(soft electronic music) >> Welcome to this CUBE Virtual Conversation. I'm your host, Rebecca Knight. Today, I'm joined by Hermanth Manda. He is the Executive Director, IBM Data and AI, responsible for Cloud Pak for Data. Thanks so much for coming on the show, Hermanth. >> Thank you, Rebecca. >> So we're talking now about the release of Cloud Pak for Data version 3.5. I want to explore it for, from a lot of different angles, but do you want to just talk a little bit about why it is unique in the marketplace, in particular, accelerating innovation, reducing costs, and reducing complexity? >> Absolutely, Rebecca. I mean, this is something very unique from an IBM perspective. Frankly speaking, this is unique in the marketplace because what we are doing is we are bringing together all of our data and AI capabilities into a single offering, single platform. And we have continued, as I said, we made it run on any cloud. So we are giving customers the flexibility. So it's innovation across multiple fronts. It's still in consolidation. It's, in doing automation and infusing collaboration and also having customers to basically modernize to the cloud-native world and pick their own cloud which is what we are seeing in the market today. So I would say this is a unique across multiple fronts. >> When we talk about any new platform, one of the big concerns is always around internal skills and maintenance tasks. What changes are you introducing with version 3.5 that does, that help clients be more flexible and sort of streamline their tasks? >> Yeah, it's an interesting question. We are doing a lot of things with respect to 3.5, the latest release. Number one, we are simplifying the management of the platform, made it a lot simpler. We are infusing a lot of automation into it. We are embracing the concept of operators that are not open shelf has introduced into the market. So simple things such as provisioning, installation, upgrades, scaling it up and down, autopilot management. So all of that is taken care of as part of the latest release. Also, what we are doing is we are making the collaboration and user onboarding very easy to drive self service and use the productivity. So overall, this helps, basically, reduce the cost for our customers. >> One of the things that's so striking is the speed of the innovation. I mean, you've only been in the marketplace for two and a half years. This is already version 3.5. Can you talk a little bit about, about sort of the, the innovation that it takes to do this? >> Absolutely. You're right, we've been in the market for slightly over two and a half years, 3.5's our ninth release. So frankly speaking, for any company, or even for startups doing nine releases in 2.5 years is unheard of, and definitely unheard of at IBM. So we are acting and behaving like a startup while addressing the go to market, and the reach of IBM. So I would say that we are doing a lot here. And as I said before, we're trying to address the unique needs of the market, the need to modernize to the cloud-native architectures to move to the cloud also while addressing the needs of our existing customers, because there are two things we are trying to focus, here. First of all, make sure that we have a modern platform across the different capabilities in data and AI, that's number one. Number two is also how do we modernize our existing install base. We have six plus billion dollar business for data and AI across significant real estates. We're providing a platform through Cloud Pak for Data to those existing install base and existing customers to more nice, too. >> I want to talk about how you are addressing the needs of customers, but I want to delve into something you said earlier, and that is that you are behaving like a startup. How do you make sure that your employees have that kind of mindset that, that kind of experimental innovative, creative, resourceful mindset, particularly at a more mature company like IBM? What kinds of skills do you try to instill and cultivate in your, in your team? >> That's a very interesting question, Rebecca. I think there's no single answer, I would say. It starts with listening to the customers, trying to pay detailed attention to what's happening in the market. How competent is it reacting. Looking at the startups, themselves. What we did uniquely, that I didn't touch upon earlier is that we are also building an open ecosystem here, so we position ourselves as an open platform. Yes, there's a lot of IBM unique technology here, but we also are leveraging open source. We are, we have an ecosystem of 50 plus third party ISVs. So by doing that, we are able to drive a lot more innovation and a lot faster because when you are trying to do everything by yourself, it's a bit challenging. But when you're part of an open ecosystem, infusing open source and third party, it becomes a lot easier. In terms of culture, I just want to highlight one thing. I think we are making it a point to emphasize speed over being perfect, progress over perfection. And that, I think, that is something net new for IBM because at IBM, we pride ourselves in quality, scalability, trying to be perfect on day one. I think we didn't do that in this particular case. Initially, when we launched our offense two and a half years back, we tried to be quick to the market. Our time to market was prioritized over being perfect. But now that is not the case anymore, right? I think we will make sure we are exponentially better and those things are addressed for the past two and one-half years. >> Well, perfect is the enemy of the good, as we know. One of the things that your customers demand is flexibility when building with machine learning pipeline. What have you done to improve IBM machine learning tools on this platform? >> So there's a lot of things we've done. Number one, I want to emphasize our building AI, the initial problem that most of our customers concerned about, but in my opinion, that's 10% of the problem. Actually deploying those AI models or managing them and covering them at scales for the enterprise is a bigger challenge. So what we have is very unique. We have the end-to-end AI lifecycle, we have tools for all the way from building, deploying, managing, governing these models. Second is we are introducing net new capabilities as part of a latest release. We have this call or this new service called WMLA, Watson Machine Learning Accelerator that addresses the unique challenges of deep learning capabilities, managing GPUs, et cetera. We are also making the auto AI capabilities a lot more robust. And finally, we are introducing a net new concept called Federator Learning that allows you to build AI across distributed datasets, which is very unique. I'm not aware of any other vendor doing this, so you can actually have your data distributed across multiple clouds, and you can build an aggregated AI model without actually looking at the data that is spread across these clouds. And this concept, in my opinion, is going to get a lot more traction as we move forward. >> One of the things that IBM has always been proud of is the way it partners with ISVs and other vendors. Can you talk about how you work with your partners and foster this ecosystem of third-party capabilities that integrate into the platform? >> Yes, it's always a challenge. I mean, for this to be a platform, as I said before, you need to be open and you need to build an ecosystem. And so we made that a priority since day one and we have 53 third party ISVs, today. It's a chicken and egg problem, Rebecca, because you need to obviously showcase success and make it a priority for your partners to onboard and work with you closely. So, we obviously invest, we co-invest with our partners and we take them to market. We have different models. We have a tactical relationship with some of our third party ISVs. We also have a strategic relationship. So we partner with them depending on their ability to partner with us and we go invest and make sure that we are not only integrating them technically, but also we are integrating with them from a go-to-market perspective. >> I wonder if you can talk a little bit about the current environment that we're in. Of course, we're all living through a global health emergency in the form of the COVID-19 pandemic. So much of the knowledge work is being done from home. It is being done remotely. Teams are working asynchronously over different kinds of digital platforms. How have you seen these changes affect the team, your team at IBM, what kinds of new kinds of capabilities, collaborations, what kinds of skills have you seen your team have to gain and have to gain quite quickly in this environment? >> Absolutely. I think historically, IBM had quite a, quite a portion of our workforce working remotely so we are used to this, but not at the scale that the current situation has compelled us to. So we made a lot more investments earlier this year in digital technologies, whether it is Zoom and WebEx or trying to use tools, digital tools that helps us coordinate and collaborate effectively. So part of it is technical, right? Part of it is also a cultural shift. And that came all the way from our CEO in terms of making sure that we have the necessary processes in place to ensure that our employees are not in getting burnt out, that they're being productive and effective. And so a combination of what I would say, technical investments, plus process and leadership initiatives helped us essentially embrace the changes that we've seen, today. >> And I want you to close us out, here. Talk a little bit about the future, both for Cloud Pak for Data, but also for the companies and clients that you work for. What do you see in the next 12 to 24 months changing in the term, in terms of how we have re-imagined the future of work. I know you said this was already version nine. You've only been in the marketplace for, for not even three years. That's incredible innovation and speed. Talk a little bit about changes you see coming down the pike. >> So I think everything that we have done is going to get amplified and accelerated as we move forward, shift to cloud, embracing AI, adopting AI into business processes to automate and amplify new business models, collaboration, to a certain extent, consolidation of the different offerings into platforms. So all of this, we, I obviously see that being accelerated and that acceleration will continue as we move forward. And the real challenge I see with our customers and all the enterprises is, I see them in two buckets. There's one bucket which are resisting change, like to stick to the old concepts, and there's one bucket of enterprises who are embracing the change and moving forward, and actually get accelerating this transformation and change. I think it will be successful over the next one to five years. You know, it could be under the other bucket and if you're not, I think it's, you're going to get, you're going to miss out and that is getting amplified and accelerated, as we speak. >> So for those ones in the bucket that are resistant to the change, how do you get them onboard? I mean, this is classic change management that they teach at business schools around the world. But what are some advice that you would have to those who are resisting the change? >> So, and again, frankly speaking, we, at IBM, are going through that transition so I can speak from experience. >> Rebecca: You're drinking the Kool-Aid. >> Yeah, when, when I think, one way to address this is basically take one step at a time, like as opposed to completely revolutionizing the way you do your business. You can transform your business one step at a time while keeping the end objective as your goal, as your end goal. So, and it just want a little highlight that with full factor, that's exactly what we are enabling because what we do is we enable you to actually run anywhere you like. So if most of your systems, most of your data and your models, and analytics are on-premise, you can actually start your journey there while you plan for the future of a public cloud or a managed service. So my advice is pretty simple. You start the journey, but you can take, you can, you don't need to, you don't need to do it as a big bang. You, it could be a journey, it could be a gradual transformation, but you need to start the journey today. If you don't, you're going to miss out. >> Baby steps. Hey Hermanth Manda, thank you so much for joining us for this Virtual CUBE Conversation >> Thank you very much, Rebecca. >> I'm Rebecca Knight, stay tuned for more of theCUBE Virtual. (soft electronic music)

>> [Announcer] Live from Times Square in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to theCUBE's special presentation. We're covering IBM's announcements today around AI. IBM, as theCUBE does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on theCUBE, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching theCUBE.

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.

>> Live from Times Square in New York City, it's The Cube covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to The Cube's special presentation. We're covering IBM's announcements today around AI. IBM, as The Cube does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on The Cube, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching The Cube.

>> 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.