>> [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.

>> 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. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)

>> Live from Times Square in New York City, it's theCUBE. Covering IBMs Change the Game, Winning With AI. Brought to you by, IBM. >> Hi, everybody, we're back. My name is Dave Vellante and you're watching, theCUBE. The leader in live tech coverage. We're here with Scott Hebner who's the VP of marketing for IBM analytics and AI. Scott, it's good to see you again, thanks for coming back on theCUBE. >> It's always great to be here, I love doing these. >> So one of the things we've been talking about for quite some time on theCUBE now, we've been following the whole big data movement since the early Hadoop days. And now AI is the big trend and we always ask is this old wine, new bottle? Or is it something substantive? And the consensus is, it's real, it's real innovation because of the data. What's your perspective? >> I do think it's another one of these major waves, and if you kind of go back through time, there's been a series of them, right? We went from, sort of centralized computing into client server, and then we went from client server into the whole world of e-business in the internet, back around 2000 time frame or so. Then we went from internet computing to, cloud. Right? And I think the next major wave here is that next step is AI. And machine learning, and applying all this intelligent automation to the entire system. So I think, and it's not just a evolution, it's a pretty big change that's occurring here. Particularly the value that it can provide businesses is pretty profound. >> Well it seems like that's the innovation engine for at least the next decade. It's not Moore's Law anymore, it's applying machine intelligence and AI to the data and then being able to actually operationalize that at scale. With the cloud-like model, whether its OnPrem or Offprem, your thoughts on that? >> Yeah, I mean I think that's right on 'cause, if you kind of think about what AI's going to do, in the end it's going to be about just making much better decisions. Evidence based decisions, your ability to get to data that is previously unattainable, right? 'Cause it can discover things in real time. So it's about decision making and it's about fueling better, and more intelligent business processing. Right? But I think, what's really driving, sort of under the covers of that, is this idea that, are clients really getting what they need from their data? 'Cause we all know that the data's exploding in terms of growth. And what we know from our clients and from studies is only about 15% of what business leaders believe that they're getting what they need from their data. Yet most businesses are sitting on about 80% of their data, that's either inaccessible, un-analyzed, or un-trusted, right? So, what they're asking themselves is how do we first unlock the value of all this data. And they knew they have to do it in new ways, and I think the new ways starts to talk about cloud native architectures, containerization, things of that nature. Plus, artificial intelligence. So, I think what the market is starting to tell us is, AI is the way to unlock the value of all this data. And it's time to really do something significant with it otherwise, it's just going to be marginal progress over time. They need to make big progress. >> But data is plentiful, insights aren't. And part of your strategy is always been to bring insights out of that dividend and obviously focused on clients outcomes. But, a big part of your role is not only communicating IBMs analytic and AI strategy, but also helping shape that strategy. How do you, sort of summarize that strategy? >> Well we talk about the ladder to AI, 'cause one thing when you look at the actual clients that are ahead of the game here, and the challenges that they've faced to get to the value of AI, what we've learned, very, very clearly, is that the hardest part of AI is actually making your data ready for AI. It's about the data. It's sort of this notion that there's no AI without a information architecture, right? You have to build that architecture to make your data ready, 'cause bad data will be paralyzing to AI. And actually there was a great MIT Sloan study that they did earlier in the year that really dives into all these challenges and if I remember correctly, about 81% of them said that the number one challenge they had is, their data. Is their data ready? Do they know what data to get to? And that's really where it all starts. So we have this notion of the ladder to AI, it's several, very prescriptive steps, that we believe through best practices, you need to actually take to get to AI. And once you get to AI then it becomes about how you operationalize it in the way that it scales, that you have explainability, you have transparency, you have trust in what the model is. But it really much is a systematical approach here that we believe clients are going to get there in a much faster way. >> So the picture of the ladder here it starts with collect, and that's kind of what we did with, Hadoop, we collected a lot of data 'cause it was inexpensive and then organizing it, it says, create a trusted analytics foundation. Still building that sort of framework and then analyze and actually start getting insights on demand. And then automation, that seems to be the big theme now. Is, how do I get automation? Whether it's through machine learning, infusing AI everywhere. Be a blockchain is part of that automation, obviously. And it ultimately getting to the outcome, you call it trust, achieving trust and transparency, that's the outcome that we want here, right? >> I mean I think it all really starts with making your data simple and accessible. Which is about collecting the data. And doing it in a way you can tap into all types of data, regardless of where it lives. So the days of trying to move data around all over the place or, heavy duty replication and integration, let it sit where it is, but be able to virtualize it and collect it and containerize it, so it can be more accessible and usable. And that kind of goes to the point that 80% of the enterprised data, is inaccessible, right? So it all starts first with, are you getting all the data collected appropriately, and getting it into a way that you can use it. And then we start feeding things in like, IOT data, and sensors, and it becomes real time data that you have to do this against, right? So, notions of replicating and integrating and moving data around becomes not very practical. So that's step one. Step two is, once you collect all the data doesn't necessarily mean you trust it, right? So when we say, trust, we're talking about business ready data. Do people know what the data is? Are there business entities associated with it? Has it been cleansed, right? Has it been take out all the duplicate data? What do you when a situation with data, you know you have sources of data that are telling you different things. Like, I think we've all been on a treadmill where the phone, the watch, and the treadmill will actually tell you different distances, I mean what's the truth? The whole notion of organizing is getting it ready to be used by the business, in applying the policies, the compliance, and all the protections that you need for that data. Step three is, the ability to build out all this, ability to analyze it. To do it on scale, right, and to do it in a way that everyone can leverage the data. So not just the business analysts, but you need to enable everyone through self-service. And that's the advancements that we're getting in new analytics capabilities that make mere mortals able to get to that data and do their analysis. >> And if I could inject, the challenge with the sort of traditional decision support world is you had maybe two, or three people that were like, the data gods. You had to go through them, and they would get the analysis. And it's just, the agility wasn't there. >> Right. >> So you're trying to, democratizing that, putting it in the hands. >> Absolutely. >> Maybe the business user's not as much of an expert as the person who can build theCUBE, but they could find new use cases, and drive more value, right? >> Actually, from a developer, that needs to get access, and analytics infused into their applications, to the other end of the spectrum which could be, a marketing leader, a finance planner, someone who's planning budgets, supply chain planner. Right, so it's that whole spectrum, not only allowing them to tap into, and analyze the data and gain insights from it, but allow them to customize how they do it and do it in a more self-service. So that's the notion of scale on demand insights. It's really a cultural thing enabled through the technology. With that foundation, then you have the ability to start infuse, where I think the real power starts to kick in here. So I mean, all that's kind of making your data ready for AI, right? Then you start to infuse machine learning, everywhere. And that's when you start to build these models that are self-learning, that start to automate the ability to get to these insights, and to the data. And uncover what has previously been unattainable, right? And that's where the whole thing starts to become automated and more real time and more intelligent. And that's where those models then allow you to do things you couldn't do before. With the data, they're saying they're not getting access to. And then of course, once you get the models, just because you have good models doesn't mean that they've been operationalized, that they've been embedded in applications, embedded in business process. That you have trust and transparency and explainability of what it's telling you. And that's that top tier of the ladder, is really about embedding it, right, so that into your business process in a way that you trust it. So, we have a systematic set of approaches to that, best practices. And of course we have the portfolio that would help you step up that ladder. >> So the fat middle of this bell curve is, something kind of this maturity curve, is kind of the organize and analyze phase, that's probably where most people are today. And what's the big challenge of getting up that ladder, is it the algorithms, what is it? >> Well I think it, it clearly with most movements like this, starts with culture and skills, right? And the ability to just change the game within an organization. But putting that aside, I think what's really needed here is an information architecture that's based in the agility of a cloud native platform, that gives you the productivity, and truly allows you to leverage your data, wherever it resides. So whether it's in the private cloud, the public cloud, on premise, dedicated no matter where it sits, you want to be able to tap into all that data. 'Cause remember, the challenge with data is it's always changing. I don't mean the sources, but the actual data. So you need an architecture that can handle all that. Once you stabilize that, then you can start to apply better analytics to it. And so yeah, I think you're right. That is sort of the bell curve here. And with that foundation that's when the power of infusing machine learning and deep learning and neuronetworks, I mean those kind of AI technologies and models into it all, just takes it to a whole new level. But you can't do those models until you have those bottom tiers under control. >> Right, setting that foundation. Building that framework. >> Exactly. >> And then applying. >> What developers of AI applications, particularly those that have been successful, have told us pretty clearly, is that building the actual algorithms, is not necessarily the hard part. The hard part is making all the data ready for that. And in fact I was reading a survey the other day of actual data scientists and AI developers and 60% of them said the thing they hate the most, is all the data collection, data prep. 'Cause it's so hard. And so, a big part of our strategy is just to simplify that. Make it simple and accessible so that you can really focus on what you want to do and where the value is, which is building the algorithms and the models, and getting those deployed. >> Big challenge and hugely important, I mean IBM is a 100 year old company that's going through it's own digital transformation. You know, we've had Inderpal Bhandari on talking about how to essentially put data at the core of the company, it's a real hard problem for a lot of companies who were not born, you know, five or, seven years ago. And so, putting data at that core and putting human expertise around it as opposed to maybe, having whatever as the core. Humans or the plant or the manufacturing facility, that's a big change for a lot of organizations. Now at the end of the day IBM, and IBM sells strategy but the analytics group, you're in the software business so, what offerings do you have, to help people get there? >> Well in the collect step, it's essentially our hybrid data management portfolio. So think DB2, DB2 warehouse, DB2 event store, which is about IOT data. So there's a set of, and that's where big data in Hadoop and all that with Wentworth's, that's where that all fits in. So building the ability to access all this data, virtualize it, do things like Queryplex, things of that nature, is where that all sits. >> Queryplex being that to the data, virtualization capability. >> Yeah. >> Get to the data no matter where it is. >> To find a queary and don't worry about where it resides, we'll figure that out for you, kind of thought, right? In the organize, that is infosphere, so that's basically our unified governance and integration part of our portfolio. So again, that is collecting all this, taking the collected data and organizing it, and making sure you're compliant with whatever policies. And making it, you know, business ready, right? And so infosphere's where you should look to understand that portfolio better. When you get into scale and analytics on demand, that's Cognos analytics, it is our planning analytics portfolio. And that's essentially our business analytics part of all this. And some data science tools like, SPSS, we're doing statistical analysis and SPSS modeler, if we're doing statistical modeling, things of that nature, right? When you get into the automate and the ML, everywhere, that's Watson Studio which is the integrated development environment, right? Not just for IBM Watson, but all, has a huge array of open technologies in it like, TensorFlow and Python, and all those kind of things. So that's the development environment that Watson machine learning is the runtime that will allow you to run those models anywhere. So those are the two big pieces of that. And then from there you'll see IBM building out more and more of what we already have. But we have Watson applications. Like Watson Assistant, Watson Discovery. We have a huge portfolio of Watson APIs for everything from tone to speech, things of that nature. And then the ability to infuse that all into the business processes. Sort of where you're going to see IBM heading in the future here. >> I love how you brought that home, and we talked about the ladder and it's more than just a PowerPoint slide. It actually is fundamental to your strategy, it maps with your offerings. So you can get the heads nodding, with the customers. Where are you on this maturity curve, here's how we can help with products and services. And then the other thing I'll mention, you know, we kind of learned when we spoke to some others this week, and we saw some of your announcements previously, the Red Hat component which allows you to bring that cloud experience no matter where you are, and you've got technologies to do that, obviously, you know, Red Hat, you guys have been sort of birds of a feather, an open source. Because, your data is going to live wherever it lives, whether it's on Prem, whether it's in the cloud, whether it's in the Edge, and you want to bring sort of a common model. Whether it's, containers, kubernetes, being able to, bring that cloud experience to the data, your thoughts on that? >> And this is where the big deal comes in, is for each one of those tiers, so, the DB2 family, infosphere, business analytics, Cognos and all that, and Watson Studio, you can get started, purchase those technologies and start to use them, right, as individual products or softwares that service. What we're also doing is, this is the more important step into the future, is we're building all those capabilities into one integrated unified cloud platform. That's called, IBM Cloud Private for data. Think of that as a unified, collaborative team environment for AI and data science. Completely built on a cloud native architecture of containers and micro services. That will support a multi cloud environment. So, IBM cloud, other clouds, you mention Red Hat with Openshift, so, over time by adopting IBM Cloud Private for data, you'll get those steps of the ladder all integrated to one unified environment. So you have the ability to buy the unified environment, get involved in that, and it all integrated, no assembly required kind of thought. Or, you could assemble it by buying the individual components, or some combination of both. So a big part of the strategy is, a great deal of flexibility on how you acquire these capabilities and deploy them in your enterprise. There's no one size fits all. We give you a lot of flexibility to do that. >> And that's a true hybrid vision, I don't have to have just IBM and IBM cloud, you're recognizing other clouds out there, you're not exclusive like some companies, but that's really important. >> It's a multi cloud strategy, it really is, it's a multi cloud strategy. And that's exactly what we need, we recognize that most businesses, there's very few that have standardized on only one cloud provider, right? Most of them have multiples clouds, and then it breaks up of dedicated, private, public. And so our strategy is to enable this capability, think of it as a cloud data platform for AI, across all these clouds, regardless of what you have. >> All right, Scott, thanks for taking us through the strategies. I've always loved talking to you 'cause you're a clear thinker, and you explain things really well in simple terms, a lot of complexity here but, it is really important as the next wave sets up. So thanks very much for your time. >> Great, always great to be here, thank you. >> All right, good to see you. All right, thanks for watching everybody. We are now going to bring it back to CubeNYC so, thanks for watching and we will see you in the afternoon. We've got the panel, the influencer panel, that I'll be running with Peter Burris and John Furrier. So, keep it right there, we'll be right back. (upbeat music)

(upbeat music) >> Live from Time Square in New York City, it's The Cube. Covering IBM's change the game, winning with AI. Brought to you by IBM. >> Hi everybody, welcome back to The Big Apple. My name is Dave Vellante. We're here in the Theater District at The Westin Hotel covering a Special Cube event. IBM's got a big event today and tonight, if we can pan here to this pop-up. Change the game: winning with AI. So IBM has got an event here at The Westin, The Tide at Terminal 5 which is right up the Westside Highway. Go to IBM.com/winwithAI. Register, you can watch it online, or if you're in the city come down and see us, we'll be there. Uh, we have a bunch of customers will be there. We had Rob Thomas on earlier, he's kind of the host of the event. IBM does these events periodically throughout the year. They gather customers, they put forth some thought leadership, talk about some hard dues. So, we're very excited to have John Thomas here, he's a distinguished engineer and Director of IBM Analytics, long time Cube alum, great to see you again John >> Same here. Thanks for coming on. >> Great to have you. >> So we just heard a great case study with Niagara Bottling around the Data Science Elite Team, that's something that you've been involved in, and we're going to get into that. But give us the update since we last talked, what have you been up to?? >> Sure sure. So we're living and breathing data science these days. So the Data Science Elite Team, we are a team of practitioners. We actually work collaboratively with clients. And I stress on the word collaboratively because we're not there to just go do some work for a client. We actually sit down, expect the client to put their team to work with our team, and we build AI solutions together. Scope use cases, but sort of you know, expose them to expertise, tools, techniques, and do this together, right. And we've been very busy, (laughs) I can tell you that. You know it has been a lot of travel around the world. A lot of interest in the program. And engagements that bring us very interesting use cases. You know, use cases that you would expect to see, use cases that are hmmm, I had not thought of a use case like that. You know, but it's been an interesting journey in the last six, eight months now. >> And these are pretty small, agile teams. >> Sometimes people >> Yes. use tiger teams and they're two to three pizza teams, right? >> Yeah. And my understanding is you bring some number of resources that's called two three data scientists, >> Yes and the customer matches that resource, right? >> Exactly. That's the prerequisite. >> That is the prerequisite, because we're not there to just do the work for the client. We want to do this in a collaborative fashion, right. So, the customers Data Science Team is learning from us, we are working with them hand in hand to build a solution out. >> And that's got to resonate well with customers. >> Absolutely I mean so often the services business is like kind of, customers will say well I don't want to keep going back to a company to get these services >> Right, right. I want, teach me how to fish and that's exactly >> That's exactly! >> I was going to use that phrase. That's exactly what we do, that's exactly. So at the end of the two or three month period, when IBM leaves, my team leaves, you know, the client, the customer knows what the tools are, what the techniques are, what to watch out for, what are success criteria, they have a good handle of that. >> So we heard about the Niagara Bottling use case, which was a pretty narrow, >> Mm-hmm. How can we optimize the use of the plastic wrapping, save some money there, but at the same time maintain stability. >> Ya. You know very, quite a narrow in this case. >> Yes, yes. What are some of the other use cases? >> Yeah that's a very, like you said, a narrow one. But there are some use cases that span industries, that cut across different domains. I think I may have mentioned this on one of our previous discussions, Dave. You know customer interactions, trying to improve customer interactions is something that cuts across industry, right. Now that can be across different channels. One of the most prominent channels is a call center, I think we have talked about this previously. You know I hate calling into a call center (laughter) because I don't know Yeah, yeah. What kind of support I'm going to get. But, what if you could equip the call center agents to provide consistent service to the caller, and handle the calls in the best appropriate way. Reducing costs on the business side because call handling is expensive. And eventually lead up to can I even avoid the call, through insights on why the call is coming in in the first place. So this use case cuts across industry. Any enterprise that has got a call center is doing this. So we are looking at can we apply machine-learning techniques to understand dominant topics in the conversation. Once we understand with these have with unsupervised techniques, once we understand dominant topics in the conversation, can we drill into that and understand what are the intents, and does the intent change as the conversation progress? So you know I'm calling someone, it starts off with pleasantries, it then goes into weather, how are the kids doing? You know, complain about life in general. But then you get to something of substance why the person was calling in the first place. And then you may think that is the intent of the conversation, but you find that as the conversation progresses, the intent might actually change. And can you understand that real time? Can you understand the reasons behind the call, so that you could take proactive steps to maybe avoid the call coming in at the first place? This use case Dave, you know we are seeing so much interest in this use case. Because call centers are a big cost to most enterprises. >> Let's double down on that because I want to understand this. So you basically doing. So every time you call a call center this call may be recorded, >> (laughter) Yeah. For quality of service. >> Yeah. So you're recording the calls maybe using MLP to transcribe those calls. >> MLP is just the first step, >> Right. so you're absolutely right, when a calls come in there's already call recording systems in place. We're not getting into that space, right. So call recording systems record the voice calls. So often in offline batch mode you can take these millions of calls, pass it through a speech-to-text mechanism, which produces a text equivalent of the voice recordings. Then what we do is we apply unsupervised machine learning, and clustering, and topic-modeling techniques against it to understand what are the dominant topics in this conversation. >> You do kind of an entity extraction of those topics. >> Exactly, exactly, exactly. >> Then we find what is the most relevant, what are the relevant ones, what is the relevancy of topics in a particular conversation. That's not enough, that is just step two, if you will. Then you have to, we build what is called an intent hierarchy. So this is at top most level will be let's say payments, the call is about payments. But what about payments, right? Is it an intent to make a late payment? Or is the intent to avoid the payment or contest a payment? Or is the intent to structure a different payment mechanism? So can you get down to that level of detail? Then comes a further level of detail which is the reason that is tied to this intent. What is a reason for a late payment? Is it a job loss or job change? Is it because they are just not happy with the charges that I have coming? What is a reason? And the reason can be pretty complex, right? It may not be in the immediate vicinity of the snippet of conversation itself. So you got to go find out what the reason is and see if you can match it to this particular intent. So multiple steps off the journey, and eventually what we want to do is so we do our offers in an offline batch mode, and we are building a series of classifiers instead of classifiers. But eventually we want to get this to real time action. So think of this, if you have machine learning models, supervised models that can predict the intent, the reasons, et cetera, you can have them deployed operationalize them, so that when a call comes in real time, you can screen it in real time, do the speech to text, you can do this pass it to the supervise models that have been deployed, and the model fires and comes back and says this is the intent, take some action or guide the agent to take some action real time. >> Based on some automated discussion, so tell me what you're calling about, that kind of thing, >> Right. Is that right? >> So it's probably even gone past tell me what you're calling about. So it could be the conversation has begun to get into you know, I'm going through a tough time, my spouse had a job change. You know that is itself an indicator of some other reasons, and can that be used to prompt the CSR >> Ah, to take some action >> Ah, oh case. appropriate to the conversation. >> So I'm not talking to a machine, at first >> no no I'm talking to a human. >> Still talking to human. >> And then real time feedback to that human >> Exactly, exactly. is a good example of >> Exactly. human augmentation. >> Exactly, exactly. I wanted to go back and to process a little bit in terms of the model building. Are there humans involved in calibrating the model? >> There has to be. Yeah, there has to be. So you know, for all the hype in the industry, (laughter) you still need a (laughter). You know what it is is you need expertise to look at what these models produce, right. Because if you think about it, machine learning algorithms don't by themselves have an understanding of the domain. They are you know either statistical or similar in nature, so somebody has to marry the statistical observations with the domain expertise. So humans are definitely involved in the building of these models and claiming of these models. >> Okay. >> (inaudible). So that's who you got math, you got stats, you got some coding involved, and you >> Absolutely got humans are the last mile >> Absolutely. to really bring that >> Absolutely. expertise. And then in terms of operationalizing it, how does that actually get done? What tech behind that? >> Ah, yeah. >> It's a very good question, Dave. You build models, and what good are they if they stay inside your laptop, you know, they don't go anywhere. What you need to do is, I use a phrase, weave these models in your business processes and your applications. So you need a way to deploy these models. The models should be consumable from your business processes. Now it could be a Rest API Call could be a model. In some cases a Rest API Call is not sufficient, the latency is too high. Maybe you've got embed that model right into where your application is running. You know you've got data on a mainframe. A credit card transaction comes in, and the authorization for the credit card is happening in a four millisecond window on the mainframe on all, not all, but you know CICS COBOL Code. I don't have the time to make a Rest API call outside. I got to have the model execute in context with my CICS COBOL Code in that memory space. >> Yeah right. You know so the operationalizing is deploying, consuming these models, and then beyond that, how do the models behave over time? Because you can have the best programmer, the best data scientist build the absolute best model, which has got great accuracy, great performance today. Two weeks from now, performance is going to go down. >> Hmm. How do I monitor that? How do I trigger a loads map for below certain threshold. And, can I have a system in place that reclaims this model with new data as it comes in. >> So you got to understand where the data lives. >> Absolutely. You got to understand the physics, >> Yes. The latencies involved. >> Yes. You got to understand the economics. >> Yes. And there's also probably in many industries legal implications. >> Oh yes. >> No, the explainability of models. You know, can I prove that there is no bias here. >> Right. Now all of these are challenging but you know, doable things. >> What makes a successful engagement? Obviously you guys are outcome driven, >> Yeah. but talk about how you guys measure success. >> So um, for our team right now it is not about revenue, it's purely about adoption. Does the client, does the customer see the value of what IBM brings to the table. This is not just tools and technology, by the way. It's also expertise, right? >> Hmm. So this notion of expertise as a service, which is coupled with tools and technology to build a successful engagement. The way we measure success is has the client, have we built out the use case in a way that is useful for the business? Two, does a client see value in going further with that. So this is right now what we look at. It's not, you know yes of course everybody is scared about revenue. But that is not our key metric. Now in order to get there though, what we have found, a little bit of hard work, yes, uh, no you need different constituents of the customer to come together. It's not just me sending a bunch of awesome Python Programmers to the client. >> Yeah right. But now it is from the customer's side we need involvement from their Data Science Team. We talk about collaborating with them. We need involvement from their line of business. Because if the line of business doesn't care about the models we've produced you know, what good are they? >> Hmm. And third, people don't usually think about it, we need IT to be part of the discussion. Not just part of the discussion, part of being the stakeholder. >> Yes, so you've got, so IBM has the chops to actually bring these constituents together. >> Ya. I have actually a fair amount of experience in herding cats on large organizations. (laughter) And you know, the customer, they've got skin in the IBM game. This is to me a big differentiator between IBM, certainly some of the other technology suppliers who don't have the depth of services, expertise, and domain expertise. But on the flip side of that, differentiation from many of the a size who have that level of global expertise, but they don't have tech piece. >> Right. >> Now they would argue well we do anybodies tech. >> Ya. But you know, if you've got tech. >> Ya. >> You just got to (laughter) Ya. >> Bring those two together. >> Exactly. And that's really seems to me to be the big differentiator >> Yes, absolutely. for IBM. Well John, thanks so much for stopping by theCube and explaining sort of what you've been up to, the Data Science Elite Team, very exciting. Six to nine months in, >> Yes. are you declaring success yet? Still too early? >> Uh, well we're declaring success and we are growing, >> Ya. >> Growth is good. >> A lot of lot of attention. >> Alright, great to see you again, John. >> Absolutely, thanks you Dave. Thanks very much. Okay, keep it right there everybody. You're watching theCube. We're here at The Westin in midtown and we'll be right back after this short break. I'm Dave Vellante. (tech music)

>> Live from Time Square in New York City, it's theCUBE, covering IBM's Change the Game, Winning with AI. (theCUBE theme music) Brought to you by IBM. >> Hello everybody, welcome back to theCUBE's special presentation. We're covering IBM's announcement. Changing the Game, Winning with AI is the theme of IBM. And IBM has these customer meet-ups, analyst meet-ups, partner meet-ups and they do this in conjunction with Strata every year. And theCUBE has been there covering 'em. I'm Dave Vellante with us is James Wade, who's the Director of Application Hosting at Guidewell, and Hemanth Manda, who's the Director of Platform Offerings at IBM. Gentlemen, welcome to theCUBE thanks for coming on. >> Thank you. >> Hemanth, let's start with you. Platform offerings. A lot of platforms inside of IBM. What do you mean platform offerings? Which one are you responsible for? >> Yeah, so IBM's data and analytics portfolio is pretty wide. It's close to six billion dollar business. And we have hundred plus products. What we are trying to do, is we're trying to basically build a platform through IBM Cloud Private for Data. Bring capabilities that cuts across our portfolio and build upon it. We also make it open. Support multiple clouds and support other partners who wants to run on the platform. So that's what I'm leading. >> Okay, great and we'll come back and talk about that. But James, tell us more about Guidewell. Where are you guys based? What'd you do and what's your role? >> Guidewell is the largest insurer in the sate of Florida. We have about six and a half million members. We also do about 38, 39% of the government processing for MediCare, MediCaid claims. Very large payer. We've also recently moved in over the provider space. We actually have clinics throughout the state of Florida where our members can go in and actually get services there. So we're actually morphing as a company, away from just an insurance company, really to a healthcare company. Very exciting time to be there. We've doubled in size in the last six years from a six billion dollar company to a, I mean from an eight billion dollar company to an 18 billion dollar company. >> So both health insurer and provider, bringing those two worlds together. And the thinking there is just more efficient, you'd be able to drive efficiencies obviously out of your business, right? >> Yup, yes. I mean, the ultimate goal for us is just to have better health outcomes for our members. And the way you deliver that is, one, you do the insurance right, you do it well. You make sure that their processed and handled properly, that they're getting all the services that they need. But two, from a provider space, how do you take the information that you have about your members and use them in a provider space to make sure they're getting the right prescriptions at the right time, for the right situations that they're having, whatever's going on in their life. >> And keeping cost down. I mean, there's a lot of finger pointing in the industry. If you bring those two areas together, you know, now they got a single throat to choke, >> That's right, we get that too. (laughing) >> Buck stops with you. Okay, and you're responsible for the entire application portfolio across the insurance and the clinical side? >> Yes, I have, you know, be it both sides, we have Guidewell as the holding company, we have multiple companies underneath it. So all of those companies roll up into a single kind of IT infrastructure. And I manage that for them, for the entire company. >> Okay. Talk about the big drivers in you business. Obviously on the insurance side, it's the claims system is the life blood, the agency system to deal with, the channel. And now of course, you've got the clinical thing to worry about, but so, talk about sort of the drivers of your business and what's changing. >> Right, I mean, the biggest change we've had, obviously in last few years, has been the Affordable Care Act. It changed the way that, you know, from a group policy where if you're a big corporation and you work for a big corporation, that company actually buys insurance for you and provides it to their employees. Well now the individual market has grown significantly. We're still a group policy insurance company, don't get me wrong, we have a great portfolio of companies that we work with, but we also now sell directly to individuals. So they're in the consumer space directly. And that's just a different way of interacting with folks. You have to have sales sites. You have to have websites that are up, where folks can come and browse your products. You have to interface with government websites. Like CMS has their site where they set up and you're able to buy products through that. So it's really changed our marketing and sales channels completely. And on the back side, the volume of growth, I mean, with the new individual insurance market we've grown in size significantly in our number of members. And that's really stressed our IT systems, it's stressed our database environment. And it's really stressed our ability to kind of analyze the thing that we're doing. And make sure that we're processing claims efficiently and making sure that the members are getting what they expect from us. So, the velocity and change in size has really stressed us. >> Yeah, so you got the Affordable Care Act and some uncertainties around that, the regulations around that. You've got things like EMR and meaningful use that you got to worry about. So a lot of complexity in the application portfolio. And Hemanth, I imagine this is not a unique discussion that you have with some of your insurance clients and healthcare folks, although, you guys are a little different in that you're bringing those two worlds together. But your thoughts on what you're seeing the marketplace. >> Yeah, so I mean, this is not unique because the data is exploding and there are multiple data sources spread across multiple clouds. So in terms of trying to get a sense of where the data is, how to actually start leveraging it, how to govern it, how to analyze it, is a problem that is across all industry verticals. And especially as we are going through digital transformation right, trying to leverage and monetize your data becomes even more important. So. >> Yeah, so, well let's talk a little bit about the data. So your data, like a lot of companies, you must have a lot of data silos. And we have said on theCUBE a lot, that the innovation engine in the future is data. Applying machine intelligence to that data. Using cloud models, whether that cloud is in a private cloud or a public cloud or now even at the edge. But having a cloud-like experience for scale and agility is critical. So, that seems to be the innovation, whereas, last 20, 30 years the innovation has been you know kind of Moore's Law and being able to get the latest and greatest systems, so I can get data out of my data warehouse faster. So change in the innovation engine driven by data what are you seeing James? >> I mean, absolutely. Again, we go back to the mission of the company. It's to provide better health outcomes for our members, right. And IT, and using the data that we collect more effectively and efficiently, allows us to do that. I mean we, if you take, you know, across the board, you may have four or five doctors that you're working with and they've prescribed multiple things to you, but they're not talking. They have no idea what your other doctor is doing with you, unless you tell 'em and a lot of people forget. So just as an example, we would know as the payer, what you've been prescribed, what you've been using for multiple years. If we see something, using AI, machine learning, that you've just been prescribed is going to have a detrimental impact to something else that you're doing, we can alert you. We can send you SMS messages, we can send you emails, we could alert your doctors. Just to say, hey this could be a problem and it could cause a prescription collision and you can end up in the hospital or worse. And that's just one example of the things that we look at everyday to try to better the outcome for our members. But, you know, that's just the first layer. What else can you do with that? Are there predictive medicines? Are there things we could alert your doctors to, that we're seeing from other places, or populations, that kind of match, you know, your current, you know, kind of what you look like, what you do, what you think, what you're using. All the information we have about you, can we predict health outcomes down the future and let your doctors know? So, exciting time to be in this industry. >> Let's talk about the application architecture to support that outcome, because you know, you're not starting from a green field. You probably got some Cobalt running and it works, you can't mess with that stuff. And traditionally you built, especially in a regulated industry, you're building applications that are hardened. And as I said you have this data silo that really, you know, it's like, it works, don't touch it. How much of a challenge is it for you to enter this sort of new era? And how are you getting there? I'd like to understand, IBM's role as well. >> Well we, it's very challenging, number one. You have your, I don't want to call it legacy 'cause that makes it sound bad, but you do have kind of your legacy environments where we're collecting the information. It's kind of like the silos that have gathered the information, the sales information, the claims information, that type of stuff. But those may not be the best systems currently, to actually do the processing and the data analysis and having the machine learning run against it. So we have, you know, really complex ETL, you know, moving data from our kind of legacy environments in to these newer open source models that you guys support with, you know, IBM Cloud Private for Data. But basically, moving into these open source areas where we can kind of focus our tools on it and learn from that data. So that, you know, having your legacy environment and moving it to the new environment where you can do this processing, has been a challenge. I mean the velocity of change in the new environment, the types of databases that are out there Hadoop and then the products that you guys have that run through the information, that's one of the bigger challenges that we have. Our company is very supportive of IT, they give us plenty of budget, they give us plenty of resources. But even with all of the support that we get, the velocity of change in the new environment, in the AI space and the machine learning, is very difficult to keep up with. >> Yeah and you can't just stop doing what your doing in the existing environment, you still got to make changes to it. You got regulatory, you got hippo stuff that you've got to deal with. So you can't just freeze your code there. So, are things like containers and, you know, cloud native techniques coming into play? >> Absolutely, absolutely. We're developing all, you know, we kind of drew a line in the sand, our CIO about two years ago, line in the sand, everything that we develop now is in our cloud-first strategy. That doesn't necessarily mean it's going to go into the external cloud. We have an internal cloud that we have. And we have a very large power environment at Guidewell. Our mainframe is still sort of a cloud-like infrastructure. So, we developed it to be cloud native, cloud-first. And then if it, you know, more than likely stays in our four walls, but there's also the option that we can move it out. Move it to various clouds that are out there. As an IBM Cloud, Amazon, Microsoft, Google, any of those clouds. So we're developing with a cloud-first strategy all of the new things. Now, like you said, the legacy side, we have to maintain. I mean, still the majority of our business is processing claims for our members, right, and that's still in that kind of legacy environment. Runs on a mainframe in the power environment today. So we have to keep it up and running as well. >> How large of organization are you, head count wise? >> We have about 2,100 IT people at Guidewell. Probably a 17,000 person organization. So there is a significant percentage of the population of our employees that are IT directly. >> I was at a, right 'cause it is a IT heavy business, always has been. I was at a conference recently and they threw out a stat that the average organization has eight clouds. And I said, "we're like a 60 person company "and we have eight clouds." I mean you must have 8,000 clouds. (laughing) Imagine when you through in the SAS and so forth. But, you mentioned a number of other clouds. You mentioned IBM Cloud and some others. So, it's a multi-cloud world. >> Yes, yes. >> Okay, so I'm interested in how IBM is approaching that, right. You're not just saying, okay, IBM Cloud or nothing, I think, you know. And cloud is defined on-prem, off-prem, maybe now at the edge, your thoughts. >> Yeah, so, absolutely, I think that is our strategy. We would like to support all the clouds out there, we don't want to discriminate one versus the other. We do have our own public cloud, but what our strategy is, to support our products and platforms on any cloud. For example, IBM Cloud Private for Data, it can run in the data center, it can provide the benefits of the cloud within your firewall. But if you want to deploy it on any other public cloud infrastructures, such as Amazon or Red Hat OpenStack, we do support it. We are also looking to expand that support to Microsoft and Google in the future. So we are going forward with the multi-cloud strategy. Also, if you look at IBM's strength, right, we have significant on-premise business, right, that's our strength. So we want to basically start with enterprise-out. So by focusing on private cloud, and making sure that customers can actually move their offerings and products to private cloud, we are essentially providing a path for our customers and clients to move cloud, embrace cloud. So that's been our approach. >> So James, I'm interested in how you guys look at cloud-first. When you say cloud-first, first of all, I'm hearing, it's not about where it goes, it's about the experience. So we're going to bring the cloud model to the data, wherever the data lives. It's in the public cloud, of course it's cloud. If we bring it on-prem, we want a cloud-like experience. How do you guys looks at that cloud-like experience? Is it utility pricing, is it defined in sort of agility terms? Maybe you could elaborate. >> Actually, we're trying to go with the agility piece first, right. The hardest thing right now is to keep up with the pace that customers demand. I mean, you know, my boss Paul Stallings always talks about, you know, consumer-grade is now the industrial strength. Now you go home at night, your network at home is very fast to your PC. Your phone, you just hit an app, you always expect it to work. Well, we have to be able to provide that same level of support and reliability in the applications we're deploying inside of our infrastructure. So, to do that, you have to be fast, you have to be agile. And our cloud-first being, how do you get things to market faster, right. So you can build service faster build out your networks faster and build you databases faster. Already have like defined sizes, click a button and it's there. On-demand infrastructure, much like they do in the public loud, We want to have that internally. But second, and our finance department would tell you, is that, you know, most important is the utility piece. So once you can define these individuals modules that you can hit a button and immediately spin up and instantiate, you should be able to figure out what that cost the company. How do you define what a server cost? Total cost of ownership through the lifetime that server is for the company. Because if we can lower thar cost, if we can do these things very well, automate 'em, get the data where it needs to be, spin up quickly, we can reduce our administrative cost and then pass those savings right back to our members. You know, if we can find a way to save your grandmother $20 a month off her health insurance, that can make a lot of difference in a person's life, right. Just by cutting our cost on the IT side, we can deliver savings back to the company. And that's very key to us. >> And in terms of sort of what goes where, I guess it's a function of the physics, right, if there's latencies involved, the economics, which you mentioned are critical obviously in your business. And I guess the laws, you know, the edicts of the government-- >> Yes and the various contracts that you sign with companies. I mean, there's some companies that we deal with it in the state of Florida that want their data to stay in that sate of Florida. Well if you move it out to a various cloud provider, you don't know which data center that it's in. So you have to go, there's the laws and regulations based on your contracts. But you're exactly right. It's what have you signed up for, what've you agreed to, what are your member comfortable with as to where the data can actually go? >> How does IBM help Guidewell and other companies sort of mange through that complexity? >> Yeah, absolutely. So I think, in addition to what James mentioned, right, it's also about agility. Because for example, if you look at insurance applications, there's a specific time period where you probably would expect 10x of load, right. So you should be able to easily scale up and down. And also, as you're changing your business model, if you have new laws, or if you want to go after new businesses, you should be able to easily embrace that, right. So cloud provides sort of flexibility and elasticity and also the agility. So that's one. The other thing that you mentioned around regulation, especially in healthcare and also too with financial services industry. So what we're trying to do is, on our platform, we would like to actually have industry-specific accelerators. We've been working with fortune 500 companies for the last 30, 40 years. So we've gained a depth of knowledge that we currently have within our company. So we want to basically start exposing the accelerators. And this is on our roadmap and will be available fairly quickly. So that's one approach we're taking. The other approach we're taking is, we're also working with our business partners and technology partners because we do believe, in today's world, you cannot go after an opportunity all by yourself. You need to build an ecosystem and that's what we're doing. We're trying to work with, basically, specialty vendors who might be focused on that particular vertical, who can bring the depth in knowledge that we might not be having. And work with them and team up, so that they can build their solutions on top of the platform. So that's another approach that we're taking. >> So I got to ask you, I always ask this question of customers. Why IBM? >> I mean, this, you guys have been a part of our business for so long. You have very detailed sales guys that are embed really with our IT folks. You understand our systems. You understand what we do, when we do it, why we do it. You understand our business cycle. IBM really invests in their customers and understanding what they're doing, what they need to be done. And quite honestly, you guys bring some ideas to the table we haven't even thought of. You have such a breadth of understanding, and you're dealing with so many other companies, you'll see things out there that could be a nugget that we could use. And IBM's never shied of bringing that to us. Just a history and a legacy of really bringing innovative solutions to us to really help our business. And very companies out there really get to know a company's business, as well as IBM does. >> Hemanth I'll give you the last word. We got Change the Game, Winning with AI tonight You go to IBM.com/winwithAI and register there. I just did, I'm part of the analyst program. So, Hemanth, last word for you. >> Yeah, so, I think the world is changing really fast and unless enterprises embrace cloud and embrace artificial intelligence and cloud base their data to monetize new business models, it very hard to compete. Like, digital transformation is impacting every industry vertical, including IBM. So, I think going after this opportunistically is critical. And IBM Cloud Private for Data, the platform provides this. And please join us today, it's going to be a great event. And I look forward to meeting you guys, thank you. >> Awesome, and definitely agree. It's all about your digital meets data, applying machine intelligence, machine learning, AI, to that data. Being able to run it in a cloud-like model so you can scale, you can be fast. That's the innovation sandwich for the future. It's not just about the speed of the processor, or the size of the disk drive, or the flash or whatever is. It's really about that combination. theCUBE bringing you all the intelligence we can find. You're watching CUBE NYC. We'll be right back right after this short break. (theCUBE theme music)

>> 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.

>> 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. >> Hi everybody, welcome back to theCUBE's special presentation. We're here at the Western Hotel and the theater district covering IBM's announcements. They've got an analyst meeting today, partner event. They've got a big event tonight. IBM.com/winwithAI, go to that website, if you're in town register. You can watch the webcast online. You'll see this very cool play of Vince Lombardy, one of his famous plays. It's kind of a power sweep right which is a great way to talk about sort of winning and with X's and O's. So anyway, Daniel Hernandez is here the vice president of IBM analytics, long time Cube along. It's great to see you again, thanks for coming on. >> My pleasure Dave. >> So we've talked a number of times. We talked earlier this year. Give us the update on momentum in your business. You guys are doing really well, we see this in the quadrants and the waves, but your perspective. >> Data science and AI, so when we last talked we were just introducing something called IBM Club Private for data. The basic idea is anybody that wants to do data science, data engineering or building apps with data anywhere, we're going to give them a single integrated platform to get that done. It's going to be the most efficient, best way to do those jobs to be done. We introduced it, it's been a resounding success. Been rolling that out with clients, that's been a whole lot of fun. >> So we talked a little bit with Rob Thomas about some of the news that you guys have, but this is really your wheelhouse so I'm going to drill down into each of these. Let's say we had Rob Beerden on yesterday on our program and he talked a lot about the IBM Red Hat and Hortonworks relationship. Certainly they talked about it on their earnings call and there seems to be clear momentum in the marketplace. But give us your perspective on that announcement. What exactly is it all about? I mean it started kind of back in the ODPI days and it's really evolved into something that now customers are taking advantage of. >> You go back to June last year, we entered into a relationship with Hortonworks where the basic primacy, was customers care about data and any data driven initiative was going to require data science. We had to do a better job bringing these eco systems, one focused on kind of Hadoop, the other one on classic enterprise analytical and operational data together. We did that last year. The other element of that was we're going to bring our data science and machine learning tools and run times to where the data is including Hadoop. That's been a resounding success. The next step up is how do we proliferate that single integrated stack everywhere including private Cloud or preferred Clouds like Open Shift. So there was two elements of the announcement. We did the hybrid Cloud architecture initiative which is taking the Hadoop data stack and bringing it to containers and Kubernetes. That's a big deal for people that want to run the infrastructure with Cloud characteristics. And the other was we're going to bring that whole stack onto Open Shift. So on IBM's side, with IBM Cloud Private for data we are driving certification of that entire stack on OpenShift so any customer that's betting on OpenShift as their Cloud infrastructure can benefit from that and the single integrated data stack. It's a pretty big deal. >> So OpenShift is really interesting because OpenShift was kind of quiet for awhile. It was quiest if you will. And then containers come on the scene and OpenShift has just exploded. What are your perspectives on that and what's IBM's angle on OpenShift? >> Containers of Kubernetes basically allow you to get Cloud characteristics everywhere. It used to be locked in to kind of the public Cloud or SCP providers that were offering as a service whether PAS OR IAS and Docker and Kubernetes are making the same underline technology that enabled elasticity, pay as you go models available anywhere including your own data center. So I think it explains why OpenShift, why IBM Cloud Private, why IBM Club Private for data just got on there. >> I mean the Core OS move by Red Hat was genius. They picked that up for the song in our view anyway and it's really helped explode that. And in this world, everybody's talking about Kubernetes. I mean we're here at a big data conference all week. It used to be Hadoop world. Everybody's talking about containers, Kubernetes and Multi cloud. Those are kind of the hot trends. I presume you've seen the same thing. >> 100 percent. There's not a single client that I know, and I spend the majority of my time with clients that are running their workloads in a single stack. And so what do you do? If data is an imperative for you, you better run your data analytic stack wherever you need to and that means Multi cloud by definition. So you've got a choice. You can say, I can port that workload to every distinct programming model and data stack or you can have a data stack everywhere including Multi clouds and Open Shift in this case. >> So thinking about the three companies, so Hortonworks obviously had duped distro specialists, open source, brings that end to end sort of data management from you know Edge, or Clouds on Prim. Red Hat doing a lot of the sort of hardcore infrastructure layer. IBM bringing in the analytics and really empowering people to get insights out of data. Is that the right way to think about that triangle? >> 100 percent and you know with the Hortonworks and IBM data stacks, we've got our common services, particularly you're on open meta data which means wherever your data is, you're going to know about it and you're going to be able to control it. Privacy, security, data discovery reasons, that's a pretty big deal. >> Yeah and as the Cloud, well obviously the Cloud whether it's on Prim or in the public Cloud expands now to the Edge, you've also got this concept of data virtualization. We've talked about this in the past. You guys have made some announcements there. But let's put a double click on that a little bit. What's it all about? >> Data virtualization been going on for a long time. It's basic intent is to help you access data through whatever tools, no matter where the data is. Traditional approaches of data virtualization are pretty limiting. So they work relatively well when you've got small data sets but when you've got highly fragmented data, which is the case in virtually every enterprise that exists a lot of the undermined technology for data virtualization breaks down. Data coming through a single headnote. Ultimately that becomes the critical issue. So you can't take advantage of data virtualization technologies largely because of that when you've got wide scale deployments. We've been incubating technology under this project codename query plex, it was a code name that we used internally and that we were working with Beta clients on and testing it out, validating it technically and it was pretty clear that this is a game changing method for data virtualization that allows you to drive the benefits of accessing your data wherever it is, pushing down queries where the data is and getting benefits of that through highly fragmented data landscape. And so what we've done is take that extremely innovated next generation data virtualization technology include it in our data platform called IBM Club Private for Data, and made it a critical feature inside of that. >> I like that term, query plex, it reminds me of the global sisplex. I go back to the days when actually viewing sort of distributed global systems was very, very challenging and IBM sort of solved that problem. Okay, so what's the secret sauce though of query plex and data virtualization? How does it all work? What's the tech behind it? >> So technically, instead of data coming and getting funneled through one node. If you ever think of your data as kind of a graph of computational data nodes. What query plex does is take advantage of that computational mesh to do queries and analytics. So instead of bringing all the data and funneling it through one of the nodes, and depending on the computational horsepower of that node and all the data being able to get to it, this just federates it out. It distributes out that workload so it's some magic behind the scenes but relatively simple technique. Low computing aggregate, it's probably going to be higher than whatever you can put into that single node. >> And how do customers access these services? How long does it take? >> It would look like a standard query interface to them. So this is all magic behind the scenes. >> Okay and they get this capability as part of what? IBM's >> IBM's Club Private for Data. It's going to be a feature, so this project query plex, is introduced as next generation data virtualization technology which just becomes a part of IBM Club Private for Data. >> Okay and then the other announcement that we talked to Rob, I'd like to understand a little bit more behind it. Actually before we get there, can we talk about the business impact of query plex and data virtualization? Thinking about it, it dramatically simplifies the processes that I have to go through to get data. But more importantly, it helps me get a handle on my data so I can apply machine intelligence. It seems like the innovation sandwich if you will. Data plus AI and then Cloud models for scale and simplicity and that's what's going to drive innovation. So talk about the business impact that people are excited about with regard to query plex. >> Better economics, so in order for you to access your data, you don't have to do ETO in this particular case. So data at rest getting consumed because of this online technology. Two performance, so because of the way this works you're actually going to get faster response times. Three, you're going to be able to query more data simply because this technology allows you to access all your data in a fragmented way without having to consolidate it. >> Okay, so it eliminates steps, right, and gets you time to value and gives you a bigger corporate of data that you can the analyze and drive inside. >> 100 percent. >> Okay, let's talk about stack overflow. You know, Rob took us through a little bit about what that's, what's going on there but why stack overflow, you're targeting developers? Talk to me more about that. >> So stack overflow, 50 million active developers each month on that community. You're a developer and you want to know something, you have to go to stack overflow. You think about data science and AI as disciplines. The idea that that is only dermained to AI and data scientists is very limiting idea. In order for you to actually apply artificial intelligence for whatever your use case is instead of a business it's going to require multiple individuals working together to get that particular outcome done including developers. So instead of having a distinct community for AI that's focused on AI machine developers, why not bring the artificial intelligence community to where the developers already are, which is stack overflow. So, if you go to AI.stackexchange.com, it's going to be the place for you to go to get all your answers to any question around artificial intelligence and of course IBM is going to be there in the community helping out. >> So it's AI.stackexchange.com. You know, it's interesting Daniel that, I mean to talk about digital transformation talking about data. John Furrier said something awhile back about the dots. This is like five or six years ago. He said data is the new development kit and now you guys are essentially targeting developers around AI, obviously a data centric. People trying to put data at the core of the organization. You see that that's a winning strategy. What do you think about that? >> 100 percent, I mean we're the data company instead of IBM, so you're probably asking the wrong guy if you think >> You're biased. (laughing) >> Yeah possibly, but I'm acknowledged. The data over opinions. >> Alright, tell us about tonight what we can expect? I was referencing the Vince Lombardy play here. You know, what's behind that? What are we going to see tonight? >> We were joking a little bit about the old school power eye formation, but that obviously works for your, you're a New England fan aren't you? >> I am actually, if you saw the games this weekend Pat's were in the power eye for quite a bit of the game which I know upset a lot of people. But it works. >> Yeah, maybe we should of used it as a Dallas Cowboy team. But anyways, it's going to be an amazing night. So we're going to have a bunch of clients talking about what they're doing with AI. And so if you're interested in learning what's happening in the industry, kind of perfect event to get it. We're going to do some expert analysis. It will be a little bit of fun breaking down what those customers did to be successful and maybe some tips and tricks that will help you along your way. >> Great, it's right up the street on the west side highway, probably about a mile from the Javis Center people that are at Strata. We've been running programs all week. One of the themes that we talked about, we had an event Tuesday night. We had a bunch of people coming in. There was people from financial services, we had folks from New York State, the city of New York. It was a great meet up and we had a whole conversation got going and one of the things that we talked about and I'd love to get your thoughts and kind of know where you're headed here, but big data to do all that talk and people ask, is that, now at AI, the conversation has moved to AI, is it same wine, new bottle, or is there something substantive here? The consensus was, there's substantive innovation going on. Your thoughts about where that innovation is coming from and what the potential is for clients? >> So if you're going to implement AI for let's say customer care for instance, you're going to be three wrongs griefs. You need data, you need algorithms, you need compute. With a lot of different structure to relate down to capture data wasn't captured until the traditional data systems anchored by Hadoop and big data movement. We landed, we created a data and computational grid for that data today. With all the advancements going on in algorithms particularly in Open Source, you now have, you can build a neuro networks, you can do Cisco machine learning in any language that you want. And bringing those together are exactly the combination that you need to implement any AI system. You already have data and computational grids here. You've got algorithms bringing them together solving some problem that matters to a customer is like the natural next step. >> And despite the skills gap, the skill gaps that we talked about, you're seeing a lot of knowledge transfer from a lot of expertise getting out there into the wild when you follow people like Kirk Born on Twitter you'll see that he'll post like the 20 different models for deep learning and people are starting to share that information. And then that skills gap is closing. Maybe not as fast as some people like but it seems like the industry is paying attention to this and really driving hard to work toward it 'cause it's real. >> Yeah I agree. You're going to have Seth Dulpren, I think it's Niagara, one of our clients. What I like about them is the, in general there's two skill issues. There's one, where does data science and AI help us solve problems that matter in business? That's really a, trying to build a treasure map of potential problems you can solve with a stack. And Seth and Niagara are going to give you a really good basis for the kinds of problems that we can solve. I don't think there's enough of that going on. There's a lot of commentary communication actually work underway in the technical skill problem. You know, how do I actually build these models to do. But there's not enough in how do I, now that I solved that problem, how do we marry it to problems that matter? So the skills gap, you know, we're doing our part with our data science lead team which Seth opens which is telling a customer, pick a hard problem, give us some data, give us some domain experts. We're going to be in the AI and ML experts and we're going to see what happens. So the skill problem is very serious but I don't think it's most people are not having the right conversations about it necessarily. They understand intuitively there's a tech problem but that tech not linked to a business problem matters nothing. >> Yeah it's not insurmountable, I'm glad you mentioned that. We're going to be talking to Niagara Bottling and how they use the data science elite team as an accelerant, to kind of close that gap. And I'm really interested in the knowledge transfer that occurred and of course the one thing about IBM and companies like IBM is you get not only technical skills but you get deep industry expertise as well. Daniel, always great to see you. Love talking about the offerings and going deep. So good luck tonight. We'll see you there and thanks so much for coming on theCUBE. >> My pleasure. >> Alright, keep it right there everybody. This is Dave Vellanti. We'll be back right after this short break. You're watching theCUBE. (upbeat music)

>> Male Voice: From around the globe, it's theCUBE covering HPE Discover Virtual Experience brought to you by HPE. >> Hi everybody, you're watching theCUBE. And this is Dave Vellante in our continuous coverage of the Discover 2020 Virtual Experience, HPE's virtual event, theCUBE is here, theCUBE virtual. We're really excited, we got a great session here. We're going to dig deep into machine intelligence and artificial intelligence. Dr. Arti Garg is here. She's the Head of Advanced AI Solutions and Technologies at Hewlett Packard Enterprise. And she's joined by Dr. Sorin Cheran, who is the Vice President of AI Strategy and Solutions Group at HPE. Folks, great to see you. Welcome to theCUBE. >> Hi. >> Hi, nice to meet you, hello! >> Dr. Cheran, let's start with you. Maybe talk a little bit about your role. You've had a variety of roles and maybe what's your current situation at HPE? >> Hello! Hi, so currently at HPE, I'm driving the Artificial Intelligence Strategy and Solution group who is currently looking at how do we bring solutions across the HPE portfolio, looking at every business unit, but also on the various geos. At the same time, the team is responsible for building the strategy around the AI for the entire company. We're working closely with the field, we're working closely with the things that are facing the customers every day. And we're also working very closely with the various groups in order to make sure that whatever we build holds water for the entire company. >> Dr. Garg, maybe you could share with us your focus these days? >> Yeah, sure, so I'm also part of the AI Strategy and Solutions team under Sorin as our new vice president in that role, and what I'm focused on is really trying to understand, what are some of the emerging technologies, whether those be things like new processor architectures, or advanced software technologies that could really enhance what we can offer to our customers in terms of AI and exploring what makes sense and how do we bring them to our customers? What are the right ways to package them into solutions? >> So everybody's talking about how digital transformation has been accelerated. If you're not digital, you can't transact business. AI infused into every application. And now people are realizing, "Hey, we can't solve all the world's problems with labor." What are you seeing just in terms of AI being accelerated throughout the portfolio and your customers? >> So that's a very good idea, because we've been talking about digital transformation for some time now. And I believe most of our customers believed initially that the one thing they have is time thinking that, "Oh yes I'm going to somehow at one point apply AI "and somehow at one point "I'm going to figure out how to build the data strategy, "or how to use AI in my different line of businesses." What happened with COVID-19 and in this area is that we lost one thing: time. So I think discussed what they see in our customers is the idea of accelerating their data strategy accelerating, moving from let's say an environment where they would compute center models per data center models trying to understand how do they capture data, how they accelerate the adoption of AI within the various business units, why? Because they understand that currently the way they are actually going to the business changed completely, they need to understand how to adapt a new business model, they need to understand how to look for value pools where there are none as well. So most of our customers today, while initially they spend a lot of time in an never ending POC trying to investigate where do they want to go. Currently they do want to accelerate the application of AI models, the build of data strategies, how then they use all of this data? How do they capture the data to make sure that they look at new business models, new value pools, new customer experience and so on and so forth. So I think what they've seen in the past, let's say three to six months is that we lost time. But the shift towards an adoption of analytics, AI and data strategy is accelerated a lot, simply because customers realize that they need to get ahead of the game. >> So Dr. Garg, what if you could talk about how HPE is utilizing machine intelligence during this pandemic, maybe helping some of your customers, get ahead of it, or at least trying to track it. How are you applying AI in this context? >> So I think that Sorin sort of spoke to one of the things with adopting AI is, it's very transformational for a business so it changes how you do things. You need to actually adopt new processes to take advantage of it. So what I would say is right now we're hearing from customers who recognize that the context in which they are doing their work is completely different. And they're exploring how AI can help them really meet the challenges of those context. So one example might be how can AI and computer vision be coupled together in a way that makes it easier to reopen stores, or ensures that people are distancing appropriately in factories. So I would say that it's the beginning of these conversations as customers as businesses try to figure out how do we operate in the new reality that we have? And I think it's a pretty exciting time. And I think just to the point that Sorin just made, there's a lot of openness to new technologies that there wasn't before, because there's this willingness to change the business processes to really take advantage of any technologies. >> So Dr. Cheran, I probably should have started here but help us understand HPE's overall strategy with regard to AI. I would certainly know that you're using AI to improve IT, the InfoSite product and capability via the Nimble acquisition, et cetera, and bringing that across the portfolio. But what's the strategy for HPE? >> So, yeah, thank you. That's (laughs) a good question. So obviously you started with a couple of our acquisition in the past because obviously Nimble and then we talked a lot about our efforts to bring InfoSite across the portfolio. But currently, in the past couple of months, let's say close to a year, we've been announcing a lot of other acquisitions and we've been talking about Tuteybens, we've been talking about Scytale we've been talking about Cray, and so on, so forth, and now what we're doing at HPE is to bring all of this IP together into one place and try to help our customers within their region out. If you're looking at what, for example, what did they actually get when Cray play was not only the receiver, but we also acquire and they also have a lot of software and a lot of IP around optimization and so on and so forth. Also within our own labs, we've been investigating AI around like, for example, some learning or accelerators or a lot of other activity. So right now what we're trying to help our customers with is to understand how do they lead from the production stage, from the POC stage to the production stage. So (mumbles) what we are trying to do is we are trying to accelerate their adoption of AI. So simply starting from an optimized platform infrastructure up to the solution they are actually going to apply or to use to solve their business problems and wrapping all of that around with services either consumed on-prem as a service and so on. So practically what we want to do is we want to help our customers optimize, orchestrate and operationalize AI. Because the problem of our customers is not to start in our PLC, the problem is how do I then take everything that I've been developing or working on and then put it in production at the edge, right? And then keep it, maintaining production in order to get insights and then actually take actions that are helping the enterprise. So basically, we want to be data driven assets in cloud enable, and we want to help our customers move from POC into production. >> Or do you work with obviously a lot of data folks, companies or data driven data scientists, you are hands on practitioners in this regard. One of the challenges that I hear a lot from customers is they're trying to operationalize AI put AI into production, they have data in silos, they spend all their time, munging data, you guys have made a number of acquisitions. Not a list of which is prey, obviously map of, data specialist, my friend Kumar's company Blue Data. So what do you see as HPE's role in terms of helping companies operationalize AI. >> So I think that a big part of operationalizing AI moving away from the PLC to really integrate AI into the business processes you have and also the sort of pre existing IT infrastructure you talked about, you might already have siloed data. That's sort of something we know very well at HPE, we understand a lot of the IT that enterprises already have the incumbent IT and those systems. We also understand how to put together systems and integrated systems that include a lot of different types of computing infrastructure. So whether that being different types of servers and different types of storage, we have the ability to bring all of that together. And then we also have the software that allows you to talk to all of these different components and build applications that can be deployed in the real world in a way that's easy to maintain, and scale and grow as your AI applications will almost invariably get more complex involved, more outputs involved and more input. So one of the important things as customers try to operationalize AI is think is knowing that it's not just solving the problem you're currently solving. It's not just operationalizing the solution you have today, it's ensuring that you can continue to operationalize new things or additional capabilities in the future. >> I want to talk a little bit about AI for good. We talked about AI taking away jobs, but the reality is, when you look at the productivity data, for instance, in the United States, in Europe, it's declining and it has for the last several decades and so I guess my point is that we're not going to be able to solve some of the world problems in the coming decades without machine intelligence. I mean you think about health care, you think about feeding populations, you think about obviously paying things like pandemics, climate change, energy alternatives, et cetera, productivity is coming down. Machines are potential opportunity. So there's an automation imperative. And you feel, Dr. Cheran, the people who are sort of beyond that machines replacing human's issue? Is that's still an item or has the pandemic sort of changed that? >> So I believe it is, so it used to be a very big item, you're right. And every time we were speaking at a conference and every time you're actually looking at the features of AI, right? Two scenarios are coming to plays, right? The first one where machines are here, actually take a walk, and then the second one as you know even a darker version where terminator is coming, yes and so forth, right? So basically these are the two, is the lesser evil in the greater evil and so on and so forth. And we still see that regular thing coming over and over again. And I believe that 2019 was the year of reckoning, where people are trying to realize that not only we can actually take responsible AI, but we can actually create an AI that is trustworthy, an AI that is fair and so on and so forth. And that we also understood in 2019 it was highly debated everywhere, which part of our jobs are going to be replaced like the parts that are mundane, or that can actually be easily automated and so on and so forth. With the COVID-19 what happened is that people are starting to look at AI differently, why? Because people are starting to look at data differently. And looking at data differently, how do I actually create this core of data which is trusted, secure and so on and so forth, and they are trying to understand that if the data is trusted and secure somehow, AI will be trusted and secure as well. Now, if I actually shifted forward, as you said, and then I try to understand, for example on the manufacturing floor, how do I add more machines? Or how do I replace humans with machines simply because, I need to make sure that I am able to stay in production and so on and so forth. From their perspective, I don't believe that the view of all people are actually looking at AI from the job marketplace perspective changed a lot. The view that actually changes how AI is helping us better certain prices, how AI is helping us, for example, in health care, but the idea of AI actually taking part of the jobs or automating parts of the jobs, we are not actually past yet, even if 2018 and even more so in 2019, it was the year also where actually AI through automation replaced the number of jobs but at the same time because as I was saying the first year where AI created more jobs it's because once you're displacing in one place, they're actually creating more work more opportunities in other places as well. But still, I don't believe the feeling changed. But we realize that AI is a lot more valuable and it can actually help us through some of our darkest hours, but also allow us to get better and faster insights as well. >> Well, machines have always replaced humans and now for the first time in history doing so in a really cognitive functions in a big way. But I want to ask you guys, I'll start with Dr. Arti, a series of questions that I think underscore the impact of AI and the central role that it plays in companies digital transformations, we talk about that a lot. But the questions that I'm going to ask you, I think will hit home just in terms of some hardcore examples, and if you have others I'd love to hear them but I'm going to start with Arti. So when do you think Dr. or machines will be able to make better diagnoses than doctors? We're actually there today already? >> So I think it depends a little bit on how you define that. And I'm just going to preface this by saying both of my parents are physicians. So I have a little bit of bias in this space. But I think that humans can bring creativity in a certain type of intelligence that it's not clear to me. We even know how to model with the computer. And so diagnoses have sometimes two components. One is recognizing patterns and being able to say, "I'm going to diagnose this disease that I've seen before." I think that we are getting to the place where there are certain examples. It's just starting to happen where you might be able to take the data that you need to make a diagnosis as well understood. A machine may be able to sort of recognize those subtle patterns better. But there's another component of doing diagnosis is when it's not obvious what you're looking for. You're trying to figure out what is the actual sort of setup diseases I might be looking at. And I think that's where we don't really know how to model that type of inspiration and creativity that humans still bring to things that they do, including medical diagnoses. >> So Dr. Cheran my next question is, when do you think that owning and driving your own vehicle will become largely obsolete? >> (laughs) Well, I believe my son is six year old now. And I believe, I'm working with a lot of companies to make sure that he will not get his driving license with his ID, right? So depending who you're asking and depending the level of autonomy that you're looking at, but you just mentioned the level five most likely. So there are a lot of dates out there so some people actually say 2030. I believe that my son in most of the cities in US but also most of the cities in Europe, by the time he's 18 in let's say 2035, I'll try to make sure that I'm working with the right companies not to allow them to get the driving license. >> I'll let my next question is from maybe both of you can answer. Do you take the traditional banks will lose control of payment system? >> So that's an interesting question, because I think it's broader than an AI question, right? I think that it goes into some other emerging technologies, including distributed ledgers and sort of the more secure forms of blockchain. I think that's a challenging question to my mind, because it's bigger than the technology. It's got Economic and Policy implications that I'm not sure I can answer. >> Well, that's a great answer, 'cause I agree with you already. I think that governments and banks have a partnership. It's important partnership for social stability. But similar we've seen now, Dr. Cheran in retail, obviously the COVID-19 has affected retail in a major way, especially physical retail, do you think that large retail stores are going to go away? I mean, we've seen many in chapter 11. At this point, how much of that is machine intelligence versus just social change versus digital transformation? It's an interesting question, isn't it? >> So I think most of the... Right now the retailers are here to stay I guess for the next couple of years. But moving forward, I think their capacity of adapting to stores like to walk in stores or to stores where basically we just go in and there are no shop assistants and just you don't even need the credit card to pay you're actually being able to pay either with your face or with your phone or with your small chips and so on and so forth. So I believe currently in the next couple of years, obviously they are here to stay. Moving forward then we'll get artificial intelligence, or robotics applied everywhere in the store and so on and so forth. Most likely their capacity of adapting to the new normal, which is placing AI everywhere and optimizing the walk in through predicting when and how to guide the customers to the shop, and so on and so forth, would allow them to actually survive. I don't believe that everything is actually going to be done online, especially from the retailer perspective. Most of the... We've seen a big shift at COVID-19. But what I was reading the other day, especially in France that the counter has opened again, we've seen a very quick pickup in the retailers of people that actually visiting the stores as well. So it's going to be some very interesting five to 10 years, and then most of the companies that have adapted to the digital transformation and to the new normal I think they are here to stay. Some of them obviously are going to take sometime. >> I mean, I think it's an interesting question too that you really sort of triggering in my mind is when you think about the framework for how companies are going to come back and come out of this, it's not just digital, that's a big piece of it, like how digital businesses, can they physically distance? I mean, I don't know how sports arenas are going to be able to physically distance that's going to be interesting to see how essential is the business and if you think about the different industries that it really is quite different across those industries. And obviously, digital plays a big factor there, but maybe we could end on that your final thoughts and maybe any other other things you'd like to share with our audience? >> So I think one of the things that's interesting anytime you talk about adopting a new technology, and right now we're happening to see this sort of huge uptick in AI adoption happening right at the same time but this sort of massive shift in how we live our lives is happening and sort of an acceptance, I think that can't just go back to the way things work as you mentioned, they'll probably be continued sort of desire to maintain social distancing. I think that it's going to force us to sort of rethink why we do things the way we do now, a lot, the retail, environments that we have the transportation solutions that we have, they were adapted in many cases in a very different context, in terms of what people need to do on a day-to-day basis within their life. And then what were the sort of state of technologies available. We're sort of being thrust and forced to reckon with like, what is it I really need to do to live my life and then what are the technologies I have available to meet to answer that and I think, it's really difficult to predict right now what people will think is important about a retail experience, I wouldn't be surprised if you start to find in person retail actually be much less, technologically aided, and much more about having the ability to talk to a human being and get their opinion and maybe the tactile sense of being able to like touch new clothes, or whatever it is. And so it's really difficult I think right now to predict what things are going to look like maybe even a year or two from now from that perspective. I think that what I feel fairly confident is that people are really starting to understand and engage with new technologies, and they're going to be really open to thinking about what those new technologies enable them to do in this sort of new way of living that we're going to probably be entering pretty soon. >> Excellent! All right, Sorin, bring us home. We'll give you the last word on this topic. >> Now, so I wanted to... I agree with Arti because what these three months of staying at home and of busy shutting down allowed us to do was to actually have a very big reset. So let's say a great reset but basically we realize that all the things we've taken from granted like our freedom of movement, our technology, our interactions with each other, and also for suddenly we realize that everything needs to change. And the only one thing that we actually kept doing is interacting with each other remotely, interacting with each other with our peers in the house, and so on and so forth. But the one thing that stayed was generating data, and data was here to stay because we actually leave traces of data everywhere we go, we leave traces of data when we put our watch on where we are actually playing with our phone, or to consume digital and so on and so forth. So what these three months reinforced for me personally, but also for some of our customers was that the data is here to stay. And even if the world shut down for three months, we did not generate less data. Data was there on the contrary, in some cases, more data. So the data is the main enabler for the new normal, which is going to pick up and the data will actually allow us to understand how to increase customer experience in the new normal, most likely using AI. As I was saying at the beginning, how do I actually operate new business model? How do I find, who do I partner with? How do I actually go to market together? How do I make collaborations more secure, and so on and so forth. And finally, where do I actually find new value pools? For example, how do I actually still enjoy for having a beer in a pub, right? Because suddenly during the COVID-19, that wasn't possible. I have a very nice place around the corner, but it's actually cheaply stuff. I'm not talking about beer but in general, I mean, so the finance is different the pools of data, the pools (mumbles) actually, getting values are different as well. So data is here to stay, and the AI definitely is going to be accelerated because it needs to use data to allow us to adopt the new normal in the digital transformation. >> A lot of unknowns but certainly machines and data are going to play a big role in the coming decade. I want to thank Dr. Arti Garg and Dr. Sorin Cheran for coming on theCUBE. It's great to have you. Thank you for a wonderful conversation. Really appreciate it. >> Thank you very much. >> Thanks so much. >> All right. And thank you for watching everybody. This is Dave Vellante for theCUBE and the HPE 2020 Virtual Experience. We'll be right back right after this short break. (upbeat music)

- [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.