>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering Data Works Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of Dataworks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Cindy Maike. She is the VP Industry Solutions and GM Insurance and Healthcare at Hortonworks. Thanks so much for coming on theCUBE, Cindy. >> Thank you, thank you, look forward to it. >> So, before the cameras were rolling we were talking about the business case for data, for data analytics. Walk our viewers through how you, how you think about the business case and your approach to sort of selling it. >> So, when you think about data and analytics, I mean, as industries we've been very good sometimes at doing kind of like the operational reporting. To me that's looking in the rearview mirror, something's already happened, but when you think about data and analytics, especially big data it's about what questions haven't I been able to answer. And, a lot of companies when they embark on it they're like, let's do it for technology's sake, but from a business perspective when we, as our industry GMs we are out there working with our customers it's like, what questions can't you answer today and how can I look at existing data on new data sources to actually help me answer questions. I mean, we were talking a little bit about the usage of sensors and so forth around telematics and the insurance industry, connected homes, connective lives, connected cars, those are some types of concepts. In other industries we're looking at industrial internet of things, so how do I actually make the operations more efficient? How do I actually deploy time series analysis to actually help us become more profitable? And, that's really where companies are about. You know, I think in our keynote this morning we were talking about new communities and it's what does that mean? How do we actually leverage data to either monetize new data sources or make us more profitable? >> You're a former insurance CFO, so let's delve into that use case a little bit and talk about the questions that I haven't asked yet. What are some of those and how are companies putting this thing to work? >> Yeah so, the insurance industry you know, it's kind of frustrating sometimes where as an insurance company you sit there and you always monitor what your combined ratio is, especially if you're a property casualty company and you go, yeah, but that tells me information like once a month, you know, but I was actually with a chief marketing officer recently and she's like, she came from the retail industry and she goes, I need to understand what's going on in my business on any given day. And so, how can we leverage better real time information to say, what customers are we interacting with? You know, what customers should we not be interacting with? And then you know, the last thing insurance companies want to do is go out and say, we want you as a customer and then you decline their business because they're not risk worthy. So, that's where we're seeing the insurance industry and I'll focus a lot on insurance here, but it's how do we leverage data to change that customer engagement process, look at connected ecosystems and it's a good time to be well fundamentally in the insurance industry, we're seeing a lot of use cases, but also in the retail industry, new data opportunities that are out there. We talked a little bit before the interview started on shrinkage and you know, the retail industry's especially in the food, any type of consumer type packages, we're starting to see the usage of sensors to actually help companies move fresh food around to reduce their shrinkage. You know, we've got. >> Sorry, just define shrinkage, 'cause I'm not even sure I understand, it's not that your gapple is getting smaller. It refers to perishable goods, you explain it. >> Right, so you're actually looking at, how do we make sure that my produce or items that are perishable, you know, I want to minimize the amount of inventory write offs that I have to do, so that would be the shrinkage and this one major retail chain is, they have a lot of consumer goods that they're actually saying, you know what, their shrinkage was pretty high, so they're now using sensors to help them monitor should we, do we need to move certain types of produce? Do we need to look at food before it expires you know, to make sure that we're not doing an inventory write off. >> You say sensors and it's kind of, are you referring to cameras taking photos of the produce or are you referring to other types of chemical analysis or whatever it might be, I don't know. >> Yeah, so it's actually a little bit of both. It's how do I actually you know, looking at certain types of products, so we all know when you walk into a grocery store or some type of department store, there's cameras all over the place, so it's not just looking at security, but it's also looking at you know, are those goods moving? And so, you can't move people around a store, but I can actually use the visualization and now with deep machine learning you can actually look at that and say, you know what, those bananas are getting a little ripe. We need to like move those or we need to help turn the inventory. And then, there's also things with bar coding you know, when you think of things that are on the shelves. So, how do I look at those bar codes because in the past you would've taken somebody down the isle. They would've like checked that, but no, now we're actually looking up the bar codes and say, do we need to move this? Do we need to put these things on sale? >> At this conference we're hearing just so much excitement and talk about data as the new oil and it is an incredible strategic asset, but you were also saying that it could become a liability. Talk about the point at which it becomes a liability. >> It becomes a liability when one, we don't know what to do with it, or we make decisions off of data data, so you think about you know, I'll give you an example, in the healthcare industry. You know, medical procedures have changed so immensely. The advancement in technology, precision medicine, but if we're making healthcare decisions on medical procedures from 10 years ago, so you really need to say how do I leverage you know, newer data stats, so over time if you make your algorithms based on data that's 10, 20 years old, it's good in certain things, but you know, you can make some bad business decisions if the data is not recent. So, that's when I talk about the liability aspect. >> Okay, okay, and then, thinking about how you talk with, collaborate with customers, what is your approach in the sense of how you help them think through their concerns, their anxieties? >> So, a lot of times it's really kind of understanding what's their business strategy. What are their financial, what are their operational goals? And you say, what can we look at from a data perspective, both data that we have today or data that we can acquire from new data sources to help them actually achieve their business goals and you know, specifically in the insurance industry we focus on top line growth with growing your premium or decreasing your combined ratio. So, what are the types of data sources and the analytical use cases that we can actually you know, use? See the exact same thing in manufacturing, so. >> And, have customer attitudes evolved over time since you've been in the industry? How would you describe their mindsets right now? >> I think we still have some industries that we struggle with, but it's actually you know, I mentioned healthcare, the way we're seeing data being used in the healthcare industry, I mean, it's about precision medicine. You look at gnomics research. It says that if people like 58 percent of the world's population would actually do a gnomics test if they could actually use that information. So, it's interesting to see. >> So, the struggle is with people's concern about privacy encroachment, is that the primary struggle? >> There's a little bit of that and companies are saying, you know, I want to make sure that it's not being used against me, but there was actually a recent article in Best Review, which is an insurance trade magazine, that says, you know, if I have, actually have a gnomic test can the insurance industry use that against me? So, I mean, there's still a little bit of concern. >> Which is a legitimate concern. >> It is, it is, absolutely and then also you know, we see globally with just you know, the General Data Protection act, the GDPR, you know, how are companies using my information and data? So you know, consumers have to be comfortable with the type of data, but outside of the consumer side there's so much data in the industry and you made the comment about you know, data's the new oil. I have a thing, against, with that is, but we don't use oil straight in a car, we don't use crude putting in a car, so once we do something with it which is the analytical side, then that's where we get the business end side. So, data for data's sake is just data. It's the business end sites is what's really important. >> Looking ahead at Hortonworks five, 10 years from now I mean, how much, how much will your business account for the total business of Hortonworks do you think, in the sense of as you've said, this is healthcare and insurance represents such huge potential possibilities and opportunities for the company? Where do you see the trajectory? >> The trajectory I believe is really in those analytical apps, so we were working with a lot of partners that are like you know, how do I accelerate those business value because like I said, it's like we're not just into data management, we're in the data age and what does that mean? It's like turning those things into business value and I've got to be able to I think from an industry perspective, you know be working with the right partners and then also customers because they lack some of the skillsets. So, who can actually accelerate the time to value of using data for profitability? >> Is your primary focus area at helping regulated industries with their data analytics challenges and using IOT or does it also cover unregulated? >> Unregulated as well. >> Are the analytics requirements different between regulated and unregulated in terms of the underlying capabilities they require in terms of predictive modeling, of governance and so forth and how does Hortonworks differentiate their response to those needs? >> Yeah, so it varies a little bit based upon their regulations. I mean, even if you look at life sciences, life sciences is very, very regulated on how long do I have to keep the data? How can I actually use the data? So, if you look at those industries that maybe aren't regulated as much, so we'll get away from financial services, highly regulated across all different areas, but I'll also look at say business insurance, not as much regulated as like you and I as consumers, because insurance companies can use any type of data to actually do the pricing and doing the underwriting and the actual claims. So, still regulated based upon the solvency, but not regulated on how we use it to evaluate risk. Manufacturing, definitely some regulation there from a work safety perspective, but you can use the data to optimize your yields you know, however you see fit. So, we see a mixture of everything, but I think from a Hortonworks perspective it's being able to share data across multiple industries 'cause we talk about connected ecosystems and connected ecosystems are really going to change business of the future. >> So, how so? I mean, especially in bringing it back to this conference, to Data Works, and the main stage this morning we heard so much about these connected communities and really it's all about the ecosystem, what do you see as the biggest change going forward? >> So, you look at, and I'll give you the context of the insurance industry. You look at companies like Arity, which is a division of All State, what they're doing actually working with the car manufacturers, so at some point in time you know, the automotive industry, General Motors tried this 20 years ago, they didn't quite get it with On Star and GMAC Insurance. Now, you actually have the opportunity with you know, maybe on the front man for the insurance industry. So, I can now start to collect the data from the vehicle. I'm using that for driving of the vehicle, but I can also use it to help a driver make safer driving. >> And upsize their experience of actually driving, making it more pleasant as well as safer. There's many layers of what can be done now with the same data. Some of those uses impinge or relate to regulated concern or mandatory concerns, then some are purely for competitive differentiation of the whole issue of experience. >> Right, and you think about certain aspects that the insurance industry just has you know, a negative connotation and we have an image challenge on what data can and cannot be used, so, but a lot of people opt in to an automotive manufacturer and share that type of data, so moving forward who's to say with the connected ecosystem I still have the insurance company in the background doing all the underwriting, but my distribution channel is now the car dealer. >> I love it, great. That's a great note to end on. Thanks so much for coming on theCUBE. Thank you Cindy. I'm Rebecca Knight for James Kobielus. We will have more from theCUBE's live coverage of Data Works in just a little bit. (upbeat music)

>> Narrator: From Berlin, Germany, it's theCUBE. Covering Data Works Summit, Europe 2018. Brought to you by, Horton Works. >> Well hello, welcome to theCUBE. I am James Kobielus. I'm the lead analyst within the Wikbon Team at Silicon Angled Media, focused on big data analytics. And big data analytics is what Data Works Summit is all about. We are at Data Works Summit 2018 in Berlin, Germany. We are on day two, and I have, as my special guest here, Pankaj Sodhi, who is the big data practice lead with Accenture. He's based in London, and he's here to discuss really what he's seeing in terms of what his clients are doing with Big DSO. Hello, welcome Pankaj, how's it going? >> Thank you Jim, very pleased to be there. >> Great, great, so what are you seeing in terms of customers adoption of the dupe and so forth, big data platforms, for what kind of use cases are you seeing? GDPR is coming down very quickly, and we saw this poll this morning that John Chrysler, of Horton Works, did from the stage, and it's a little bit worrisome if you're an enterprise data administrator. Really, in enterprise period, because it sounds like not everybody in this audience, in fact a sizeable portion, is not entirely ready to comply with GDRP on day one, which is May 25th. What are you seeing, in terms of customer readiness, for this new regulation? >> So Jim, I'll answer the question in two ways. One was, just in terms of, you know, the adoption of Hadoop, and then, you know, get into GDPR. So in regards to Hadoop adoption, I think I would place clients in three different categories. The first ones are the ones that have been quite successful in terms of adoption of Hadoop. And what they've done there is taken a very use case driven approach to actually build up the capabilities to deploy these use cases. And they've taken an additive approach. Deployed hybrid architectures, and then taken the time. >> Jim: Hybrid public, private cloud? >> Cloud as well, but often sort of, on premise. Hybrid being, for example, with an EDW and product type AA. In that scenario, they've taken the time to actually work out some of the technical complexities and nuances of deploying these pipelines in production. Consequently, what they're in a good position to do now, is to leverage the best of Cloud computing, open so its technology, while it's looking at making the best getting the investment protection that they have from the premise deployments as well. So they're in a fairly good position. Another set of customers have done successful pilots looking at either optimization use cases. >> Jim: How so, Hadoob? >> Yes, leveraging Hadoob. Either again from a cost optimization play or potentially a Bon Sand escape abilities. And there in the process of going to production, and starting to work out, from a footprint perspective, what elements of the future pipelines are going to be on prim, potentially with Hadoop, or on cloud with Hadoop. >> When you say the pipeline in this context, what are you referring to? When I think of pipeline, in fact in our coverage of pipeline, it refers to an end to end life cycle for development and deployment and management of big data. >> Pankaj: Absolutely >> And analytics, so that's what you're saying. >> So all the way from ingestion to curation to consuming the data, through multiple different access spots, so that's the full pipeline. And I think what the organizations that have been successful have done is not just looked at the technology aspect, which is just Hadoop in this case, but looked at a mix of architecture, delivery approaches, governance, and skills. So I'd like to bring this to life by looking at advanced analytics as a use case. So rather than take the approach of lets ingest all data in a data lake, it's been driven by a use case mapped to a set of valuable data sets that can be ingested. But what's interesting then is the delivery approach has been to bring together diverse skill sets. For example, date engineers, data scientists, data ops and visualization folks, and then use them to actually challenge architecture and delivery approach. I think this is where, the key ingredient for success, which is, for me, the modern sort of Hadoob's pipeline, need to be iteratively built and deployed, rather than linear and monolithic. So this notion of, I have raw data, let me come up a minimally curated data set. And then look at how I can do future engineering and build an analytical model. If that works, and I need to enhance, get additional data attributes, I then enhance the pipeline. So this is already starting to challenge organizations architecture approaches, and how you also deploy into production. And I think that's been one of the key differences between organizations that have embarked on the journey, ingested the data, but not had a path to production. So I think that's one aspect. >> How are the data stewards of the world, or are they challenging the architecture, now that GDPR is coming down fast and furious, we're seeing, for example Horton Works architecture for data studio, are you seeing did the data govern as the data stewards of the world coming, sitting around the virtual table, challenging this architecture further to evolve? >> I think. >> To enable privacy by default and so forth? >> I think again, you know the organizations that have been successful have already been looking at privacy by design before GDPR came along. Now one of the reasons a lot of the data link implementation haven't been as successful, is the business haven't had the ability to actually curate the data sets, work out what the definitions are, what the curation levels are. So therefore, what we see with business glossaries, and sort of data architectures, from a GDPR perspective, we see this as an opportunity rather than a threat. So to actually make the data usable in the data lakes, we often talk to clients about this concept of the data marketplace. So in the data marketplace, what you need to have, is well curated data sets. The proper definition such will, for business glossary or a data catalog, underpin by the right user access model, and available for example through a search or API's. So, GDPR actually is. >> There's not a public market place, this is an architectural concept. >> Yes. >> It could be inside, completely inside, the private data center, but it's reusable data, it's both through API, and standard glossaries and meta data and so forth, is that correct? >> Correct, so data marketplace is reusable, both internally, for example, to unlock access to data scientists who might want to use the data set and then put that into a data lab. It can also be extended, from an APR perspective, for a third party data market place for exchanging data with consumers or third parties as organizations look at data monetization as well. And therefore, I think the role of data stewards is changing around a bit. Rather than looking at it from a compliance perspective, it's about how can we make data usable to the analysts and the data scientists. So actually focusing on getting the right definitions upfront, and as we curate and publish data, and as we enrich it, what's the next definition that comes of that? And actually have that available before we publish the data. >> That's a fascinating concept. So, the notion of a data steward or a data curator. It's sort of sounds like you're blending them. Where the data curator, their job, part of it, very much of it, involves identifying the relevance of data and the potential reusability and attractiveness of that data for various downstream uses and possibly being a player in the ongoing identification of the monetize-ability of data elements, both internally and externally in the (mumbles). Am I describing correctly? >> Pankaj: I think you are, yes. >> Jim: Okay. >> I think it's an interesting implication for the CDO function, because, rather than see the function being looked at as a policy. >> Jim: The chief data officer. >> Yes, chief data officer functions. So rather than imposition of policies and standards, it's about actually trying to unlock business values. So rather than look at it from a compliance perspective, which is very important, but actually flip it around and look at it from a business value perspective. >> Jim: Hmm. >> So for example, if you're able to tag and classify data, and then apply the right kind of protection against it, it actually helps the data scientists to use that data for their models. While that's actually following GDPR guidelines. So it's a win-win from that perspective. >> So, in many ways, the core requirement for GDPR compliance, which is to discover an inventory and essentially tag all of your data, on a fine grade level, can be the greatest thing that ever happened to data monetization. In other words, it's the foundation of data reuse and monetization, unlocking the true value to your business of the data. So it needn't be an overhead burden, it can be the foundation for a new business model. >> Absolutely, Because I think if you talk about organizations becoming data driven, you have to look at what does the data asset actually mean. >> Jim: Yes. >> So to me, that's a curated data set with the right level of description, again underpinned by the right authority of privacy and ability to use the data. So I think GDPR is going to be a very good enabler, so again the small minority of organizations that have been successful have done this. They've had business laws freeze data catalogs, but now with GDPR, that's almost I think going to force the issue. Which I think is a very positive outcome. >> Now Pankaj, do you see any of your customers taking this concept of curation and so forth, the next step in terms of there's data assets but then there's data derived assets, like machine learning models and so forth. Data scientists build and train and deploy these models and algorithms, that's the core of their job. >> Man: Mhmm. >> And model governance is a hot hot topic we see all over. You've got to have tight controls, not just on the data, but on the models, 'cause they're core business IP. Do you see this architecture evolving among your customer so that they'll also increasingly be required to want to essentially catalog the models and identify curate them for re-usability. Possibly monetization opportunities. Is that something that any of your customers are doing or exploring? >> Some of our customers are looking at that as well. So again, initially, exactly it's an extension of the marketplace. So while one aspect of the marketplace is data sets, you can then combine to run the models, The other aspect is models that you can also search for and prescribe data. >> Jim: Yeah, like pre-trained models. >> Correct. >> Can be golden if they're pre trained and the core domain for which they're trained doesn't change all that often, they can have a great after market value conceivably if you want to resell that. >> Absolutely, and I think this is also a key enabler for the way data scientists and data engineers expect to operate. So this notion of IDs of collaborative notebooks and so forth, and being able to soft of share the outputs of models. And to be able to share that with other folks in the team who can then maybe tweak it for a different algorithm, is a huge, I think, productivity enabler, and we've seen. >> Jim: Yes. >> Quite a few of our technology partners working towards enabling these data scientists to move very quickly from a model they may have initially developed on a laptop, to actually then deploying the (mumbles). How can you do that very quickly, and reduce the time from an ideal hypothesis to production. >> (mumbles) Modularization of machine learning and deep learning, I'm seeing a lot of that among data scientists in the business world. Well thank you, Pankaj, we're out of time right now. This has been very engaging and fascinating discussion. And we thank you very much for coming on theCUBE. This has been Pankaj Sodhi of Accenture. We're here at Data Works Summit 2018 in Berlin, Germany. Its been a great show, and we have more expert guests that we'll be interviewing later in the day. Thank you very much, Pankaj. >> Thank you very much, Jim.

>> Narrator: Live from San Jose, in the heart of Silicon Valley, it's theCUBE! Covering Data Works Summit 2017. Brought to you by Hortonworks. >> Welcome back to theCUBE. I'm Lisa Martin with my co-host George Gilbert. We are live on day one of the Data Works Summit in San Jose in the heart of Silicon Valley. Great buzz in the event, I'm sure you can see and hear behind us. We're very excited to be joined by a couple of fellows from IBM. A very longstanding Hortonworks partner that announced a phenomenal suite of four new levels of that partnership today. Please welcome Asad Mahmood, Analytics Cloud Solutions Specialist at IBM, and medical doctor, and Linton Ward, Distinguished Engineer, Power Systems OpenPOWER Solutions from IBM. Welcome guys, great to have you both on the queue for the first time. So, Linton, software has been changing, companies, enterprises all around are really looking for more open solutions, really moving away from proprietary. Talk to us about the OpenPOWER Foundation before we get into the announcements today, what was the genesis of that? >> Okay sure, we recognized the need for innovation beyond a single chip, to build out an ecosystem, an innovation collaboration with our system partners. So, ranging from Google to Mellanox for networking, to Hortonworks for software, we believe that system-level optimization and innovation is what's going to bring the price performance advantage in the future. That traditional seamless scaling doesn't really bring us there by itself but that partnership does. >> So, from today's announcements, a number of announcements that Hortonworks is adopting IBM's data science platforms, so really the theme this morning of the keynote was data science, right, it's the next leg in really transforming an enterprise to be very much data driven and digitalized. We also saw the announcement about Atlas for data governance, what does that mean from your perspective on the engineering side? >> Very exciting you know, in terms of building out solutions of hardware and software the ability to really harden the Hortonworks data platform with servers, and storage and networking I think is going to bring simplification to on-premises, like people are seeing with the Cloud, I think the ability to create the analyst workbench, or the cognitive workbench, using the data science experience to create a pipeline of data flow and analytic flow, I think it's going to be very strong for innovation. Around that, most notable for me is the fact that they're all built on open technologies leveraging communities that universities can pick up, contribute to, I think we're going to see the pace of innovation really pick up. >> And on that front, on pace of innovation, you talked about universities, one of the things I thought was really a great highlight in the customer panel this morning that Raj Verma hosted was you had health care, insurance companies, financial services, there was Duke Energy there, and they all talked about one of the great benefits of open source is that kids in universities have access to the software for free. So from a talent attraction perspective, they're really kind of fostering that next generation who will be able to take this to the next level, which I think is a really important point as we look at data science being kind of the next big driver or transformer and also going, you know, there's not a lot of really skilled data scientists, how can that change over time? And this is is one, the open source community that Hortonworks has been very dedicated to since the beginning, it's a great it's really a great outcome of that. >> Definitely, I think the ability to take the risk out of a new analytical project is one benefit, and the other benefit is there's a tremendous, not just from young people, a tremendous amount of interest among programmers, developers of all types, to create data science skills, data engineering and data science skills. >> If we leave aside the skills for a moment and focus on the, sort of, the operationalization of the models once they're built, how should we think about a trained model, or, I should break it into two pieces. How should we think about training the models, where the data comes from and who does it? And then, the orchestration and deployment of them, Cloud, Edge Gateway, Edge device, that sort of thing. >> I think it all comes down to exactly what your use case is. You have to identify what use case you're trying to tackle, whether that's applicable to clinical medicine, whether that's applicable to finance, to banking, to retail or transportation, first you have to have that use case in mind, then you can go about training that model, developing that model, and for that you need to have a good, potent, robust data set to allow you to carry out that analysis and whether you want to do exploratory analysis or you want to do predictive analysis, that needs to be very well defined in your training stage. Once you have that model developed, then we have certain services, such as Watson Machine Learning, within data science experience that will allow you to take that model that you just developed, just moments ago, and just deploy that as a restful API that you can then embed into an application and to your solution, and in that solution you can basically use across industry. >> Are there some use cases where you have almost like a tiering of models where, you know, there're some that are right at the edge like, you know, a big device like a car and then, you know, there's sort of the fog level which is the, say, cell towers or other buildings nearby and then there's something in the Cloud that's sort of like, master model or an ensemble of models, I don't assume that's like, Evel Knievel would say you know, "Don't try that at home," but sort-of, is the tooling being built to enable that? >> So the tooling is already in existence right now. You can actually go ahead right now and be able to build out prototypes, even full-level, full-range applications right on the Cloud, and you can do that, you can do that thanks to Data Science Experience, you can do that thanks to IBM Bluemix, you can go ahead and do that type of analysis right there and not only that, you can allow that analysis to actually guide you along the path from building a model to building a full-range application and this is all happening on the Cloud level. We can talk more about it happening on on-premise level but on the Cloud level specifically, you can have those applications built on the fly, on the Cloud and have them deployed for web apps, for moblie apps, et cetera. >> One of the things that you talked about is use cases in certain verticals, IBM has been very strong and vertically focused for a very long time, but you kind of almost answered the question that I'd like to maybe explore a little bit more about building these models, training the models, in say, health care or telco and being able to deploy them, where's the horizontal benefits there that IBM would be able to deliver faster to other industries? >> Definitely, I think the main thing is that IBM, first of all, gives you that opportunity, that platform to say that hey, you have a data set, you have a use case, let's give you the tooling, let's give you the methodology to take you from data, to a model, to ultimately that full range application and specifically, I've built some applications specific to federal health care, specifically to address clinical medicine and behavioral medicine and that's allowed me to actually use IBM tools and some open source technologies as well to actually go out and build these applications on the fly as a prototype to show, not only the realm, the art of the possible when it comes to these technologies, but also to solve problems, because ultimately, that's what we're trying to accomplish here. We're trying to find real-world solutions to real-world problems. >> Linton, let me re-direct something towards you about, a lot of people are talking about how Moore's law slowing down or even ending, well at least in terms of speed of processors, but if you look at the, not just the CPU but FPGA or Asic or the tensor processing unit, which, I assume is an Asic, and you have the high speed interconnects, if we don't look at just, you know what can you fit on one chip, but you look at, you know 3D what's the density of transistors in a rack or in a data center, is that still growing as fast or faster, and what does it mean for the types of models that we can build? >> That's a great question. One of the key things that we did with the OpenPOWER Foundation, is to open up the interfaces to the chip, so with NVIDIA we have NVLink, which gives us a substantial increase in bandwidth, we have created something called OpenCAPI, which is a coherent protocol, to get to other types of accelerators, so we believe that hybrid computing in that form, you saw NVIDIDA on-stage this morning, and we believe especially for deploring the acceleration provided for GPUs is going to continue to drive substantial growth, it's a very exciting time. >> Would it be fair to say that we're on the same curve, if we look at it, not from the point of view of, you know what can we fit on a little square, but if we look at what can we fit in a data center or the power available to model things, you know Jeff Dean at Google said, "If Android users "talk into their phones for two to three minutes a day, "we need two to three times the data centers we have." Can we grow that price performance faster and enable sort of things that we did not expect? >> I think the innovation that you're describing will, in fact, put pressure on data centers. The ability to collect data from autonomous vehicles or other N points is really going up. So, we're okay for the near-term but at some point we will have to start looking at other technologies to continue that growth. Right now we're in the throws of what I call fast data versus slow data, so keeping the slow data cheaply and getting the fast data closer to the compute is a very big deal for us, so NAND flash and other non-volatile technologies for the fast data are where the innovation is happening right now, but you're right, over time we will continue to collect more and more data and it will put pressure on the overall technologies. >> Last question as we get ready to wrap here, Asad, your background is fascinating to me. Having a medical degree and working in federal healthcare for IBM, you talked about some of the clinical work that you're doing and the models that you're helping to build. What are some of the mission critical needs that you're seeing in health care today that are really kind of driving, not just health care organizations to do big data right, but to do data science right? >> Exactly, so I think one of the biggest questions that we get and one of the biggest needs that we get from the healthcare arena is patient-centric solutions. There are a lot of solutions that are hoping to address problems that are being faced by physicians on a day-to-day level, but there are not enough applications that are addressing the concerns that are the pain points that patients are facing on a daily basis. So the applications that I've started building out at IBM are all patient-centric applications that basically put the level of their data, their symptoms, their diagnosis, in their hands alone and allows them to actually find out more or less what's going wrong with my body at any particular time during the day and then find the right healthcare professional or the right doctor that is best suited to treating that condition, treating that diagnosis. So I think that's the big thing that we've seen from the healthcare market right now. The big need that we have, that we're currently addressing with our Cloud analytics technology which is just becoming more and more advanced and sophisticated and is trending towards some of the other health trends or technology trends that we have currently right now on the market, including the Blockchain, which is tending towards more of a de-centralized focus on these applications. So it's actually they're putting more of the data in the hands of the consumer, of the hands of the patient, and even in the hands of the doctor. >> Wow, fantastic. Well you guys, thank you so much for joining us on theCUBE. Congratulations on your first time being on the show, Asad Mahmood and Linton Ward from IBM, we appreciate your time. >> Thank you very much. >> Thank you. >> And for my co-host George Gilbert, I'm Lisa Martin, you're watching theCUBE live on day one of the Data Works Summit from Silicon Valley but stick around, we've got great guests coming up so we'll be right back.

>> Narrator: Covering Data Works Summit Europe 2017 brought to you by Horton Works. (uptempo, energetic music) >> Okay, welcome back everyone. We're here live in Germany at Munich for DataWorks Summit 2017, formerly Hadoop Summit. I'm John Furrier, my co-host Dave Vellante. Our next guest is Mike Merritt-Holmes, is senior Vice President of Global Services Strategy at Think Big, a Teradata company, formerly the co-founder of the Big Data Partnership merged in with Think Big and Teradata. Mike, welcome to The Cube. >> Mike: Thanks for having me. >> Great having an entrepreneur on, you're the co-founder, which means you've got that entrepreneurial blood, and I got to ask you, you know, you're in the big data space, you got to be pretty pumped by all the hype right now around AI because that certainly gives a lot of that extra, extra steroid of recognition. People love AI it gives a face to it, and certainly IOT is booming as well, Internet of Things, but big data's cruising along. >> I mean it's a great place to be. The train is certainly going very, very quickly right now. But the thing for us is, we've been doing data science and AI and trying to build business outcomes, and value for businesses for a long time. It's just great now to see this really, the data science and AI both were really starting to take effect and so companies are starting to understand it and really starting to really want to embrace it which is amazing. >> It's inspirational too, I mean I have a bunch of kids in my family, some are in college and some are in high school, even the younger generation are getting jazzed up on just software, right, but the big data stuffs been cruising along now. It's been a good, decade now of really solid DevOps culture, cloud now accelerating, but now the customers are forcing the vendors to be very deliberate in delivering great product, because the demand (chuckling) for real time, the demand for more stuff, is at an all time high. Can you elaborate your thoughts on, your reaction to what customers are doing, because they're the ones driving everyone, not to create friction, to create simplicity. >> Yeah, and you know, our customers are global organizations, trying to leverage this kind of technology, and they are, you know, doing an awesome amount of stuff right now to try to move them from, effectively, a step change in their business, whether it's, kind of, shipping companies doing preventive asset maintenance, or whether it's retailers looking to target customers in a more personalized way, or really understand who their customers are, where they come from, they're leveraging all those technologies, and really what they're doing is pushing the boundaries of all of them, and putting more demands on all of the vendors in the space to say, we want to do this quicker, faster, but more easily as well. >> And then the things that you're talking about, I want to get your thoughts on, because this is the conversation that you're having with customers, I want to extract is, have those kind of data-driven mindset questions, have come out the hype of the Hadoob. So, I mean we've been on a hype cycle for awhile, but now its back to reality. Where are we with the customer conversations, and, from your stand point, what are they working on? I mean, is it mostly IT conversation? Is it a frontoffice conversation? Is it a blend of both? Because, you know, data science kind of threads both sides of the fence there. >> Yeah, I mean certainly you can't do big data without IT being involved, but since the start, I mean, we've always been engaged with the business, it's always been about business outcome, because you bring data into a platform, you provide all this data science capability, but unless you actually find ROI from that, then there's no point, because you want to be moving the business forward, so it's always been about business engagement, but part of that has always been also about helping them to change their mindset. I don't want a report, I want to understand why you look at that report and what's the thing you're looking for, so we can start to identify that for you quicker. >> What's the coolest conversation you've been in, over the past year? >> Uh, I mean, I can't go into too much details, but I've had some amazing conversations with companies like Lego, for instance, they're an awesome company to work with. But when you start to see some of the things we're doing, we're doing some amazing object recognition with deep-learning in Japan. We're doing some ford analytics in the Nordics with deep-learning, we're doing some amazing stuff that's really pushing the boundaries, and when you start to put those deep-learning aspects into real world applications, and you start to see, customers clambering over to want to be part of that, it's a really exciting place to be. >> Let me just double-click on that for a second, because a lot of, the question I get a lot on The Cube, and certainly off-camera is, I want to do deep-learning, I want to do AI, I love machine learning, I hear, oh, it's finally coming to reality so people see it forming. How do they get started, what are some of the best practices of getting involved in deep-learning? Is it using open-source, obviously, is one avenue, but what advice would you give customers? >> From a deep-learning perspective, so I think first of all, I mean, a lot of the greatest deep-learning technologies, run open-source, as you rightly said, but I think actually there's a lot of tutorials and stuff on there, but really what you need is someone who has done it before, who knows where the pitfalls are, but also know when to use the right technology at the right time, and also to know around some of the aspects about whether using a deep-learning methodology is going to be the right approach for your business problem. Because a lot of companies are, like, we want to use this deep-learning thing, its amazing, but actually its not appropriate, necessarily, for the use case you're trying to draw from. >> It's the classic holy grail, where is it, if you don't know what you're looking for, it's hard to know when to apply it. >> And also, you've got to have enough data to utilize those methods as well, so. >> You hear a lot about the technical complexity associated with Hadoop specifically, but just ol' big data generally. I wonder if you could address that, in terms of what you're seeing, how people are dealing with that technical complexity but what other headwinds are there, in terms of adopting these new capabilities. >> Yeah, absolutely, so one of the challenges that we still see is that customers are struggling to leverage value from their platform, and normally that's because of the technical complexities. So we really, we introduced to the open-source world last month Kaylo, something you can download free of charge. It's completely open-source on the Apache license, and that really was about making it easier for customers to start to leverage the data on the platform, to self-serve injection onto that, and for data scientists to wrangle the data better. So, I think there's a real push right now about that next level up, if you like, in the technology stack to start to enable non-technical users to start to do interesting things on the platform directly, rather than asking someone to do it for them. And that, you know, we've had technologies in the PI space like Tableau, and, obviously, the (mumbling) did a data-warehouse solutions on Teradata that have been giving customers something, before and previously, but actually now they're asking for more, not just that, but more as well. And that's where we are starting to see the increases. >> So that's sort of operationalizing analytics as an example, what are some of the business complexities and challenges of actually doing that? >> That's a very good question, because, I think, when you find out great insight, and you go, wow you've built this algorithm, I've seen things I've never seen before, then the business wants to have that always on they want to know that it's that insight all the time is it changing, is it going up, is it going down do I need to change my business decisions? And doing that and making that operational means, not only just deploying it but also monitoring those models, being able to keep them up to date regularly, understanding whether those things are still accurate or not, because you don't want to be making business decisions, on algorithms that are now a bit stale. So, actually operationalizing it, is about building out an entire capability that's keeping these things accurate, online, and, therefore, there's still a bit of work to do, I think, actually in the marketplace still, around building out an operational capability. >> So you kind of got bottom-up, top-down. Bottom-up is the you know the Hadoop experiments, and then top-down is CXO saying we need to do big data. Have those two constituencies come together now, who's driving the bus? Are they aligned or is it still, sort of, a mess organizationally? >> Yeah, I mean, generally, in the organization, there's someone playing the Chief Data Officer, whether they have that as a title or a roll, ultimately someone is in charge of generating value from the data they have in the organization. But they can't do that with IT, and I think where we've seen companies struggle is where they've driven it from the bottom-up, and where they succeed is where they drive it from the top-down, because by driving it from the top-down, you really align what you're doing with the business and strategy that you have. So, the company strategy, and what you're trying to achieve, but ultimately, they both need to meet in the middle, and you can't do one without the other. >> And one of our practitioner friends, who's describing this situation in our office in Palo Alto, a couple of weeks ago. he said, you know, the challenge we have as an organization is, you've got top people saying alright, we're moving. And they start moving, the train goes, and then you've got kind of middle management, sort of behind them, and then you got the doers that are far behind, and aligning those is a huge challenge for this particular organization. How do you recommend organizations to address that alignment challenge, does Think Big have capabilities to help them through that, or is that, sort of, you got to call Accenture? >> In essence, our reason for being is to help with those kind of things, and, you know, whether it's right from the start, so, oh, my God, my Chief Data Officer or my CEO is saying we need to be doing this thing right now, come on, let's get on with it, and we help them to understand what does that mean, what are the use cases, how, where's the value going to come from, what's that architecting to look like, or whether its helping them to build out capability, in terms of data science or building out the cluster itself, and then managing that and providing training for staff. Our whole reason for being is supporting that transformation as a business, from, oh, my God, what do I do about this thing, to, I'm fully embracing it, I know what's going on, I'm enabling my business, and I'm completely comfortable with that world. >> There was a lot talk three, or four or five years ago, about the ROI of so-called big data initiatives, not being really, you know, there were edge cases which were huge ROI, but there was a lot of talk about not a lot of return. My question is, has that, first question, has that changed, are you starting to see much bigger phone numbers coming back where the executives are saying yeah, lets double down on this. >> Definitely, I'm definitely seeing that. I mean, I think it's fair to say that companies are a bit nervous about reporting their ROI around this stuff, in some cases, so there's more ROI out there than you necessarily see out in the public place, but-- >> Why is that? Because they don't want to expose to the competition, or they don't want to front run their earnings, or whatever it is? >> They're trying to get a competitive edge. The minute you start saying, we're doing this, their competitors have an opportunity to catch up. >> John: Very secretive. >> Yeah and I think, it's not necessarily about what they're doing, it's about keeping the edge over their customers, really, over their competitors. So, but what we're seeing is that many customers are getting a lot of ROI more recently because they're able to execute better, rather than being struggling with the IT problems, and even just recently, for instance, we had a customer of ours, the CEO phones us up and says, you know what, we've got this problem with our sales. We don't really know why this is going down, you know, in this country, in this part of the world, it's going up, in this country, it's going down, we don't know why, and that's making us very nervous. Could you come in and just get the data together, work out why it's happening, so that we can understand what it is. And we came in, and within weeks, we were able to give them a very good insight into exactly why that is, and they changed their strategy, moving forward, for the next year, to focus on addressing that problem, and that's really amazing ROI for a company to be able to get that insight. Now, we're working with them to operationalize that, so that particular insight is always available to them, and that's an example of how companies are now starting to see that ROI come through, and a lot of it is about being able to articulate the right business question, rather than trying to worry about reports. What is the business question I'm trying to solve or answer, and that's when you can start to see the ROI come through. >> Can you talk about the customer orientation when they get to that insight, because you mentioned earlier that they got used to the reports, and you mentioned visualization, Tableau, they become table states, once you get addicted to the visualization, you want to extract more insights so the pressure seems to be getting more insight. So, two questions, process gap around what they need to do process-wise, and then just organizational behavior. Are they there mentally, what are some of the criteria in your mind, in your experiments, with customers around the processes that they go through, and then organizational mindset. >> Yeah, so what I would say is, first of all, from an organizational mindset perspective, it's very important to start educating, not just the analysis team, but the entire business on what this whole machine-learning, big data thing is all about, and how to ask the right questions. So, really starting to think about the opportunities you have to move your business forward, rather than what you already know, and think forward rather than retrospective. So, the other thing we often have to teach people, as well, is that this isn't about what you can get from the data warehouse, or replacing your data warehouse or anything like that. It's about answering the right questions, with the right tools, and here is a whole set of tools that allow you to answer different questions that you couldn't before, so leverage them. So, that's very important, and so that mindset requires time actually, to transform business into that mindset, and a lot of commitment from the business to make that happen. >> So, mindset first, and then you look at the process, then you get to the product. >> Yep, so, and basically, once you have that mindset, you need to set up an engine that's going to run, and start to drive the ROI out, and the engine includes, you know, your technical folk, but also your business users, and that engine will then start to build up momentum. The momentum builds more interest, and, overtime, you start to get your entire business into using these tools. >> It kind of makes sense, just kind of riffing in real time here, so the product-gap conversation should probably come after you lay that out first, right? >> Totally, yeah, I mean, you don't choose a product before you know what you need to do with it. So, but actually often companies don't know what they need to do with it, because they've got the wrong mindset in the first place. And so part of the road map stuff that we do, that we have a road map offering, is about changing that mindset, and helping them to get through that first stage, where we start to put, articulate the right use cases, and that really is driving a lot of value for our customers. Because they start from the right place-- >> Sometimes we hear stories, like the product kind of gives them a blind spot, because they tend to go into, with a product mindset first, and that kind of gives them some baggage, if you will. >> Well, yeah, because you end up with a situation, where you go, you get a product in, and then you say what can we do with it. Or, in fact, what happens is the vendor will say, these are the things you could do, and they give you use cases. >> It constrains things, forecloses tons of opportunities, because you're stuck within a product mindset. >> Yeah, exactly that, and you're not, you don't want to be constrained. And that's why open-source, and the kind of ecosystem that we have within the big data space is so powerful, because there's so many different tools for different things but don't choose your tool until you know what you're trying to achieve. >> I have a market question, maybe you just give us opinion, caveat, if you like, it's sort of a global, macro view. When we started first looking at the big data market, we noticed right away the dominant portion of revenue was coming from services. Hardware was commodity, so, you know, maybe sort of less than you would, obviously, in a mainframe world, and open-source software has a smaller contribution, so services dominated, and, frankly, has continued to dominate, since the early days. Do you see that changing, or do you think those percentages, if you will, will stay relatively constant? >> Well, I think it will change over time, but not in the near future, for sure, there's too much advancement in the technology landscape for that to stop, so if you had a set of tools that weren't really evolving, becoming very mature, and that's what tools you had, ultimately, the skill sets around them start to grow, and it becomes much easier to develop stuff, and then companies start to build out industry- or solutions-specific stuff on top, and it makes it very easy to build products. When you have an ecosystem that's evolving, growing with the speed it is, you're constantly trying to keep up with that technology, and, therefore, services have to play an awful big part in making sure that you are using the right technology, at the right time, and so, for the near future, for certain, that won't change. >> Complexity is your friend. >> Yeah, absolutely. Well, you know, we live in a complex world, but we live and breathe this stuff, so what's complex to some is not to us, and that's why we add value, I guess. >> Mike Merritt-Holmes here inside The Cube with Teradata Think Big. Thanks for spending the time sharing your insights. >> Thank you for having me. >> Understand the organizational mindset, identify the process, then figure out the products. That's the insight here on The Cube, more coverage of Data Works Summit 2017, here in Germany after this short break. (upbeat electronic music)

>> Announcer: Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering AWS Summit 2016. (upbeat electronic music) >> Hello everyone, welcome to theCUBE. Here, live in Silicon Valley, at Amazon Web Services, AWS Summit, in Silicon Valley. I'm John Furrier, this is theCUBE, our flagship program. We go out to the events and extract the signal from the noise. I'm here with my co-host. Introducing Lisa Martin on theCUBE, new host. Lisa, you look great. Our first guest here is David Richards, CEO of WANdisco. Welcome to theCUBE, good to see you. >> Good to see you, John, as always. >> So, I've promised a special CUBE presentation, $20 bill here that I owe David. We played golf on Friday, our first time out in the year. He sandbagged me, he's a golfer, he's a pro. I don't play very often. There's your winnings, there you go, $20, I paid. (smooching) (laughing) I did not well challenge your swing, so it's been paid. Great fun, good to see you. >> It was great fun and I'm sorry that I cheated a little bit, mirror in the bathroom still running through your ears. >> I love the English style. Like all the inner gain and playing music on the course, it was great a great time. When we went golfing last week, we were talking, just kind of had a social get-together but we were talking about some things on the industry mind right now. And you had some interesting color around your business. We talked about your strategy of OEMing your core technology to IBM and also you have other business deals. Can you share some light on your strategy at WANdisco with your core IP, and how that relates to what's going on in this phenom called Amazon Web Services? They've been running the table on the enterprise now and certainly public cloud for years. $10 billion, Wikibon called that years ago. We see that trajectory not stopping but clearly the enterprise cloud is what they want. Do you have a deal with Amazon? Are you talking to them and what is that impact your business? >> Well I mean the wonderful thing is if you go to AWS Marketplace, you go to that front page, we're one of the feature products on the front page of the AWS Marketplace, so I think that tells you that we're pretty strategic with Amazon. We're solving a big problem for them which is the movement of data in and out of public cloud. But you asked an interesting question about our business model. When we first came into the whole big date marketplace we went for the whole direct selling thing like everybody does, but that doesn't give you a lot of operational leverage. I mean we're in accounts with IBM right now, you mentioned earlier, MR technology. At a big automotive company they have 72 enterprise sales guys, 72. We could never get to that scale any time soon. >> And you have relationships too. So it's not like they like, you know, just knocking on doors selling used cars. They are strategic high-end enterprise sales. >> Exactly. That gives us a tremendous amount of operational leverage and AWS is one of the great stories, will be one of the great IT stories of the century. To go from zero to 15 billion. If AWS was an independent company, faster than any other enterprise software company in the history of mankind, is just incredible. >> Yeah, well, enterprise obviously, they care about hybrid cloud, which you know all about through your IBM relationship. Andy Jassy at Amazon, the CEO now of Amazon. Newly announced title, he's certainly SVP, basically he's been the CEO of Amazon. He's been on record, certainly on stage, and on theCUBE saying, why do even companies need data centers? That kind of puts you out of business. You have a data center product, or is the cloud just one big data center? Will there ultimately be no data center at all? What's your thoughts? >> That's a great question. We see the cloud as just one great big data center or actually many great big data centers. And how you actually integrate those together, how you move data between data centers, how you arbitrage been cloud vendors. Are you really going to put all your eggs into one basket? You're going to put everything into AWS. Everything into Azure. I don't think you will. I think you'll need to move data around between those different data centers and then how about high availability? How do you solve that problem? Well WANdisco solves that problem as well. >> So a couple of questions for you David. One of the things that Dr. Wood said in the keynote today was friends don't let friends build data centers. So I wanted to get your take on that as well as from an IBM perspective. We just talked about the OEM opportunity that you're working there to get to those large enterprises. Does that mean that you're shifting your focus for enterprise towards IBM? Where does that leave WANdisco and Amazon as we see Amazon making a big push to the enterprise? >> So I think that was some big news that came out last week that was missed largely by the industry, which was the FCA, the financial regulatory authority in the United Kingdom, came out and said, we see no reason why banks cannot move to cloud from a regulatory perspective. That was one of the big fears that we all had which is are banks actually going to be able to move core infrastructure into a public cloud environment? Well now it turns out they can. So we're all in on cloud. I mean, we can see, if you look at the partnerships that we're focused on, it's the sort of four/five cloud vendors. It's the IBM, the AWS, Azure, Oracle, when they finally built that cloud, and so on. They're the key partnerships that we see in the marketplace. That will be our go-to market strategy. That is our go-to market strategy. >> So one of the things that's clear is the data value and you do a lot of replications. So one of the things that, I forget which CUBE segment we've done over the years, that's Hurricane Sandy I think it was, in New York City. You guys were instrumental in keeping the up-time and availability. >> Lisa mentioned, Amazon vis-a-vis IBM, obviously two different strategies, kind of converging in on the same customer. Amazon's had problems with availability zones and they're rushing and running like the wind to put up new data centers. They just announced a new data center in India just recently. Andy Jassy and team were out there kicking that off. So they're rushing to put points of presence, if you will, for lack of a better word, around the world. Does that fit into your availability concept and how do customers engage with you guys with specifically that kind of architecture developing very fast? >> I think that's a really great question. There are problems, there have been historic problems with general availability in cloud. There are lots of 15-minute outages and so on that cost billions and billions of dollars. We're working very closely and I can't say too much about it with the teams that are focused on enabling availability. Clearly the IBM OEM is very focused on the movement of data from the hybrid cloud, I'm from a data availability perspective. But there's a great deal of value in data that sits in cloud and I think you'll see us do more and more deals around general cloud availability moving forward. >> Is there a specific on that front project that you can share with us where you've really helped a customer gain significant advantage by working with AWS and facilitating those availability objectives, security compliance? >> So, one of the big use cases that we see, and it's kind of all happening at once really, is I built an on-premise infrastructure to store lots and lots of data, now I need to run compute and analytics against that data and I'm not going to build a massive redundant infrastructure on-premise in order to do that, so I need to figure out a way to move that data in and out of cloud without interruption to service. And when we are talking about large volumes of data, you simply can't move transactional data in and out of cloud using existing technology. AWS offers something called Snowball where you put it into a rugged ICE drive and then you ship it to them, but that's not really streaming analytics is it? Most of our use cases today are either involved in either the migration of data from on-premise into cloud infrastructure, or the movement of data for an atemporal basis so I can run compute against that data and taking advantage of the elastic compute available in cloud. They are really the two major use cases that web, and we're working with a lot of customers right now that have those exact problems. >> So majority of your customers are more using hybrid cloud versus all in the public cloud? >> Hybrid falls into two categories. I'm going to use hybrid in order to migrate data because I need to keep on using it while it's moving. And secondly I need to use hybrid because I need to build a compute infrastructure that I simply can't build behind firewall. I need to build it in cloud. >> So the new normal is the cloud. There was a tweet here that says, database migration, now we can have an Oracle Exadata data dispute that we're ready to throw into the river. (David laughs) Database migration is a big thing and you mentioned it on the first question that moving in and out of the cloud is a top concern for enterprises. This is one of those things, it's the elephant in the room, so to speak. No pun intended AKA Hadoop. Moving the data around is a big deal and you don't want to get a roach motel situation where you can check in and can't check out. That is the lock-in that enterprise customers are afraid of with Amazon. You're thoughts there, and what do you guys offer your customers. And if you can give some color on this whole database migration issue, real, not real? >> The big problem that the Hadoop market has had from a growth perspective is applications. And why they had a problem, well it's the concept of data gravity. The way that the AWS execs will look at their business the way that the Azure execs will look at their business at Microsoft. They will look at how much data they actually have. Data gravity. The implication being if I have data then the applications follow. The whole point of cloud is that I can build my applications on that ubiquitous infrastructure. We want to be the kings of moving data around right? Wherever the data lands is where the applications follow. If the applications follow, you have a business. If the applications don't follow, then it's probably a roach motel situation, as you so quaintly put it. But basically the data is temporal. It will move back to where the applications are going to be. So where the applications are, and it's who is going to be the king of applications, will actually win this race. >> So, question, in terms of migration, we're hearing a lot about mass migration. Amazon's even doing partner competency programs for migration. Not to trivialize it, talk to us about some of the challenges that you are helping customers overcome when they sort of don't know where to start when it comes to that data problem? >> If it's batch data, if it's stuff that I'm only going to touch if it's an archive, that I only going to touch once in a blue moon, then I can put it into Snowball and I can ship my Snowball device. I can sort of press the pause button akin to when I'm copying files into a network drive where you can't edit them, and then wait for two months, three months. Wait for them to turn up in AWS and that's fine. If it's transactional data where maybe 80% of my data set changes on a daily basis and I've got petabyte scale data to move, that's a hard problem. That requires active transactional data migration. That's a big mouthful, but that's really important for run-time transactional data. That's the problem that we solve. We enable customers, without interruption to service to move a massive scale active transactional data into cloud without any interruption of service. So I can still use it while it's moving. >> One of the things we were talking about before you came on was the whole global economy situation. I think a year and a half ago, or two years ago, you predicted the housing bubble bursting in London. You're in the London Exchange, you're a public company. Brexit, EU. These are huge issues that are going to impact, certainly North America looking healthy right now but some are saying that there's a big challenge and certainly the uncertainty of the U.S. presidency candidates that are lack of thereof. The general sentiment in the U.S. We're in a world of turmoil. So specifically the Brexit situation. You guys are in London. What does this impact your business and is that going to happen? Or give us some color and insight into what the countrymen are thinking over there. >> Okay, so, I get asked by, I live here of course, and I've lived here for 19 years. It feels like I'm recolonizing sometimes, I have to say. No, I'm joking. I get asked by a lot of Americans what the situation is with Brexit and why it happened. And for that you have to look at economics. If you sort of take a step back, in Northern Europe nine of the 10 poorest parts of Northern Europe are in the U.K. And one, only one of the top 10 richest parts is in the U.K. and that's London. So basically outside of London the U.K. has a really big problem. Those people are dissatisfied. When people are dissatisfied, if they're not benefiting from an economic upturn, if governments make it, like the conservative government for the past four years made huge cuts, those people don't benefit, and they really feel pissed off and they will vote against the government. >> John: So protest vote pretty much? >> Brexit was really, I think, a protest vote. It's people dissatisfied. It's people voting basically anti-immigration which is, being in the U.S., is a really foreign thing to us. >> But there are some implications to business. I mean obviously there's filings, there's legal issues, obviously currency. Have you been impacted positively, negatively and what is the outlook on WANdisco's business going forward with the Brexit uncertainty and/or impact? >> We're in great shape because we buy pounds. We buy labor that's now discounted by 20% in the U.K. I just got back from the U.K. If you want to go on vacation, Americans, anywhere, go to London this summer and go shopping because everything is humongously discounted for us American's right now. It's a great time to be there. So from a WANdisco perspective-- >> John: How does that affect the housing bubble too? >> I said to you about a year ago that the London housing market was akin to the jewelry shops that existed in Hong Kong a few years ago, where the Chinese used to come over and basically launder money by buying huge diamonds and bars of gold and things. If you look at the London housing market it is primarily fueled by the Saudis and by the Russians who have been buying Hyde Park Corner 100 million pounds, $160 million, well $140 million now, apartments and so on in London. Now seven, and I repeat seven housing funds in the U.K. last week canceled redemptions. Which means that they can foresee liquidity problems coming in those funds. I think you're about to see a housing crash in London, the like of which we've never seen before, and I think it would be very sad and I think that will make people really question the Brexit decision. >> John: So sell London property now people? >> Yes. >> Before the crash. >> And go shopping, I heard the go shopping. So following along that, you talked about the significant differential between London and the rest of the U.K. You're from Sheffield, you're very proud of that. You've also been proud of your business really helping to fuel that economy. How do you think Brexit is going to affect WANdisco in your home area of Sheffield. >> I don't think it really will. I think our employees there, relative terms, very well paid. They're working on interesting things. They're working very closely with the AWS team, for example, the S3 team, the MR team. And building our technology, we're liaising very closely with them. They're doing lots of interesting things. I suspect their vacations into Europe and their vacations to the United States have just gone up by about 20% which will reduce the amount of beer that they can drink. It's a big beer drinking part of the world in Sheffield. Sheffield is, in terms of cost of living, is relatively low compared to the rest of the U.K. and I think those people will be pretty happy. >> David, I appreciate you coming on theCUBE. I want to give you the final word here on the segment because you're a chief executive officer of a public company. You've been in the industry for awhile. You've seen the trials and tribulations of the Hadoop ecosystem. Now basically branded as the data ecosystem. As Hortonworks has recently announced, Hadoop Summit is now being called Data Works Summit. They're moving from the word Hadoop to Data. Clearly that's impacting all the trends. Cloud data, mobile is really the key. I want you, and I'm sure you get this question a lot, I would like you to take a minute and explain to the audience that's watching, what's this phenom of Amazon Web Services really all about? What's all the hub-bub about? Why is everyone fawning over Amazon now? When you go back five years ago, or 10 years ago when it started, they were ridiculed. I remember when this started I loved it, but they were looked at as just a kind of a tinkering environment. Now they're the behemoth and just on an unstoppable run and certainly the expansion has been fantastic under Andy Jassy's leadership. How do you explain it to normal people what's going on at Amazon? Take a minute please. >> So Amazon is, and that's a brilliant question, by the way. Amazon is the best investor-relation story ever, and I mean ever. What Bezos did is never talked about the potential size of the market. Never talked about this thing was going to generate lots of cash. He just said, you know what, we're building this little internet thing. It might, it might not work. It's not going to make any money. And then in the blink of an eye, it's a $15 billion revenue business growing faster than any other part of his business and throwing off cash like there's no tomorrow. It is just the most non-obvious story in technology, in business, of any public company ever. I mean AWS, arguably, as a stand alone entity, is almost worth as much as Oracle. An unbelievable, an unbelievable story and to do that with all the complexity. I mean mean running a public company with shareholder expectations, with investor relations where you have to constantly be positive about what's going on. For him to do that and never talk about making a profit, never talk about this becoming a multi-billion dollar segment of their business, is the most incredible thing. >> So they've been living the agile. Certainly that's the business story, but they've been living the agile story relative to announcing the slew of new products. Basic building blocks S3, EC2 to start with, as the story goes from Andy Jassy himself, and then a slew of new services. It's a tsunami of every event of new services. What is the disruptive enabler? What's the disruption under the hood for Amazon? How do you explain that? >> Well, I mean what they did is they took a really simple concept. They said, okay, storage, how do we make storage completely elastic, completely public, in a way that we can use the public internet to get data in and out of it. Right? That sounds simple. What they actually built underneath the covers was an extremely complex thing called object store. Everybody else in the industry completely missed this. Oracle missed it, Microsoft missed it, everybody missed it. Now we're all playing catch-up trying to develop this thing called object store. It's going to take over, I mean, somebody said to me, what's the relevance of Hadoop in cloud? And you have to ask that question. It's a relevant question. Do you really need it when you've got object store? Show me side-by-side, object store versus every, you know, Net Apple, Teradata, or any of those guys. Show me side-by-side the difference between the two things. There ain't a lot. >> Amazon Web Service is a company that can put incumbents out of business. David, thanks so much. As we always say, what inning are we in? It's really a double-header. Game one swept by Amazon Web Services. Game two is the enterprise and that's really the story here at Amazon Web Services Summit in Silicon Valley. Can Amazon capture the enterprise? Their focus is clear. We're theCUBE. I'm John Furrier with Lisa Martin. We'll be right back with more after this short break. (techno music)