they are not as sophisticated and well-resourced as a company like the New York Power Authority or our investor on utilities across the nation. But as the saying goes, we're only as strong as our weakest link. And so we need- >> Terrific. >> we need to address the issues of our smaller utilities as well. >> Yeah, terrific. Do you see a potential for more collaboration between the larger utilities and the smaller ones? What do you see as the next phase of defense? >> Well, in fact, General Alexander's company, IronNet and NYPA are working together to help bring in the 51 smaller utilities here in New York in their collective defense tool, the IronDefense or the IronDome as we call it here in New York. We had a meeting the other day, where even thinking about bringing in critical state agencies and authorities. The Metropolitan Transportation Authority, Port Authority of New York and New Jersey, and other relevant critical infrastructure state agencies to be in this cloud and to be in this radar of cybersecurity. And the beauty of what IronNet is bringing to this arrangement is they're trying to develop a product that can be scalable and affordable by those smaller utilities. I think that's important because if we can achieve that, then we can replicate this across the country where you have a lot of smaller utilities and rural cooperative systems. >> Yeah. Terrific. Well, Gil, staying with you. I'd love to learn more about what was the solution that worked so well for you? >> In cybersecurity, you need public-private partnerships. So we have private companies like IronNet that we're partnering with and others, but also partnering with state and federal government because they have a lot of resources. So the key to all of this is bringing all of that information together and being able to react, the General mentioned, network speed, we call it machine speed, has to be quick and we need to protect and or isolate and be able to recover it and be resilient. So that's the beauty of this solution that we're currently developing here in New York. >> Terrific. Well, thank you for those points. Shifting back to General Alexander. With your depth of experience in the defense sector, in your view, how can we stay in front of the attacks, mitigate them, and then respond to them before any damage is done? >> So having run our nations, the offense. I know that the offense has the upper hand almost entirely because every company and every agency defends itself as an isolated entity. Think about 50 mid-sized companies, each with 10 people, they're all defending themselves and they depend on that defense individually and they're being attacked individually. Now take those 50 companies and their 10 people each and put them together and collect the defense where they share information, they share knowledge. This is the way to get out in front of the offense, the attackers that you just asked about. And when people start working together, that knowledge sharing and crowdsourcing is a solution for the future because it allows us to work together where now you have a unified approach between the public and private sectors that can share information and defend each of the sectors together. That is the future of cybersecurity. What makes it possible is the cloud, by being able to share this information into the cloud and move it around the cloud. So what Amazon has done with AWS has exactly that. It gives us the platform that allows us to now share that information and to go at network speed and share it with the government in an anonymized way. I believe that will change radically how we think about cybersecurity. >> Yeah. Terrific. Well, you mention data sharing, but how is it now a common tactic to get the best out of the data? And now, how is it sharing data among companies accelerated or changed over the past year? And what does it look like going forward when we think about moving out of the pandemic? >> So first, this issue of sharing data, there's two types of data. One about the known threats. So sharing that everybody knows because they use a signature-based system and a set of rules. That shared and that's the common approach to it. We need to go beyond that and share the unknown. And the way to share the unknown is with behavioral analytics. Detect behaviors out there that are anonymous or anomalous, are suspicious and are malicious and share those and get an understanding for what's going on in company A and see if there's correlations in B, C and D that give you insights to suspicious activity. Like solar winds, recognizes solar winds at 18,000 companies, each defending themselves. None of them were able to recognize that. Using our tools, we did recognize it in three of our companies. So what you can begin to see is a platform that can now expand and work at network speed to defend against these types of attacks. But you have to be able to see that information, the unknown unknowns, and quickly bring people together to understand what that means. Is this bad? Is this suspicious? What do I need to know about this? And if I can share that information anonymized with the government, they can reach in and say, this is bad. You need to do something about it. And we'll take the responsibility from here to block that from hitting our nation or hitting our allies. I think that's the key part about cybersecurity for the future. >> Terrific. General Alexander, ransomware of course, is the hottest topic at the moment. What do you see as the solution to that growing threat? >> So I think, a couple things on ransomware. First, doing what we're talking about here to detect the phishing and the other ways they get in is an advanced way. So protect yourself like that. But I think we have to go beyond, we have to attribute who's doing it, where they're doing it from and hold them accountable. So helping provide that information to our government as it's going on and going after these guys, making them pay a price is part of the future. It's too easy today. Look at what happened with the DarkSide and others. They hit Colonial Pipeline and they said, oh, we're not going to do that anymore. Then they hit a company in Japan and prior to that, they hit a company in Norway. So they're attacking and they pretty much operate at will. Now, let's indict some of them, hold them accountable, get other governments to come in on this. That's the way we stop it. And that requires us to work together, both the public and private sector. It means having these advanced tools, but also that public and private partnership. And I think we have to change the rhetoric. The first approach everybody takes is, Colonial, why did you let this happen? They're a victim. If they were hit with missiles, we wouldn't be asking that, but these were nation state like actors going after them. So now our government and the private sector have to work together and we need to change that to say, they're victim, and we're going to go after the guys that did this as a nation and with our allies. I think that's the way to solve it. >> Yeah. Well, terrific. Thank you so much for those insights. Gil, I'd also like to ask you some key questions and of course, certainly people today have a lot of concerns about security, but also about data sharing. How are you addressing those concerns? >> Well, data governance is critical for a utility like the New York Power Authority. A few years ago, we declared that we aspire to be the first end-to-end digital utility. And so by definition, protecting the data of our system, our industrial controls, and the data of our customers are paramount to us. So data governance, considering data or treating data as an asset, like a physical asset is very, very important. So we in our cybersecurity, plans that is a top priority for us. >> Yeah. And Gil thinking about industry 4.0, how has the surface area changed with Cloud and IoT? >> Well, it's grown significantly. At the power authority, we're installing sensors and smart meters at our power plants, at our substations and transmission lines, so that we can monitor them real time, all the time, know their health, know their status. Our customers we're monitoring about 15 to 20,000 state and local government buildings across our states. So just imagine the amount of data that we're streaming real time, all the time into our integrated smart operations center. So it's increasing and it will only increase with 5G, with quantum computing. This is just going to increase and we need to be prepared and integrate cyber into every part of what we do from beginning to end of our processes. >> Yeah. And to both of you actually, as we see industry 4.0 develop even further, are you more concerned about malign actors developing more sophistication? What steps can we take to really be ahead of them? Let's start with General Alexander. >> So, I think the key differentiator and what the energy sector is doing, the approach to cybersecurity is led by CEOs. So you bring CEOs like Gil Quiniones in, you've got other CEOs that are actually bringing together forums to talk about cybersecurity. It is CEO led. That the first part. And then the second part is how do we train and work together, that collective defense. How do we actually do this? I think that's another one that NYPA is leading with West Point in the Army Cyber Institute. How can we start to bring this training session together and train to defend ourselves? This is an area where we can uplift our people that are working in this process, our cyber analysts if you will at the security operations center level. By training them, giving them hard tests and continuing to go. That approach will uplift our cybersecurity and our cyber defense to the point where we can now stop these types of attacks. So I think CEO led, bring in companies that give us the good and bad about our products. We'd like to hear the good, we need to hear the bad, and we needed to improve that, and then how do we train and work together. I think that's part of that solution to the future. >> And Gil, what are your thoughts as we embrace industry 4.0? Are you worried that this malign actors are going to build up their own sophistication and strategy in terms of data breaches and cyber attacks against our utility systems? What can we do to really step up our game? >> Well, as the General said, the good thing with the energy sector is that on the foundational level, we're the only sector with mandatory regulatory requirements that we need to meet. So we are regulated by the Federal Energy Regulatory Commission and the North American Electric Reliability Corporation to meet certain standards in cyber and critical infrastructure. But as the General said, the good thing with the utility is by design, just like storms, we're used to working with each other. So this is just an extension of that storm restoration and other areas where we work all the time together. So we are naturally working together when it comes to to cyber. We work very closely with our federal government partners, Department of Homeland Security, Department of Energy and the National Labs. The National Labs have a lot of expertise. And with the private sector, like great companies like IronNet, NYPA, we stood up an excellence, center of excellence with private partners like IronNet and Siemens and others to start really advancing the art of the possible and the technology innovation in this area. And as the governor mentioned, we partnered with West Point because just like any sporting or just any sport, actual exercises of the red team, green team, and doing that constantly, tabletop exercises, and having others try and breach your walls. Those are good exercises to really be ready against the adversaries. >> Yeah. Terrific. Thank you so much for those insights. General Alexander, now I'd like to ask you this question. Can you share the innovation strategy as the world moves out of the pandemic? Are we seeing new threats, new realities? >> Well, I think, it's not just coming out of the pandemic, but the pandemic actually brought a lot of people into video teleconferences like we are right here. So more people are working from home. You add in the 5G that Gil talked about that gives you a huge attack surface. You're thinking now about instead of a hundred devices per square kilometer up to a million devices. And so you're increasing the attack surface. Everything is changing. So as we come out of the pandemic, people are going to work more from home. You're going to have this attack surface that's going on, it's growing, it's changing, it's challenging. We have to be really good about now, how we trained together, how we think about this new area and we have to continue to innovate, not only what are the cyber tools that we need for the IT side, the internet and the OT side, operational technology. So those kinds of issues are facing all of us and it's a constantly changing environment. So that's where that education, that training, that communication, working between companies, the customers, the NYPA's and the IronNet's and others and then working with the government to make sure that we're all in sync. It's going to grow and is growing at an increased rate exponentially. >> Terrific. Thank you for that. Now, Gil, same question for you. As a result of this pandemic, do you see any kind of new realities emerging? What is your position? >> Well, as the General said, most likely, many companies will be having this hybrid setup. And for company's life like mine, I'm thinking about, okay, how many employees do I have that can access our industrial controls in our power plants, in our substations, and transmission system remotely? And what will that mean from a risk perspective, but even on the IT side, our business information technology. You mentioned about the Colonial Pipeline type situation. How do we now really make sure that our cyber hygiene of our employees is always up-to-date and that we're always vigilant from potential entry whether it's through phishing or other techniques that our adversaries are using. Those are the kinds of things that keep myself like a CEO of a utility up at night. >> Yeah. Well, shifting gears a bit, this question for General Alexander. How come supply chain is such an issue? >> Well, the supply chain, of course, for a company like NYPA, you have hundreds or thousands of companies that you work with. Each of them have different ways of communicating with your company. And in those communications, you now get threats. If they get infected and they reach out to you, they're normally considered okay to talk to, but at the same time that threat could come in. So you have both suppliers that help you do your job. And smaller companies that Gil has, he's got the 47 munis and four co-ops out there, 51, that he's got to deal with and then all the state agencies. So his ecosystem has all these different companies that are part of his larger network. And when you think about that larger network, the issue becomes, how am I going to defend that? And I think, as Gil mentioned earlier, if we put them all together and we operate and train together and we defend together, then we know that we're doing the best we can, especially for those smaller companies, the munis and co-ops that don't have the people and a security ops centers and other things to defend them. But working together, we can help defend them collectively. >> Terrific. And I'd also like to ask you a bit more on IronDefense. You spoke about its behavioral capabilities, it's behavioral detection techniques, excuse me. How is it really different from the rest of the competitive landscape? What sets it apart from traditional cybersecurity tools? >> So traditional cybersecurity tools use what we call a signature-based system. Think of that as a barcode for the threat. It's a specific barcode. We use that barcode to identify the threat at the firewall or at the endpoint. Those are known threats. We can stop those and we do a really good job. We share those indicators of compromise in those barcodes, in the rules that we have, Suricata rules and others, those go out. The issue becomes, what about the things we don't know about? And to detect those, you need behavioral analytics. Behavioral analytics are a little bit noisier. So you want to collect all the data and anomalies with behavioral analytics using an expert system to sort them out and then use collected defense to share knowledge and actually look across those. And the great thing about behavioral analytics is you can detect all of the anomalies. You can share very quickly and you can operate at network speed. So that's going to be the future where you start to share that, and that becomes the engine if you will for the future radar picture for cybersecurity. You add in, as we have already machine learning and AI, artificial intelligence, people talk about that, but in this case, it's a clustering algorithms about all those events and the ways of looking at it that allow you to up that speed, up your confidence in and whether it's malicious, suspicious or benign and share that. I think that is part of that future that we're talking about. You've got to have that and the government can come in and say, you missed something. Here's something you should be concerned about. And up the call from suspicious to malicious that gives everybody in the nation and our allies insights, okay, that's bad. Let's defend against it. >> Yeah. Terrific. Well, how does the type of technology address the President's May 2021 executive order on cybersecurity as you mentioned the government? >> So there's two parts of that. And I think one of the things that I liked about the executive order is it talked about, in the first page, the public-private partnership. That's the key. We got to partner together. And the other thing it went into that was really key is how do we now bring in the IT infrastructure, what our company does with the OT companies like Dragos, how do we work together for the collective defense for the energy sector and other key parts. So I think it is hit two key parts. It also goes on about what you do about the supply chain for software were all needed, but that's a little bit outside what we're talking about here today. The real key is how we work together between the public and private sector. And I think it did a good job in that area. >> Terrific. Well, thank you so much for your insights and to you as well, Gil, really lovely to have you both on this program. That was General Keith Alexander, Founder and Co-CEO of IronNet Cybersecurity, as well as Gil Quiniones, the President and CEO of the New York Power Authority. That's all for this session of the 2021 AWS Global Public Sector Partner Awards. I'm your host for theCUBE, Natalie Erlich. Stay with us for more coverage. (bright music)

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> The steepest drop in the stock market since June 11th flipped the narrative and sent investors scrambling. Tech got hammered after a two-month run, and people are asking questions. Is this a bubble popping, or is it a healthy correction? Are we now going to see a rotation into traditional stocks, like banks and maybe certain cyclicals that have lagged behind the technology winners? Hello, everyone, and welcome to this week's episode of Wikibon's CUBE Insights powered by ETR. In this Breaking Analysis, we want to give you our perspective on what's happening in the technology space and unpack what this sentiment flip means for the balance of 2020 and beyond. Let's look at what happened on September 3rd, 2020. The tech markets recoiled this week as the NASDAQ Composite dropped almost 5% in a single day. Apple's market cap alone lost $178 billion. The Big Four: Apple, Microsoft, Amazon, and Google lost a combined value that approached half a trillion dollars. For context, this number is larger than the gross domestic product for countries as large as Thailand, Iran, Austria, Norway, and even the UAE, and many more. The tech stocks that have been running due to COVID, well, they got crushed. These are the ones that we've highlighted as best positioned to thrive during the pandemic, you know, the work-from-home, SaaS, cloud, security stocks. We really have been talking about names like Zoom, ServiceNow, Salesforce, DocuSign, Splunk, and the security names like CrowdStrike, Okta, Zscaler. By the way, DocuSign and CrowdStrike and Okta all had nice earnings beats, but they still got killed underscoring the sentiment shift. Now the broader tech market was off as well on sympathy, and this trend appears to be continuing into the Labor Day holiday. Now why is this happening, and why now? Well, there are a lot of opinions on this. And first, many, like myself, are relatively happy because this market needed to take a little breather. As we've said before, the stock market, it's really not reflecting the realities of the broader economy. Now as we head into September in an election year, uncertainty kicks in, but it really looks like this pullback was fueled by a combination of an overheated market and technical factors. Specifically, take a look at volatility indices. They were high and rising, yet markets kept rising along with them. Robinhood millennial investors who couldn't bet on sports realized that investing in stocks was as much of a rush and potentially more lucrative. The other big wave, which was first reported by the Financial Times, is that SoftBank made a huge bet on tech and bought options tied to around $50 billion worth of high-flying tech stocks. So the option call volumes skyrocketed. The call versus put ratio was getting way too hot, and we saw an imbalance in the market. Now market makers will often buy an underlying stock to hedge call options to ensure liquidity in these cases. So to be more specific, delta in options is a measure of the change in the price of an option relative to the underlying stock, and gamma is a measure of the volatility of the delta. Now usually, volatility is relatively consistent on both sides of the trade, the calls and the puts, because investors often hedge their bets. But in the case of many of these hot stocks, like Tesla, for example, you've seen the call skew be much greater than the skew in the downside. So let's take an example. If people are buying cheap out of the money calls, a market maker might buy the underlying stock to hedge for liquidity. And then if Elon puts out some good news, which he always does, the stock goes up. Market makers have to then buy more of the underlying stock. And then algos kick in to buy even more. And then the price of the call goes up. And as it approaches it at the money price, this forces market makers to keep buying more of that underlying stock. And then the melt up until it stops. And then the market flips like it did this week. When stock prices begin to drop, then market makers were going to rebalance their portfolios and their risk and sell their underlying stocks, and then the rug gets pulled out from the markets. And that's really why some of the stocks that have run dropped so precipitously. Okay, why did I spend so much time on this, and why am I not freaking out? Because I think these market moves are largely technical versus fundamental. It's not like 1999. We had a double whammy of technical rug pulls combined with poor underlying fundamentals for high-flying companies like CMGI and Internet Capital Group, whose businesses, they were all about placing bets on dot-coms that had no business models other than non-monetizable eyeballs. All right, let's take a look at the NASDAQ and dig into the data a little bit. And I think you'll see what I mean and why I'm not too concerned. This is a year-to-date chart of the NASDAQ, and you can see it bottomed on March 23rd at 6,860. And then ran up until June 11th and had that big drop, but was still elevated at 9,492. And then it ran up to over 12,000 and hit an all-time high. And then you see the big drop. And that trend continued on Friday morning. The NASDAQ Composite traded below 11,000. It actually corrected to 10% of its high, 9.8% to be precise, and then it snapped back. But even at its low, that's still up over 20% for the year. In the year of COVID, would that have surprised you in March? It certainly would have surprised me. So to me, this pullback is sort of a relief. It's good and actually very normal and quite predictable. Now the exact timing of these pullbacks, of course, on the other hand is not entirely predictable. Not at all, frankly, at least for this observer. So the big question is where do we go from here? So let's talk about that a little bit. Now the economy continues to get better. Take a look at the August job report; it was good. 1.4 million new jobs, 340,000 came from the government. That was positive numbers. And the other good news is it translates into a drop in unemployment under 10%. It's now at 8.4%. And this is really good relative to expectations. Now the sell-off continued, which suggested that the market wanted to keep correcting, so that's good. Maybe some buying opportunities would emerge in over the next several months, the market snapped back, but for those who have been waiting, I think that's going to happen. And so that snapback, maybe that's an indicator that the market wants to keep going up, we'll see. But I think there are more opportunities ahead because there's really so much uncertainty. What's going to happen with the next round of the stimulus? The jobs report, maybe that's a catalyst for compromise between the Democrats and the Republicans, maybe. The US debt is projected to exceed 100% of GDP this calendar year. That's the highest it's been since World War II. Does that give you a good feeling? That doesn't give me a good feeling. And when we talk about the election, that brings additional uncertainty. So there's a lot to think about for the markets. Now let's talk about what this means for tech. Well, as we've been projecting for months with our colleagues at ETR, despite what's going on in the stock market and its rise, there's those real tech winners, we still see a contraction in 2020 for IT spend of minus 5 to 8%. And we talk a lot about the bifurcation in the market due to COVID accelerating some of these trends that were already in place, like digital transformation and SaaS and cloud. And then the work-from-home kicks in with other trends like video conferencing and the shift to security spend. And we think this is going to continue for years. However, because these stocks have run up so much, they're going to have very tough compares in 2021. So maybe time for a pause. Now let's take a look at the IT spending macroeconomics. This data is from a series of surveys that ETR conducted to try to better understand spending patterns due to COVID. Those yellow slices of the pies show the percent of customers that indicate that their budgets will be impacted by coronavirus. And you can see there's a steady increase from mid-March, which blend into April, and then you can see the June data. It goes from 63% saying yes, which is very high, to 78%, which is very, very high. And the bottom part of the chart shows the degree of that change. So 22% say no change in the latest survey, but you can see much more of a skew to the red declines on the left versus the green upticks on the right-hand side of the chart. Now take a look at how IT buyers are seeing the response to the pandemic. This chart shows what companies are doing as a result of COVID in another recent ETR survey. Now of course, it's no surprise, everybody's working from home. Nobody's traveling for business, not nobody, but most people aren't, we know that. But look at the increase in hiring freezes and freezing new IT deployments, and the sharp rise in layoffs. So IT is yet again being asked to do more with less. They're used to it. Well, we see this driving an acceleration to automation, and that's going to benefit, for instance, the RPA players, cloud providers, and modern software vendors. And it will also precipitate a tailwind for more aggressive AI implementations. And many other selected names are going to continue to do well, which we'll talk about in a second, but they're in the work-from-home, the cloud, the SaaS, and the modern data sectors. But the problem is those sectors are not large enough to offset the declines in the core businesses of the legacy players who have a much higher market share, so the overall IT spend declines. Now where it gets kind of interesting is the legacy companies, look, they all have growth businesses. They're making acquisitions, they're making other bets. IBM, for example, has its hybrid cloud business in Red Hat, Dell has VMware and it's got work-from-home solutions, Oracle has SaaS and cloud, Cisco has its security business, HPE, it's as a service initiative, and so forth. And again, these businesses are growing faster, but they are not large enough to offset the decline in core on-prem legacy and drive anything more than flat growth, overall, for these companies at best. And by the time they're large enough, we'll be into the next big thing, so the cycle continues. But these legacy companies are going to compete with the upstarts, and that's where it gets interesting. So let's get into some of the specific names that we've been talking about for over a year now and make some comments around their prospects. So what we want to do is let's start with one of our favorites: Snowflake. Now Snowflake, along with Asana, JFrog, Sumo Logic, and Unity, has a highly anticipated upcoming IPO. And this chart shows new adoptions in the database sector. And you can see that Snowflake, while down from the October 19th survey, is far outpacing its competitors, with the exception of Google, where BigQuery is doing very well. But you see Mongo and AWS remain strong, and I'm actually quite encouraged that it looks like Cloudera has righted the ship and you kind of saw that in their earnings recently. But my point is that Snowflake is a share gainer, and we think will likely continue to be one for a number of quarters and years if they can execute and compete with the big cloud players, and that's a topic that we've covered extensively in previous Breaking Analysis segments, and, as you know, we think Snowflake can compete. Now let's look at automation. This is another space that we've been talking about quite a bit, and we've largely focused on two leaders: UiPath and Automation Anywhere. But I have to say, I still like Blue Prism. I think they're well-positioned. And I especially like Pegasystems, which has, for years, been embarking on a broader automation agenda. What this chart shows is net score or spending velocity data for those customers who said they were decreasing spend in 2020. Those red bars that we showed earlier are the ones who are decreasing. And you can see both Automation Anywhere and UiPath show elevated levels within that base where spending is declining, so that's a real positive. Now Microsoft, as we've reported, is elbowing its way into the market with what is currently an inferior point product, but, you know, it's Microsoft, so we can't ignore that. And finally, let's have a look at the all-important security sector, which we've covered extensively and put out a report recently. So what this next chart does is cherry-picks of a few of our favorite names, and it shows the net score or spending momentum and the granularity for some of the leaders and emerging players. All of these players are in the green, as you can see in the upper right, and they all have decent presence in the dataset as indicated by the shared NS. Okta is at the top of the list with 58% net score. Palo Alto, they're a more mature player, but still, they have an elevated net score. CrowdStrike's net score dropped this quarter, which was a bit of a concern, but it's still high. And it followed by SailPoint and Zscaler, who are right there. The big three trends in this space right now are cloud security, identity access management, and endpoint security. Those are the tailwinds, and we think these trends have legs. Remember, net score in this survey is a forward-looking metric, so we'll come back and look at the next survey, which is running this month in the field from ETR. Now everyone on this chart has reported earnings, except Zscaler, which reports on September 9th, and all of these companies are doing well and exceeding expectations, but as I said earlier, next year's compares won't be so easy. Oh, and by the way, their stock prices, they all got killed this week as a result of the rug pull that we explained earlier. So we really feel this isn't a fundamental problem for these firms that we're talking about. It's more of a technical in the market. Now Automation Anywhere and UiPath, you really don't know because they're not public and I think they need to get their house in order so they can IPO, so we'll see when they make it to public markets. I don't think that's an if, that I think they will IPO, but the fact that they haven't filed yet says they're not ready. Now why wouldn't you IPO if you are ready in this market despite the recent pullbacks? Okay, let's summarize. So listen, all you new investors out there that think stock picking is easy, look, any fool can make money in a market that goes up every day, but trees don't grow to the moon and there are bulls and bears and pigs, and pigs get slaughtered. And I can throw a dozen other cliches at you, but I am excited that you're learning. You maybe have made a few bucks playing the options game. It's not as easy as you might think. And I'm hoping that you're not trading on margin. But look, I think there are going to be some buying opportunities ahead, there always are, be patient. It's very hard, actually impossible, to time markets, and I'm a big fan of dollar-cost averaging. And young people, if you make less than $137,000 a year, load up on your Roth, it's a government gift that I wish I could have tapped when I was a newbie. And as always, please do your homework. Okay, that's it for today. Remember, these episodes, they're all available as podcasts, wherever you listen, so please subscribe. I publish weekly on wikibon.com and siliconangle.com, so check that out, and please do comment on my LinkedIn posts. Don't forget, check out etr.plus for all the survey action. Get in touch on Twitter, I'm @dvellante, or email me at david.vellante@siliconangle.com. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, everyone. Be well, and we'll see you next time. (gentle upbeat music)

>> Woman: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Okay welcome back already Jeff Frick here with theCUBE. We're in our Palo Alto Studios here. Can't believe we just turned the calendar on September the 1st of 2020. What a year, it's cruising by. And one of the big topics obviously is working from home, we're seeing more and more companies telling everybody to expect to work from home through the end of the year or into next year, some are even saying indefinitely. And we've got an expert coming on the show that we're excited to have back. It's Dr. Karen Sobel Lojeski. She is the founder and CEO and author of "Virtual Distance and the Virtual Distance Company". Karen, great to see you. >> Great to see you too Jeff, thanks for having me. >> Absolutely, so I wanted to get you back on for a couple reasons. One is we first met at the ACGSV, Association for Corporate Growth Silicon Valley 2018 Awards, about two years ago was summer of 2018. And at that point, you introduced me to the concept and our audience, to the concept of virtual distance, which if I can summarize is basically communicating through devices versus face-to-face, like we're doing here. And the bad things that come from that and challenges and this and the other. Who knew that two years from then we would all be forced and not asked, but forced to basically go to a work-from-home environment and increase the frequency and use of using electronic devices to communicate not only for work, but also for social stuff, for school, for everything, so, oh my goodness, you happen to be in the right place at the right time for not necessarily the greatest of reasons, but wow, I mean, how amazing this transformation that we've all been forced to since the middle of March. First off, get your thoughts on that and then we'll dive into what people should be thinking about, what people should be doing about it and how they can, I want to say make the most, but it does kind of make the most of, not necessarily the greatest situation. >> Yeah, well, I could have never imagined when we were sitting out at that round table outside the room where we had dinner that we'd be here two years later, right, talking about virtual distances, you said in the context of everyone having to be isolated from each other and working from home. Obviously, like everyone on the planet, I think I would never have wanted to see this happen. But I feel fortunate in a way to have put this out there many years ago because today it's serving a lot of different organizations, corporations, schools, even government organizations to have a very steady framework that's based on 15 years of data, to understand how to make the best, as you said, of this situation and to reduce some of the negative consequences of virtual distance and actually use the framework as a way to get to know people better and really see them more as human beings in a way that helps them through not just their work life, but also through the family challenges that they're having with every kid now, sort of going back to school, many of them online, there's a lot of virtual distance that can crop up even in the house. But I guess I just, I'm glad that I discovered virtual distance, and that it's useful in this time. >> Right, right. So let's jump into it. And actually I want to skip to the end of the book before we get into the beginning of the book because you talked about leadership and when this thing first hit, we had a number of leaders from the community, talking about leading through trying times. And most great leaders know that their primary job is really communication, right? Communication to their teams, communication to their constituents, communication to their customers. COVID has really changed the communication challenges and increase them dramatically and most of the stuff we're hearing is that leaders need to communicate more frequently and in more variety, both in terms of topics as well as communication forms. How does that kind of jive with your studies on virtual distance and leadership, given the fact that there aren't a lot of other options in terms of face-to-face or a little bit more intimate things? They have to use these electronic means. So what tips do you have for leaders, as they suddenly were told everybody's working from home starting like tomorrow? >> Yeah, well, it's funny that you asked me that because we learned early on when I started looking at this phenomenon in the early 2000s. We learned early on that it actually takes a lot more work and time to lead virtually than it does in more traditional environments. And the reason is because a leader really has to bring forward a lot of context that tends to go underground or become invisible about other people when we're working virtually. So the leader already was under a lot of pressure if you will, to communicate much more than they had been in more traditional settings because a lot of the information and knowledge and intelligence if you will, about the company was available in the context of the environment and other people. So leaders were already on track to having to communicate much more in order to make make remote work and virtual work work. Well, which of course it can. >> Right. >> But what happened was, we found that when suddenly a light switch is turned off, leaders needed to communicate even more. And that is kind of standard crisis management leadership. We talked a little bit about that in the past, right? So we can look at the situation we're in as not just an acute crisis that came to bear in early January and then sort of everything locking down in March. But we can kind of look at this as a long-term leadership crisis management strategy on top of just over communicating to do better in virtual space. And in a crisis management situation you definitely want to have even more communication, but it's also an opportunity actually to develop other leaders behind you on teams that can also communicate as well, to share that responsibility, to share that leadership commitment to a lot of communication during times like this, that actually works really well. >> Right, 'cause one of the things you talked about that's super, super important, more important actually than physical distance or the virtual distance is what you called the affinity distance, and I think it ties back to another point in the book in terms of clarity of communication from the leadership. What are the goals, what is the vision? And reinforcing that at a rate and frequency much higher than they've ever done before to build that affinity so people can continue to feel like they're part of something beyond more just the tasks and the roles and the assignments that I have to do every day. >> Yeah, that's exactly right, Jeff. So again, we found early on. And it was a surprise to us at first, but then became kind of obvious that people tend to think that the real challenge with virtual work is physical distance, right, sort of the space between us in terms of a geography or a geographic separation. And what we learned early on through the statistics, as well as sort of common sense was that actually physical distance had the least impact on corporate outcomes than any of the other three factors. So the affinity distance piece is really all about, how do I gain an affinity for someone when I really don't know that much about them. And I don't know much about their context in the moment that we're talking, and I also just know less about them in general when we're virtual. >> Right. >> So affinity distance is much more important than the physical separation because it's what holds us together and allows us to build very, very deep relationships which we can count on and trust no matter what the situation is. And yeah, doing that in these times is very important. >> So it's funny, right? 'Cause so much of the problems that we have with communications are in the subtle feedback mechanisms that aren't necessarily in the overt communication and as you said, those can be lost in a lot of channels. What's kind of (chuckles) interesting that's going on with COVID is we're actually seeing a side of people that we never did see in the physical space, right. Now we're literally being invited into everyone's home. I mean, I'm in your home office, I can see your books on your bookshelf and people are bringing people into their home which they may not have done before or been comfortable. Not only that, but the spouse is there, he or she is working from home. The kids are there, they're doing their school from home, the occasional dog or pet or other thing kind of jumping through the screen. So it's this weird kind of juxtaposition. On one hand you've lost a whole lot of kind of subtle communication reinforcers. On the other hand, you're getting kind of a whole new kind of the human side aspect in terms of who these people are and what they're all about, that you never necessarily had before. So I think the blending of the whole self is probably been elevated, even though the communication challenges without having kind of all these subtle feedback loops that we really rely on, are gone. So when you think about communication and communication methods based on communication messages and what you're trying to do, how do you tell people to think about that? What types of communications should be done in which ways to make them the most effective and avoid some of the real problems that come from the wrong type of communication on the wrong type of channel? >> Yeah, so first of all, you make some great points. Because it really is when we invite people into our home via these kind of video links, people see a different side of us, a contextualized side to us that they normally wouldn't see. And that opens the door, as you said, to having other communications. I think before I get directly to your question, one thing that strikes me about what you say is that this is truly a shared experience, right? So all of us are being impacted by COVID-19, the economics of the situation, the childcare issues that are raised by the situation, the community issues that we all have in our towns or cities. And we're sharing that experience, which is a great jumping off point in terms of communications because we actually have a very similar context from which were working. In terms of which communications to use when. This is a really important question, I had a person from a very, very large tech company that people use every day to go look for things on the Internet, call me and tell me at one point early, sort of early on in the pandemic that some of his people were starting to beg him to turn off the video screens. (chuckles) And just use audio because sometimes when we're overwhelmed with a crisis the video can be helpful, but it can also sort of be overwhelming. So it's important to understand sort of when to discern, when to use audio and when to use visual, when to use email and when to use tax. And the basic tips here is that email has really never been good to explain ourselves to other people. It's been great to set up lunch dates or an appointment and things like that. So email should be used pretty sparingly. Audio is really great if we don't have video, but we also just kind of need a rest from video. And we also need to really focus on a person's voice very, very intensely. So if we're trying to solve a really critical problem that's a little bit conceptual, sometimes audio can can be more helpful. Video is obviously great because it gives us all this context and it allows people to see into our home and hear our cats kind of screaming at each other which is happening right now in my house. But it also lets us see each other's expressions and a little bit of the facial communication that we need in order to know if people are okay with what we're saying, if they're quizzical and looking like they kind of don't understand et cetera, The overarching goal of communications in a situation like this, that I talk a lot about in the book, is to mix up modes of communication as much as you can think about that, right? Because we get context as I've just explained in different ways through different modes. And so if we mix it up, if I say well, I've talked to Jeff a lot over video maybe I'll just give him a call today. Or I've been using a lot of email to talk to one of my colleagues in Norway, maybe I should really try to set up a video call that is very helpful because it gives us dimensionality to someone's personality as well as their context. >> Yeah, that's a really interesting point. I think most people are always saying turn on the video, turn on the video, we want to see everybody's face but as this thing continues to go and go and go and it's going to go for the foreseeable future, and people are going to get fatigue, right, people are getting Zoom fatigue. That's a really interesting and simple way to I think, kind of lessen the stress a little bit by telling people, let's just turn the video off. We don't necessarily need to see each other, we know what we look like. And if you feel some reason to turn it on, you can turn it on, but having that as an option, I think that's a really insightful. And the other thing I want to focus on is it's not all negative, right? I mean, there's a lot of studies about the open office plan, which didn't necessarily work so well, and we've had conversations with a lot of people that say, just because you throw everybody in a room together doesn't mean that they're necessarily going to communicate more and there aren't necessarily the water cooler chatter that you're kind of hoping for. And in fact, you have a bunch of stats in the book here about remote workers having actually a lot of success. They have less trouble with technology, they can cope best with multiple projects. There's so many less interruptions, (chuckles) assuming the rest of the family has a place to work. But you don't get kind of the work interruptions that you would in terms of actually getting projects done. So, it's not all bad. And I think there's a lot of things that we can help people think about to really take advantage or make the most of the opportunity, to take advantage is probably the wrong word. So, vary communications, frequency in communications is certainly a good one. What are other ways that people kind of build trust? 'Cause you talk a lot about trust and feeling part of something bigger and not letting the individual tasks and the little day-to-day things that we do get in the way of still feeling like you belong to something that's important, that you care about, with your teammates that you want to move forward. >> Yeah, so the it's a great question, and again I think, obviously, amongst sort of the darkness there's always sort of opportunities to see some light. And I think one of the ways that we can see light through working this way at this time is to expand our understanding of the people that we're working with, right? And we can do that in a framework, it doesn't have to be haphazard. So when we look at affinity, what we really want to do is to bring forward the way people feel about their value systems, what's important to them about work in sort of pre-COVID or BC, right before COVID, but also what's important to them about their family life or about the situation that's happening, that's interacting with and integrating with their work life. So asking those questions in ways that are not guised, but sort of directly asking them things about what they value? How they feel that they're interdependent on other people? Why other people are important to them in their work, as well as just in their day-to-day lives? Those are the kinds of opportunities for questions around things that are not work related, are not party Friday, which are also kind of fun things right? But that get more to the core of who a person is, that whole person that you were talking about. And that allows us to see so much more deeply, ironically, into that human being. And when you talk about purpose, and really wanting to feel like we're part of something bigger than ourselves, those kinds of insights that build affinity help us help other people. So, we tend to focus on task orientation and goals and deliverables and all that which is absolutely critical for business continuity, and to get through the day and focus our attention. But actually what makes people feel really good about their day as a person is often how they can help other people. And so if we draw this closer affinity, we can actually figure out ways to help other people. And that just lifts everybody up and makes the work product actually even better. >> Right, right, I've always ascribed to the theory that right, if you spend your work helping other people do their work better, easier, get roadblocks out of the way, whatever, be an enabler, then you're getting this multiplier effect because I'm doing my work and I'm helping somebody else be more efficient. And it's a very different way to kind of think about work in terms of helping everybody be more effective, more efficient, and as you said, you get this great multiplier effect, but I want to shift gears a little bit. And this sentence, just jumped out of your book. I'm actually going to read from it, that despite the fact that many leadership challenges are new, we continue to over rely on management thinking and solutions that are fundamentally designed around outdated assumptions. I mean, to me this is such a huge thing. We had Martin Mikason at the beginning of this process and his great line, and he's managed remote companies for years and multiple companies. And he said, it's so easy to fake it in the office, right? It's so easy to look busy. (Karen chuckles) Whereas when you're working from home, the only thing you have to show is your output. And that's what you're graded on, your output. And yet when this thing first hit, we saw all types of new products coming out that are basically spyware for the employees, how often are you sitting in front of your computer? How often are you on a Zoom call? How often are you, doing these things? And it's striking to me that it's such an outdated way to measure activity, versus a way to measure outcome and output and what are you trying to do? I mean, it just drives me crazy to hear those things, I just love to get your take that people still are mixed up about what they're supposed to be measuring and what the purpose of the whole task is, which is to get output done not just to be busy and sit in Zoom calls all day. >> It's so true. So there's sort of two prongs to that question. And two very important things to look at. So one is how do we measure productivity, right among knowledge workers, which has been the topic of a lot of conversation. And the other thing is, what have leadership models been built off of in the past, right? If you just take the first thing first. Productivity today, if you go to the Bureau of Labor Statistics website, you will still see productivity defined as how many widgets can I produce in an hour. That's still today, how we measure productivity, even though (chuckles) all of our output or most of our output, right, is coming from our knowledge, our thinking, our problem solving. (clears throat) So the notion of productivity feels very heavy handed to a lot of people, because it's still rooted literally economics wise in this notion of x widgets per hour, which just doesn't fit. And that comes through the second point, which is our leadership models, right? So I talked in the book and I've been talking about this for many years, because it just jumped out at me when I started to do this research, is that if you look at most leadership models today, any one of them, pick whatever one you like, transformational leadership, transactional leadership, situational leadership or whatever it might be. Those leadership models were built mainly in the 1950s. And some of them came later in the 80s. We have a few new ones, (clears throat) excuse me that have come after the internet, but not too many. And fundamentally, if you look at the communication mode of leaders in the 50s, and the 80s, it was face-to-face or phone. I mean, just by definition, was in person or via phone. But that assumption doesn't hold true anymore and hasn't held true for a good 15 years. And yet, in every business school today, we still use those leadership models as sort of our first run at how to lead. It's not that they're not useful and helpful and don't have extremely good words of advice for leaders. But the main thing leaders do is communicate. So if the fundamental channel over which leaders are communicating has completely changed, it seems natural that we should be looking for new leadership models (chuckles) that fit our times a little bit better. Taking pieces of the best of those leadership models, but really turning them on their head and saying, what's really a better approach when fundamentally our communication mode itself, it has completely changed. >> Right right. >> And that's what we do as leaders. >> And I do just want to say a word. We're talking about working from home and knowledge workers and unfortunately, there's a whole lot of people going through COVID right now that don't have that option, right. If you're in the travel industry, if you're in the hospitality industry, if you're in a lot of services industries, if you are a plumber, you can't go virtual as a plumber, unfortunately. So just to acknowledge that, what we're talking about applies to a lot of people, but certainly not everyone and everyone doesn't have these options. So I just wanted to mention that but before we wrap, Karen, the thing that struck me, as you're talking about kind of the 50s and the organizational structure, was it was really command and control and just top down hierarchies that dictated what people did. And then you as you said, your job was to put so many widgets on the widget receiver per hour, and that's what you were graded on. Where in knowledge workers, it's a very different thing. And in fact, you shouldn't tell people how to do things, you should tell people what the objectives are, and then see what they come up with. And hopefully, they'll come up with lots of different ways to achieve the objective, most of which that management has never thought of, they're not down in the weeds, and you get all kinds of interesting and diversity of opinion and different approaches. And kind of a DevOps mentality where you try lots of things and you'll find new ways to get it done. So I want to close out on this final kind of communication piece for leadership. And this is the why. I think back in the 50s, I don't know that the why we was that important. Or maybe it was and I'm not giving it enough credit. But today the why is so important. That is such a big piece of why do I come to work every day? And why am I important to work with my colleagues and move this mission forward. And so whenever you can just share, how important the why is today, and then how important the why is in trying to build a culture and hold people together when they are now by rule distributed all over the place. Talk a little bit about the why. >> Yeah, I love that question, Jeff. Because in the book, I talk a lot about Taylorism. And Taylor was the founder of like bureaucratic management and leadership and he actually despised the worker. (chuckles) There's actually a little piece in the book where he's testifying to Congress and saying that the man who handles pig iron, a type of steel, wasn't intelligent enough to understand what pig iron really was, he got a lot of flak for that. (chuckles) So as we've evolved, right, and as we've grown as organizations into knowledge workers, and I think your point about not everyone is a quote unquote, knowledge worker, is really, really important. The bottom line is, we're trying to measure our output and the value of our work by these older standards. And so people are struggling a little bit with that sort of disconnect, and looking for why, what purpose do they have? What is their bigger purpose? How are they connected to the organization in new ways? And there's actually an excellent analogy in the Navy. Is has its traditions in the Navy, called Commander's Intent which I talk about. So if you think of ships that used to sail, right out to sea, and they had lots of goals about either taking over a certain country or whatever it was they were doing, they couldn't be together, right. So we've been working remotely for a very long time. So the commander would gather all of his lieutenants, and basically tell them what his or, there were no hers at that time, but what his intentions were. And the lieutenants, the captains of the other ships, would go out to each ship, and they wouldn't follow a blueprint tactical plan they would just have the Commander's Intent as their guide. And then they were free actually, to use whatever strategies and tactics that they thought of and that worked in their context in order to fulfill the Commander's Intent, but they weren't given a blueprint. Their goal was really to use their own smarts, their own critical thinking in order to carry forward that intent. And I think that idea is very powerful today because I think if leaders can focus on helping their workers, their employees, their ecosystem partners, supply chain partners, whatever it may be, understand what the intent of the company is, and show that they trust the employees or the partner to deliver on that intent, with whatever means and creativity and imagination, guided by the intent, can be used and selected from on their day-to-day lives, people will feel so much more empowered and still get to the same outcome or actually better, than if they're told do A, B, C and D. So this idea of leader intent, I think would serve companies really well during this time, and if I could just add one other quick thing. There's another idea that comes out of sort of the military that I used and doing some work with leadership crisis management after 9-11. Around this notion of net-centricity. Net-centricity is sort of allowing people on the ground to sort of form their own networks and push information up to leadership so that they can make certain decisions and then push those decisions down with an intention back to the ground, so that this network can operate with some freedom and flexibility. And I think corporations can put net-centricity actually into place in a structured way and they'll find themselves with a lot more flexibility, higher levels of business continuity and effectiveness, and perhaps, most importantly, giving a sense of more meaningfulness and purpose and powerfulness, or self actualization back to the worker. >> Right, right, as you're speaking the word I just can't get out of my head is trust, right? It's so much about trust. And then giving people the power, enabling people the power that you trust to go do the jobs that you've hired them to do. And then to the other point that we talked about, then as a leader, help them remove roadblocks. Give them the tools, do the things that you can do to help them do their job better, versus to your point, being super prescriptive on the road actions that you wish that they would do, and then managing to the completion of the road, actions versus the accomplishment of the bigger task. It seems so simple, it's so hard for so many people to grok. It just, it still just amazes me that so many folks are unfortunately still stuck in that old paradigm. But you can't anymore 'cause everybody's (chuckles) working from home, so you better get with the program. >> (clears throat) Yeah, I'm sorry, I have a little frog in my throat. But you can. And just to add to what you're saying. I think the best thing that leaders can do is also expand their understanding of the worker as no longer just coming to work in some kind of bubble. They're coming to work with all kinds of personal situations. And I've had clients who have sort of tried to get away from that and keep the worker in a bubble. And I think, to be successful as we get through this sort of long-term leadership crisis, I think it's important to lean in to the chaos. Lean into the complexities that COVID, the pandemic, the economic situation bring and see the corporation and their role as leaders as trying to help that whole person with the complexities of their life, as opposed to trying to divorce them from their life, because that has not worked. And what works best, and I've seen this over and over again, is that companies that lean into the crisis, embrace it, and really try to help that whole employee who's coming to work in their house, really, really works very well. >> Yeah, it's going to be interesting as we come out of the summer and go back into the fall, which is the traditional season of kids going back to school and everybody kind of going back to work, and in our world conferences, and it's kind of the ramp up of a busy activity until we get kind of to the Christmas season again coming off of summer, now knowing that isn't a temporary situation, this isn't going away anytime soon. I mean, we used to talk about the new normal in March or April and May. Well now talking about the new normal in September, October, November and into 2021 is a whole different deal. So to your point, I think that's a great tip, lean in, do the best you can, learn from the experts. You don't need to do it by yourself. There's lots of documentation out there. Darren Murph has stuff up from GitHub. Or excuse me GitLab. There's lot of good information. So you do have to kind of buy into it and embrace it, 'cause it's not it's not going away. So these are great tips Karen and I give you this, the last word before we sign off. Of all the work you've done, all the clients you've worked with, a couple of two or three really good nuggets that are really simple things that everybody should be thinking about and doing today. >> I think, there's the Waldorf Schools out by you on the west coast, right, have a motto that they use for education. And it it says in through the heart out through the mind. And I think more than ever, leadership and business can borrow that idea. I think we have to sort of look at things in through the heart. And then, distribute our directions and our leadership out through the mind. At the end of the day (chuckles) we're all human beings that are all struggling in this shared experience, something that has literally never happened on planet earth with 8 billion people, connected through technology with a global pandemic. And so if we kind of can make a shift and think about taking things in through the heart and then delivering out through the mind. I think that a lot of people will feel that compassion. And that will translate into the kind of trust that we're trying to build between all of us to get through it together. And I think when we do that, I have a lot of confidence in the human spirit that we will get through it. People will be able to look back and say, yes, this was very difficult and horrific on many levels, but at the end of the day, maybe there's a little bit of a renaissance in how we sort of look at each other and treat each other with compassion and some love and joy, even in the worst of times. I think that translates over any communication medium (chuckles) including the one we're using today. >> Well, Karen, thank you for the time and thank you for closing this with a little bit of light. Congrats again on the book, "The Power of Virtual Distance", I'm sure it's available everywhere. And again, great to see you. >> Thank you so much Jeff, you too. >> All right. >> Take care. >> She's Karen, I'm Jeff, you're watching theCUBE. Thanks for watching. We'll see you next time. (soothing music)

(upbeat music) >> From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studios today. We're having a CUBE Conversation around a really interesting topic. It's applied AI, applied machine learning. You know, we hear a lot about artificial intelligence and machine learning in kind of the generic sense, but I think really, where we're going to see a lot of the activity is when that's applied to specific solutions and specific applications. And we're really excited to have our next guest. He's applying AI and machine learning in a really interesting and important space. So joining us from San Francisco is Bryton Shang. He's the founder and CEO of Aquabyte. Bryton great to see you. >> Yeah, Jeff. Great to be here. >> I can't believe it's been almost a year since we met at a Kosta Noah event. I looked it up June of last year. Wow, how time flies. But before we get into it, give everyone just kind of the quick overview of what you guys are up to at Aquabyte. >> Aquabyte's a company, we're building software to be able to help fish farmers. It's computer vision and machine learning software based on a camera that takes pictures of a fish in a fish pen, analyzes those images and helps the farmer understand the health of the fish, the weight of the fish, how much to feed and generally better manage their farms. >> It's such a great story. So for those people that haven't seen it, I encourage you to jump on the internet and look up the AWS special that Werner did on Aquabyte last year. It's a really nice piece, really gets into the technology and a lot of the fun part of the story. I really enjoyed it and you know, congratulations to you for getting featured in that AWS piece. But let's go to how did you get here? I mean, you're really interesting guy. You're a multiple company founder coming out of Princeton, in most of your startup role, your startups are all about, Applied Mathematics and Statistics but you've been in everything from finance and trading to looking at cells in the context of Cancer. How did you get to Aquabyte? Was it the technology? And then you found a cool solution? Or did you hear about, you know, an interesting problem and you thought, you know, I have just the trick to help attack that problem. >> Well, so I had studied Operations Research and Financial Engineering at Princeton, which I guess we would call nowadays, like modern day machine learning and data science. So that was something as you mentioned, first I'd apply it to algorithmic trading, and then got on to more general applications of computer vision for example, in cancer detection. The idea to apply machine learning talk to aquaculture, came from a number of different sources. One was from a previous co-founder who had been doing some investigation in the fish farming space, had a business school classmate who owned a fish farm. And also growing up in Ithaca, New York near to Cornell I had a family friend who is a professor of aquaculture. And really just to learn about fish farming and overfishing and the idea that over half the fish we eat nowadays are coming from fish farms and that you could use machine learning and computer vision to make these farms more efficient. That being very interesting and compelling. >> So it's really interesting. One of the things that jumped out from me when I watched the piece with Werner was the amazing efficiency on the feed to protein output in fish farming. I had no idea that it was so high, it's basically approaching one to one really interesting opportunity. And I had no idea to that, as you said over 50% of the world's seafood that's consumed was commercially farmed. So really a giant opportunity and so great space to be in a lot of environmental impacts. So but how did you decide to find an entree? We know where to find an entree for machine learning to make a big impact in this industry. >> So it came from a couple different angles. First, there's been applications of machine learning computer vision and other industries that served as good parallels where we're using cameras to be able to take images and then use computer vision to derive insight from those images. For example, just take aquaculture where you're using cameras to spray weeds to understand crop yield. And so there's good parallels and other industries. aquaculture specifically, I was also looking at what was coming out in the machine learning literature in terms of using cameras to size fish. And so the idea that you could use cameras to size fish was very interesting because then you can use that to figure out growth rates and feeding. And as I developed my idea, it really became clear that you could use computer vision and machine learning to do a wide range of things at the farm and so, it started with this idea about using cameras to size fish and then it became monitoring health and sea lice and parasites and then ultimately, all the aspects of the farm that you would want to manage. >> And correct me for wrong, but do you guys identify individual fish within the population within that big net and then you're basically tracking individuals and then aggregating that to see the health of the whole population. >> That's right, the spot pattern on the fish is unique and we have an algorithm that's able to use that to determine each individual fish via the spot pattern. >> Wow. And then how long once, once you kind of got together with the farmers to really start to say, wow, we can use this application for, as you said, worrying about lice and disease control and oh wow, we can use this application to measure growth. So now we know the health of the environment or wow, now we know the size so we can impact our harvest depending on what our customers are looking for. I assume there's all kinds of ways you can slice and dice the data that comes out of the system into actual information that can be applied in lots of different ways. >> Right So I started the company back in 2017. And if you think about aquaculture, it's actually a hugely international industry 99% outside the US, and within aquaculture, very quickly zeroed in on salmon farming, and specifically salmon farming in Norway. Norway produces about half of the world's farmed salmon and ended up going there for a conference Aqua Nor August of 2017 and whilst there had my idea and a prototype for sizing the fish with a camera, but then also realized in Norway they have recently passed regulations around counting sea lice on the fish so this is parasite that attaches to the fish and is regulated and pretty much every country that grows fish in the ocean and farmers asked me then, okay, if you could use the camera to size fish, can you also count sea lice? And can you also detect the appetite? And then it just turned into this more platform approach where this single camera could do a wide variety of application. >> That's awesome. And I'm just curious to get your take on, the acceptance and really the excitement around, you know, kind of application of machine learning in this computer vision in terms of the digital transformation of commercial fish farming, because once it sounds like once they discovered the power of this thing, they very quickly saw lots of different applications, and I assume continue to see kind of new applications to apply this to transform their business. >> Right, I would say fish farming itself is already fairly highly mechanized. So you're dealing with fairly rough conditions in the ocean. And a lot of the equipment there is already mechanized. So you have automatic feeders, you have feeding systems. That said, there isn't too much computer vision machine learning in the industry. Today, a lot of that is fairly new to the farmers. That said they were open to trying out the technology, especially when it helps save labor at the farm. And it's something that they have familiarity with, with some of the applications for example, with Tesla with their autopilot and other examples that you could point to in common day use. >> That's interesting that you brought up Tesla, I was going to say that the Tesla had an autonomous driving day presentation. I don't know, it's probably been a year or so now but really long in-depth presentations by some of his key technical people around the microprocessor and AI and machine learning and a whole thing about computer vision. And, you know, there's this great debate about, can you can you have an autonomous car without Lidar and I love the great quote from that thing was you "Lions don't have Lidar "and they chase down gazelles all day long." So, we can do a lot with our vision. I'm curious, some of the specific challenges within working in your environment within working in water and working with all kinds of crazy light conditions. It's funny on that Tesla, they talked about really some of the more challenging environments being like a tunnel, inside of a tunnel with wet pavement. So, kind of reflections and these kind of metric conditions that make it much harder. What are some of the special challenges you guys had to overcome? And how much, is it really the technology? Or is it really being done in the software and the algorithms and the analyzing or is it basically a bunch of pixel dots? >> Right. The basic technology is based on similar, it's a serial camera that takes images of the fish. Now, a lot of the special challenges we deal with relate to the underwater domain. So underwater, you're dealing with a rough environment, there could be particles in the water, specularity some reflections underwater, you're dealing with practical challenges such as algae, but even the behavior of the fish, are they swimming by the camera? Or do you want to position your camera in the pen. Also, water itself has interesting optical properties. So the deeper you go, it affects the wavelength that's hitting the camera. And also you have specialized optics where the focal length and other aspects of the optics are affected underwater. And so a lot of the specific expertise we've developed is understanding how to sense properly underwater. Some of that is handled by the mechanical design. A lot of it is also handled by the software, where on the camera we have GPUs that are processing the images and using deep learning computer vision algorithms to identify fish parts and sea lice and other aspects of the fish. >> It's crazy, and how many fish are in one you know, individuals are in one of these nets. >> So single pen can have as much as 100,000. Where actually in one pen, which is I think it's the largest salmon farm in Norway based on an oil rig called the ocean farm where they have 2 million fish in a single pen. >> 2 million fish, and you're in that one. >> Right, yes. >> And you've identified all 2 million fish or do you work on some sampling? Or how do you make sure every fish eventually swims by the camera? Or does the camera move around inside that population? That's an amazing amount of fish. >> So I think we'll eventually get to the point where we can identify every single fish in the pen and use that to track individual health and growth. Well we practice what we use the individual recognition algorithm the deal is to de-duplicate fish. So a common question we get asked is okay, what if the same fish swims by the camera twice, and so it's used to de-duplicate fish But I think eventually you'd be able to survey the entire population. >> That's crazy. So where do you guys go next Bryton, again you've brought your analytical brain to a number of problems. Do you see kind of expanding the use within the fish industry and kind of a vertical player? Do you see really a horizontal play in different parts of agriculture and beyond to apply some of the techniques and the IP that you guys have built up so far? >> Well, starting with Norwegian salmon, we want to bring this to other countries around the world for other species. So we've expanded to our second species, which is a rainbow trout. We also are, starting with computer vision are building this very interesting data set which we can use to enable other applications. Eventually, we'll get to the point where that data allows us to run fully autonomous fish farms. Right now the limitations of fish farming is that it needs to be close to the shore. So you can have people go to the farms. And once you have fully autonomous fish farms, then you can have fish farms in the open ocean, fish farms on land. And with the world being 70% water, we're only producing about 5% of the protein from the oceans. And so it presents a massive opportunity for us to be able to increase the amount of world's demand for protein. Also given that we're running out of land to grow crops. >> Wow, that's amazing. We're only getting 5% of our food protein out of the ocean at this stage? >> Right, right. >> That is crazy. I thought it would be much higher than that. Well, certainly a really cool opportunity and, a kind of a really awesome little documentary by Werner and the team, definitely go watch it if you haven't seen it. So I just give you the last word as you've been in this industry and really seen kind of the transformative potential of something like computer vision in commercial fishing and who would have even thought that, six or seven years ago? How does that help you kind of think forward, kind of the opportunity really to use these types of applications like computer vision and machine learning to advance something so important, like food creation for our world. >> I think there's definitely a lot of opportunities to be able to use machine learning computer vision, similar technologies to help make these industries a lot more efficient. Also a lot more environmentally sustainable. I'd say something like this industry, like aquaculture, it's not so apparent just if you're in the valley, and even in the US just because 99% of it happens outside the US and so to be able to be familiar with the industry to know that it exists and to build applications itself is a bit of a challenge. I would say that is changing. One of the things that actually came out a couple weeks ago was an executive order to actually start kick starting offshore aquaculture in the US. So it is starting in the US. But more generally, I do think there's a massive opportunity to be able to apply machine and computer vision in new industries that previously haven't been addressed. >> Yeah, that's great. And I just love how you got kind of a single source of data, but really the information that you can apply and the applications you can apply are actually quite broad. It's a super use case. Well, Bryton, thanks for spending a few minutes. I've really enjoyed the story. Congratulations on your funding rounds and your continued success. >> Thanks, and really appreciate to be on and yeah, hope to continue to help bring the world more sustainable seafood. >> Absolutely. Well, thanks a lot Bryton. So he's Bryton and I'm Jeff. You're watching theCUBE. We'll see you next time, thanks for watching. (upbeat music)

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat welcome back this is the cubes coverage of Red Hat summit 2020 of course this year instead of all gathering together in San Francisco we're getting to talk to red hat executives their partners and their customers where they are around the globe I'm your host Stu minimun and happy to welcome to the program Nick Barr said who is the senior director of Technology Strategy at Red Hat he happens to be on a boat in the Bahamas so Nick thanks so much for joining us hey thank you for inviting me it's a great pleasure to be here and it's a great pleasure to work for a company that has always dealt with remote people so it's really easy for us to kind of thing yeah Nick you know it's interesting I've been saying probably for the last 10 years that the challenge of our time is really distributed systems you know from a software standpoint that's what we talked about and even more so today and number one of course the current situation with the global plan global pandemic but number two the topic we're gonna talk to you about is edge and 5g it's obviously gotten a lot of hype so before we get into that - training Nick you know you came into Red Hat through an acquisition so give us a little bit about your background and what you work on Baretta about five years ago company I was working for involves got acquired by read at and I've been very lucky in that acquisition where I found a perfect home to express my talent I've been free software advocate for the past 20-some years always been working in free software for the past 20 years and Red Hat is really wonderful for that yeah it's addressing me ok yeah I remember back the early days we used to talk about free software now we don't talk free open-source is what we talk about you know dream is a piece of what we're doing but yeah let's talk about you know Ino Vaughn's I absolutely remember the they were a partner of Red Hat talked to them a lot at some of the OpenStack goes so I I'm guessing when we're talking about edge these are kind of the pieces coming together of what red had done for years with OpenStack and with NFB so what what what's the solution set you're talking about Ferguson side how you're helping your customers with these blue well clearly the solution we are trying to put together as to combine what people already have with where they want to go our vision for the future is a vision where openshift is delivering a common service on any platform including hardware at the far edge on a model where both viens and containers can be hosted on the same machine however there is a long road to get there and until we can fulfill all the needs we are going to be using combination of openshift OpenStack and many other product that we have in our portfolio to fulfill the needs of our customer we've seen for example a Verizon starting with OpenStack quite a few years ago now going with us with openshift that they're going to place on up of OpenStack or directly on bare metal we've seen other big telcos use tag in very successful to deploy their party networks there is great capabilities in the existing portfolio we are just expanding that simplifying it because when we are talking about the edge we are talking about managing thousands if not millions of device and simplicity is key if you do not want to have your management box in Crete excellent so you talked a lot about the service providers obviously 5g as a big wave coming a lot of promise as what it will enable both for the service providers as well as the end-users help us understand where that is today and what we should expect to see in the coming years though so in respect of 5g there is two reason why 5g is important one it is B it is important in terms of ad strategy because any person deploying 5g will need to deploy computer resources much closer to the antenna if they want to be able to deliver the promise of 5g and the promise of very low latency the second reason it is important is because it allows to build a network of things which do not need to be interconnected other than through a 5g connection and this simplifies a lot some of the edge application that we are going to see where sensors needs to provide data in a way where you're not necessarily always connected to a physical network and maintaining a Wi-Fi connection is really complex and costly yeah Nick a lot of pieces that sometimes get confused or conflated I want you to help us connect the dots between what you're talking about for edge and what's happening the telcos and the the broader conversation about hybrid cloud or red hat calls at the O the open hybrid cloud because you know there were some articles that were like you know edge is going to kill the cloud I think we all know an IP nothing ever dies everything is all additive so how do these pieces all go together so for us at reddit it's very important to build edge as an extension of our open hybrid cloud strategy clearly what we are trying to build is an environment where developers can develop workloads once and then can the administrator that needs to deploy a workload or the business mode that means to deploy a workload can do it on any footprint and the edge is just one of these footprint as is the cloud as is a private environment so really having a single way to administer all these footprints having a single way to define the workloads running on it is really what we are achieving today and making better and better in the years to come um the the reality of [Music] who process the data as close as possible to where the data is being consumed or generated so you have new footprints - let's say summarize or simplify or analyze the data where it is being used and then you can limit the traffic to a more central site to only the essential of it is clear that we've the current growth of data there won't be enough capacity to have all the data going directly to the central part and this is what the edge is about making sure we have intermediary of points of processing yeah absolutely so Nikki you talked about OpenStack and OpenShift of course there's open source project with with OpenStack openshift the big piece of that is is kubernetes when it comes to edge are there other open source project the parts of the foundations out there that we should highlight when looking at these that's Luke oh there is a tremendous amount of projects that are pertaining to the edge read ad carry's many of these projects in its portfolio the middleware components for example Quercus or our amq mechanism caki are very important components we've got storage solutions that are super important also when you're talking about storing or handling data you've got in our management portfolio two very key tool one called ansible that allows to configure remotely confidence that that is super handy when you need to reconfigure firewall in Mass you've got another tool that he's a central piece of our strategy which is called a CM read at forgot the name of the product now we are using the acronym all the time which is our central management mechanism just delivered to us through IBM so this is a portfolio wide we are making and I forgot the important one which is real that Enterprise Linux which is delivering very soon a new version that is going to enable easier management at the edge yeah well of course we know that well is you know the core foundational piece with most of the solution in a portfolio that's really interesting how you laid that out though as you know some people on the outside look and say ok Red Hat's got a really big portfolio how does it all fit together you just discussed that all of these pieces become really important when when they come together for the edge so maybe uh you know one of the things when we get together summit of course we get to hear a lot from your your your customer so any customers you can talk about that might be a good proof point for these solutions that you're talking about today so right now most of the proof points are in the telco industry because these are the first one that have made the investment in it and when we are talking about their eyes and we are talking about a very large investment that is reinforced in their strategy we've got customers in telco all over the world that are starting to use our products to deploy their 5g networks and we've got lots of customer starting to work with us on creating their tragedy for in other vertical particularly in the industrial and manufacturing sector which is our necks and ever after telco yet yeah well absolutely Verizon a customer I'm well familiar with when it comes to what they've been used with Red Hat I'd interviewed them it opens back few years back when they talked about that those nmv type solutions you brought a manufacturing so that brings up one of the concerns when you talk about edge or specifically about IOT environment when we did some original research looking at the industrial Internet the boundaries between the IT group and the OT which heavily lives lives in manufacturing wouldn't they did they don't necessarily talk or work together so Houser had had to help to make sure that customers you know go through these transitions Plus through those silos and can take advantage of these sorts of new technologies well obviously you you have to look at a problem in entirety you've got to look at the change management aspect and for this you need to understand how people interact together if you intend on modifying the way they work together you also need to ensure that the requirements of one are not impeding the yeah other the man an environment of a manufacturer is really important especially when we are talking about dealing with IOT sensors which have very limited security capability so you need to add in the appropriate security layers to make what is not secure secure and if you don't do that you're going to introduce a friction and you also need to ensure that you can delegate administration of the component to the right people you cannot say Oh from now on all of what you used to be controlling on a manufacturing floor is now controlled centrally and you have to go through this form in order to have anything modified so having the flexibility in our tooling to enable respect of the existing organization and handle a change management the appropriate way is our way to answer this problem right Nick last thing for you obviously this is a maturing space lots of age happening so gives a little bit of a look forward as to what users should be affecting and you know what what what pieces will the industry and RedHat be working on that bring full value out of the edge and find a solution so as always any such changes are driven by the application and what we are seeing is in terms of application a very large predominance of requirements for AI ml and data processing capability so reinforcing all the components around this environment is one of our key addition and that we are making as we speak you can see Chris keynote which is going to demonstrate how we are enabling a manufacturer to process the signal sent from multiple sensors through an AI and during early failure detection you can also expect us to enable more and more complex use case in terms of footprint right now we can do very small data center that are residing on three machine tomorrow we'll be able to handle remote worker nodes that are on a single machine further along we'll be able to deal with disconnected node a single machine acting as a cluster all these are elements that are going to allow us to go further and further in the complication of the use cases it's not the same thing when you have to connect a manufacturer that is on solid grounds with fiber access or when you have to connect the Norway for example or a vote and talk about that too Nick thank you so much for all the updates no there's some really good breakouts I'm sure there's lots on the Red Hat website find out more about edge in five B's the Nick bark set thanks so much for joining us thank you very much for having me all right back with lots more covered from Red Hat summit 2020 I'm stoom in a man and thanks though we for watching the queue [Music]

>> Announcer: Live from Orlando Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity. >> Good morning everyone. You are watching theCUBE's live coverage of Microsoft Ignite 2019 here in Orlando, Florida. I'm your host Rebecca Knight, co-hosting alongside of Stu Miniman. We are joined by Donovan Brown. He is the Principal Cloud Advocate Manager of Methods and Practices Organizations at Microsoft. (laughing) A mouthful of a title. >> Yes. >> Rebecca: We are thrilled to welcome you on. >> Thank you so much. >> You are the man in the black shirt. >> I have been dubbed the man in the black shirt. >> So tell us what that's all about. You're absolutely famous. Whenever we were saying Donovan Brown's going to be here. "The man in the black shirt?" >> Yes. >> So what's that about? >> So it was interesting. The first time I ever got to keynote in an event was in New York in 2015 for Scott Guthrie, the guy who only wears a red shirt. And I remember, I was literally, and this is no exaggeration, wearing this exact black shirt, right, because I bring it with me and I can tell because the tag in the back is worn more than the other black shirts I have just like this one. And I bring this one out for big events because I was in a keynote yesterday and I knew I was going to be on your show today. And I wore it and it looked good on camera. I felt really good. I'm an ex-athlete. We're very superstitious. I'm like I have to wear that shirt in every keynote that I do from now on because if you look further back, you'll see me in blue shirts and all other colored shirts. But from that day forward, it's going to be hard pressed for you to find me on camera on stage without this black shirt on or a black shirt of some type. And there's a really cool story about the black shirt that was. This is what\ I knew it was a thing. So I pack about six or seven black shirts in every luggage. I'm flying overseas to Germany to go Kampf to do a keynote for, I think it was Azure Saturday. Flights were really messed up. they had to check my bag which makes me very uncomfortable because they lose stuff. I'm not too worried about it, it'll be okay. Check my bag, get to Europe. They've been advertising that the black shirt is coming for months and they lose my luggage. And I am now, heart's pounding out of my chest. (laughing) We go to the airport. I'm shopping in the airport because I don't even have luggage. I cannot find a black shirt and I am just thinking this is devastating. How am I going to go to a conference who's been promoting "the black shirt's coming" not wearing a black shirt? And my luggage does not show up. I show up at the event I'm thinking okay, maybe I'll get lucky and the actual conference shirt will be black and then we're all good. I walk in and all I see are white shirts. I'm like this could not be worse. And then now the speakers show up. They're wearing blue shirts, I'm like this cannot be happening. So I'm depressed, I'm walking to the back and everyone's starts saying, "Donovan's here, Donovan's here." And I'm looking to find my polo, my blue polo I'm going to put on. They're like no, no, no, no Donovan. They printed one black shirt just for me. I was like oh my goodness, this is so awesome. So I put the black shirt on, then I put a jacket on over it and I go out and I tell the story of how hard it was to get here, that they lost my luggage, I'm not myself without a black shirt. But this team had my back. And when I unzipped my shirt, the whole place just starts clapping 'cause I'm wearing >> Oh, I love it. >> a black shirt. >> Exactly. So now to be seen without a black shirt is weird. Jessica Dean works for me. We were in Singapore together and it was an off day. So I just wore a normal shirt. She had to take a double take, "Oh no, is that Donovan, my manager "'cause he's not wearing a black shirt?" I don't wear them all the time but if I'm on camera, on stage you're going to see me in a black shirt. >> Rebecca: All right, I like it. >> Well, Donovan, great story. Your team, Methods and Practices makes up a broad spectrum of activities and was relatively recently rebranded. >> Yeah. >> We've talked to some of your team members on theCUBE before, so tell our audience a little bit about the bridges Microsoft's building to help the people. >> Great. No, so that's been great. Originally, I built a team called The League. Right, there's a really small group of just DevOps focused diehards. And we still exist. A matter of fact, we're doing a meet and greet tonight at 4:30 where you can come and meet all five of the original League members. Eventually, I got tasked with a much bigger team. I tell the story. I was in Norway, I went to sleep, I had four direct reports. I literally woke up and I had 20 people reporting to me and I'm like what just happened? And the team's spanned out a lot more than just DevOps. So having it branded as the DevOps Guy doesn't really yield very well for people who aren't diehard DevOps people. And what we feared was, "Donovan there's people who are afraid of DevOps "who now report to you." You can't be that DevOps guy anymore. You have to broaden what you do so that you can actually focus on the IT pros in the world, the modern operations people, the lift and shift with Jeremy, with what Jeramiah's doing for me right, with the lift and shift of workloads . And you still have to own DevOps. So what I did is I pulled back, reduced my direct reports to four and now I have teams underneath me. Abel Wang now runs DevOps. He's going to be the new DevOps guy for me. Jeramiah runs our lift and shift. Rick Klaus or you know the Hat, he runs all my IT Pro and then Emily who's just an amazing speaker for us, runs all of my modern operations. So we span those four big areas right. Modern operations which is sort of like the ops side of DevOps, IT pros which are the low level infrastructure, diehard Windows server admins and then we have DevOps run by Abel which is still, the majority of The League is over there. And then we have obviously the IT pros, modern ops, DevOps and then the left and shift with Jeramiah. >> I'd like to speak a little bit as to why you've got these different groups? How do you share information across the teams but you know really meet customers where they are and help them along 'cause my background's infrastructure. >> Donovan: Sure. >> And that DevOps, was like that religion pounding at you, that absolutely, I mean, I've got a closet full of hoodies but I'm not a developer. Understand? >> Understood. (laughs) It's interesting because when you look at where our customers are today, getting into the cloud is not something you do overnight. It takes lots of steps. You might start with a lift and shift, right? You might start with just adding some Azure in a hybrid scenario to your on-prem scenario. So my IT pros are looking after that group of people that they're still on prem majority, they're trying to dip those toes into the cloud. They want to start using things like file shares or backups or something that they can have, disaster recovery offsite while they're still running the majority of what they're doing on-prem. So there's always an Azure pool to all four of the teams that I actually run. But I need them to take care of where our customers are today and it's not just force them to be where we want them tomorrow and they're not ready to go there. So it's kind of interesting that my team's kind of have every one of those stages of migration from I'm on-prem, do I need to lift and shift do I need to do modern operations, do I need to be doing full-blown DevOps pull all up? So, I think it's a nice group of people that kind of fit the spectrum of where our customers are going to be taking that journey from where they are to enter the cloud. So I love it. >> One of the things you said was getting to the cloud doesn't happen overnight. >> No, it does not. >> Well, you can say that again because there is still a lot of skepticism and reluctance and nervousness. How do you, we talked so much about this digital transformation and technology is not the hard part. It's the people that pose the biggest challenges to actually making it happen. >> Donovan: Right. >> So we're talking about meeting customers where they are in terms of the tools they need. But where do you meet them in terms of where they are just in their approach and their mindset, in terms of their cloud readiness? >> You listen. Believe it or not, you can't just go and tell people something. You need to listen to them, find out what hurts and then start with that one thing is what I tell people. Focus on what hurts the most first. Don't do a big bang change of any type. I think that's a recipe for disaster. There's too many variables that could go wrong. But when I sit down with a customer is like tell me where you are, tell me what hurts, like what are you afraid of? Is it a compliancies? Let me go get you in contact with someone who can tell you about all the comp. We have over 90 certifications on Azure. Let me. whatever your fear is, I bet you I can get you in touch with someone that's going to help you get past that fear. But I don't say just lift, shift, move it all like stop wasting, like no. Let's focus on that one thing. And what you're going to do is you're going to start to build confidence and trust with that customer. And they know that I'm not there just trying to rip and replace you and get out high levels of ACR. I'm trying to succeed with you, right, empower every person in every organization on the planet to achieve more. You do that by teaching them first, by helping them first. You can sell them last, right? You shouldn't have to sell them at all once they trust that what we we're trying to do together is partner with you. I look at every customer more as a partner than a customer, like how can I come with you and we do better things together than either one of us could have done apart. >> You're a cloud psychologist? Almost, right because I always put myself in their position. If I was a customer, what would I want that vendor to do for me? How would they make me feel comfortable and that's the way that I lead. Right, I don't want you going in there selling anything right. We're here to educate them and if we're doing our job on the product side, the answer is going to be obvious that you need to be coming with us to Azure. >> All right. So Donovan, you mentioned you used to be an athlete? >> Donovan: Yes. >> According to your bio, you're still a bit of an athlete. >> Donovan: A little bit, a little bit. >> So there's the professional air hockey thing which has a tie to something going on with the field. Give us a little bit of background. I've got an air hockey table in my basement. Any tips for those of us that aren't, you know? You were ranked 11th in the world. >> At one point, yeah, though I went to the World Championships. It was interesting because that World Championships I wasn't prepared. My wife plays as well. We were like we're just going to go, we're going to support the tournament. We had no expectations whatsoever. Next thing you know, I'm in the round playing for the top 10 in the world. And that's when it got too serious for me and I lost, because I started taking it too serious. I put too much pressure on myself. But professionally, air hockey's like professional foosball or pool. It's grown men taking this sport way too seriously. It's the way I'd describe it. It is not what you see at Chuck E. Cheese. And what was interesting is Damien Brady who works for me found that there is an AI operated air hockey table here on this floor. And my wife was like, oh my gosh, we have to find this machine. Someone tape Donovan playing it. Six seconds later, my first shot I scored it. And I just looked at the poor people who built it and I'm like yeah, I'm a professional air hockey player. This thing is so not ready for professional time but they took down all my information and said we'd love to consult with you. I said I'd love to consult with you too because this could be a lot of fun. Maybe also a great way for professionals to practice, right, because you don't always have someone who's willing to play hours and hours which it takes to get at the professional level. But to have an AI system that I could even teach up my attack, forcing me to play outside of my comfort zone, to try something other than a left wall under or right well over but have to do more cuts because it knows to search for that. I can see a lot of great applications for the professionalized player with this type of AI. It would actually get a lot better. Literally, someone behind me started laughing. "That didn't take long" because it in six seconds I had scored on it already. I'm like okay, I was hoping it was going to be harder than this. >> I'm thinking back to our Dave Cahill interview of AI for everyone, and this is AI for professional air hockey players. >> It is and in one of my demos, Kendra Havens showed AI inside of your IDE. And I remember I tell the story that I remember I started writing software back in the 90s. I remember driving to a software store. You remember we used to have to drive and you'd buy a box and the box would be really heavy because the manuals are in there, and not to mention a stack of floppy discs that you're going to spend hours putting in your computer. And I bought visual C++ 1.52 was my first compiler. I remember going home so excited. And it had like syntax highlighting and that was like this cool new thing and you had all these great breakpoints and line numbers. And now Kendra's on stage typing this repetitives task and then the editor stops her and says, "It looks like you need to do this a little bit more. "You want me to do this for you?" And I'm like what just happened? This is not syntax highlighting. This is literally watching what you do, identifying a repetitive task, seeing the pattern in your code and suggesting that I can finish writing this code for you. It's unbelievable. >> You bring up a great point. Back when I used to write, it was programming. >> Yes. >> And we said programming was you learn the structure, you learn the logic and you write all the lines of what's going to be there. Coding on the other hand usually is taking something that is there, pulling in the pieces, making the modification. >> Right. >> It sounds like we're talking about even the next generation where the intelligence is going to take over. >> It's built right inside of your IDE which is amazing. You were talking about artificial intelligence, not only for the air hockey. But I love the fact that in Azure, we have so many cognitive services and you just like pick these off the shelf. When I wanted to learn artificial intelligence when I was in the university, you had to go for another language called Lisp. That scared half of us away from artificial intelligence because you have to learn another language just to go do this cool thing that back then was very difficult to do and you could barely get it to play chess, let alone play air hockey. But today, cognitive services search, decision-making, chat bots, they're so easy. Anyone, even a non developer, can start adding the power of AI into their products thanks to the stuff that we're doing in Azure. And this is just lighting up all these new possibilities for us, air hockey, drones that are able to put out fires. I've just seen amazing stuff where they're able to use AI and they add it with as little as two lines of code. And all of a sudden, your app is so much more powerful than it was before. >> Donovan, one of the things that really struck me over the last couple years, looking at Microsoft, is it used to be, you'd think about the Microsoft stack. When I think about developers it's like, oh wait are you a .NET person? Well, you're going to be there. The keynote this morning, one of your team members was on stage with Scott Hanselman and was you know choose your language, choose your tools and you're going to have all of them out there. So talk to us a little bit about that transition inside Microsoft. >> Sure. One of the mantras that I've been saying for a while is "any language, any platform". No one believes me . So I had to start proving it. I'm like so I got on stage one year. It was interesting and this is a really rough year because I flew with three laptops. One had Mac OS on it, one of them had Linux on it and one of them had Windows. And what I did is I created a voting app and what I would do is I'd get on stage and say okay everyone that's in this session, go to this URL and start voting. They got to pick what computer I use, they got to pick what language I programmed in and they got to pick where in Azure-eyed I deployed it to. Was it to an app service was it to Docker? I'm like I'm going to prove to you I can do any language in any platform. So I honestly did not know what demo I was going to do. 20 minutes later, after showing them some slides, I would go back to the app and say what did you pick? And I would move that computer in front of me and right there on stage completely create a complete CI/CD pipeline for the language that that audience chose to whatever resources that they wanted on whatever platform that they wanted me. Was like, have I proven this to you enough or not? And I did that demo for an entire year. Any language that you want me to program in and any platform you want me to target, I'm going to do that right now and I don't even know what it's going to be. You're going to choose it for me. I can't remember the last time I did a .NET demo on stage. I did Python this week when I was on stage with Jason Zander. I saw a lot of Python and Go and other demos this year. We love .NET. Don't get us wrong but everyone knows we can .NET. What we're trying to prove right now is that we can do a lot of other things. It does not matter what language you program in. It does not matter where you want to deploy. Microsoft is here to help you. It's a company created by developers and we're still obsessed with developers, not just .NET developers, all developers even the citizen developer which is a developer which is a developer who doesn't have to see the code anymore but wants to be able to add that value to what they're doing in their organization. So if you're a developer, Microsoft is here to help full-stop. It's a powerful mission and a powerful message that you are really empowering everyone here. >> Donovan: Right. >> Excellent. >> And how many developers only program in one language now, right? I thought I remember I used to be a C++ programmer and I thought that was it, right. I knew the best language, I knew the fastest language. And then all of a sudden, I knew CSharp and I knew Java and I knew JavaScript and I brought a lot of PowerShell right now and I write it on and noticed like wow, no one knows one language. But I never leave Visual Studio code. I deploy all my workloads into Azure. I didn't have to change my infrastructure or my tools to switch languages. I just switched languages that fit whatever the problem was that I was trying to solve. So I live the mantra that we tell our customers. I don't just do .NET development. Although I love .NET and it's my go-to language if I'm starting from scratch but sometimes I'm going to go help in an open source project that's written in some other language and I want to be able to help them. With Visual Studio online, we made that extremely easy. I don't even have to set up my development machine anymore. I can only click a link in a GitHub repository and the environment I need will be provisioned for me. I'll use it, check in my commits and then throw it away when I'm done. It's the world of being a developer now and I always giggle 'cause I'm thinking I had to drive to a store and buy my first compiler and now I can have an entire environment in minutes that is ready to rock and roll. It's just I wish I would learn how to program now and not when I was on bulletin boards asking for help and waiting three days for someone to respond. I didn't have Stack Overflow or search engines and things like that. It's just an amazing time to be a developer. >> Yes, indeed. Indeed it is Donovan Brown, the man in the black shirt. Thank you so much for coming on theCUBE. >> My pleasure. Thank you for having me. >> It was really fun. Thank you. >> Take care. >> I'm Rebecca Knight for Stu Miniman. Stay tuned for more of theCUBE's live coverage of Microsoft Ignite. (upbeat music)

>> Live from Washington, D.C., it's theCUBE. Covering Inforum, DC 2018. Brought to you by Infor. >> We are back this afternoon here in Washington, D.C., at the Walter Washington Convention Center. As we continue our coverage here of Inforum 2018 along with Dave Vellante, I'm John Walls, and we now welcome Mr. Cormack Watters to the program today, EVP of Emea and APAC at Infor. Cormack, good to see you sir. >> Nice to be here. >> So, we're going to talk about Guinness, over in Ireland (chuckling). Cormack's from Dublin, so we had a little conversation. We're getting a primer here. >> It's actually the best conversation we should have, right? >> Right, we'll save that for the end. How about that? So, you're fairly new, right? About a year or so. >> Ten months or so, not that I'm counting it by the day >> No no no, always going forward, never backward. But a big plate you have, right, with EMEA and APAC? Different adoptions, different viewpoints, different perspectives... We've talked a lot really kind of focusing domestically here for the past couple of days. Your world's a little different than that though, right? >> It is. It is. And it's very good that you've actually recognized it because that's actually the biggest challenge that we have. To be a little bit humble about it, I think we've got world-class products and solutions. I actually fundamentally believe that. But we have lots of different languages, cultures, and localization requirements in the multiple Countries that we look after. So, it's great to have great products, but it needs to be in French, Spanish, Portuguese, Italian, Swedish, Norwegian, Finish, Arabic, which most of them are. Customers realize that we are actually international and localized for many, many markets. But now we've become an intriguing option for them, if you're a multi-national business, with subsidiaries all over the world. So, it's good that Infor is big enough to do that. We need to do a better job of letting everybody know that we've done that, if that makes any sense. >> Sure. >> So what's happening in Europe? Europe's always pockets, there's no..I mean.. Yes, EU but there's really still no one Europe. What's going on? Obviously, we have Brexit hanging over our head. I felt like U.S. markets are maybe a little bit overheated in Europe has potential upside. >> Yeah >> And it seems like others seem to agree with that. What happening on the ground? Any specific, interesting areas? Is Southern Europe still a concern? Maybe you can give us an update? >> Yeah, so Brexit is quite a dominant conversation. I am from Ireland. I live in Dublin, but I'm working all over Europe, the Middle East, Africa and the Far East. So, I don't get to be at home very often, except the weekends. London is really our regional headquarters from a European perspective, and Brexit is on everybody's mind. Interestingly, when you go outside the UK, Brexit is not such a big topic because... That's Europe. And they kind of go, "Well if you don't want to be here, then you don't need to be here." Right? So it's a little bit of that, and they're saying, "Well, we'd like for them to stay, but if they don't want to stay, well, don't wait around." But in the UK, it's causing a lot of uncertainty. And the UK's one of our biggest markets. It's a lot of uncertainty, and what would be best is if we just knew what was going to happen, and then we could deal with it. And actually, once we know what's going to happen, that's going to bring a degree of change. And change, from our industry perspective means there's going to be some requirements that emerge. So, we need to be ready to serve those, which is opportunity. But the uncertainty is just slowing down investment. So, we need that to be resolved. >> So, clarity obviously is a good thing obviously a good thing in any market. Are there any hotspots? >> Yeah, actually for us, we're doing, for us the Hotspots right now, we're doing incredibly well in Germany. Which, one of our lesser known competitors is a small Company called SAP. And they're headquartered in Germany. It's quite interesting to see that we're actually taking a lot of market there in Germany, which is fantastic. That's a little bit unexpected, but it's going very well right now. We're seeing a ton of activity in the Asia Pacific, I would say that region is probably our fastest growing in all of Infor. And consistently so for several quarters and maybe past a year at this point. So Asia Pacific, Germany, U.K., and then as it happens, we are doing very well in Southern Europe, which is a combination of countries really. France, Italy, Spain, Portugal and Greece. Hard to put it down to which particular Country is doing well, but there seems to be a general uplift in that region. Because they were hit the hardest, arguably, by the crash back in 2008. So they've definitely come out of that now. >> And when they come out, excuse me I'm sorry John, but, they come out, Cloud becomes more important to them, Right? >> Yeah, I mean, absolutely. Anyone who's been delaying investment for years, can actually leapfrog what's been happening and jump straight to what you might call the future. So lots of Companies, lots of our Customers, are trying to simplify their Business. So Cloud is a great equalizer. We believe in your, what we call Last Mile of Functionality per industry. And that should make the projects shorter, more compact more predictable and the infrastructure worries go away, because that's our responsibility to the Customers. >> We definitely so that in the U.S., 2008-2009, CFO's came in said shift to the Cloud, because we want to shift Capx to Opx, and when we came out of the downturn, they said "wow this stuff works pretty well, double down on it" and then there were other business benefits that they wanted to accelerate, and so maybe Southern Europe was a little bit behind >> I think that may be the case right, and they are picking up. And what we're seeing are a lot of other advantages. Not to make this a sale's pitch, but, I am here so >> Go for it >> You've got a microphone >> I've got a microphone and I'm Irish, so I've got to talk right? What the Cloud is actually doing is, lots of Companies have put in big ERP over the years, the decades. And then they get stuck at various points and maybe years behind, because upgrades become painful and really want to avoid them. So what they're seeing is, if they can get onto the Cloud, they never need to upgrade again. Because it's always current, because we upgrade it every week, or every month and they're never falling behind. So they want to be ready to take advantage of the innovations that they know about and those that they don't even know about. So by keeping on the latest version, that opportunities open to them. Also, there's a big issue in Europe specifically about a thing called GDPR, which is data protection. Security. So we believe that we can do a better job of providing that, than any individual Company. Because we provide it for everybody, our resources can be deployed once and then deployed many times. Where as if you're an individual customer, you've got to have that speciality and put it in place. So GDPR is a genuine issue in Europe, because, the fines are absolutely huge if a Company is found to breach it. >> It's become a template for the globe now, California's started moving in that direction, GDPR has set the frame work. >> Well and just to follow up on that, and now you're dealing with a very different regulatory climate, then certainly here in the United States. And many U.S. Companies are finding that out, as we know. Overseas right now. So how do you deal with that in terms of, this kind of balkanized approach that you have, that you know that what's working here doesn't necessarily translate to overseas, and plus you have, you know, you're serving many masters and not just one or two. >> What's happening is the guys in our RND have done very well, is they understand the requirement of, in this instance, GDPR. They look at the other regulatory requirements, lets say in Australia, which is subtly different, but it is different, and they can take, well what do we have to do? What's the most extreme we have to achieve? And if we do that across our suite into our platform suite, the N4RS, that can then be applied to all the applications. And then becomes relevant to the U.S. So it's almost like some requirement across the seas, being deployed then becoming really relevant back here because over here you do need to be aware of the data protection, as well, it's just not as formalized yet. >> It's coming >> A Brewing issue right? >> What about Asia Pacific? So you have responsibility for Japan, and China, and the rest of the region. >> Right >> Which you are sort of re-distinct... >> Really are right? There are several sub regions in the one region. The team down there, as I say, arguably the most successful team in Infor right now, so Helen and the crew. So you see Australia, New Zealand then you see Southeast Asia, then you see China, Japan and so on. So different dynamics and different markets, some more mature than others, Japan is very developed by very specific. You do need very specialized local skills to succeed. Arguably Australia, New Zealand is not that similar from say some of the European Countries. Even though there are differences and I would never dream to tell an Australian or a New Zealander that they are the same as Europeans, cuz I get it. I smile when people say "you're from the U.K and you're not from Ireland?" I understand the differentiation. (laugher) And Southeast Asia, there's a ton of local custom, local language, local business practice that needs to be catered for. We seem to be doing okay down there. As I say, fastest growing market at scale. It's not like it's growing ridiculously fast but from a small base. It's as a big market already and growing the fastest. >> And China, what's that like? You have to partner up? >> Oh yeah >> To the JV in China? >> You have to partner up, there are several of the key growth markets that it's best to go in with partners. Customers like to see we've got a presence. So that they can touch and feel that Infor entity. We can't achieve the scale we need, and the growth we want fast enough without partnering. So we have to go with partners to get us the resources that we need. >> And in the Middle East, so my business partner, Co-Host, John Furrier, is on a Twenty Hour flight to Bahrain. The Cube Bahrain. Bahrain was the first Country in the Middle East to declare Cloud first. AWS is obviously part of that story, part of your story. So what's going on over there? Is it a growing market? Is it sort of something you're still cracking? >> No, no, again it's growing. We have several key markets down there, big in hospitality in that part of the world. Hotels, tourism obviously. Shopping, very interesting markets, and Healthcare, interestingly enough. I think arguably some of the worlds best Hospitals are in that region. Definitely the best funded Hospitals. >> Probably the most comfortable. (laughter) >> So again part of our stent is the number of industries we serve, so if you can put in our platform as it were, then you could have multiple of the industry flavors applied. Because what's interesting in that part of World, there seem to be a number of, I guess we call them conglomerates. So maybe family owned, or region owned, and they have just a different array of businesses all under the one ownership. So you would have a retailer that's also doing some tourism, that's also doing some manufacturing. So we can put our platform in, and then those industry flavors they can get one solution to cover it all. Which is a little bit unusual, and works for us. >> Your scope is enormous. I mean essentially you're the head of Non-U.S. I mean is that right? >> Yeah, and Latin America as well. >> That's part of it? That's not... >> Excluding the Americas. So there's Americas and then everything else, and you're everything else. >> I missed a meeting you see so they just gave it to me >> What you raised your hand at the wrong time? >> I wasn't there (laughter) >> So how do you organize to be successful? You obviously have to have strong people in the region. >> Right. So the key is people, right. We organize somewhat differently to over here. We've gone for a regional model, so I have six sub-regions, that I worry about. So four in Europe, the Nordic Countries. Scandinavian, Sweden, Norway, Finland, Denmark. We call Western, which is Ireland, U.K. and the Benelux. Germany is Central and East, and then Southern is the Latin Country, Spain, Portugal, Greece and so. Then we've got the Middle East, and Africa, and then we got Asia Pacific. I've got six regional teams, all headed by a regional leader, and each of them are trying to be as self contained as they can. And where we see we've got an opportunity to move into something new, we've got one team working with me directly as an incubator. For example, we're driving a specific focus on Healthcare, in our part of the world, because it's very big over here. We haven't quite cracked the code over there. When we get some scale, then it'll move into the regions, but for now that's incubating under me. >> And, what about in Country? Do you have Country Managers? One in the U.K., one in France, one in Germany. >> We have what we call local leaders, right? So in some cases it could be a sales oriented individual, it could be consulting, others it could be the local HR guy. So that's more for us to make sure we're building a sense of community within Infor. Rather than it being more customer facing. We're still trying to make sure that there is a reasonably scarcity of senior skills. So regionalizing lets us deploy across several Countries, and that works with the customer base, but for employees we need local leaders to give them a sense of feeling home and attached. >> So the regions are kind of expertise centers if you will? >> Yes >> So I was going to ask about product expertise, where does that come from? It's not parachuted in from the U.S. I presume? >> No, we're pretty much self-sufficient actually, which is great. So from both what we call solution consulting, which is the product expertise, and then consulting which is the product deployment. And we're doing more and more of our deployments with Partners. As I say, we need to really rapidly embrace that partner ecosystem to give us the growth opportunity. RND, is all over the World. That's not under my direct control. So for a major suites, take for example, LN, happens to be headquartered out of Barneveld, in the Netherlands. From a Historic perspective, which is great. And Stockholm, which is also great. But a lot of the development resource room in Nila and in India. So we work closely with the guys, even though they don't actually report to me. >> And out of the whole area, the area of your responsibility what's the best growth opportunity? We all think of China, but that's been fits and starts for a lot of people. >> Yeah, yeah I think we've got multiple opportunities, you can look at it a few ways. You can look at it geographically, and you would say China. You can look at Eastern Europe, and you can look at Africa. There's a ton of opportunity in those regions, geographically. Interestingly we are also at a point where I think the Nordics, and we've got a very solid base Historically, and so on. But we probably haven't put enough focus on there in recent times, that the opportunities are really scaled in Nordics is really quite significant. And then they can look at it from a Product Perspective. So for example, we have, what we believe to be World Leading, and actually a Company called Gartner would equally agree with us. Enterprise Asset Management, EAM, that's a product suite that can fit across all of our industries. I think that could well be the significant growth area for us across the entire six regions. And it's a huge focus for us here at the conference actually. So we can do it by product, EAM, Healthcare, or by Region. I think Eastern Europe, China, and Africa, as well as the Nordics. >> And the other big opportunity is just share gains, market share gains, particularly in Europe, I would think, with your background. >> Yup. Completely, I mean, that's why I said, it's really interesting that we are winning market share in Germany. Who'd of thought that a few years ago? That's a big market, I mean, Germany, U.K., France, Italy. They're huge. Right, I mean U.K., is what, Sixty-Five Million People? It's a big economy, so we've got many of the worlds G7, in our backyard. So we just really need to double down on those, and give them the opportunities to grow that we need. >> And just back to Japan for a second. Japan has traction, it takes a long time to crack Japan. I know it first from personal experiences. >> Yeah, Okay, Interesting. >> Yeah you just got to go many many times and meet people. >> That's it, Right. And it's a different culture, of when you think they're saying yes and you think they're there, that's just yes to the next step. (laughter) >> Alright, so it does take time to get there. We've actually cracked it to some extent, that we've now got some solid referenceability, and some good wind. We need local leaders in Japan, to really crack the code there. >> And then once you're in, you're in. >> I think that once you've proven yourself, it's a lot of word of mouth and referencing. >> Well I hope you get home this weekend. Are you headed home? >> Yes! Actually I'm lucky enough. My Wife is originally from Chicago. So she and our Daughter have come over for the weekend, to go sight seeing in Washington. So that'll be fun. So we'll be going home on Sunday. >> Your adopted home for the weekend then. >> That's exactly right. >> Well we'll talk Guinness in just a bit. Thanks for the time though, we appreciate it. >> Thank you Gentlemen. >> Good to see you, Sir. Alright, back with more here from Inforum 2018, and you're watching Live, on theCube, here in D.C. (electronic music)

live from Washington DC if the queue covering in forum DC 2018 brought to you by in for and welcome back here on the cube inform 2018 we're live in Washington DC continuing our day to coverage here on the cube along with de Ville on tape I'm John Wallace it's now a pleasure as well to welcome Eric Noren to the cube is the managing director of the CFO and enterprise value consulting at Accenture good morning Eric Harry a good morning to see you guys glad to have you with us we appreciate the time yeah let's talk about first the relationship assurance your and in for I know you've had you've been elsewhere right doing some other things with other folks and have kind of migrated back into the in four fold what led to that and what kind of successes are you having well so we're very excited about the partnership with with in for this is kind of like the really the second year for us right now as we go into the second year the first year was really driven from the partnership and the work that we do at Koch Industries and that that client experience kind of led us into a variety of different paths of partnership with with in for we've been doing work with with in for products for many years but we just our alliances just kind of blossomed in this past year into a variety of different areas focusing on the cloud suite financials focusing on GT Nexus in the supply chain space and now we're getting more and more excited about bursts and we're also getting very excited about the the whole the way the infor OS platform is just blossoming and and being tailored to a variety different industries and you've got you've got three offerings right if I remember right that you're taking out that you're taking to your client base as we speak once you give us a rundown of what you're up to well in our practice we have in our CFO and enterprise value practice we have an offering that's all around digital finance that's one of our biggest areas and that's really all just about the intersection of platform technology and how it enables the next generation of the finance function for the CFO so that we cloud that could also include things like you know automation and artificial intelligence applied to the finance function we see in our recent research here that CFO role as pivoting really not to be not really as focused on the books and records and being the controllers right but the CFOs role is now becoming more focused on being the digital steward the value architect of the enterprise and so the core of Finance is being digitized so that the transaction handling can be done more in an automated and efficient way and then freeing up the talent to focus on analytics and value-add and that really allows the CFO to focus more on driving insights into the business driving growth and what we call enterprise value so I totally agree the role of the CFO is transforming quite dramatically you know long gone in my view anyway are the days of CFO equals bean-counter this is a little there's a controller for that and no bean counter by the way is not a pejorative I run a business and I'm happy when people are counting those beans but it's not the CFO's role they're really transforming you see some Rockstar CFOs certainly in the tech industry like Scarpelli Tom sweet to just name a couple right reporting still matters compliance still matters but the CFO is taking a much more strategic role I'm really interested in this this this digitization of finance double-click on that yeah what does that specifically mean maybe you could give us some examples well I think that a couple things one is cloud right also I would say one thing is how transaction handling is moving from paper into all aspects of touchless transaction handling one is that harnessing the data to for transaction so it's touchless between vendors and customers and how that just flows through the system in a more digital way less paper more digital more touchless integration more automation right and then with that platform enabling things like artificial intelligence or machine learning being applied to these patterns of transaction handling so it can do the compliance checking in the reconciliation and so that the accountants right are enabling these algorithms to check things and don't have to do it themselves right but then there's also this whole context of of digital sort of process automation that that yields new ways of working you know new ways of looking at efficiency in terms of how and where the work is done right there was a view of like shared services and how we enable a digital operating model where there is there's work that can be done you know in with business unit intimacy and then there's work that can be done from other locations but then enabled by digital technology that's common and standardized right in a common platform that's also scalable and flexible and so putting all those things together is what we call digital finance I love this conversation and Accenture is like the best of the best you guys gets deep industry expertise and domain expertise I'm interested in Eric and in what the organizational structure looks like because when we talk about digital you're talking about data yeah and when you talking about data you're talking about monetization in some way shape or form not people I think got confused in the early days of big data so we can sell our data and more importantly as how data contributes to the monetization of the company sure and and how you can harness that and invest in that and that's really where the CFO comes in but he or she is not an expert at at digital not an X not a chief data officer or chief digital officer but they are an enabler they got to understand the strategy they got to pay for the strategy and maybe help course-correct it so what are you seeing is the right organizational regime to take advantage of digital well I think it first off it's integrated and it's and it's and it's focused on integration and collaboration for sure I think that there's a role where finance has the the business acumen and the insights to find out where the the story of enterprise value where it is now where it could be relative to the drivers of the business and but what's going on in the industry or the adjacent industries they can take advantage of so it's really all about you know a partnership between you know let's say finance right and let's say bringing in new talent and skills like data scientists and all those kind of you know digital skills and integrating it into finance so that it could be more accessible and then and then translate it into opportunities for for the business units so so a couple examples could be just one just getting a when we say monetization I think there's two things one is cost reduction where could you just use data to just understand the business in all aspects of where costs and how they're behaving and just being farm Warp know precise about where there are opportunities to reduce costs increase your bottom line right and that that in of those is value then there's the other side on you know revenue up left where there could be optimization of pricing optimization of your discounting strategy all those things that get into maintaining and improving your revenue without any additional cost of goods sold correct cost of sales right exactly that's a great example rights right your your operating structures it stays the same they're getting more leverage out of that that's writing and then there's other things where there's adjacent opportunities in to just gain market share right just to say well where there's opportunities with and really what we want to say is that by applying all this intelligence it's focused on really the theme is focused on customer experiences like what are the customer experiences that could be enabled with digital digital technologies in a seamless touchless way that are just differentiating the company you know in the market customers are and I think the world is changing its disrupting so the ways in which customers are interacting with businesses are expecting these kind of digital experiences very much inspired by a lot of the digital native companies they're out there in the market so the traditional companies that don't have those experience need to catch up and invest in these kind of customer experiences give me an example I mean how about expectations and and so let's say for example if you're a telco alright and you've got experiences that are about paying your bill or experiences have to do with services that you need by going to a call center all right now maybe you can have you know the traditional route of talking to someone or maybe there's a way you can go between the information and the channels that you have between your telephone your the mobile app between the website being able to talk to someone and having chat bots and the mix and how you coordinate all those different experiences so that that the customer can come in and get their questions answered in a very efficient way in some cases the the chat BOTS and the kind of sophistication that they can have to to to address the customers question right on the spot in a very timely way helps them just say I got my question solved and I'm happy with that experience right same thing with having information about I'm getting a you know service supply to my home how do I know that I'm having that same certainty of the service supply to the home much like the certainty that consumers are experiencing kind of like when they get an uber and they're like hey I know that the car is only five minutes away and it's coming and I have that certainty of an experience now that's being applied to other kind of customer experience it's a lot of situation I'm there at three things so first was saved money you know example RP a jerk something to help you drop money to the bottom line just cutting out mundane tasks yeah the top the top line operating leverage and that's around analytics may be optimizing pricing was the example you gave now the third I'll call Tam expansion which is which is really gaining share you leveraging your digital strategy to maybe try to be an incumbent disruptor just disrupt before you get disrupted now that last one has more risk associated with it because there are there are additional cost you've got other cost of goods sold you go to market cost but the reward could be you know huge these are the conversations is a great great proxy for the conversations that are going on with your clients yeah absolutely and I think that look you know there's the the market is going through changes constant disruption is coming in different forms whether it be through technology or other kind of industry integrations and you know they're different in the different we I specialize and more the communications me in technology industry alright and so those those are where I spend most of my time and and what's going on in communications right now and what's going on with communications and media is a quite interesting time on how content and distribution of content is changing and the way that the next generation of consumers are going to think about you know consuming media and how advertising is distributed we're going through a tremendous transformation in that space and all the companies are kind of racing to to be have that advantage of how they connect with the consumers at scale in a seamless connected way so that they have that that that ability to continue to serve them in new and innovative ways so let's talk about them so you said comms and media are we talking telecoms yeah okay and then tech industry is in IT technical yeah I mean tech suppliers tech suppliers yes girls just go and and companies like novo those kind of companies that are in that those guys are pretty forward-thinking in terms of technology adoption oh absolutely okay the telco business is really interesting right now though absolutely hardened infrastructures they get over the top suppliers coming in the cost per per bit is going down but they can't charge more you know this you know very well yeah they're going through some really radical transformation at the same time they have a huge opportunity with content yes you see and people make some moves yes absolutely about what's going on in that business a little bit more well you know there was the recent you know Comcast just an acquisition of sky is quite Norway we got 18 t going through the Time Warner thing and then you have so that's a Content play that I think is just frees up some opportunities for for companies like Comcast and AT&T you know to start really servicing their customers and a new profound way you know to be able to say it could be you know content that is suited to different demographics and to get those consumers at scale not only to keep them you know comfortable with the and and very delighted if you will with the kind of wireless service and flexibility they have with that but then to be able to see all the range of content that it could be consuming all of which is coming back to those companies as data as the consumers are watching all this content and having better control visibility of all the different patterns that they're seeing in the use of this content so they can then in turn shape different kinds of programming and shape different kinds of advertising programs that are tailored to those demographics and there's an it there's an underlying infrastructure transformation that's going on so it's something as basic as you know things like network function virtualization not to get too geeky out here but I'm trying to to make their their infrastructure more agile so they can compete with the OTT suppliers and they're trying to vertically integrate as content yes Rogers absolutely in this whole next wave of 5g is is a huge thing that's gonna come to us and that's that's a big disruption that's just starting and will happen in the next three to five years that will level be coming due so everybody's trying to get digital right yeah yeah yo that you talk to but do you do you when you go beneath that to the organization it's harder to get people you know to actually move do you get do you see a sense of complacency of people saying well you know not we're doing pretty well in our industry or I'll be retired before this all happens I mean how do you compel well I think that I mean that does exist in certain industries and certain types of companies you know I think that's the whole point about talent right and I think when we come back and look at talent is really when we think about change not only is the technology changing but the the talent that's available not only in the finance function but in all parts of an enterprise the the the the next generation of folks that are going into the workforce are just coming from a different place in terms of how they use technology in their lifestyle but how they want to apply to their as a customer but then how they want to do it as an employee and so for when we have that conversation about well what is the future going to look like a lot of it will come down to well what does digital mean as an experience for your consumer and your customer but also what does it mean for the talent and and we believe that look talent is a critical asset in every and every company it's the biggest asset that we have in a center right so how do we inspire and have folks have been enabled to use digital technologies to have that entrepreneurial you know sort of platform to use these digitally native tools that's really the key and I think that any kind of you know CFO that's like thinking about betting on the future that talent is very much a part of that stories it's definitely about technology is very important it's an enabler it's a platform however it's the talent that will be using the platform to take those info sites and drive growth in the wild card is data all right that's the new oh yeah absolutely I mean when a variable in the equation yeah this data putting data at the sort of score of your organization and having the talent that knows how they'll exploit your day that's right and I think it's like when I think about talent there's I mean there's different specializations right but I think the talent is really about the collaboration you see people who are able to work with other different cross functions and say well how do we how do we build and find this together how do we discover where the opportunity the insight is together right and you know there's you know there's differences between you know stuff which I said like the you know things that are known and we just optimized what we have and then there's going into the new areas right that I haven't been discovered yet and I think that the thing about the the the talent that's curious you know we like the way to think about like okay curious about what could be or what's out there and using data not as a as a hurdle but harnessing the power of data to go into these areas and start exploring and using all those different tools to explore where could we go and one of the things it's doing is it's not about you know we talked about analytics and some of the tools that are out there it's not about necessarily precision in this moment it's about direction of where you can go and exploring and continuing to find the facts that support investment it's your point I mean the the tools and the tech aren't the hard part it's the it's the unknown it's the people right you know the processes around that right getting everybody on the same page to collaborate it's like old dogs new tricks I mean I mean so yeah never simplifying but you you are trying to bring new tricks yeah to folks and there's a generational awareness that you're the difference between the people they have coming up and where they said that's right and we think that look you know by bringing the fresh new talent in to the organization that and of itself has has the team operating and working differently because not only they have new tools but there's new a new way of talent being integrated you know new talent and experienced talent you know seeing how these things come together to wither with a mandate again on superior business outcomes like let's go after these prizes it's worth it to get this right to make these investments because if we get it right there's an opportunity to grow revenue to grow to grow profitably to gain market share right so there's a there's a it's hard okay there's culture change and change this is normal okay digital transformation is not an easy thing to do all companies go through you know different things but it's worth it in the end yeah and it enforced talked a lot at this show about new new ways to work what I call new ways to and I think there's some substance there yeah absolutely Eric thank you and for the record we are always open to new tricks we do like new tricks okay good it'd be good to have you with us okay my pleasure guys Norman ceinture back with more on the key we're live here in Washington DC [Music]

(electronic dance music) (Intel Jingle) (ethereal electronic dance music) ♪ Okay ♪ (upbeat techno dance music) ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Yeah everybody get loose yeah ♪ ♪ Yeah ♪ ♪ Ye-yeah yeah ♪ ♪ Yeah yeah ♪ ♪ Everybody everybody yeah ♪ ♪ Whoo whoo ♪ ♪ Whoo whoo ♪ ♪ Whoo yeah ♪ ♪ Everybody get loose whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ >> As a courtesy to the presenters and those around you, please silence all mobile devices, thank you. (electronic dance music) ♪ Everybody get loose ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ (upbeat salsa music) ♪ Ha ha ha ♪ ♪ Ah ♪ ♪ Ha ha ha ♪ ♪ So happy ♪ ♪ Whoo whoo ♪ (female singer scatting) >> Ladies and gentlemen, please take your seats. Our program will begin momentarily. ♪ Hey ♪ (female singer scatting) (male singer scatting) ♪ Hey ♪ ♪ Whoo ♪ (female singer scatting) (electronic dance music) ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ Red don't go ♪ ♪ All hands are in don't go ♪ ♪ In don't go ♪ ♪ Oh red go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are red don't go ♪ ♪ All hands are in red red red red ♪ ♪ All hands are in don't go ♪ ♪ All hands are in red go ♪ >> Ladies and gentlemen, there are available seats. Towards house left, house left there are available seats. If you are please standing, we ask that you please take an available seat. We will begin momentarily, thank you. ♪ Let go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ (upbeat electronic dance music) ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ I live ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Hey ♪ ♪ Yeah ♪ ♪ Oh ♪ ♪ Ah ♪ ♪ Ah ah ah ah ah ah ♪ ♪ Just make me ♪ ♪ Just make me ♪ (bouncy techno music) >> Ladies and gentlemen, once again we ask that you please take the available seats to your left, house left, there are many available seats. If you are standing, please make your way there. The program will begin momentarily, thank you. Good morning! This is Lenovo Transform 2.0! (keyboard clicks) >> Progress. Why do we always talk about it in the future? When will it finally get here? We don't progress when it's ready for us. We need it when we're ready, and we're ready now. Our hospitals and their patients need it now, our businesses and their customers need it now, our cities and their citizens need it now. To deliver intelligent transformation, we need to build it into the products and solutions we make every day. At Lenovo, we're designing the systems to fight disease, power businesses, and help you reach more customers, end-to-end security solutions to protect your data and your companies reputation. We're making IT departments more agile and cost efficient. We're revolutionizing how kids learn with VR. We're designing smart devices and software that transform the way you collaborate, because technology shouldn't just power industries, it should power people. While everybody else is talking about tomorrow, we'll keep building today, because the progress we need can't wait for the future. >> Please welcome to the stage Lenovo's Rod Lappen! (electronic dance music) (audience applauding) >> Alright. Good morning everyone! >> Good morning. >> Ooh, that was pretty good actually, I'll give it one more shot. Good morning everyone! >> Good morning! >> Oh, that's much better! Hope everyone's had a great morning. Welcome very much to the second Lenovo Transform event here in New York. I think when I got up just now on the steps I realized there's probably one thing in common all of us have in this room including myself which is, absolutely no one has a clue what I'm going to say today. So, I'm hoping very much that we get through this thing very quickly and crisply. I love this town, love New York, and you're going to hear us talk a little bit about New York as we get through here, but just before we get started I'm going to ask anyone who's standing up the back, there are plenty of seats down here, and down here on the right hand side, I think he called it house left is the professional way of calling it, but these steps to my right, your left, get up here, let's get you all seated down so that you can actually sit down during the keynote session for us. Last year we had our very first Lenovo Transform. We had about 400 people. It was here in New York, fantastic event, today, over 1,000 people. We have over 62 different technology demonstrations and about 15 breakout sessions, which I'll talk you through a little bit later on as well, so it's a much bigger event. Next year we're definitely going to be shooting for over 2,000 people as Lenovo really transforms and starts to address a lot of the technology that our commercial customers are really looking for. We were however hampered last year by a storm, I don't know if those of you who were with us last year will remember, we had a storm on the evening before Transform last year in New York, and obviously the day that it actually occurred, and we had lots of logistics. Our media people from AMIA were coming in. They took the, the plane was circling around New York for a long time, and Kamran Amini, our General Manager of our Data Center Infrastructure Group, probably one of our largest groups in the Lenovo DCG business, took 17 hours to get from Raleigh, North Carolina to New York, 17 hours, I think it takes seven or eight hours to drive. Took him 17 hours by plane to get here. And then of course this year, we have Florence. And so, obviously the hurricane Florence down there in the Carolinas right now, we tried to help, but still Kamran has made it today. Unfortunately, very tragically, we were hoping he wouldn't, but he's here today to do a big presentation a little bit later on as well. However, I do want to say, obviously, Florence is a very serious tragedy and we have to take it very serious. We got, our headquarters is in Raleigh, North Carolina. While it looks like the hurricane is just missing it's heading a little bit southeast, all of our thoughts and prayers and well wishes are obviously with everyone in the Carolinas on behalf of Lenovo, everyone at our headquarters, everyone throughout the Carolinas, we want to make sure everyone stays safe and out of harm's way. We have a great mixture today in the crowd of all customers, partners, industry analysts, media, as well as our financial analysts from all around the world. There's over 30 countries represented here and people who are here to listen to both YY, Kirk, and Christian Teismann speak today. And so, it's going to be a really really exciting day, and I really appreciate everyone coming in from all around the world. So, a big round of applause for everyone whose come in. (audience applauding) We have a great agenda for you today, and it starts obviously a very consistent format which worked very successful for us last year, and that's obviously our keynote. You'll hear from YY, our CEO, talk a little bit about the vision he has in the industry and how he sees Lenovo's turned the corner and really driving some great strategy to address our customer's needs. Kirk Skaugen, our Executive Vice President of DCG, will be up talking about how we've transformed the DCG business and once again are hitting record growth ratios for our DCG business. And then you'll hear from Christian Teismann, our SVP and General Manager for our commercial business, get up and talk about everything that's going on in our IDG business. There's really exciting stuff going on there and obviously ThinkPad being the cornerstone of that I'm sure he's going to talk to us about a couple surprises in that space as well. Then we've got some great breakout sessions, I mentioned before, 15 breakout sessions, so while this keynote section goes until about 11:30, once we get through that, please go over and explore, and have a look at all of the breakout sessions. We have all of our subject matter experts from both our PC, NBG, and our DCG businesses out to showcase what we're doing as an organization to better address your needs. And then obviously we have the technology pieces that I've also spoken about, 62 different technology displays there arranged from everything IoT, 5G, NFV, everything that's really cool and hot in the industry right now is going to be on display up there, and I really encourage all of you to get up there. So, I'm going to have a quick video to show you from some of the setup yesterday on a couple of the 62 technology displays we've got on up on stage. Okay let's go, so we've got a demonstrations to show you today, one of the greats one here is the one we've done with NC State, a high-performance computing artificial intelligence demonstration of fresh produce. It's about modeling the population growth of the planet, and how we're going to supply water and food as we go forward. Whoo. Oh, that is not an apple. Okay. (woman laughs) Second one over here is really, hey Jonas, how are you? Is really around virtual reality, and how we look at one of the most amazing sites we've got, as an install on our high-performance computing practice here globally. And you can see, obviously, that this is the Barcelona supercomputer, and, where else in New York can you get access to being able to see something like that so easily? Only here at Lenovo Transform. Whoo, okay. (audience applauding) So there's two examples of some of the technology. We're really encouraging everyone in the room after the keynote to flow into that space and really get engaged, and interact with a lot of the technology we've got up there. It seems I need to also do something about my fashion, I've just realized I've worn a vest two days in a row, so I've got to work on that as well. Alright so listen, the last thing on the agenda, we've gone through the breakout sessions and the demo, tonight at four o'clock, there's about 400 of you registered to be on the cruise boat with us, the doors will open behind me. the boat is literally at the pier right behind us. You need to make sure you're on the boat for 4:00 p.m. this evening. Outside of that, I want everyone to have a great time today, really enjoy the experience, make it as experiential as you possibly can, get out there and really get in and touch the technology. There's some really cool AI displays up there for us all to get involved in as well. So ladies and gentlemen, without further adieu, it gives me great pleasure to introduce to you a lover of tennis, as some of you would've heard last year at Lenovo Transform, as well as a lover of technology, Lenovo, and of course, New York City. I am obviously very pleasured to introduce to you Yang Yuanqing, our CEO, as we like to call him, YY. (audience applauding) (upbeat funky music) >> Good morning, everyone. >> Good morning. >> Thank you Rod for that introduction. Welcome to New York City. So, this is the second year in a row we host our Transform event here, because New York is indeed one of the most transformative cities in the world. Last year on this stage, I spoke about the Fourth Industrial Revolution, and our vision around the intelligent transformation, how it would fundamentally change the nature of business and the customer relationships. And why preparing for this transformation is the key for the future of our company. And in the last year I can assure you, we were being very busy doing just that, from searching and bringing global talents around the world to the way we think about every product and every investment we make. I was here in New York just a month ago to announce our fiscal year Q1 earnings, which was a good day for us. I think now the world believes it when we say Lenovo has truly turned the corner to a new phase of growth and a new phase of acceleration in executing the transformation strategy. That's clear to me is that the last few years of a purposeful disruption at Lenovo have led us to a point where we can now claim leadership of the coming intelligent transformation. People often asked me, what is the intelligent transformation? I was saying this way. This is the unlimited potential of the Fourth Industrial Revolution driven by artificial intelligence being realized, ordering a pizza through our speaker, and locking the door with a look, letting your car drive itself back to your home. This indeed reflect the power of AI, but it just the surface of it. The true impact of AI will not only make our homes smarter and offices more efficient, but we are also completely transformed every value chip in every industry. However, to realize these amazing possibilities, we will need a structure built around the key components, and one that touches every part of all our lives. First of all, explosions in new technology always lead to new structures. This has happened many times before. In the early 20th century, thousands of companies provided a telephone service. City streets across the US looked like this, and now bundles of a microscopic fiber running from city to city bring the world closer together. Here's what a driving was like in the US, up until 1950s. Good luck finding your way. (audience laughs) And today, millions of vehicles are organized and routed daily, making the world more efficient. Structure is vital, from fiber cables and the interstate highways, to our cells bounded together to create humans. Thankfully the structure for intelligent transformation has emerged, and it is just as revolutionary. What does this new structure look like? We believe there are three key building blocks, data, computing power, and algorithms. Ever wondered what is it behind intelligent transformation? What is fueling this miracle of human possibility? Data. As the Internet becomes ubiquitous, not only PCs, mobile phones, have come online and been generating data. Today it is the cameras in this room, the climate controls in our offices, or the smart displays in our kitchens at home. The number of smart devices worldwide will reach over 20 billion in 2020, more than double the number in 2017. These devices and the sensors are connected and generating massive amount of data. By 2020, the amount of data generated will be 57 times more than all the grains of sand on Earth. This data will not only make devices smarter, but will also fuel the intelligence of our homes, offices, and entire industries. Then we need engines to turn the fuel into power, and the engine is actually the computing power. Last but not least the advanced algorithms combined with Big Data technology and industry know how will form vertical industrial intelligence and produce valuable insights for every value chain in every industry. When these three building blocks all come together, it will change the world. At Lenovo, we have each of these elements of intelligent transformations in a single place. We have built our business around the new structure of intelligent transformation, especially with mobile and the data center now firmly part of our business. I'm often asked why did you acquire these businesses? Why has a Lenovo gone into so many fields? People ask the same questions of the companies that become the leaders of the information technology revolution, or the third industrial transformation. They were the companies that saw the future and what the future required, and I believe Lenovo is the company today. From largest portfolio of devices in the world, leadership in the data center field, to the algorithm-powered intelligent vertical solutions, and not to mention the strong partnership Lenovo has built over decades. We are the only company that can unify all these essential assets and deliver end to end solutions. Let's look at each part. We now understand the important importance data plays as fuel in intelligent transformation. Hundreds of billions of devices and smart IoTs in the world are generating better and powering the intelligence. Who makes these devices in large volume and variety? Who puts these devices into people's home, offices, manufacturing lines, and in their hands? Lenovo definitely has the front row seats here. We are number one in PCs and tablets. We also produces smart phones, smart speakers, smart displays. AR/VR headsets, as well as commercial IoTs. All of these smart devices, or smart IoTs are linked to each other and to the cloud. In fact, we have more than 20 manufacturing facilities in China, US, Brazil, Japan, India, Mexico, Germany, and more, producing various devices around the clock. We actually make four devices every second, and 37 motherboards every minute. So, this factory located in my hometown, Hu-fi, China, is actually the largest laptop factory in the world, with more than three million square feet. So, this is as big as 42 soccer fields. Our scale and the larger portfolio of devices gives us access to massive amount of data, which very few companies can say. So, why is the ability to scale so critical? Let's look again at our example from before. The early days of telephone, dozens of service providers but only a few companies could survive consolidation and become the leader. The same was true for the third Industrial Revolution. Only a few companies could scale, only a few could survive to lead. Now the building blocks of the next revolution are locking into place. The (mumbles) will go to those who can operate at the scale. So, who could foresee the total integration of cloud, network, and the device, need to deliver intelligent transformation. Lenovo is that company. We are ready to scale. Next, our computing power. Computing power is provided in two ways. On one hand, the modern supercomputers are providing the brute force to quickly analyze the massive data like never before. On the other hand the cloud computing data centers with the server storage networking capabilities, and any computing IoT's, gateways, and miniservers are making computing available everywhere. Did you know, Lenovo is number one provider of super computers worldwide? 170 of the top 500 supercomputers, run on Lenovo. We hold 89 World Records in key workloads. We are number one in x86 server reliability for five years running, according to ITIC. a respected provider of industry research. We are also the fastest growing provider of hyperscale public cloud, hyper-converged and aggressively growing in edge computing. cur-ges target, we are expand on this point soon. And finally to run these individual nodes into our symphony, we must transform the data and utilize the computing power with advanced algorithms. Manufactured, industry maintenance, healthcare, education, retail, and more, so many industries are on the edge of intelligent transformation to improve efficiency and provide the better products and services. We are creating advanced algorithms and the big data tools combined with industry know-how to provide intelligent vertical solutions for several industries. In fact, we studied at Lenovo first. Our IT and research teams partnered with our global supply chain to develop an AI that improved our demand forecasting accuracy. Beyond managing our own supply chain we have offered our deep learning supply focused solution to other manufacturing companies to improve their efficiency. In the best case, we have improved the demand, focused the accuracy by 30 points to nearly 90 percent, for Baosteel, the largest of steel manufacturer in China, covering the world as well. Led by Lenovo research, we launched the industry-leading commercial ready AR headset, DaystAR, partnering with companies like the ones in this room. This technology is being used to revolutionize the way companies service utility, and even our jet engines. Using our workstations, servers, and award-winning imaging processing algorithms, we have partnered with hospitals to process complex CT scan data in minutes. So, this enable the doctors to more successfully detect the tumors, and it increases the success rate of cancer diagnosis all around the world. We are also piloting our smart IoT driven warehouse solution with one of the world's largest retail companies to greatly improve the efficiency. So, the opportunities are endless. This is where Lenovo will truly shine. When we combine the industry know-how of our customers with our end-to-end technology offerings, our intelligent vertical solutions like this are growing, which Kirk and Christian will share more. Now, what will drive this transformation even faster? The speed at which our networks operate, specifically 5G. You may know that Lenovo just launched the first-ever 5G smartphone, our Moto Z3, with the new 5G Moto model. We are partnering with multiple major network providers like Verizon, China Mobile. With the 5G model scheduled to ship early next year, we will be the first company to provide a 5G mobile experience to any users, customers. This is amazing innovation. You don't have to buy a new phone, just the 5G clip on. What can I say, except wow. (audience laughs) 5G is 10 times the fast faster than 4G. Its download speed will transform how people engage with the world, driverless car, new types of smart wearables, gaming, home security, industrial intelligence, all will be transformed. Finally, accelerating with partners, as ready as we are at Lenovo, we need partners to unlock our full potential, partners here to create with us the edge of the intelligent transformation. The opportunities of intelligent transformation are too profound, the scale is too vast. No company can drive it alone fully. We are eager to collaborate with all partners that can help bring our vision to life. We are dedicated to open partnerships, dedicated to cross-border collaboration, unify the standards, share the advantage, and market the synergies. We partner with the biggest names in the industry, Intel, Microsoft, AMD, Qualcomm, Google, Amazon, and Disney. We also find and partner with the smaller innovators as well. We're building the ultimate partner experience, open, shared, collaborative, diverse. So, everything is in place for intelligent transformation on a global scale. Smart devices are everywhere, the infrastructure is in place, networks are accelerating, and the industries demand to be more intelligent, and Lenovo is at the center of it all. We are helping to drive change with the hundreds of companies, companies just like yours, every day. We are your partner for intelligent transformation. Transformation never stops. This is what you will hear from Kirk, including details about Lenovo NetApp global partnership we just announced this morning. We've made the investments in every single aspect of the technology. We have the end-to-end resources to meet your end-to-end needs. As you attend the breakout session this afternoon, I hope you see for yourself how much Lenovo has transformed as a company this past year, and how we truly are delivering a future of intelligent transformation. Now, let me invite to the stage Kirk Skaugen, our president of Data Center growth to tell you about the exciting transformation happening in the global Data C enter market. Thank you. (audience applauding) (upbeat music) >> Well, good morning. >> Good morning. >> Good morning! >> Good morning! >> Excellent, well, I'm pleased to be here this morning to talk about how we're transforming the Data Center and taking you as our customers through your own intelligent transformation journey. Last year I stood up here at Transform 1.0, and we were proud to announce the largest Data Center portfolio in Lenovo's history, so I thought I'd start today and talk about the portfolio and the progress that we've made over the last year, and the strategies that we have going forward in phase 2.0 of Lenovo's transformation to be one of the largest data center companies in the world. We had an audacious vision that we talked about last year, and that is to be the most trusted data center provider in the world, empowering customers through the new IT, intelligent transformation. And now as the world's largest supercomputer provider, giving something back to humanity, is very important this week with the hurricanes now hitting North Carolina's coast, but we take this most trusted aspect very seriously, whether it's delivering the highest quality products on time to you as customers with the highest levels of security, or whether it's how we partner with our channel partners and our suppliers each and every day. You know we're in a unique world where we're going from hundreds of millions of PCs, and then over the next 25 years to hundred billions of connected devices, so each and every one of you is going through this intelligent transformation journey, and in many aspects were very early in that cycle. And we're going to talk today about our role as the largest supercomputer provider, and how we're solving humanity's greatest challenges. Last year we talked about two special milestones, the 25th anniversary of ThinkPad, but also the 25th anniversary of Lenovo with our IBM heritage in x86 computing. I joined the workforce in 1992 out of college, and the IBM first personal server was launching at the same time with an OS2 operating system and a free mouse when you bought the server as a marketing campaign. (audience laughing) But what I want to be very clear today, is that the innovation engine is alive and well at Lenovo, and it's really built on the culture that we're building as a company. All of these awards at the bottom are things that we earned over the last year at Lenovo. As a Fortune now 240 company, larger than companies like Nike, or AMEX, or Coca-Cola. The one I'm probably most proud of is Forbes first list of the top 2,000 globally regarded companies. This was something where 15,000 respondents in 60 countries voted based on ethics, trustworthiness, social conduct, company as an employer, and the overall company performance, and Lenovo was ranked number 27 of 2000 companies by our peer group, but we also now one of-- (audience applauding) But we also got a perfect score in the LGBTQ Equality Index, exemplifying the diversity internally. We're number 82 in the top working companies for mothers, top working companies for fathers, top 100 companies for sustainability. If you saw that factory, it's filled with solar panels on the top of that. And now again, one of the top global brands in the world. So, innovation is built on a customer foundation of trust. We also said last year that we'd be crossing an amazing milestone. So we did, over the last 12 months ship our 20 millionth x86 server. So, thank you very much to our customers for this milestone. (audience applauding) So, let me recap some of the transformation elements that have happened over the last year. Last year I talked about a lot of brand confusion, because we had the ThinkServer brand from the legacy Lenovo, the System x, from IBM, we had acquired a number of networking companies, like BLADE Network Technologies, et cetera, et cetera. Over the last year we've been ramping based on two brand structures, ThinkAgile for next generation IT, and all of our software-defined infrastructure products and ThinkSystem as the world's highest performance, highest reliable x86 server brand, but for servers, for storage, and for networking. We have transformed every single aspect of the customer experience. A year and a half ago, we had four different global channel programs around the world. Typically we're about twice the mix to our channel partners of any of our competitors, so this was really important to fix. We now have a single global Channel program, and have technically certified over 11,000 partners to be technical experts on our product line to deliver better solutions to our customer base. Gardner recently recognized Lenovo as the 26th ranked supply chain in the world. And, that's a pretty big honor, when you're up there with Amazon and Walmart and others, but in tech, we now are in the top five supply chains. You saw the factory network from YY, and today we'll be talking about product shipping in more than 160 countries, and I know there's people here that I've met already this morning, from India, from South Africa, from Brazil and China. We announced new Premier Support services, enabling you to go directly to local language support in nine languages in 49 countries in the world, going directly to a native speaker level three support engineer. And today we have more than 10,000 support specialists supporting our products in over 160 countries. We've delivered three times the number of engineered solutions to deliver a solutions orientation, whether it's on HANA, or SQL Server, or Oracle, et cetera, and we've completely reengaged our system integrator channel. Last year we had the CIO of DXE on stage, and here we're talking about more than 175 percent growth through our system integrator channel in the last year alone as we've brought that back and really built strong relationships there. So, thank you very much for amazing work here on the customer experience. (audience applauding) We also transformed our leadership. We thought it was extremely important with a focus on diversity, to have diverse talent from the legacy IBM, the legacy Lenovo, but also outside the industry. We made about 19 executive changes in the DCG group. This is the most senior leadership team within DCG, all which are newly on board, either from our outside competitors mainly over the last year. About 50 percent of our executives were now hired internally, 50 percent externally, and 31 percent of those new executives are diverse, representing the diversity of our global customer base and gender. So welcome, and most of them you're going to be able to meet over here in the breakout sessions later today. (audience applauding) But some things haven't changed, they're just keeping getting better within Lenovo. So, last year I got up and said we were committed with the new ThinkSystem brand to be a world performance leader. You're going to see that we're sponsoring Ducati for MotoGP. You saw the Ferrari out there with Formula One. That's not a surprise. We want the Lenovo ThinkSystem and ThinkAgile brands to be synonymous with world record performance. So in the last year we've gone from 39 to 89 world records, and partners like Intel would tell you, we now have four times the number of world record workloads on Lenovo hardware than any other server company on the planet today, with more than 89 world records across HPC, Java, database, transaction processing, et cetera. And we're proud to have just brought on Doug Fisher from Intel Corporation who had about 10-17,000 people on any given year working for him in workload optimizations across all of our software. It's just another testament to the leadership team we're bringing in to keep focusing on world-class performance software and solutions. We also per ITIC, are the number one now in x86 server reliability five years running. So, this is a survey where CIOs are in a blind survey asked to submit their reliability of their uptime on their x86 server equipment over the last 365 days. And you can see from 2016 to 2017 the downtime, there was over four hours as noted by the 750 CXOs in more than 20 countries is about one percent for the Lenovo products, and is getting worse generation from generation as we went from Broadwell to Pearlie. So we're taking our reliability, which was really paramount in the IBM System X heritage, and ensuring that we don't just recognize high performance but we recognize the highest level of reliability for mission-critical workloads. And what that translates into is that we at once again have been ranked number one in customer satisfaction from you our customers in 19 of 22 attributes, in North America in 18 of 22. This is a survey by TVR across hundreds of customers of us and our top competitors. This is the ninth consecutive study that we've been ranked number one in customer satisfaction, so we're taking this extremely seriously, and in fact YY now has increased the compensation of every single Lenovo employee. Up to 40 percent of their compensation bonus this year is going to be based on customer metrics like quality, order to ship, and things of this nature. So, we're really putting every employee focused on customer centricity this year. So, the summary on Transform 1.0 is that every aspect of what you knew about Lenovo's data center group has transformed, from the culture to the branding to dedicated sales and marketing, supply chain and quality groups, to a worldwide channel program and certifications, to new system integrator relationships, and to the new leadership team. So, rather than me just talk about it, I thought I'd share a quick video about what we've done over the last year, if you could run the video please. Turn around for a second. (epic music) (audience applauds) Okay. So, thank you to all our customers that allowed us to publicly display their logos in that video. So, what that means for you as investors, and for the investor community out there is, that our customers have responded, that this year Gardner just published that we are the fastest growing server company in the top 10, with 39 percent growth quarter-on-quarter, and 49 percent growth year-on-year. If you look at the progress we've made since the transformation the last three quarters publicly, we've grown 17 percent, then 44 percent, then 68 percent year on year in revenue, and I can tell you this quarter I'm as confident as ever in the financials around the DCG group, and it hasn't been in one area. You're going to see breakout sessions from hyperscale, software-defined, and flash, which are all growing more than a 100 percent year-on-year, supercomputing which we'll talk about shortly, now number one, and then ultimately from profitability, delivering five consecutive quarters of pre-tax profit increase, so I think, thank you very much to the customer base who's been working with us through this transformation journey. So, you're here to really hear what's next on 2.0, and that's what I'm excited to talk about today. Last year I came up with an audacious goal that we would become the largest supercomputer company on the planet by 2020, and this graph represents since the acquisition of the IBM System x business how far we were behind being the number one supercomputer. When we started we were 182 positions behind, even with the acquisition for example of SGI from HP, we've now accomplished our goal actually two years ahead of time. We're now the largest supercomputer company in the world. About one in every four supercomputers, 117 on the list, are now Lenovo computers, and you saw in the video where the universities are said, but I think what I'm most proud of is when your customers rank you as the best. So the awards at the bottom here, are actually Readers Choice from the last International Supercomputing Show where the scientific researchers on these computers ranked their vendors, and we were actually rated the number one server technology in supercomputing with our ThinkSystem SD530, and the number one storage technology with our ThinkSystem DSS-G, but more importantly what we're doing with the technology. You're going to see we won best in life sciences, best in data analytics, and best in collaboration as well, so you're going to see all of that in our breakout sessions. As you saw in the video now, 17 of the top 25 research institutions in the world are now running Lenovo supercomputers. And again coming from Raleigh and watching that hurricane come across the Atlantic, there are eight supercomputers crunching all of those models you see from Germany to Malaysia to Canada, and we're happy to have a SciNet from University of Toronto here with us in our breakout session to talk about what they're doing on climate modeling as well. But we're not stopping there. We just announced our new Neptune warm water cooling technology, which won the International Supercomputing Vendor Showdown, the first time we've won that best of show in 25 years, and we've now installed this. We're building out LRZ in Germany, the first ever warm water cooling in Peking University, at the India Space Propulsion Laboratory, at the Malaysian Weather and Meteorological Society, at Uninett, at the largest supercomputer in Norway, T-Systems, University of Birmingham. This is truly amazing technology where we're actually using water to cool the machine to deliver a significantly more energy-efficient computer. Super important, when we're looking at global warming and some of the electric bills can be millions of dollars just for one computer, and could actually power a small city just with the technology from the computer. We've built AI centers now in Morrisville, Stuttgart, Taipei, and Beijing, where customers can bring their AI workloads in with experts from Intel, from Nvidia, from our FPGA partners, to work on their workloads, and how they can best implement artificial intelligence. And we also this year launched LICO which is Lenovo Intelligent Compute Orchestrator software, and it's a software solution that simplifies the management and use of distributed clusters in both HPC and AI model development. So, what it enables you to do is take a single cluster, and run both HPC and AI workloads on it simultaneously, delivering better TCO for your environment, so check out LICO as well. A lot of the customers here and Wall Street are very excited and using it already. And we talked about solving humanity's greatest challenges. In the breakout session, you're going to have a virtual reality experience where you're going to be able to walk through what as was just ranked the world's most beautiful data center, the Barcelona Supercomputer. So, you can actually walk through one of the largest supercomputers in the world from Barcelona. You can see the work we're doing with NC State where we're going to have to grow the food supply of the world by 50 percent, and there's not enough fresh water in the world in the right places to actually make all those crops grow between now and 2055, so you're going to see the progression of how they're mapping the entire globe and the water around the world, how to build out the crop population over time using AI. You're going to see our work with Vestas is this largest supercomputer provider in the wind turbine areas, how they're working on wind energy, and then with University College London, how they're working on some of the toughest particle physics calculations in the world. So again, lots of opportunity here. Take advantage of it in the breakout sessions. Okay, let me transition to hyperscale. So in hyperscale now, we have completely transformed our business model. We are now powering six of the top 10 hyperscalers in the world, which is a significant difference from where we were two years ago. And the reason we're doing that, is we've coined a term called ODM+. We believe that hyperscalers want more procurement power than an ODM, and Lenovo is doing about $18 billion of procurement a year. They want a broader global supply chain that they can get from a local system integrator. We're more than 160 countries around the world, but they want the same world-class quality and reliability like they get from an MNC. So, what we're doing now is instead of just taking off the shelf motherboards from somewhere, we're starting with a blank sheet of paper, we're working with the customer base on customized SKUs and you can see we already are developing 33 custom solutions for the largest hyperscalers in the world. And then we're not just running notebooks through this factory where YY said, we're running 37 notebook boards a minute, we're now putting in tens and tens and tens of thousands of server board capacity per month into this same factory, so absolutely we can compete with the most aggressive ODM's in the world, but it's not just putting these things in in the motherboard side, we're also building out these systems all around the world, India, Brazil, Hungary, Mexico, China. This is an example of a new hyperscale customer we've had this last year, 34,000 servers we delivered in the first six months. The next 34,000 servers we delivered in 68 days. The next 34,000 servers we delivered in 35 days, with more than 99 percent on-time delivery to 35 data centers in 14 countries as diverse as South Africa, India, China, Brazil, et cetera. And I'm really ashamed to say it was 99.3, because we did have a forklift driver who rammed their forklift right through the middle of the one of the server racks. (audience laughing) At JFK Airport that we had to respond to, but I think this gives you a perspective of what it is to be a top five global supply chain and technology. So last year, I said we would invest significantly in IP, in joint ventures, and M and A to compete in software defined, in networking, and in storage, so I wanted to give you an update on that as well. Our newest software-defined partnership is with Cloudistics, enabling a fully composable cloud infrastructure. It's an exclusive agreement, you can see them here. I think Nag, our founder, is going to be here today, with a significant Lenovo investment in the company. So, this new ThinkAgile CP series delivers the simplicity of the public cloud, on-premise with exceptional support and a marketplace of essential enterprise applications all with a single click deployment. So simply put, we're delivering a private cloud with a premium experience. It's simple in that you need no specialists to deploy it. An IT generalist can set it up and manage it. It's agile in that you can provision dozens of workloads in minutes, and it's transformative in that you get all of the goodness of public cloud on-prem in a private cloud to unlock opportunity for use. So, we're extremely excited about the ThinkAgile CP series that's now shipping into the marketplace. Beyond that we're aggressively ramping, and we're either doubling, tripling, or quadrupling our market share as customers move from traditional server technology to software-defined technology. With Nutanix we've been public, growing about more than 150 percent year-on-year, with Nutanix as their fastest growing Nutanix partner, but today I want to set another audacious goal. I believe we cannot just be Nutanix's fastest growing partner but we can become their largest partner within two years. On Microsoft, we are already four times our market share on Azure stack of our traditional business. We were the first to launch our ThinkAgile on Broadwell and on Skylake with the Azure Stack Infrastructure. And on VMware we're about twice our market segment share. We were the first to deliver an Intel-optimized Optane-certified VSAN node. And with Optane technology, we're delivering 50 percent more VM density than any competitive SSD system in the marketplace, about 10 times lower latency, four times the performance of any SSD system out there, and Lenovo's first to market on that. And at VMworld you saw CEO Pat Gelsinger of VMware talked about project dimension, which is Edge as a service, and we're the only OEM beyond the Dell family that is participating today in project dimension. Beyond that you're going to see a number of other partnerships we have. I'm excited that we have the city of Bogota Columbia here, an eight million person city, where we announced a 3,000 camera video surveillance solution last month. With pivot three you're going to see city of Bogota in our breakout sessions. You're going to see a new partnership with Veeam around backup that's launching today. You're going to see partnerships with scale computing in IoT and hyper-converged infrastructure working on some of the largest retailers in the world. So again, everything out in the breakout session. Transitioning to storage and data management, it's been a great year for Lenovo, more than a 100 percent growth year-on-year, 2X market growth in flash arrays. IDC just reported 30 percent growth in storage, number one in price performance in the world and the best HPC storage product in the top 500 with our ThinkSystem DSS G, so strong coverage, but I'm excited today to announce for Transform 2.0 that Lenovo is launching the largest data management and storage portfolio in our 25-year data center history. (audience applauding) So a year ago, the largest server portfolio, becoming the largest fastest growing server OEM, today the largest storage portfolio, but as you saw this morning we're not doing it alone. Today Lenovo and NetApp, two global powerhouses are joining forces to deliver a multi-billion dollar global alliance in data management and storage to help customers through their intelligent transformation. As the fastest growing worldwide server leader and one of the fastest growing flash array and data management companies in the world, we're going to deliver more choice to customers than ever before, global scale that's never been seen, supply chain efficiencies, and rapidly accelerating innovation and solutions. So, let me unwrap this a little bit for you and talk about what we're announcing today. First, it's the largest portfolio in our history. You're going to see not just storage solutions launching today but a set of solution recipes from NetApp that are going to make Lenovo server and NetApp or Lenovo storage work better together. The announcement enables Lenovo to go from covering 15 percent of the global storage market to more than 90 percent of the global storage market and distribute these products in more than 160 countries around the world. So we're launching today, 10 new storage platforms, the ThinkSystem DE and ThinkSystem DM platforms. They're going to be centrally managed, so the same XClarity management that you've been using for server, you can now use across all of your storage platforms as well, and it'll be supported by the same 10,000 plus service personnel that are giving outstanding customer support to you today on the server side. And we didn't come up with this in the last month or the last quarter. We're announcing availability in ordering today and shipments tomorrow of the first products in this portfolio, so we're excited today that it's not just a future announcement but something you as customers can take advantage of immediately. (audience applauding) The second part of the announcement is we are announcing a joint venture in China. Not only will this be a multi-billion dollar global partnership, but Lenovo will be a 51 percent owner, NetApp a 49 percent owner of a new joint venture in China with the goal of becoming in the top three storage companies in the largest data and storage market in the world. We will deliver our R and D in China for China, pooling our IP and resources together, and delivering a single route to market through a complementary channel, not just in China but worldwide. And in the future I just want to tell everyone this is phase one. There is so much exciting stuff. We're going to be on the stage over the next year talking to you about around integrated solutions, next-generation technologies, and further synergies and collaborations. So, rather than just have me talk about it, I'd like to welcome to the stage our new partner NetApp and Brad Anderson who's the senior vice president and general manager of NetApp Cloud Infrastructure. (upbeat music) (audience applauding) >> Thank You Kirk. >> So Brad, we've known each other a long time. It's an exciting day. I'm going to give you the stage and allow you to say NetApp's perspective on this announcement. >> Very good, thank you very much, Kirk. Kirk and I go back to I think 1994, so hey good morning and welcome. My name is Brad Anderson. I manage the Cloud Infrastructure Group at NetApp, and I am honored and privileged to be here at Lenovo Transform, particularly today on today's announcement. Now, you've heard a lot about digital transformation about how companies have to transform their IT to compete in today's global environment. And today's announcement with the partnership between NetApp and Lenovo is what that's all about. This is the joining of two global leaders bringing innovative technology in a simplified solution to help customers modernize their IT and accelerate their global digital transformations. Drawing on the strengths of both companies, Lenovo's high performance compute world-class supply chain, and NetApp's hybrid cloud data management, hybrid flash and all flash storage solutions and products. And both companies providing our customers with the global scale for them to be able to meet their transformation goals. At NetApp, we're very excited. This is a quote from George Kurian our CEO. George spent all day yesterday with YY and Kirk, and would have been here today if it hadn't been also our shareholders meeting in California, but I want to just convey how excited we are for all across NetApp with this partnership. This is a partnership between two companies with tremendous market momentum. Kirk took you through all the amazing results that Lenovo has accomplished, number one in supercomputing, number one in performance, number one in x86 reliability, number one in x86 customers sat, number five in supply chain, really impressive and congratulations. Like Lenovo, NetApp is also on a transformation journey, from a storage company to the data authority in hybrid cloud, and we've seen some pretty impressive momentum as well. Just last week we became number one in all flash arrays worldwide, catching EMC and Dell, and we plan to keep on going by them, as we help customers modernize their their data centers with cloud connected flash. We have strategic partnerships with the largest hyperscalers to provide cloud native data services around the globe and we are having success helping our customers build their own private clouds with just, with a new disruptive hyper-converged technology that allows them to operate just like hyperscalers. These three initiatives has fueled NetApp's transformation, and has enabled our customers to change the world with data. And oh by the way, it has also fueled us to have meet or have beaten Wall Street's expectations for nine quarters in a row. These are two companies with tremendous market momentum. We are also building this partnership for long term success. We think about this as phase one and there are two important components to phase one. Kirk took you through them but let me just review them. Part one, the establishment of a multi-year commitment and a collaboration agreement to offer Lenovo branded flash products globally, and as Kurt said in 160 countries. Part two, the formation of a joint venture in PRC, People's Republic of China, that will provide long term commitment, joint product development, and increase go-to-market investment to meet the unique needs to China. Both companies will put in storage technologies and storage expertise to form an independent JV that establishes a data management company in China for China. And while we can dream about what phase two looks like, our entire focus is on making phase one incredibly successful and I'm pleased to repeat what Kirk, is that the first products are orderable and shippable this week in 160 different countries, and you will see our two companies focusing on the here and now. On our joint go to market strategy, you'll see us working together to drive strategic alignment, focused execution, strong governance, and realistic expectations and milestones. And it starts with the success of our customers and our channel partners is job one. Enabling customers to modernize their legacy IT with complete data center solutions, ensuring that our customers get the best from both companies, new offerings the fuel business success, efficiencies to reinvest in game-changing initiatives, and new solutions for new mission-critical applications like data analytics, IoT, artificial intelligence, and machine learning. Channel partners are also top of mind for both our two companies. We are committed to the success of our existing and our future channel partners. For NetApp channel partners, it is new pathways to new segments and to new customers. For Lenovo's channel partners, it is the competitive weapons that now allows you to compete and more importantly win against Dell, EMC, and HP. And the good news for both companies is that our channel partner ecosystem is highly complementary with minimal overlap. Today is the first day of a very exciting partnership, of a partnership that will better serve our customers today and will provide new opportunities to both our companies and to our partners, new products to our customers globally and in China. I am personally very excited. I will be on the board of the JV. And so, I look forward to working with you, partnering with you and serving you as we go forward, and with that, I'd like to invite Kirk back up. (audience applauding) >> Thank you. >> Thank you. >> Well, thank you, Brad. I think it's an exciting overview, and these products will be manufactured in China, in Mexico, in Hungary, and around the world, enabling this amazing supply chain we talked about to deliver in over 160 countries. So thank you Brad, thank you George, for the amazing partnership. So again, that's not all. In Transform 2.0, last year, we talked about the joint ventures that were coming. I want to give you a sneak peek at what you should expect at future Lenovo events around the world. We have this Transform in Beijing in a couple weeks. We'll then be repeating this in 20 different locations roughly around the world over the next year, and I'm excited probably more than ever about what else is coming. Let's talk about Telco 5G and network function virtualization. Today, Motorola phones are certified on 46 global networks. We launched the world's first 5G upgradable phone here in the United States with Verizon. Lenovo DCG sells to 58 telecommunication providers around the world. At Mobile World Congress in Barcelona and Shanghai, you saw China Telecom and China Mobile in the Lenovo booth, China Telecom showing a video broadband remote access server, a VBRAS, with video streaming demonstrations with 2x less jitter than they had seen before. You saw China Mobile with a virtual remote access network, a VRAN, with greater than 10 times the throughput and 10x lower latency running on Lenovo. And this year, we'll be launching a new NFV company, a software company in China for China to drive the entire NFV stack, delivering not just hardware solutions, but software solutions, and we've recently hired a new CEO. You're going to hear more about that over the next several quarters. Very exciting as we try to drive new economics into the networks to deliver these 20 billion devices. We're going to need new economics that I think Lenovo can uniquely deliver. The second on IoT and edge, we've integrated on the device side into our intelligent devices group. With everything that's going to consume electricity computes and communicates, Lenovo is in a unique position on the device side to take advantage of the communications from Motorola and being one of the largest device companies in the world. But this year, we're also going to roll out a comprehensive set of edge gateways and ruggedized industrial servers and edge servers and ISP appliances for the edge and for IoT. So look for that as well. And then lastly, as a service, you're going to see Lenovo delivering hardware as a service, device as a service, infrastructure as a service, software as a service, and hardware as a service, not just as a glorified leasing contract, but with IP, we've developed true flexible metering capability that enables you to scale up and scale down freely and paying strictly based on usage, and we'll be having those announcements within this fiscal year. So Transform 2.0, lots to talk about, NetApp the big news of the day, but a lot more to come over the next year from the Data Center group. So in summary, I'm excited that we have a lot of customers that are going to be on stage with us that you saw in the video. Lots of testimonials so that you can talk to colleagues of yourself. Alamos Gold from Canada, a Canadian gold producer, Caligo for data optimization and privacy, SciNet, the largest supercomputer we've ever put into North America, and the largest in Canada at the University of Toronto will be here talking about climate change. City of Bogota again with our hyper-converged solutions around smart city putting in 3,000 cameras for criminal detection, license plate detection, et cetera, and then more from a channel mid market perspective, Jerry's Foods, which is from my home state of Wisconsin, and Minnesota which has about 57 stores in the specialty foods market, and how they're leveraging our IoT solutions as well. So again, about five times the number of demos that we had last year. So in summary, first and foremost to the customers, thank you for your business. It's been a great journey and I think we're on a tremendous role. You saw from last year, we're trying to build credibility with you. After the largest server portfolio, we're now the fastest-growing server OEM per Gardner, number one in performance, number one in reliability, number one in customer satisfaction, number one in supercomputing. Today, the largest storage portfolio in our history, with the goal of becoming the fastest growing storage company in the world, top three in China, multibillion-dollar collaboration with NetApp. And the transformation is going to continue with new edge gateways, edge servers, NFV solutions, telecommunications infrastructure, and hardware as a service with dynamic metering. So thank you for your time. I've looked forward to meeting many of you over the next day. We appreciate your business, and with that, I'd like to bring up Rod Lappen to introduce our next speaker. Rod? (audience applauding) >> Thanks, boss, well done. Alright ladies and gentlemen. No real secret there. I think we've heard why I might talk about the fourth Industrial Revolution in data and exactly what's going on with that. You've heard Kirk with some amazing announcements, obviously now with our NetApp partnership, talk about 5G, NFV, cloud, artificial intelligence, I think we've hit just about all the key hot topics. It's with great pleasure that I now bring up on stage Mr. Christian Teismann, our senior vice president and general manager of commercial business for both our PCs and our IoT business, so Christian Teismann. (techno music) Here, take that. >> Thank you. I think I'll need that. >> Okay, Christian, so obviously just before we get down, you and I last year, we had a bit of a chat about being in New York. >> Exports. >> You were an expat in New York for a long time. >> That's true. >> And now, you've moved from New York. You're in Munich? >> Yep. >> How does that feel? >> Well Munich is a wonderful city, and it's a great place to live and raise kids, but you know there's no place in the world like New York. >> Right. >> And I miss it a lot, quite frankly. >> So what exactly do you miss in New York? >> Well there's a lot of things in New York that are unique, but I know you spent some time in Japan, but I still believe the best sushi in the world is still in New York City. (all laughing) >> I will beg to differ. I will beg to differ. I think Mr. Guchi-san from Softbank is here somewhere. He will get up an argue very quickly that Japan definitely has better sushi than New York. But obviously you know, it's a very very special place, and I have had sushi here, it's been fantastic. What about Munich? Anything else that you like in Munich? >> Well I mean in Munich, we have pork knuckles. >> Pork knuckles. (Christian laughing) Very similar sushi. >> What is also very fantastic, but we have the real, the real Oktoberfest in Munich, and it starts next week, mid-September, and I think it's unique in the world. So it's very special as well. >> Oktoberfest. >> Yes. >> Unfortunately, I'm not going this year, 'cause you didn't invite me, but-- (audience chuckling) How about, I think you've got a bit of a secret in relation to Oktoberfest, probably not in Munich, however. >> It's a secret, yes, but-- >> Are you going to share? >> Well I mean-- >> See how I'm putting you on the spot? >> In the 10 years, while living here in New York, I was a regular visitor of the Oktoberfest at the Lower East Side in Avenue C at Zum Schneider, where I actually met my wife, and she's German. >> Very good. So, how about a big round of applause? (audience applauding) Not so much for Christian, but more I think, obviously for his wife, who obviously had been drinking and consequently ended up with you. (all laughing) See you later, mate. >> That's the beauty about Oktoberfest, but yes. So first of all, good morning to everybody, and great to be back here in New York for a second Transform event. New York clearly is the melting pot of the world in terms of culture, nations, but also business professionals from all kind of different industries, and having this event here in New York City I believe is manifesting what we are trying to do here at Lenovo, is transform every aspect of our business and helping our customers on the journey of intelligent transformation. Last year, in our transformation on the device business, I talked about how the PC is transforming to personalized computing, and we've made a lot of progress in that journey over the last 12 months. One major change that we have made is we combined all our device business under one roof. So basically PCs, smart devices, and smart phones are now under the roof and under the intelligent device group. But from my perspective makes a lot of sense, because at the end of the day, all devices connect in the modern world into the cloud and are operating in a seamless way. But we are also moving from a device business what is mainly a hardware focus historically, more and more also into a solutions business, and I will give you during my speech a little bit of a sense of what we are trying to do, as we are trying to bring all these components closer together, and specifically also with our strengths on the data center side really build end-to-end customer solution. Ultimately, what we want to do is make our business, our customer's businesses faster, safer, and ultimately smarter as well. So I want to look a little bit back, because I really believe it's important to understand what's going on today on the device side. Many of us have still grown up with phones with terminals, ultimately getting their first desktop, their first laptop, their first mobile phone, and ultimately smartphone. Emails and internet improved our speed, how we could operate together, but still we were defined by linear technology advances. Today, the world has changed completely. Technology itself is not a limiting factor anymore. It is how we use technology going forward. The Internet is pervasive, and we are not yet there that we are always connected, but we are nearly always connected, and we are moving to the stage, that everything is getting connected all the time. Sharing experiences is the most driving force in our behavior. In our private life, sharing pictures, videos constantly, real-time around the world, with our friends and with our family, and you see the same behavior actually happening in the business life as well. Collaboration is the number-one topic if it comes down to workplace, and video and instant messaging, things that are coming from the consumer side are dominating the way we are operating in the commercial business as well. Most important beside technology, that a new generation of workforce has completely changed the way we are working. As the famous workforce the first generation of Millennials that have now fully entered in the global workforce, and the next generation, it's called Generation Z, is already starting to enter the global workforce. By 2025, 75 percent of the world's workforce will be composed out of two of these generations. Why is this so important? These two generations have been growing up using state-of-the-art IT technology during their private life, during their education, school and study, and are taking these learnings and taking these behaviors in the commercial workspace. And this is the number one force of change that we are seeing in the moment. Diverse workforces are driving this change in the IT spectrum, and for years in many of our customers' focus was their customer focus. Customer experience also in Lenovo is the most important thing, but we've realized that our own human capital is equally valuable in our customer relationships, and employee experience is becoming a very important thing for many of our customers, and equally for Lenovo as well. As you have heard YY, as we heard from YY, Lenovo is focused on intelligent transformation. What that means for us in the intelligent device business is ultimately starting with putting intelligence in all of our devices, smartify every single one of our devices, adding value to our customers, traditionally IT departments, but also focusing on their end users and building products that make their end users more productive. And as a world leader in commercial devices with more than 33 percent market share, we can solve problems been even better than any other company in the world. So, let's talk about transformation of productivity first. We are in a device-led world. Everything we do is connected. There's more interaction with devices than ever, but also with spaces who are increasingly becoming smart and intelligent. YY said it, by 2020 we have more than 20 billion connected devices in the world, and it will grow exponentially from there on. And users have unique personal choices for technology, and that's very important to recognize, and we call this concept a digital wardrobe. And it means that every single end-user in the commercial business is composing his personal wardrobe on an ongoing basis and is reconfiguring it based on the work he's doing and based where he's going and based what task he is doing. I would ask all of you to put out all the devices you're carrying in your pockets and in your bags. You will see a lot of you are using phones, tablets, laptops, but also cameras and even smartwatches. They're all different, but they have one underlying technology that is bringing it all together. Recognizing digital wardrobe dynamics is a core factor for us to put all the devices under one roof in IDG, one business group that is dedicated to end-user solutions across mobile, PC, but also software services and imaging, to emerging technologies like AR, VR, IoT, and ultimately a AI as well. A couple of years back there was a big debate around bring-your-own-device, what was called consumerization. Today consumerization does not exist anymore, because consumerization has happened into every single device we build in our commercial business. End users and commercial customers today do expect superior display performance, superior audio, microphone, voice, and touch quality, and have it all connected and working seamlessly together in an ease of use space. We are already deep in the journey of personalized computing today. But the center point of it has been for the last 25 years, the mobile PC, that we have perfected over the last 25 years, and has been the undisputed leader in mobility computing. We believe in the commercial business, the ThinkPad is still the core device of a digital wardrobe, and we continue to drive the success of the ThinkPad in the marketplace. We've sold more than 140 million over the last 26 years, and even last year we exceeded nearly 11 million units. That is about 21 ThinkPads per minute, or one Thinkpad every three seconds that we are shipping out in the market. It's the number one commercial PC in the world. It has gotten countless awards but we felt last year after Transform we need to build a step further, in really tailoring the ThinkPad towards the need of the future. So, we announced a new line of X1 Carbon and Yoga at CES the Consumer Electronics Show. And the reason is not we want to sell to consumer, but that we do recognize that a lot of CIOs and IT decision makers need to understand what consumers are really doing in terms of technology to make them successful. So, let's take a look at the video. (suspenseful music) >> When you're the number one business laptop of all time, your only competition is yourself. (wall shattering) And, that's different. Different, like resisting heat, ice, dust, and spills. Different, like sharper, brighter OLA display. The trackpoint that reinvented controls, and a carbon fiber roll cage to protect what's inside, built by an engineering and design team, doing the impossible for the last 25 years. This is the number one business laptop of all time, but it's not a laptop. It's a ThinkPad. (audience applauding) >> Thank you very much. And we are very proud that Lenovo ThinkPad has been selected as the best laptop in the world in the second year in a row. I think it's a wonderful tribute to what our engineers have been done on this one. And users do want awesome displays. They want the best possible audio, voice, and touch control, but some users they want more. What they want is super power, and I'm really proud to announce our newest member of the X1 family, and that's the X1 extreme. It's exceptionally featured. It has six core I9 intel chipset, the highest performance you get in the commercial space. It has Nvidia XTX graphic, it is a 4K UHD display with HDR with Dolby vision and Dolby Atmos Audio, two terabyte in SSD, so it is really the absolute Ferrari in terms of building high performance commercial computer. Of course it has touch and voice, but it is one thing. It has so much performance that it serves also a purpose that is not typical for commercial, and I know there's a lot of secret gamers also here in this room. So you see, by really bringing technology together in the commercial space, you're creating productivity solutions of one of a kind. But there's another category of products from a productivity perspective that is incredibly important in our commercial business, and that is the workstation business . Clearly workstations are very specifically designed computers for very advanced high-performance workloads, serving designers, architects, researchers, developers, or data analysts. And power and performance is not just about the performance itself. It has to be tailored towards the specific use case, and traditionally these products have a similar size, like a server. They are running on Intel Xeon technology, and they are equally complex to manufacture. We have now created a new category as the ultra mobile workstation, and I'm very proud that we can announce here the lightest mobile workstation in the industry. It is so powerful that it really can run AI and big data analysis. And with this performance you can go really close where you need this power, to the sensors, into the cars, or into the manufacturing places where you not only wannna read the sensors but get real-time analytics out of these sensors. To build a machine like this one you need customers who are really challenging you to the limit. and we're very happy that we had a customer who went on this journey with us, and ultimately jointly with us created this product. So, let's take a look at the video. (suspenseful music) >> My world involves pathfinding both the hardware needs to the various work sites throughout the company, and then finding an appropriate model of desktop, laptop, or workstation to match those needs. My first impressions when I first seen the ThinkPad P1 was I didn't actually believe that we could get everything that I was asked for inside something as small and light in comparison to other mobile workstations. That was one of the I can't believe this is real sort of moments for me. (engine roars) >> Well, it's better than general when you're going around in the wind tunnel, which isn't alway easy, and going on a track is not necessarily the best bet, so having a lightweight very powerful laptop is extremely useful. It can take a Xeon processor, which can support ECC from when we try to load a full car, and when we're analyzing live simulation results. through and RCFT post processor or example. It needs a pretty powerful machine. >> It's come a long way to be able to deliver this. I hate to use the word game changer, but it is that for us. >> Aston Martin has got a lot of different projects going. There's some pretty exciting projects and a pretty versatile range coming out. Having Lenovo as a partner is certainly going to ensure that future. (engine roars) (audience applauds) >> So, don't you think the Aston Martin design and the ThinkPad design fit very well together? (audience laughs) So if Q, would get a new laptop, I think you would get a ThinkPad X P1. So, I want to switch gears a little bit, and go into something in terms of productivity that is not necessarily on top of the mind or every end user but I believe it's on top of the mind of every C-level executive and of every CEO. Security is the number one threat in terms of potential risk in your business and the cost of cybersecurity is estimated by 2020 around six trillion dollars. That's more than the GDP of Japan and we've seen a significant amount of data breach incidents already this years. Now, they're threatening to take companies out of business and that are threatening companies to lose a huge amount of sensitive customer data or internal data. At Lenovo, we are taking security very, very seriously, and we run a very deep analysis, around our own security capabilities in the products that we are building. And we are announcing today a new brand under the Think umbrella that is called ThinkShield. Our goal is to build the world's most secure PC, and ultimately the most secure devices in the industry. And when we looked at this end-to-end, there is no silver bullet around security. You have to go through every aspect where security breaches can potentially happen. That is why we have changed the whole organization, how we look at security in our device business, and really have it grouped under one complete ecosystem of solutions, Security is always something where you constantly are getting challenged with the next potential breach the next potential technology flaw. As we keep innovating and as we keep integrating, a lot of our partners' software and hardware components into our products. So for us, it's really very important that we partner with companies like Intel, Microsoft, Coronet, Absolute, and many others to really as an example to drive full encryption on all the data seamlessly, to have multi-factor authentication to protect your users' identity, to protect you in unsecured Wi-Fi locations, or even simple things like innovation on the device itself, to and an example protect the camera, against usage with a little thing like a thinkShutter that you can shut off the camera. SO what I want to show you here, is this is the full portfolio of ThinkShield that we are announcing today. This is clearly not something I can even read to you today, but I believe it shows you the breadth of security management that we are announcing today. There are four key pillars in managing security end-to-end. The first one is your data, and this has a lot of aspects around the hardware and the software itself. The second is identity. The third is the security around online, and ultimately the device itself. So, there is a breakout on security and ThinkShield today, available in the afternoon, and encourage you to really take a deeper look at this one. The first pillar around productivity was the device, and around the device. The second major pillar that we are seeing in terms of intelligent transformation is the workspace itself. Employees of a new generation have a very different habit how they work. They split their time between travel, working remotely but if they do come in the office, they expect a very different office environment than what they've seen in the past in cubicles or small offices. They come into the office to collaborate, and they want to create ideas, and they really work in cross-functional teams, and they want to do it instantly. And what we've seen is there is a huge amount of investment that companies are doing today in reconfiguring real estate reconfiguring offices. And most of these kind of things are moving to a digital platform. And what we are doing, is we want to build an entire set of solutions that are just focused on making the workspace more productive for remote workforce, and to create technology that allow people to work anywhere and connect instantly. And the core of this is that we need to be, the productivity of the employee as high as possible, and make it for him as easy as possible to use these kind of technologies. Last year in Transform, I announced that we will enter the smart office space. By the end of last year, we brought the first product into the market. It's called the Hub 500. It's already deployed in thousands of our customers, and it's uniquely focused on Microsoft Skype for Business, and making meeting instantly happen. And the product is very successful in the market. What we are announcing today is the next generation of this product, what is the Hub 700, what has a fantastic audio quality. It has far few microphones, and it is usable in small office environment, as well as in major conference rooms, but the most important part of this new announcement is that we are also announcing a software platform, and this software platform allows you to run multiple video conferencing software solutions on the same platform. Many of you may have standardized for one software solution or for another one, but as you are moving in a world of collaborating instantly with partners, customers, suppliers, you always will face multiple software standards in your company, and Lenovo is uniquely positioned but providing a middleware platform for the device to really enable multiple of these UX interfaces. And there's more to come and we will add additional UX interfaces on an ongoing base, based on our customer requirements. But this software does not only help to create a better experience and a higher productivity in the conference room or the huddle room itself. It really will allow you ultimately to manage all your conference rooms in the company in one instance. And you can run AI technologies around how to increase productivity utilization of your entire conference room ecosystem in your company. You will see a lot more devices coming from the node in this space, around intelligent screens, cameras, and so on, and so on. The idea is really that Lenovo will become a core provider in the whole movement into the smart office space. But it's great if you have hardware and software that is really supporting the approach of modern IT, but one component that Kirk also mentioned is absolutely critical, that we are providing this to you in an as a service approach. Get it what you want, when you need it, and pay it in the amount that you're really using it. And within UIT there is also I think a new philosophy around IT management, where you're much more focused on the value that you are consuming instead of investing into technology. We are launched as a service two years back and we already have a significant number of customers running PC as a service, but we believe as a service will stretch far more than just the PC device. It will go into categories like smart office. It might go even into categories like phone, and it will definitely go also in categories like storage and server in terms of capacity management. I want to highlight three offerings that we are also displaying today that are sort of building blocks in terms of how we really run as a service. The first one is that we collaborated intensively over the last year with Microsoft to be the launch pilot for their Autopilot offering, basically deploying images easily in the same approach like you would deploy a new phone on the network. The purpose really is to make new imaging and enabling new PC as seamless as it's used to be in the phone industry, and we have a complete set of offerings, and already a significant number customers have deployed Autopilot with Lenovo. The second major offering is Premier Support, like in the in the server business, where Premier Support is absolutely critical to run critical infrastructure, we see a lot of our customers do want to have Premier Support for their end users, so they can be back into work basically instantly, and that you have the highest possible instant repair on every single device. And then finally we have a significant amount of time invested into understanding how the software as a service really can get into one philosophy. And many of you already are consuming software as a service in many different contracts from many different vendors, but what we've created is one platform that really can manage this all together. All these things are the foundation for a device as a service offering that really can manage this end-to-end. So, implementing an intelligent workplace can be really a daunting prospect depending on where you're starting from, and how big your company ultimately is. But how do you manage the transformation of technology workspace if you're present in 50 or more countries and you run an infrastructure for more than 100,000 people? Michelin, famous for their tires, infamous for their Michelin star restaurant rating, especially in New York, and instantly recognizable by the Michelin Man, has just doing that. Please welcome with me Damon McIntyre from Michelin to talk to us about the challenges and transforming collaboration and productivity. (audience applauding) (electronic dance music) Thank you, David. >> Thank you, thank you very much. >> We on? >> So, how do you feel here? >> Well good, I want to thank you first of all for your partnership and the devices you create that helped us design, manufacture, and distribute the best tire in the world, okay? I just had to say it and put out there, alright. And I was wondering, were those Michelin tires on that Aston Martin? >> I'm pretty sure there is no other tire that would fit to that. >> Yeah, no, thank you, thank you again, and thank you for the introduction. >> So, when we talk about the transformation happening really in the workplace, the most tangible transformation that you actually see is the drastic change that companies are doing physically. They're breaking down walls. They're removing cubes, and they're moving to flexible layouts, new desks, new huddle rooms, open spaces, but the underlying technology for that is clearly not so visible very often. So, tell us about Michelin's strategy, and the technology you are deploying to really enable this corporation. >> So we, so let me give a little bit a history about the company to understand the daunting tasks that we had before us. So we have over 114,000 people in the company under 170 nationalities, okay? If you go to the corporate office in France, it's Clermont. It's about 3,000 executives and directors, and what have you in the marketing, sales, all the way up to the chain of the global CIO, right? Inside of the Americas, we merged in Americas about three years ago. Now we have the Americas zone. There's about 28,000 employees across the Americas, so it's really, it's really hard in a lot of cases. You start looking at the different areas that you lose time, and you lose you know, your productivity and what have you, so there, it's when we looked at different aspects of how we were going to manage the meeting rooms, right? because we have opened up our areas of workspace, our CIO, CEOs in our zones will no longer have an office. They'll sit out in front of everybody else and mingle with the crowd. So, how do you take those spaces that were originally used by an individual but now turn them into like meeting rooms? So, we went through a large process, and looked at the Hub 500, and that really met our needs, because at the end of the day what we noticed was, it was it was just it just worked, okay? We've just added it to the catalog, so we're going to be deploying it very soon, and I just want to again point that I know everybody struggles with this, and if you look at all the minutes that you lose in starting up a meeting, and we know you know what I'm talking about when I say this, it equates to many many many dollars, okay? And so at the end the day, this product helps us to be more efficient in starting up the meeting, and more productive during the meeting. >> Okay, it's very good to hear. Another major trend we are seeing in IT departments is taking a more hands-off approach to hardware. We're seeing new technologies enable IT to create a more efficient model, how IT gets hardware in the hands of end-users, and how they are ultimately supporting themselves. So what's your strategy around the lifecycle management of the devices? >> So yeah you mentioned, again, we'll go back to the 114,000 employees in the company, right? You imagine looking at all the devices we use. I'm not going to get into the number of devices we have, but we have a set number that we use, and we have to go through a process of deploying these devices, which we right now service our own image. We build our images, we service them through our help desk and all that process, and we go through it. If you imagine deploying 25,000 PCs in a year, okay? The time and the daunting task that's behind all that, you can probably add up to 20 or 30 people just full-time doing that, okay? So, with partnering with Lenovo and their excellent technology, their technical teams, and putting together the whole process of how we do imaging, it now lifts that burden off of our folks, and it shifts it into a more automated process through the cloud, okay? And, it's with the Autopilot on the end of the project, we'll have Autopilot fully engaged, but what I really appreciate is how Lenovo really, really kind of got with us, and partnered with us for the whole process. I mean it wasn't just a partner between Michelin and Lenovo. Microsoft was also partnered during that whole process, and it really was a good project that we put together, and we hope to have something in a full production mode next year for sure. >> So, David thank you very, very much to be here with us on stage. What I really want to say, customers like you, who are always challenging us on every single aspect of our capabilities really do make the big difference for us to get better every single day and we really appreciate the partnership. >> Yeah, and I would like to say this is that I am, I'm doing what he's exactly said he just said. I am challenging Lenovo to show us how we can innovate in our work space with your devices, right? That's a challenge, and it's going to be starting up next year for sure. We've done some in the past, but I'm really going to challenge you, and my whole aspect about how to do that is bring you into our workspace. Show you how we make how we go through the process of making tires and all that process, and how we distribute those tires, so you can brainstorm, come back to the table and say, here's a device that can do exactly what you're doing right now, better, more efficient, and save money, so thank you. >> Thank you very much, David. (audience applauding) Well it's sometimes really refreshing to get a very challenging customers feedback. And you know, we will continue to grow this business together, and I'm very confident that your challenge will ultimately help to make our products even more seamless together. So, as we now covered productivity and how we are really improving our devices itself, and the transformation around the workplace, there is one pillar left I want to talk about, and that's really, how do we make businesses smarter than ever? What that really means is, that we are on a journey on trying to understand our customer's business, deeper than ever, understanding our customer's processes even better than ever, and trying to understand how we can help our customers to become more competitive by injecting state-of-the-art technology in this intelligent transformation process, into core processes. But this cannot be done without talking about a fundamental and that is the journey towards 5G. I really believe that 5G is changing everything the way we are operating devices today, because they will be connected in a way like it has never done before. YY talked about you know, 20 times 10 times the amount of performance. There are other studies that talk about even 200 times the performance, how you can use these devices. What it will lead to ultimately is that we will build devices that will be always connected to the cloud. And, we are preparing for this, and Kirk already talked about, and how many operators in the world we already present with our Moto phones, with how many Telcos we are working already on the backend, and we are working on the device side on integrating 5G basically into every single one of our product in the future. One of the areas that will benefit hugely from always connected is the world of virtual reality and augmented reality. And I'm going to pick here one example, and that is that we have created a commercial VR solution for classrooms and education, and basically using consumer type of product like our Mirage Solo with Daydream and put a solution around this one that enables teachers and schools to use these products in the classroom experience. So, students now can have immersive learning. They can studying sciences. They can look at environmental issues. They can exploring their careers, or they can even taking a tour in the next college they're going to go after this one. And no matter what grade level, this is how people will continue to learn in the future. It's quite a departure from the old world of textbooks. In our area that we are looking is IoT, And as YY already elaborated, we are clearly learning from our own processes around how we improve our supply chain and manufacturing and how we improve also retail experience and warehousing, and we are working with some of the largest companies in the world on pilots, on deploying IoT solutions to make their businesses, their processes, and their businesses, you know, more competitive, and some of them you can see in the demo environment. Lenovo itself already is managing 55 million devices in an IoT fashion connecting to our own cloud, and constantly improving the experience by learning from the behavior of these devices in an IoT way, and we are collecting significant amount of data to really improve the performance of these systems and our future generations of products on a ongoing base. We have a very strong partnership with a company called ADLINK from Taiwan that is one of the leading manufacturers of manufacturing PC and hardened devices to create solutions on the IoT platform. The next area that we are very actively investing in is commercial augmented reality. I believe augmented reality has by far more opportunity in commercial than virtual reality, because it has the potential to ultimately improve every single business process of commercial customers. Imagine in the future how complex surgeries can be simplified by basically having real-time augmented reality information about the surgery, by having people connecting into a virtual surgery, and supporting the surgery around the world. Visit a furniture store in the future and see how this furniture looks in your home instantly. Doing some maintenance on some devices yourself by just calling the company and getting an online manual into an augmented reality device. Lenovo is exploring all kinds of possibilities, and you will see a solution very soon from Lenovo. Early when we talked about smart office, I talked about the importance of creating a software platform that really run all these use cases for a smart office. We are creating a similar platform for augmented reality where companies can develop and run all their argumented reality use cases. So you will see that early in 2019 we will announce an augmented reality device, as well as an augmented reality platform. So, I know you're very interested on what exactly we are rolling out, so we will have a first prototype view available there. It's still a codename project on the horizon, and we will announce it ultimately in 2019, but I think it's good for you to take a look what we are doing here. So, I just wanted to give you a peek on what we are working beyond smart office and the device productivity in terms of really how we make businesses smarter. It's really about increasing productivity, providing you the most secure solutions, increase workplace collaboration, increase IT efficiency, using new computing devices and software and services to make business smarter in the future. There's no other company that will enable to offer what we do in commercial. No company has the breadth of commercial devices, software solutions, and the same data center capabilities, and no other company can do more for your intelligent transformation than Lenovo. Thank you very much. (audience applauding) >> Thanks mate, give me that. I need that. Alright, ladies and gentlemen, we are done. So firstly, I've got a couple of little housekeeping pieces at the end of this and then we can go straight into going and experiencing some of the technology we've got on the left-hand side of the room here. So, I want to thank Christian obviously. Christian, awesome as always, some great announcements there. I love the P1. I actually like the Aston Martin a little bit better, but I'll take either if you want to give me one for free. I'll take it. We heard from YY obviously about the industry and how the the fourth Industrial Revolution is impacting us all from a digital transformation perspective, and obviously Kirk on DCG, the great NetApp announcement, which is going to be really exciting, actually that Twitter and some of the social media panels are absolutely going crazy, so it's good to see that the industry is really taking some impact. Some of the publications are really great, so thank you for the media who are obviously in the room publishing right no. But now, I really want to say it's all of your turn. So, all of you up the back there who are having coffee, it's your turn now. I want everyone who's sitting down here after this event move into there, and really take advantage of the 15 breakouts that we've got set there. There are four breakout sessions from a time perspective. I want to try and get you all out there at least to use up three of them and use your fourth one to get out and actually experience some of the technology. So, you've got four breakout sessions. A lot of the breakout sessions are actually done twice. If you have not downloaded the app, please download the app so you can actually see what time things are going on and make sure you're registering correctly. There's a lot of great experience of stuff out there for you to go do. I've got one quick video to show you on some of the technology we've got and then we're about to close. Alright, here we are acting crazy. Now, you can see obviously, artificial intelligence machine learning in the browser. God, I hate that dance, I'm not a Millenial at all. It's effectively going to be implemented by healthcare. I want you to come around and test that out. Look at these two guys. This looks like a Lenovo management meeting to be honest with you. These two guys are actually concentrating, using their brain power to race each others in cars. You got to come past and give that a try. Give that a try obviously. Fantastic event here, lots of technology for you to experience, and great partners that have been involved as well. And so, from a Lenovo perspective, we've had some great alliance partners contribute, including obviously our number one partner, Intel, who's been a really big loyal contributor to us, and been a real part of our success here at Transform. Excellent, so please, you've just seen a little bit of tech out there that you can go and play with. I really want you, I mean go put on those black things, like Scott Hawkins our chief marketing officer from Lenovo's DCG business was doing and racing around this little car with his concentration not using his hands. He said it's really good actually, but as soon as someone comes up to speak to him, his car stops, so you got to try and do better. You got to try and prove if you can multitask or not. Get up there and concentrate and talk at the same time. 62 different breakouts up there. I'm not going to go into too much detai, but you can see we've got a very, very unusual numbering system, 18 to 18.8. I think over here we've got a 4849. There's a 4114. And then up here we've got a 46.1 and a 46.2. So, you need the decoder ring to be able to understand it. Get over there have a lot of fun. Remember the boat leaves today at 4:00 o'clock, right behind us at the pier right behind us here. There's 400 of us registered. Go onto the app and let us know if there's more people coming. It's going to be a great event out there on the Hudson River. Ladies and gentlemen that is the end of your keynote. I want to thank you all for being patient and thank all of our speakers today. Have a great have a great day, thank you very much. 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