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

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020, sponsored by Intel and AWS. >>Welcome back to the queue. It's virtual coverage of Avis reinvent 2020 I'm John furry, your host. Um, this is a cute virtual we're here. Not in, in remote. We're not in person this year, so we're doing the remote interviews. And then this segment is going to build on the quantum conversation we had last year, Richard moles, general manager of Amazon bracket and aid was quantum computing and Fernando Brandao head of quantum algorithms at AWS and Brent professor of theoretical physics at Caltech. Fernando, thanks for coming on, Richard. Thanks for joining us. >>You're welcome to be here. >>So, Fernando, first of all, love your title, quantum algorithms. That's the coolest title I've heard so far and you're pretty smart because you're a theoretical professor of physics at Caltech. So, um, which I'd never be able to get into, but I wish I could get into there someday, but, uh, thanks for coming on. Um, quantum has been quite the rage and you know, there's a lot of people talking about it. Um, it's not ready for prime time. Some say it's moving faster than others, but where are we on quantum right now? What are, what are you, what are you seeing Fernanda where the quantum, where are peg us in the evolution of, of, uh, where we are? >>Um, yeah, what quantum, uh, it's an emerging and rapidly developing fields. Uh, but we are see where are you on, uh, both in terms of, uh, hardware development and in terms of identifying the most impactful use cases of one company. Uh, so, so it's, it's, it's early days for everyone and, and we have like, uh, different players and different technologies that are being sport. And I think it's, it's, it's early, but it's exciting time to be doing quantum computing. And, uh, and it's very interesting to see the interest in industry growing and, and customers. Uh, for example, Casa from AWS, uh, being, uh, being willing to take part in this journey with us in developmental technology. >>Awesome. Richard, last year we talked to bill Vass about this and he was, you know, he set expectations really well, I thought, but it was pretty much in classic Amazonian way. You know, it makes the announcement a lot of progress then makes me give us the update on your end. You guys now are shipping brackets available. What's the update on your end and Verner mentioned in his keynote this week >> as well. Yeah, it was a, it was great until I was really looking at your interview with bill. It was, uh, that was when we launched the launch the service a year ago, almost exactly a year ago this week. And we've come a long way. So as you mentioned, we've, uh, we've, uh, we've gone to general availability with the service now that that happened in August. So now a customer can kind of look into the, uh, to the bracket console and, uh, installed programming concept computers. You know, there's, uh, there's tremendous excitement obviously, as, as you mentioned, and Fernando mentioned, you know, quantum computers, uh, we think >>Have the potential to solve problems that are currently, uh, uh, unsolvable. Um, the goal of bracket is to fundamentally give customers the ability to, uh, to go test, uh, some of those notions to explore the technology and to just start planning for the future. You know, our goal was always to try and solve some of the problems that customers have had for, you know, gee, a decade or so now, you know, they tell us from a variety of different industries, whether it's drug discovery or financial services, whether it's energy or there's chemical engineering, machine learning, you know, th the potential for quantum computer impacts may industries could potentially be disruptive to those industries. And, uh, it's, it's essential that customers can can plan for the future, you know, build their own internal resources, become experts, hire the right staff, figure out where it might impact their business and, uh, and potentially disrupt. >>So, uh, you know, in the past they're finding it hard to, to get involved. You know, these machines are very different, different technologies building in different ways of different characteristics. Uh, the tooling is very disparate, very fragmented. Historically, it's hard for companies to get access to the machines. These tend to be, you know, owned by startups or in, you know, physics labs or universities, very difficult to get access to these things, very different commercial models. Um, and, uh, as you, as you suggested, a lot of interests, a lot of hype, a lot of claims in the industry, customers want to cut through all that. They want to understand what's real, uh, what they can do today, uh, how they can experiment and, uh, and get started. So, you know, we see bracket as a catalyst for innovation. We want to bring together end-users, um, consultants, uh, software developers, um, providers that want to host services on top of bracket, try and get the industry, you know, rubbing along them. You spoke to lots of Amazonians. I'm sure you've heard the phrase innovation flywheel, plenty of times. Um, we see the same approach that we've used successfully in IOT and robotics and machine learning and apply that same approach to content, machine learning software, to quantum computing, and to learn, to bring it together. And, uh, if we get the tooling right, and we make it easy, um, then we don't see any reason why we can't, uh, you know, rapidly try and move this industry forward. And >>It was fun areas where there's a lot of, you know, intellectual computer science, um, technology science involved in super exciting. And Amazon's supposed to some of that undifferentiated heavy. >>That's what I am, you know, it's like, >>There's a Maslow hierarchy of needs in the tech industry. You know, people say, Oh, why five people freak out when there's no wifi? You know, you can't get enough compute. Right. So, you know, um, compute is one of those things with machine learning is seeing the benefits and quantum there's so much benefits there. Um, and you guys made some announcements at, at re-invent, uh, around BRACA. Can you share just quickly share some of those updates, Richard? >>Sure. I mean, it's the way we innovate at AWS. You know, we, we start simple and we, and we build up features. We listen to customers and we learn as we go along, we try and move as quickly as possible. So since going public in, uh, in, in August, we've actually had a string of releases, uh, pretty consistent, um, delivering new features. So we try to tie not the integration with the platform. Customers have told us really very early on that they, they don't just want to play with the technology. They want to figure out how to, how to envisage a production quantum computing service, how it might look, you know, in the context of a broad cloud platform with AWS. So we've, uh, we launched some integration with, uh, other AWS capabilities around security, managing limits, quotas, tagging resources, that type of thing, things that are familiar to, uh, to, to, to current AWS users. >>Uh, we launched some new hardware. Uh, all of our partners D-Wave launched some, uh, uh, you know, a 5,000 cubit machine, uh, just in September. Uh, so we made that available on bracket the same day that they launched that hardware, which was very cool. Um, you know, we've made it, uh, we've, we've made it easier for researchers. We've been, you know, impressed how many academics and researchers have used the service, not just large corporations. Um, they want to have really deep access to these machines. They want to program these things at a low level. So we launched some features, uh, to enable them to do their research, but reinvent, we were really focused on two things, um, simulators and making it much easier to use, uh, hybrid systems systems that, uh, incorporate classical compute, traditional digital computing with quantum machinery, um, in the vein that follow some of the liens that we've seen, uh, in machine learning. >>So, uh, simulators are important. They're a very important part of, uh, learning how to use concepts, computers. They're always available 24, seven they're super convenient to use. And of course they're critical in verifying the accuracy of the results that we get from quantum hardware. When we launched the service behind free simulator for customers to help debug their circuits and experiments quickly, um, but simulating large experiments and large systems is a real challenge on classical computers. You know, it, wasn't hard on classical. Uh, then you wouldn't need a quantum computer. That's the whole point. So running large simulations, you know, is expensive in terms of resources. It's complicated. Uh, we launched a pretty powerful simulator, uh, back in August, which we thought at the time was always powerful managed. Quantum stimulates circuit handled 34 cubits, and it reinvented last week, we launched a new simulator, which actually the first managed simulator to use tensor network technology. >>And it can run up to 50 cubits. So we think is, we think is probably the most powerful, uh, managed quantum simulator on the market today. And customers can flip easily between either using real quantum hardware or either of our, uh, stimulators just by changing a line of code. Um, the other thing we launched was the ability to run these hybrid systems. You know, quantum computers will get more, no don't get onto in a moment is, uh, today's computers are very imperfect, you know, lots of errors. Um, we working, obviously the industry towards fault-tolerant machines and Fernando can talk about some research papers that were published in that area, but right now the machines are far from perfect. And, uh, and the way that we can try to squeeze as much value out of these devices today is to run them in tandem with classical systems. >>We think of the notion of a self-learning quantum algorithm, where you use a classical optimization techniques, such as we see machine learning to tweak and tune the parameters of a quantum algorithm to try and iterate and converge on the best answer and try and overcome some of these issues surrounding errors. That's a lot of moving parts to orchestrate for customers, a lot of different systems, a lot of different programming techniques. And we wanted to make that much easier. We've been impressed with a, a, an open projects, been around for a couple of years, uh, called penny lane after the Beatles song. And, um, so we wanted to double down on that. We were getting a lot of positive feedback from customers about the penny lane talk it, so we decided to, uh, uh, make it a first class citizen on bracket, make it available as a native feature, uh, in our, uh, in our Jupiter notebooks and our tutorials learning examples, um, that open source project has very similar, um, guiding principles that we do, you know, it's open, it's cross platform, it's technology agnostic, and we thought he was a great fit to the service. >>So we, uh, we announced that and made it available to customers and, uh, and, and, uh, already getting great feedback. So, uh, you know, finishing the finishing the year strongly, I think, um, looking forward to 2021, you know, looking forward to some really cool technology it's on the horizon, uh, from a hardware point of view, making it easy to use, um, you know, and always, obviously trying to work back from customer problems. And so congratulations on the success. I'm sure it's not hard to hire people interested, at least finding qualified people it'd be different, but, you know, sign me up. I love quantum great people, Fernando real quick, understanding the relationship with Caltech unique to Amazon. Um, tell us how that fits into the, into this, >>Uh, right. John S no, as I was saying, it's it's early days, uh, for, for quantum computing, uh, and to make progress, uh, in abreast, uh, put together a team of experts, right. To work both on, on find new use cases of quantum computing and also, uh, building more powerful, uh, quantum hardware. Uh, so the AWS center for quantum computing is based at Caltech. Uh, and, and this comes from the belief of AWS that, uh, in quantum computing is key to, uh, to keep close, to stay close of like fresh ideas and to the latest scientific developments. Right. And Caltech is if you're near one computing. So what's the ideal place for doing that? Uh, so in the center, we, we put together researchers and engineers, uh, from computer science, physics, and other subjects, uh, from Amazon, but also from all the academic institutions, uh, of course some context, but we also have Stanford and university of Chicago, uh, among others. So we broke wrongs, uh, in the beauty for AWS and for quantum computer in the summer, uh, and under construction right now. Uh, but, uh, as we speak, John, the team is busy, uh, uh, you know, getting stuff in, in temporary lab space that we have at cottage. >>Awesome. Great. And real quick, I know we've got some time pressure here, but you published some new research, give a quick a plug for the new research. Tell us about that. >>Um, right. So, so, you know, as part of the effort or the integration for one company, uh, we are developing a new cubix, uh, which we choose a combination of acoustic and electric components. So this kind of hybrid Aquacel execute, it has the promise for a much smaller footprint, think about like a few microliters and much longer storage times, like up to settlements, uh, which, which is a big improvement over the scale of the arts sort of writing all export based cubits, but that's not the whole story, right? On six, if you have a good security should make good use of it. Uh, so what we did in this paper, they were just put out, uh, is, is a proposal for an architecture of how to build a scalable quantum computer using these cubits. So we found from our analysis that we can get more than a 10 X overheads in the resources required from URI, a universal thought around quantum computer. >>Uh, so what are these resources? This is like a smaller number of physical cubits. Uh, this is a smaller footprint is, uh, fewer control lines in like a smaller approach and a consistent, right. And, and these are all like, uh, I think this is a solid contribution. Uh, no, it's a theoretical analysis, right? So, so the, uh, the experimental development has to come, but I think this is a solid contribution in the big challenge of scaling up this quantum systems. Uh, so, so, so John, as we speak like, uh, data blessed in the, for quantum computing is, uh, working on the experimental development of this, uh, a highly adequacy architecture, but we also keep exploring other promising ways of doing scalable quantum computers and eventually, uh, to bring a more powerful computer resources to AWS customers. >>It's kind of like machine learning and data science, the smartest people work on it. Then you democratize that. I can see where this is going. Um, Richard real quick, um, for people who want to get involved and participate or consume, what do they do? Give us the playbook real quick. Uh, so simple, just go to the AWS console and kind of log onto the, to the bracket, uh, bracket console, jump in, you know, uh, create, um, create a Jupiter notebook, pull down some of our sample, uh, applications run through the notebook and program a quantum computer. It's literally that simple. There's plenty of tutorials. It's easy to get started, you know, classic cloud style right now from commitment. Jump in, start simple, get going. We want you to go quantum. You can't go back, go quantum. You can't go back to regular computing. I think people will be running concert classical systems in parallel for quite some time. So yeah, this is the, this is definitely not a one way door. You know, you go explore quantum computing and see how it fits into, uh, >>You know, into the, into solving some of the problems that you wanted to solve in the future. But definitely this is not a replacement technology. This is a complimentary technology. >>It's great. It's a great innovation. It's kind of intoxicating technically to get, think about the benefits Fernando, Richard, thanks for coming on. It's really exciting. I'm looking forward to keeping up keeping track of the progress. Thanks for coming on the cube coverage of reinvent, quantum computing going the next level coexisting building on top of the shoulders of other giant technologies. This is where the computing wave is going. It's different. It's impacting people's lives. This is the cube coverage of re-invent. Thanks for watching.

>> Live from Washington, D.C., it's theCUBE! Covering Inforum D.C. 2018. Brought to you by Infor. >> Welcome back to the Walter Washington Convention Center, we're in Washington D.C., the nation's capital of course, as we continue our coverage here on theCUBE of Inforum 2018. Along with Dave Vellante, I'm John Walls, it's a pleasure welcoming Dhiraj Shah in with us, the CEO of Avaap. Dhiraj, thanks for joining us this afternoon! >> Good to see you again! >> Absolutely, big pleasure, it was great talking to you for the last two years, and a pleasure to be back here. >> Yeah, I'm always curious, I mean Avaap, I've read a little bit, I mean the five letters of Sanskrit language, what do the five letters represent? I mean how did you come up with the title? >> You know, that's the first question that gets asked, the two questions I get. >> Sorry to be cliche, but I'm just really curious! >> No, no, the two questions is, "Why did you start Avaap?" and the other question is, "What is Avaap?" and it's actually five elements in Sanskrit and each of them are tied to a cultural value that we hold at Avaap, so, Agni, which is fire stands for passion, 'cause I'm a deep believer of being very passionate in what you do; if you're passionate, you'll follow through and it won't feel like work. Water is tied to innovation, sky is tied to goals, we're very ambitious. We've been able to have a rocket ship type of growth, so far, and we continue to aspire to do more. We have Earth, which is tied to eco conscience, cause we like to be globally eco conscious and genuine in what we're doing. And then air, which is transparency. I think we live in a world that, you really don't need a lot of bureaucracy, and the more there is transparency, the better there is organizational development. >> Gotcha, well thank you, I appreciate the rundown. So services and solutions, and the relationship with Infor, walk us through that a little bit, of why you're here. >> Absolutely, so, we are Infor's most decorated partner, so I'd like to say that, because we just came off the stage getting four awards with Infor this year. >> Congratulations! Fantastic. >> Yeah, thank you very much. They were overall partner of the year five years in a row. Our partnership with Infor, started five years ago, before that it was with Lawson. So when Charles Phillips and the team came on board, I was in the back of the room, and I heard Charles kind of lay out his vision in 2012. And he said "I want to do two things, I want to make software that is industry specific." And this is coming at a time where everything was one size fits all. And he said "We want to reinvent the software that's driven for future technologies. Cloud, mobile, big data." Right? So I had a great opportunity, and we made a momentous decision of parking all our eggs in the Infor basket, and just doing Infor. And that served us well of going from 20, at that point we were like 25 employees, to having over 450 today. >> Wow! And we've talked about this in the past is you got in early, and now you're seeing some of the big guys come in, so you have to stay ahead of them. How are you doing that, and why are you succeeding? >> You know it's not necessarily always being ahead, so that actually, that's a question I got, is that Deloitte's here, Accenture's here, Capgemini is here, do you feel threatened? We actually don't, because it's a validation of what's occurring in this eco system with the big system integrators coming in. And with a rising tide, all boats rise. So we've actually partnered with some of these large SIs, because there's roles that they play and we let them do a lot of business transformation, change management, program management, and we do what we do best, which is Infor knowledge, and consulting services. >> The deep, deep Infor, that's kind of, it's ironic, right? Infor's specialty is the last mile, micro-industry capabilities, and that's really kind of how you specialize is deep Infor expertise. >> Exactly, yeah. >> So give us an example of, you go through an engagement, you got one of the big SIs and they're going to do their big global thing, business process change, they really are global in scale, et cetera. Where do you come in? where does Infor sort of, where does their micro services, or micro-function leave off, and where do you pick up? >> So yeah, I'll give you a real world example, in fact, I was just with this customer earlier this morning, Christus Health, they are one of the largest health systems in the country, 60 hospitals, close to 60 thousand employees. They're looking for transformation on their ERP, full suite, HCM, Supply Chain, Financial. Went through a large system selection process the usual competitive race with Oracle, Workday, Infor, kind of being in that race. It was down selected to Infor and Oracle as the two lenders that had full capabilities that they were looking for. And then once they made their decision on Infor as their vendor of choice, they did a services RFP, which we partnered with Deloitte, because the scope of that was, as I said earlier, around business transformation services, that we didn't have in our bag. And Deloitte does not have the 20 years of expertise, the deep Infor knowledge around the solutions of Infor, that we have within our healthcare team. So, we bridged and built an alliance, that, today is starting the project journey in Infor, Deloitte, Avaap, Christus, to make that project a success. >> In the capabilities that you, that they were looking for, that you said that Infor and Oracle had, were what? the coverage of the functionality across the suites, was it the cloud capabilities? What's the high level of that? >> So the one thing that I will tell you, is the consumer, in this case the healthcare market, if we talk about them, is getting extremely knowledgeable, so the way it's starting is around cloud. So gone are the days, I see a lot of commercials out there about real cloud, artificial cloud, private cloud, public cloud, there's a lot of education already around single tenant, and multi-tenant, and they understand. So it starts with the cloud platform, that is the software provider on a stable, secure cloud platform, and are the applications hosted on a multi-tenant, as opposed to individually hosted for each customer. And then they break it down into the different buckets of the applications, within HCM, within Supply Chain, within Financials to see what not a product features. So gone are the days of looking at feature functionality, but their business processes, and best practices. And that's really, in my opinion, where Infor really came ahead at Christus. >> In the multi-tenant verses hosted, I mean, Vodka would say, "Well why would a customer care?" I'm presuming the customer cares because when you do a software release, it's just seamless, right? Verses okay, we got to freeze the code, and do an upgrade, it's more disruptive. Is that why? >> Yes, that's definitely a large portion because over the period of time, every time there is a manufactured change on the software side, development chain, you're adding code that impacts a customer to have to take their system down, and then bring it back up, and here it's done without the customer even finding out, so it's a huge advantage. The second advantage is a cost, which in today's world not as much, because hardware's become very cheap. But it's still conquered hardware that's sitting on the premise, as opposed to individually putting it out there, as opposed to having one system that's scalable. And then your third is security, on multi-tenant capable software, it's more secure than your single tenant capability. >> And Avaap brings that to the table. So it's not, I mean Infor has the micro-vertical function, so yours is what? Onboarding, implementation, training, those kinds of things? >> Yeah, so it starts with helping them align, and educate on the system selection on what it does. So we have a offering called Align and Define that allows customers to prepare for the cloud, to take steps today, and educate them on what needs to be done. Once they do that, then it's going through the implementation process, and post-implementation is optimization. So on the optimization side, Avaap also has capabilities on our EHR side. So one of the big challenge in healthcare, is a wall that exists between the ERP and the EHR, you have your Oracle and Infor on the ERP side, and then you have Epic and Cerner on the EHR, and there's a wall there, one doesn't talk to the other. And the systems need to be really integrated, to be able to drive efficiency and cost benefits for that, so that's one of the things that we're heavily invested in. >> Well healthcare is your biggest business, right? >> Right. >> So what's goin on these days? You obviously, last sort of wave was Obamacare, Affordable Care Act, there's some uncertainty around that, certainly meaningful use is still a big deal for a lot of healthcare providers, EMR is still you know, a big deal. What are the hot trends, what are the drivers, and how are you guys responding? >> ERP. ERP is the hottest trend right now in the healthcare market, so there's a lot of fatigue with healthcare having gone through meaningful use over the last decade of spending hundreds of millions of dollars, of putting in the EHR platforms. So that fatigue, and that focus on EHR has led to no real advancement on the ERP side. And that's why we're in a midst of what I think, is one of the largest wave in the healthcare industry are on ERP platforms that we're seeing, there were 55 system selections done, just in the last 12 months. My personal view is that over the next three to five years, we're going to see 80% of healthcare systems swap or upgrade their ERP platforms. >> Wow. Okay, please, go ahead. >> So swap-- what's... the fundamental of that decision? >> So there are a lot of legacy providers, so the market is going to get consolidated, so we, I know we always talk about Oracle, Infor, Workday, but there is a lot of other providers, there's, if you count mid market and up, there's 5,000 health systems out there that's customer base. >> Very fragmented, isn't it? >> Very fragmented. >> Okay, alright. >> So there's McKesson as an example. McKesson had a big ERP platform, officially said that they are stopping development on it. And that's going to create a void that needs to be filled. There's Meditech on the lower end of the spectrum that serves these regional, individual health system that exist in rural areas. So those systems are, need to be upgraded, because the rural systems of most of anywhere else that have connectivity issues need the cloud platforms to kind of go through. >> Yeah I mean a lot of these, a lot of these healthcare platforms were, they were literally, they were born in the mini-computer era it was a mantra, let's buy a VAX, and we'll become a valuated re-seller, and healthcare was such a huge opportunity, and so under technologized, not a word but, and then over the years, these systems just kept getting updated, now they're just left with this fossilized mess, right? >> Absolutely >> And the cloud comes in and that's really driving a lot of the change. >> Yeah, and Infor couldn't be positioning itself in a better time, to make the change. I think Charles was very visionary, and kind of reinventing the old Lawson platform, and making it multi-tenant, cloud enabled, for the healthcare industry, specifically written. So the last mile functionality that we talk about in supply chain that Infor has is unmatched, in our opinion, in the field today. >> Who does that last mile functionality, if it's not embedded in the applications like Infor, is it the SI, is it some other internal software developer? >> So, the software developers as Infor is, trying to build that as much in the software as they can. But there's always extensions, which is where tools from the Infor OS, as an example come in, to allow to build the extensions that allow us to then have that capability. >> You do that work, is that right? >> We do that work, absolutely. >> Okay, and then, how do you deal with Infor in terms of just not getting in the way of their road map? Soma's got his ERD pipeline, and you don't want to just do something that he's going to do in week, a month or a year. How do you communicate with those guys, and how do you find the white space? And then does it somehow get back into the platform and become advantageous for others? >> So Soma has spent 4 billion dollars on product, that's the budget his board gave. I can't go in front of my board, ask for that kind of budget, then I'd be out. >> Well you could. >> I could, yeah >> It could be some good laughs >> Yeah, so we are realistic in what we can do. So the extensions we build are very specific, and not necessarily product centric. We have a good relationship with the product development team, that allows us to see their road map and make sure. So an example I'll give you is test automation. So we've built an automation framework using an industry recognized platform, and customized it for the ERP, for healthcare. So, regression testing is one of the largest pin point, manual, laborious, takes a business uses away. So this tool, called Avaap Test Automation, which has been in the field, we have, close to 100 customers using it, allows us to automate that entire regression testing sidle, and is an accelerator that condenses the entire implementation life cycle. >> You've got, we've talked a lot about healthcare, you have another interest inside of your business, with a little Beatles connection. So fill us in on that a little bit. >> Yeah, so two of the four awards we got, one, and I definitely want to talk on both of them, because those are important parts of our business, One is retail, we did get retail partner of the year award, and Stella McCartney, is our project that we're actively working on in UK. She, Stella McCartney, is Paul McCartney's daughter, and has built a very reputable shoe company, that's a brand highly sought after, and we're working on modernizing their ERP applications, using cloud suite fashion, which has the underlying technology base on M3 platform. >> She loves you, yeah, yeah, right? >> That's cool, that is cool! >> Absolutely! >> That's great, well Dhiraj, thanks for being here, thanks for sharing the story! >> Absolutely, thank you very much. >> Congratulations on all the progress! >> It's always good to be here! >> It is full speed ahead. Good for you. Dhiraj Shah from Avaap >> Thank you! >> Back with more on theCUBE. We're at in Informen, Informer rather, (laughs) I did it again, didn't I? >> Inforum! >> Inforum! >> I'll step in when you need me! (laughing) >> 2018, D.C. Did it again. >> Excellent! (bubbly music)

>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)

>> Narrator: Live from Nice, France. Its the Cube, covering .Next Conference 2017, Europe. Brought to you by Nutanix. >> Welcome back, I'm Stu Miniman and you're watching, Silicon Angle Medias production of the Cube. World Wide leader in live tech coverage. Happy to welcome to the program, first time guest, Anja Manuel, who's a Co-founder and partner at, Rice Hadley Gates. Thank you so much for joining us. >> Anja: Thank you for having me, Stu. >> So, I've attended all five of the Nutanix conferences. And definitely, when we get a speaker at the Key Note from R.H.G. is one of the highlights. So, Condoleezza Rice, everybody's like, how does Nutanix get Condie Rice to come in? Robert Gates, we've actually had the pleasure of having him on the Cube. We've had Stephen Hadley on in D.C. also. And a little bit different conversation than some of the, kind of, in the weeds technical discussion. So, Anja for our audience that's not familiar, give us a little bit about your background, what you led you in to be one of the founders. >> Absolutely. Well, I've done a bit of everything. I've been an investment banker, a lawyer doing international cases. I have worked at the State Department for Condie Rice, mostly on Asia issues. And, then at the very end of 2008, Condie, Steve and I founded this firm. And we feel very lucky to be working with each other and some of the great, young and already, some already large, some fast growing tech companies in the Valley. And helping them expand around the world. And it's been a particular pleasure to work with Dheeraj and his team at Nutanix. When we started with them, they were a couple hundred people. And now look around, you've got 2,000 people at this conference. So, we're very proud of them. >> Yeah, absolutely. Great growth for Nutanix, their eco-system's blossoming. One of the jokes I always have here on the Cube is, when I talk to any end user customers, its like, well your industry's not changing that much, right? And of course, it doesn't matter what industry you're in. Digital disruption is more than just what it's affecting. Globalization is just a fact of life. It brings, especially for a lot our audiences, USA based, we reach a global audience. But when we come to some of these international events, it really puts a point on some of the things going on globally. What're you talking to, when you speak to the CIOs and you're talking to Nutanix customers and partners, what are some of the big challenges? What are the things that they need to be looking at? >> Sure, globalization is happening and of course, it's more pronounced in tech. This is the first industry that really shows no sectoral boundaries. The big platform companies can basically go into any industry sector and no geographic boundaries. It's very easy to expand internationally. So, what I'm going to be talking about today on the main stage is just globalization and its backlash. As you know we've seen, after decades of evermore, open boarders, increase trade, easier immigration, and the last year or two, you've seen really the West in sort of, what I would call a defensive crouch. And there are real reasons for it in the US where you and I both live. If you are a white male, who has a high school education or less, you live on average, 10 years less than all of the very highly educated people in this room. And there is a real issue of people being left behind. And you can see that impact politically. You see it in the US, with Trump, and I would also argue on the left with Bernie Sanders. You see it with Brexit. You see it in the impact that Marine Le Pen and Aten a Tiva for Deutschland and others have had on European politics. And I would say that impact is strong, even though those right wing parties in Europe didn't win, they're setting the agenda much more than you would've seen 10 years ago. So it's something for the tech companies to consider as they keep expanding. >> Yeah, it's a trade. On the one hand, you said that there's no boundaries for tech, but one of the things a lot of the tech community, we look at, is some of those fragments that are happening. So, like, the internet. Is the internet a global internet or does China have their own internet? Will Germany just create their own internet? And how much is governance, and having data something we look and Nutanix looks at a lot, require that you have it within those boarders, and the boundaries between government and corporations now? There's certain countries where governments are heavily involved and certain ones where it almost feels that they're fighting. In the US, it's, is the government actually helping business or stopping business? >> That's right. >> Is something that we ask a lot. So I'm curious, your thoughts. >> Well, right now, we still have one global interoperable internet and that has been a huge boon to economies all around the world. Not just the American one. And it's this little known organization called ICANN, which was started in the 1990s. It has a convoluted thing called the multi stake holder model, where they say, we're going to get people, the technologists who are working on this and GOs and governments and everyone talking about how do we actually manage this thing and make sure that it stays interoperable and global. And I'm quite happy that that system of internet governance still stands and that it hasn't been taken over by individual governments or by the United Nations. You talked about data localization. It's a real issue. We see this with a lot of the tech companies that we work with out in California. More and more. You see the Russians doing it. You see the Chinese doing it. And I worry that if that trend really continues, you will have less interaction, for example, between Chinese and Americans, which is something we so dramatically need, now that our governments seem to be more and more at odds with each other. It's more important than ever that the companies and the people are talking to each other. >> Yeah, I actually, we interviewed the former president of ICANN, Fadi Chehade, a couple of years ago and he was raising red flags as to concern about would the US step back. Cause really, it put that in place, and had a very strong connection there. So would the US, kind of, advocate from some of this or how would that be involved? So you're happy with the way ICANN's going and kind of the global discussion? >> I was very happy to see that the United States allowed it to be privatized. Which is something that'd been planned for a long time. So we're quite happy that it happened the way it did. And that even the new Trump administration didn't stop that from going through, yeah. >> All right, you've written a lot about India, some of the others. How do companies, even in the global market place? Do they have to specialize in what they're doing? Certain regionalizations, that they need to do or how do they, global company, interact in some of the more emerging markets? >> Yeah, they do have to specialize. And I think sometimes, in Silicon Valley, we're so confident in our own abilities that sometimes we think, well if it's invented here, naturally the world will love it. That worked for Facebook. It worked for Google. It doesn't necessarily work for every technology company. And so, yes, of course you have to tailor it to the local market. And there are some innovations coming out of China and India that are, frankly, really impressive and we should adopt some of them. And China, the web payments infrastructure is much more advanced than what you see in the US. Lots of people do everything through their WeChat account. They pay, they interact, they talk. It's not just texting. It's a whole echo system in a way that we haven't really seen as much in the US and Europe. So we can learn from them as well. >> Yeah so another interesting topic is, Silicon Valley prides itself on being the center of innovation. What're you seeing globally, are there certain areas or pockets? Can there be other Silicon Valleys for different technologies or is Silicon Valley going to be the Silicon Valley for all of these waves? >> Well, we are the biggest Silicon Valley. And it is a very unique eco-system. I'm lucky enough to teach at Stanford and to work with some of these tech companies. The idea that a university and a venture capital eco-system and entrepreneurs all work together in something that isn't directed by the state is very very important. And you do see these springing up everywhere. You have it in Bangalore. You have it in Boston, where you're from. You have it outside of London. You're seeing a little bit in Berlin happening. You're seeing it in China in a much bigger way than I think people appreciate. I'll give you one story. I was at the Chinese World Internet Forums, sort of their vision of the world internet, a year and a half ago. And I get back to my hotel at midnight, ready to just go to bed, and there are a thousand people in the lobby. All with their phones out. And I'm wondering, who's coming? Is it Xi Xin Ping? Is it some rock star? In walks Jack Ma and the CEO of Xiaomi phones. And a huge shout goes up as if it's the Beatles. So if you're a young millennial Chinese person, you want to be Jack Ma. So innovation fever has captured them as well. >> Yeah, what about companies being global versus being based in a country? What advice do you give to how they balance that headquarters versus being a global company? >> Yeah, this is one of the ironies and all the protectionist talk you see from governments because I think the cat is out of the bag. So to speak. Every company we work with, even the very young ones, they're global from the very beginning. Even if you think your headquarters are in New York or in California, you're supply chain most likely, incorporates 10 different countries. Your customers are somewhere else. Maybe you don't advertise it because you try to be an all American company or all European company, but there's actually no such thing as a domestic company anymore. >> I want to give you the final word. Nutanix, you give some advice. I'm sure there's things we can't talk about. But how are they doing as being a global company? What are some of the things a company like Nutanix that they'll face as they expand globally? >> Yeah, Nutanix is very impressive. First of all, if you look at Dheeraj and Sudheesh and their senior management team, what I love about working with them, is that they are good technically, they're great at the people to people skills and they are instantly global just like we just talked about. If you look at their management team, they're from all over the world. And they very quickly got people out into all the different regions. I think they try to be sensitive to how their product would be used in different places around the world. So I'm quite optimistic about what they're going to be able to achieve. >> Okay, I do have one last question for you. I was just thinking about that globalization. One of the concerns we have these days is getting enough women in tech and with your global viewpoint, just women in the workforce is still something that we're challenged with in many parts of the globe. What's your take? >> Yeah, strangely, women in the workforce are doing better in China, for example, than in the US, Europe, India, other places. I love living and working in Silicon Valley. We really have a problem. And we need to do more. And it's on the stem side. It's on the investor side. You've seen all of the news coming out about how it's so much harder for a woman entrepreneurs to get funded. There's no reason. There's actually a recent study done saying that women who get funded, their companies do, on average, far better than companies founded by men. So clearly there's some problem going on here and I'm happy that Silicon Valley's finally paying attention. >> Well Anju Manuel, really appreciate you joining us for this segment. I'm Stu Miniman and we will be back with more coverage here from Nutanix .Next in Nice, France. You're watching the Cube.

okay we're back here live at the IBM iod information on demand conference hashtag IBM iod this is the cube so looking the anglo Mookie bonds flagship program we go out for the events extracting from the noise i'm john furrier might join my co-host Davey lonte and we'd love to have analysts in here and in this case former analyst James Cole Beatles welcome to back to the cube thank you very much John thank you Dave pleasure see you again finger of being at IOD you're a thought leader you are an influencer you work at IBM so you you're out there the front lines doing some great work so thank you very much tell us explains the folks out there not about the show because we've had some people coming in last year you were private in but what does this fit what is this vector in context to what's relevant the market obviously big data and analytics is the hottest thing on the planet right now and you got social business now emerging categorically here but it has a couple different flavors to it right within IBM's context yeah but the messaging is simple right you got analytics that drives value outcomes social business is the preferred way of people going to operate their businesses engagement and all that is great stuff new channels marketing eccentric cetera explain to them how I OD is fitting into these megatrends into mega trends I think the hottest trends why our customers caring about what's going on here is a lot of a lot of activity around customers what is what does IOD fit into that a bigger picture yeah well you know the world has changed the world culture has changed radically and really in the last decade or so none is everywhere in the world everything is now online and digital increasingly it's streaming in terms of culture look what's happening to Hollywood is being deconstructed by the netflixs of the world you know movies and TV and music and everything is delivered online now all engagement more more engagements with your employer with your you know with merchants with your family everywhere is online things like streaming media so if you look at how the world culture has changed I yesterday I spoke here on a topic that's near and dear to my heart called big media it's the support of the ascendance of streaming media and not just the area as I laid out but in education like MOOCs distance learning we use it internally at IBM for our think fridays and Ginni Rometty and the executive team you know every Friday its cloud or its big data or whatever you know we need all need to get up to speed on the world culture has changed now analytics is fundamental to that whole proposition in terms of world culture analytics driving gagement analytics in terms of you know in a business context analytics a 360-degree view and you have data warehouses and the master data and you have predictive models to drive segmentation and target marketing and all that good stuff you know that's been in business for a long time that those set of practices they have become prevalent in most industries now not just in say retailing you know the Amazons of the world they're pervasive across all industries big data is fundamental to that you know engagement model its social social in the sense that social is one of many channels through which business is engaged with through which many people engage the social is assumed assuming a degree of importance in the fabric of modern life that goes beyond simple you know engagement with you know brands and whatnot social is how people create is how they declare who they are it's their identity and so social in your personal life we all know about Facebook and Twitter and everything else and YouTube but social has revolutionized enterprise cultures everywhere you know we use social internally of course we use our own Lotus connections most large and even many mid-sized firms now use social for interactions among employees or throughout their Val you chain so social business is about all of that it's the b2c it's the b2b it's the e2e and employ to employ all these different models of engagement they all demand a number of things obviously the social platform they demand the data of various sorts structured unstructured in shared repositories or cubes or Mars or whatnot they it demands the the big data platforms not only at respite in motion the streaming media to make it all happen in real time so at IOD if you see what the themes are this year and really it's been a building for several years cloud everything social is running in the cloud now more and more not just public Claus but Federation's of public and private clouds it's it's all about cognitive computing which is a relatively new term in the Sun sets achieved a certain amount of vogue in the last year or so which is really fundamentally as an evolutionary trend it's basically a I for the 21st century but leveraging unstructured data and and machine learning and so forth and predictive analytics and you know well the whole world learn what metadata was with the whole NSA yeah comments no it's like me and then just to wrap it up in memory real-time blu acceleration you know you need real-time you need streaming you need collaboration and social you know peer-to-peer user-generated content all of that to make this new world culture really take off and IBM provides all that we recognize that that's where the world's going we've been orienting reorienting all of our solutions around these models cloud social increasingly going forward and you know we provide solutions that enable our customers in all industries to go there and big data is fundamental to all of that as we say we're computer science meets social science that's always been Silicon angles kind of masthead view but to unpack what you just said from the market relevance you mentioned Netflix we saw Amazon coming out their own movie they're going to go direct with their own programming so so but that speaks to the direct business model of the web was originally pioneered as hey direct business model cut the middleman out but now that dimension has been explored so that kind of what you're saying there so that's cool the end user pieces interesting image is social so what's your take on the end user orientation what's the expectation because you got social you got a trash you got in motion you got learning machines providing great recommendations got the Watson kind of yeah reasoning for people so personalization recommendation engines the sea change attention time currency big days of all those buzzwords all right what is the expectation for users in the future right now we're moving into this new world where I can self serve myself monologue based the information from the web now it's all coming at everyone real time the alarms are going off as Jeff Jonas says what is that prefer user experience the direct business model people get that I think the business to see that but now the end users are now at the center of the value proposition how do what's the role of the user now they're participating in the media there are also consumers of the media yeah and they now have different devices so what's the sources of data so fundamentally yeah the role of the consumers expectations now is always everything is always on everything is always online everything is all digital everything is all real time and streaming everything is all self-service everything is all available in the palm of my hand and then the back-end infrastructure the cross-channel infrastructure users don't care about individual socials they really don't they don't really fundamentally care about Facebook or Twitter or whatever you have they just care that what their experience is seamless as they move from one channel to another they're not perceived as channels anymore they're simply perceived as places or communities that overlap too in a dizzying array of socials thus social is where we all live and thus social increasingly is mobile increasingly mobile is you know the user expects that the handoff from my smartphone to my tablet to my laptop to my digital TV sentence and so forth that it all happens through the magic of infrastructure that it's being taken care of and they don't have to worry about that handoff it all it's all part of one seamless experience yeah they always just say the search business it's the it's the it's the intersection of contextual and behavioral yeah and now you take that online behaviors community contextual is context to what people are interested at any given time yeah it's so many longtail distributions at any given time so do you see the the new media companies that the new brands that might emerge mean there's all the talk about Marissa Mayer kind of turning over yahoo and yeah she some say putting lipstick on a pig but but but is that they're just an old older branch trying to be cool but is that what users want just like media but just user experience me like we're small media but we got big ideas but the thing is the outcomes right small frying big blues go figure are the outcomes still the same company still want to drive sales for their business sell a product provide great value you just want to find great content and find people I mean the same concept of the old web search find out and run sumit give any vision on how that environment will evolve for a user like is it going to be pushed at me do you see it a new portal developing is mmm Facebook's kind of a walled garden humble don't care about that what's your take on that the future vision of a user experience online user experience online future vision in many ways I think let's talk about Internet of Things because that keeps coming more and more into the discussion it's it's not so much that the user wants a seamless experience across channel cross device all that but a big part of that experience is the user knows that increasingly they'll have some confidence that whatever environments physical environments there in our being obviously there's privacy implications that surveillance here are being monitored and tracked and optimized to meet their requirements to some degree in other words environmental monitoring internet of things in your smart home you want to configure so you smart home so that every room that you walk into is as you as you're moving there even before you get there has already been optimized to your needs that ideally there should prediction Oh Jim's walking into the bathroom so turn the light on and also start to heat up the water because it's ten o'clock at night Jim's usually takes his bath around this time you sort of want that experience to be handled by the internet of things like nest these new tools like nest oh yeah yeah so essentially then it's my user experience is not just me interacting with devices but me simply moving through environments that are continuously optimized to my knees and needs of my family you know the whole notion of autonomous vehicles your vehicle if it's your personal vehicle then you want to always autumn optimize the experience in terms of like you know the heat setting and and the entertainment justement saan the you know the media center and they're always to be tailored to your specific needs at any point in time but also let's say you take a zipcar you rent a zipcar and you've got an ID with that company or any of the other companies that provide those on-demand rental car services ideally in this scenario that whatever vehicle you you rent through them for a few hours or so when you enter it it becomes your vehicle is completely customized to your needs because you're a loyal customer of that firm and they've got your profile information this is just a hypothetical I'm not speaking to anything that I actually know about what they're doing but fundamentally you know ideally any on-demand vehicle or conveyance or other item that you you lease in this new economy is personalized to your needs while you're using it and then as it were depersonalized when you check it back in so the next person can have it personalized to their use as long as they need it that's the vision of a big part of the vision of customer experience management personalization not just of your personal devices but personalization of almost any device or environment in which you are operating so that's one kanodia wants this question no I would ask one more question on that on the user experience came on Twitter from a big data alex says while you're on the subject which a my Alex I don't great great friend of the cube but thanks for the tweet today we don't have our crowd shado-pan we can get the chat going there but why not talk about AR and I've been in reality I mean honestly Internet of Things is now not the palm of your hand it could be on your wrist or on your clothing the wearables on the glasses and just gave out three invites to google glass so this is again another edition augmented reality is software paradigm as well what is that what is it what does that fit into that what's your take on augmented reality augmented reality ok so augmented reality is that which I don't use myself I've just simply seen it demonstrated and plenty of places so augmented reality is all about layers of additional information overlaid on whatever visual video view or image view that you happen to be carrying with you or have available to you while you're walking around in your normal life so right now conceivably if this is an AR a setting that I would environment or enabled device I would be able to see for example that ok who's in this room in the sense that who is declared that they are in this area of Mandalay Bay right now and why specifically are they doing to the extent that they allow that information to be seen and o of these people here which of these people if any might be the person I'm going to be speaking with it for 30 so that if they happen to be in this environment i can see that i can see that they're to some degree they may have indicated status waiting for james could be a list to get done with the Wikibon people oh that's kind of cool so I'd see that overlay and I walk to other parts of the Convention Center I might also see overlays as I walk around like oh there's a course down as several rooms down that I actually put in my schedule it's going to start in about five minutes I'll just duck you into there because it reminds me through the overlay that's the whole notion of personalization of the environment in which you're walking around in real time dynamically and contextual in alignment with your needs or with your requirements are in alignment also with these whatever data those environment managers wish to share to anybody who's subscribing in that contact so that's a context-aware that theme have been talking about here on textual essentially it's a public space that's personalized to your needs in the sense that you have a personalized view in a dynamically update okay that sounds like crowd chat Oh are we running a trip crouched at right now crouch at San overlay so just as lovely overlay so look to the minute social network yeah tailored to the needs of the group yep that adds value on top of that data yeah so James I gotta get your take on something so we had Merv on yesterday great Adrian with my great Buy analyst day and he was on last week at Big Data NYC you know we did our own little vent there Don coincident with hadoop world so Murph said well we're just entering the trough of disillusionment for big data yeah you love those Gartner you know I love medications tools I mean they are genius and I get him but he said that's a good thing because it goes left to right so we're making progress here ok right but I'm getting nervous the internet of things I love the concept we don't we don't work on industrial internet and you know a smarter planet it's in there so I love it but I'm getting nervous here's why I look back at a lot of the promises that were made in the BI days 360-degree other business predictive analytics a lot of things that are now talking about in the hood sort of Hadoop big data movement that we're actually fulfilling with this new wave that the old wave really wasn't able to fill because the cousin sort of distracted doing sarbanes-oxley and reporting in and balanced scorecards so so I'm nervous he's old school now it when he when he referenced is something that was hot in the mid part of the two thousand decade okay go ahead okay we had a guy on today talking about balance core would you know we're just talking about crowd chat that's the hottest day in 2013 like five years or hurt anybody mentions sarbanes-oxley so what kind of saved that whole business Roy thank you and Ron but so heavy right so what I'm nervous about as we as I've seen a number of waves over the years where the the vendor community promises a vision great vision great marketing and then all of a sudden something hotter comes along like Internet of Things and says don't know this is really it so my question to you is will help us it'll help me in my mind you know close that dissonance gap is are these two initiatives the sort of big data analytics for everybody putting analytics in the hands of business users yeah or is that sort of complementary to the internet of thing his internet of things just the new big trillion dollar market that everybody's going to go after and forget about all those promises about analytics everywhere help me sure Jay through that my job is to clarify confusion hey um you know if you look at the convergence of various call them paradigms there's a lot of big data analytics is one of them right now clearly there's cloud clearly their social there's big data analytics in mobile and there's something called Internet of Things so some some talk about smack smac social mobile analytic a que a big data cloud if you add IOT of there it's smack yet I don't think it works or smash yet but fundamentally if you think about Internet of Things it's it's all about machines or automated devices of various sorts probes and you know your smartphone and whatever I know servers or even you know the autonomous vehicles those are things that do things and you know they might be sources of data they would are they might be consumers of data they might conceivably even be intermediaries or brokers or routers or data what I'm getting at is that if you look at big data analytics I always think of it as a pipeline all data it's like data sources and data consumers and then there's all these databases and other functions that operate between them to move data and analytics and insight from one end to the other of the pipe in a conceptual way think of the internet of things as well a new category of sources of data these devices whether they be probes or monitors or your smart phones and new consumers and they all those same things are probably going to be many of them consumers of data and there's message passing among them and then the data that they passed might be passed in real time through streaming like InfoSphere streams it might be cached or stored and various intermediate databases and various analytics performed on them so think of you know I like to think of the internet of persons places and things persons that's human endpoints consumers and and sources of data that's all of us that's social places that's geospatial you know you think about it the Internet of geospatial you know geo spatial coordinates of of data and analytics and then there's things there's you know automated endpoints or you know hardware even Nana from macro to nano devices so it's just a new range of sources and and consumers of data and new types of analytics that are performed in new functions that can be performed and outcomes enable when you as it were stack in and out of things with social with claw with mobile new possibilities in terms of optimization in real time it throughout the you know the smarter planet if you think about the smarter planet vision it's all about interconnected instrumented and intelligent instrumented you know instrumentation that traditionally it suggests hardware instrumentation that's what probes our sensors and actuators that's the Internet of Things it's a fundamental infrastructure within smarter planet I'd love that thank you for clarifying i could write a blog post out of that and i think i'm very well made so um now i want to follow up and bring it back to the users I know snack and I thought you were going to say a story no smack MapReduce analytics and query or sell smack on the cube so so I want bring it back to the users so we had a great conversation yesterday actually last week I'll be met it was on off you know ah be met and he said look why are there any any you know where all the big data apps he said you need three things to for big data apps you need domain expertise you need algorithms which are free and you need data scientists like oh we'll never get there all right oh so rules really free while there are that was this argument yeah it means a source if people charge him for algorithms big trouble was this point I think okay sure so and then we had a discussion yesterday about how in the early days of the automobile industry you know the forecast was this is problematic the gap to adoption is just aren't enough chauffeurs know the premise that we were putting forth in the discussion yesterday I don't know who that was with was that with Judith it was good was that look we've got to figure out a way to get analytics in the hands of the business user we can't have to go through a data scientist or some business analyst no that's not going to work and we'll never get adoption so what what's going to bridge that gap is it is it the things you talked about before all these you know cool solutions that you guys are developing the project neo that you announce today visualization yeah there's another piece of that what puts it in the hands of guys like me that I can actually use the data in new and productive ways yeah well self-service business intelligence and visualization tools that are embedded in the very experience of using apps for example on your smartphone democratization of data science down to all of us you need the right tools you need you need the tools that the new generation of people like my children's generation just adopt and they work in there just a tune from from the cradle to working with data and visualizations and creating visual you know analytics of various sorts though they may not perceive it as being analytics they miss may perceive it as working with shapes and patterns and stuff yeah you would stop yeah so playing around you know in a sandbox i love that terminology data scientists working you know sandboxes which is data that's martes that they build to do regression analysis and segmentation and decision trees and all you know all that good stuff you know the fact is your sandbox can conceivably be completely on your handheld device with all the visualizations built-in you're simply doing searches and queries you know you're asking natural language questions you're looking at the responses you're changing your queries you're changing your visualizations and so forth to see if anything pops out at you as being significant playing around it you know it's as simple a matter that that these kinds of tools such as IBM you know cognos and so forth enable everybody to become as it worried a data scientist without having to you know become a maquette their profession it's just a part of the fabric of living in modern society where data surrounds us people are going to start playing with data and they're going to start teaching themselves all these capabilities in the same way that when they invented automobiles and you know wasn't Henry 42 invented them it was in like the late 1800s by engineers in Europe and America you know it's like we didn't all become auto mechanics you know there are trained auto mechanics but I think most human beings in the modern world know that there's a thing called an automobile that has an engine that needs gasoline and oil and occasionally needs to be brought to a professional mechanic for a repair and so forth we have many of us have a rough idea of something called a carburetor blah blah blah you know in the same way that when computers came up after world war two and then gradually invaded our lives through PCs and everything we all didn't become computer scientist but most of us have an idea of what a hard disk is most of it no most of us know something about something called software and things are called operating systems in the same way now in this new world most of us will become big data analytics geeks practical into the extent that will learn enough of the basic terms of art and the relationships among the various components to live our lives and when the stuff breaks down we call the likes of IBM to come and fix it or better yet they just buy our products and they just work magically all the time without fail conversing and comfortable with the concepts to the point which you can leverage them and what about visualization where does that fit visualization visualization is where the rubber meets the road of analytics is it's where human beings how human beings extract meaning insight fundamentally maybe that's like yeah you extracted inside a lots of different ways you do searches and so forth but to play around it to actually see you know a heat map or a geospatial map or or or you know a pie chart or whatever you see things with your eyes that you may not have realized we're there and if you can play around and play with different visualizations against the same data set things will pop out that you know the statistical model just seek the raw output of a data mining our predictive model or statistical analysis those patterns may not suggest themselves and rows of numbers that would pop out to an average human being or to a data scientist they need the visualizations to see things that you know because in other words when you think about analytics it's all about the algorithms that are drilling through the data to find those patterns but it's also about the visualizations the algorithms and you need the visualizations and of course you need the data to really enable human beings of all levels of expertise to find meaning and fundamentally visualizations are a lingua franca between non-expert human beings and expert eamon beings between data scientists visualizations are a lingua franca Hey look what I saw what do you think you know that's the whole promise of tools like concert for example we demonstrated this this morning it's a collaborative environment as sharing of visualizations and data sets and so forth among business analysts and the normal knowledge worker you know it with it you know like what do you see here's what I see what do you think I don't see that here's another visualization what do you see there oh yeah I think I see what you mean and here's my annotation about what I have broader context I've you know here's what I oh this is great that's the whole notion of humans deriving insight we derive it in socials we derive it in teams of that some Dave might be adept at seeing things that Jim is just absolutely blind to or you know Nancy might see things that both of us are applying to but we're all looking at the same pictures and we're all working with the same data part art yeah it's all so let's talk about some plumbing conversations you know one of the things that we noticed we were at the splunk conference this year's blown came out of nowhere taking log files making them manageable saving time for people so the thing that comes out of the splunk conversation is that it's just so easy to use that their customer testimonials are overwhelmingly positive around the area hey I just dumped my data into this the splunk box and it grid good stuffs happening I can search it it can give me insight save me time so that's the kind of ease of use so so how does IBM getting to that scenario because you guys have some good products we've got on the platform side but you also have some older products legacy Lotus other environments collaborative software that's all coming together in converging so how do we get to that environment where it's just that he just dumped your data in and let it do its magic well Odin go that's the very proposition that we provide with our puresystems puredata systems portfolio tree data system and big insights right for Hadoop so forth big in size you know we have an appliance now yeah we have pdh so that's the whole create load and go scenario that because Bob pidgeotto unless wretched and others demonstrated on the main stage yesterday and today so we did we do that and we are simple and straight being easy to use and so forth that's our value prop that's the whole value prop of an appliance you know simple you don't need a ton of expertise we pre build all the expert in a expertise patterns that you can use to derive quick value from this deployment we provide industry solution accelerates from machine data analytics on top of big insights to do the kinds of things you're talking about with splunk offerings so fundamentally you know that's scenario we all we and we're you know we have many fine competitors we offer that capability now in terms of the broader context you're describing we're a well-established provider of solutions we go back more than a hundred years we have many different product portfolios we have lots and lots of customers who would invested in IBM for a long time they might have our older products our newer products in various combinations we support the older generations we strive to migrate our customers to the newer releases when they're ready we don't force them to migrate so we make very we're very careful in our row maps to provide them with a migration path and to make it worth their while to upgrade when the time comes to the newer feature ok so I got it don't change gears to the to the shiny new toy conversation which is you know you know we love that in Silicon Valley what's a shiny new toy there's always an emerging markets when you have see changes like this where there's a whole the new whole new wave comes in creates new wealth old gets destructed new tags over whatever the conversation goes but I got to ask you okay well Elsa to the IBM landscape that you that you're over overlooking with big data and under the under the hood with cloud etc there's always that one thing that kind of breaks out as the leader the leading toy a shiny object that that people gravitate to as as I'm honest I won't say lost later because you got you know it's not not about giving away free it's it's the product that goes well we this is the lead horse you know and in this game right yeah so what is that what is the IBM thing right now that you're doubling down on is it blu acceleration is it incites is it point2 with a few highlights right now that's really cutting through the new the new the new soil of yeah we're developing our own rip off version of google glass thank you know I'm saying it's always I mean I'm gonna say shiny too but there's always that sexy product well I want that I want L customers name I want that product which leads more you know how she lifts for other products is there one is there a few you can talk about that you've noticed anecdotally is going to be specific data but just observational a shiny toy for the consumer market or for the business business business mark okay yeah yeah is it Watson is Watson the draw is it what's the headline looking for the lead lead dog here what's the attack there's always one an emerging market well you can put your the spot here well you could say that the funny thing is the whole notion of a shiny new toy implies something tangible when the world is gone more and more intangible in the cloud so we are moving our entire portfolio beginning links the big data analytics solutions into the cloud cloud first development going forward our other core principles for the pure data systems portfolio and the light for the shiny the shiny new thing the new cons could be shiny new concept or new paradigm yeah but the shiny new thing is the cloud the cloud is something pervasive and the cloud is something that it really multi form factors that's not very sexy but customers want flexibility you know they want to acquire the same functionality either as a licensed software package and running on commodity hardware we offer that for our big data analytics offerings or as an appliance and one sort or another that specialized particular occurrence or as a SAS cloud offering or as a capability that they can deploy in a virtualization layer on top of IBM or non-ibm hardware or they want the abilities you can mix and match those various deployment form factors so in many ways the whole notion of multi form factor flexibility is the shiny new thing it's the hybrid model for deployment of these capabilities on Prem in the cloud combination thereof that's not terribly sexy because it's totally it's totally abstract but it's totally real I mean demand wise people can see them that drives my business because when you go to the cloud I mean that's where you can really begin to scale seriously beyond the petabytes the whole notion of big media it will exist entirely in the cloud big media I like to think is the next sexy thing because streaming is coming into every aspect of human existence where stream computing a lot of people who focus on Big Data think of volume as being like big headline oh god we'd go to petabytes and exabytes and all that yeah it's important some really fixate on variety all these disparate sources of data and now we have all the sensor data and that's very important we have all the social media and everything all those new sources that's extremely important but look at the velocity everybody is expecting real-time instantaneous continuous streaming you know everything we do all of our entertainment all of our education surveillance you know everything is completely streaming I think ubiquitous streaming to every device and everybody themselves continue to continuing to stream their very lives everywhere all the time is the sexy new thing Dave and I talk about running data we coined that term running data what four years ago so I got to get you got to get kind of a thought leader they're watching us and we're watching streaming data right now from these said these are your guys are streaming this is big media give us some wanna get your thought leader perspective here some thought leader mojo around um the hashtag data economy you know you need now you're moving into a conversation with c-level folks and they said James tell me what the hell is this data economy thing right so what is the data economy in your words kind of like I mean I'll say it's a mindset I'll everything else what's your take on that we've been discussing that internally and externally at IBM we're trying to get our heads around what that means here's my take as one IBM are one thought Leigh right by the way the trick of being a thought leader is just to let your own thoughts lead you where they will turn around where all my followers yeah hopefully they want to lead you to far astray where you're out in the wilderness too long that's an important type of people are talking about because people are trying to put the definition around at economy can you actually have a business construct around yeah data here is my taken on the layers of the meaning of data economy it's monetizing your data the whole notion of monetization of your data data becomes a product that you generate internally or that you source from externally but you repackage it up and then resell with value add the whole notion of data monetization and you know implies a marketplace for data based products you know when I say data I'm using it in the broader context of it could be streaming media as the kind of one is a very valuable category of you know data like you know whatever kollywood provides so there's a whole notion of monetizing your data or providing a marketplace for others to monetize their data and you take a transaction fee from that or it also means in more of a traditional big data or data warehousing bi sense it means that you drive superior outcomes for your your own business from your own data you know through the usual method of better decision if better decisions on trustworthy data and the like so if you look at data monetization in terms of those layers including the marketplace including you know data-driven okay in many ways the whole notion of a data economy hinges on everybody's realization now that the chief resource for betterment of humanity one of the chief resources going forward for us to get smarter as a species on this planet is to continue to harness the data that we ourselves generate you know people stop what data is being the new oil what oil was there before we ever evolved but data wasn't there before we we landed on earth or before we evolved we generate that so it's our own exhaust your own exhaust that's actually a renewable resource data exhaust from data from exhausted gold that's what we say data is the data exhaust it's good if you can harness it and put it together as Jeff Jones says the puzzle piece is the picture the big picture at the smarter picture the smarter planet so on the final question I want to wrap up here to our next guest but what's going on with you these days talk about what's up with you you know you're very active on Facebook will you give a good following I'll be coming up what's happening you know I'll make sure I said big birthday for you on your Facebook page what's going on in your life I'll see you're working at IBM one of the things are interesting what's on your mind these days when you're at leisure are you hanging out you think what are you thinking about the most what are you doing with your you know things with your family's cherith let's see what's going on well I hang out at home with my wife and drink beer and listen to music and tweet about it everybody knows that stuff kind of beer do you drink whatever is on sale I'm not going to say where we buy it but it's a very nice place that whose initials are TJ but fundamentally you know my my mind is an open book because I evangelize I put my thoughts and my work thoughts and love my personal thoughts out there on socials I lived completely ons but I completely unsocial I self-edit but fundamentally the thought leadership I produce that the blogs and whatnot I produce all the time I put them out there for general discussion and I get a lot of good sort of feedback the world and including from inside of IBM I just try to stretch people's minds what's going on with me I'm just enjoying what I'm doing for a living now people save Jim you're with IBM why aren't you an analyst I'm still doing very analyst style work in in a vendor context I'm a thought leader I was a thought leader as I try to be being a thought leader is like being a humorist it's like it's a statement of your ambition not your outcome or your results yeah you can write jokes too you're blue in the face but if nobody laughs then you're not a successful comedian likewise i can write thought leadership pieces till I'm blue in the face but if nobody responds that I'm not leaving anybody anywhere i'm just going around in circles so my my ambition and every single day is to say at least one thing that might stretch somebody's box a little bit wider yeah yeah I think I think IBM smart they've been in social for a while the content markings about you know marketing to individuals yeah with credibility so I love analysts I love all my buds like like Merv and everybody else and I'm you know sort of a similar cat but you know there's a role for X analysts inside of solution providers and we have any number John Hegarty we have we have Brian Hill another X forest to write you know it's it's a you know it's a big industry but it's a small industry we have smart people on both sides of the equation solution provider and influencer my line um under people 99 seats and you know I I suck up to my superiors at IBM i suck up to any analyst who says nice things about me and hosts be on their show and i was going out of my life i'm just a big suck up well we like we like to have been looking forward to doing some crowd chats with you our new crouch an application with you guys lock you into that immediately it's a thought leader haven that the Crouch as as it turns out Dave what's your take on the analyst role at IBM just do a little analysis of the analyst at IBM which you're taken well I think it's under situation I think that the role that they that IBM's put James in is precisely the way in which corporations vendors should use former analysts they should give you a wide latitude a platform and and not try to filter you you know and you're good like that and so guess what I do the usual marketing stuff to the traditional but I do the new generation of thought leadership marketing and there's a role for both of those to me marketing have said this is if I said it was I said a hundred times marketing should be a source of value to people and it's so easy to make marketing a source of value by writing great content or producing great content so yeah that's my take on a jonathan your your marketing is a great explainer you explain the value to the market and thereby hopefully for your company generate demand hopefully in the direction of your cut your customers buying your things but that's what analysts the influencers should be explainers it's you know probably Dave I mean has influenced as influences that we are with with a qu here's my take on it when you have social media of direct full transparency there's no you can't head fake anyone anymore that all those days are gone so analyst bloggers people who are head faking a journalist's head faking the house the audiences will find out everything so to me it's like it's the metaphor of when someone knocks on your door your house and you open it up and they want to sell you something you shut the door in their face when you come in there and they say hey I want to hang out I got you know I got some free beer and a big-screen TV you want to watch some football maybe you invite him in the living room so the idea of communities and direct marketing's about when if you let them into your living room yeah you're not selling right you are creating value see what i do i drop smart i try to drop smart ideas into every conversational contacts throughout socials and also at events like i od so you know a big part of what I do is I thought leadership marketer is not just right you know you're clever blogs and all that but I simply participate in all the relevant conversations where I want I want ideas to be introduced and oh by they want way I definitely want people to be aware that I am an IBM employee and my company's provides really good products and services and support you know that's really a chief role of an evangelist in a high-tech slider that's one of the reasons why we started crouched at because the hashtag get so difficult to go deep into so creates crowd chatter let's go deeper and have a conversation and add some value to it you know it's you thinking about earned media as parents been kicked around but in communities the endorsement of trust earning a position whether you work at IBM people don't care a he works at IBM or whatever if you're creating value and you maybe have some free beer you get an entry but you win on your own merits you know I'm saying at the end of the day the content is the own merits and I think that's the open source paradigm that is hitting the content business which is community marketing if your pain-in-the-ass think you're going to get bounced out right out of the community or if you're selling something you're on so you guys do a great job really am i awesome you thank you James I really love what you add to the iod experience here with this corner and all the interviews is great great material well thanks for having us here really appreciate it I learned a lot it's been great you guys are great to work with very professional the products got great great-looking luqman portfolio hidden all hitting all the buttons there so hitting all the Gulf box so this is the cube we'll be right back with our last interview coming up shortly with Jeff Jonas he's got some surprises for us so we'll we'll see what he brings brings to his a game apparently he told me last night is bring his a-game to the cube so I'm a huge Jeff Jonas fan he's a rock star we love them on the cube iza teka athlete like yourself we write back with our next guest after this short break