>> 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)

(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 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)

(pulsing music) >> Live from Las Vegas, it's theCUBE, covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Good morning from day three of theCUBE's coverage of VMworld 2018 from the Mandalay Bay, Las Vegas. I'm Lisa Martin, and I'm joined by my co-host, Justin Warren. Good morning, Justin. >> Good morning, Lisa. >> We're excited to welcome to the first time to theCUBE Jesse Rothstein, co-founder and CTO of ExtraHop. Jesse, it's nice to meet you. >> Nice to meet you, Lisa. Thank you for having me. >> Absolutely, so ExtraHop, you guys are up in Seattle. You are one of Seattle's-- >> Sunny Seattle (Jesse chuckles). >> Sunny Seattle. So, one of the best companies up there to work for. Tell us about ExtraHop. What to you guys do in the software space? >> Great. Well, ExtraHop does network traffic analysis, and that can be applied to both performance, performance optimization, as well as cybersecurity. Now, I'm not unbiased, but what I would tell you is that ExtraHop extracts value from the wired data better than anybody else in the world, and that's our fundamental belief. We believe that if you can extract value from that wired data and insights and apply in real-time analytics and machine-learning, then this can be applied to a variety of use cases, as I said. >> That's quite interesting. Some of the use cases we were talking about off camera, some of the things around micro-segmentation, particularly for security, as you mentioned, is really important, and also in software-defined networking, the fact that you are software, and software-defined networking we've had a few guests on theCUBE so far over the last couple of days, that's something which is really experiencing a lot of growth. We have VMware who's talking about their NSX software-defined networking. Maybe you could give us a bit of detail on how ExtraHop helps in those situations. >> Well, I'm paying a lot of attention to VMware's vision and kind of the journey of NSX and software, really software-defined everything, as well as, and within NSX, you see a lot of applications towards security, kind of a zero-trust, least-privileged model, which I think is very exciting, and there's some great trends around that, but as we've also seen, it's difficult to execute. It's difficult to execute to build the policies such that they maybe don't break. From my perspective, a product like ExtraHop, as solution like ExtraHop, we work great with software-defined environments. First, because they have enabled the type of visibility that we offer in that you can tap traffic from a variety of locations for the purposes of analysis. If left to its own devices, I think these increased layers of abstraction and increased kind of policy frameworks have the potential to introduce complexity and to limit visibility, and this is where solutions like ExtraHop can provide a great deal of value. We apply to both your traditional on-prem environment as well as these hybrid and even public cloud environments. The ability to get visibility across a wide range of environments, really pervasively, in the hybrid enterprise is I think a big value that we offer. >> We are at VMworld and on day one, on Monday, Pat Gelsinger talked about the average enterprise has eight or nine clouds. I heard somebody the other day say that they had four and a half clouds. I didn't know you could have a half a cloud, but you can. Multi-cloud, a big theme here, that's more the vision and direction that VMware's going to go into, but to your point, customers are living in this world, it's not about embracing it, they're in it, but that also I think by default that can create silos that enterprises need to understand or to wrap their heads around. To your point, they have to have visibility, because the data is the power and the currency only if you can have visibility into it and actually extract insights and take action. >> Absolutely. ExtraHop customers are primarily large enterprises and carriers, and everyone single one of them is somewhere on their own cloud journey. You know, maybe they're just beginning it, maybe their quite mature, maybe their doing a lot of data center consolidation or some amount of workload migration to public cloud. No matter where they are in that journey, they require visibility into those environments, and I think it's extremely important that they have the same level of visibility that they're accustomed to in their on-prem environment, with their traditional workloads, as well as in these sort of borne-in-the-cloud workloads. But, I want to stress visibility for its own sake isn't very useful. Organizations are drowning in data, you can drown in visibility. For us, the real trick is to extract insights and bring them to your attention, and that's where we've been investing in data science and machine-learning for about four and a half to five years. This is before it became trendy as it is today. >> Superpower, like Pat called it. >> There's so much ML watching, when you walk in the show floor, almost every vendor talks about their AI and machine-learning. A lot of it's exaggerated, but what I'll say for ExtraHop, of course, ours is real, and we've been investing in this for years. Our vision was that we had this unbelievable amount of data, and when you're looking at the wired data, you're not just drinking from the firehose, you're drinking from Niagara Falls. You have all of this data, and then with machine-learning, you need to perform feature extraction on the data, that's essentially what data science teams are very good at, and then, build the ML models. Our vision was that we don't want to just give you a big pile of data or a bunch of charts and graphs, we actually want to bring things to your attention so that we can say, "Hey, Lisa, look over here, "there's something unusual happening here", or in many cases there's a potential threat or there's suspicious behavior, an indicator of compromise. That's where that sort of machine-learning I believe is the, kind of the-- well, certainly the current horizon or the state of the art for cybersecurity, and it's extremely important. >> Jessie, can you give us an example of one of your enterprise customers and how they've used ExtraHop to manage that complexity that Lisa was talking about, that visibility that they need to get through all the different layers of abstraction, and maybe, if there's one, an example of how they've done some cybersecurity thing, particularly around that machine-learning of detecting an anomaly that they need to deal with? >> Sure, I can think of a lot. One customer of mine, that unfortunately, I can't actually name them, is a very large retail customer, and what I love about them is the actually have ExtraHop deployed at thousands of retail sites, as well as their data centers and distribution centers. Not only does ExtraHop give them visibility into the logistics operations, and they've used ExtraHop to detect performance degradation and things like that, that we're preventing them from, literally preventing the trucks from rolling out. But they're also starting to use ExtraHop more and more to monitor what's going on at the retail sites, in particular, looking for potential compromises in the point-of-sale systems. We've another customer that's a large, telco carrier, and they used ExtraHop at one point to actually monitor phone activations, because this is something that can be frustrating if you buy a new phone, and maybe it's an iPhone, and you go to activate it, it has to communicate to all these different servers, it has to perform some sort of activation, and if that process is somehow slow or could take a long time, that's very frustrating to your users and your customers. They needed the ability to see what was happening, and certainly, if it was taking longer than it usually does. That's a very important use case. And then we have a number of customers on the cybersecurity side who are looking for both the ability to detect potential breaches and maybe ransomware infections, but also the ability to investigate them rapidly. This is extremely important, because in cybersecurity, you have a lot of products that are essentially alert cannons, a product that just says, "Hey, hey, look at this, look at this, look at this. "I think we found something." That just creates noise. That just creates work for cybersecurity teams. The ability to actually surface high-quality anomaly and threats and streamline and even automate the workflows for investigation is super important. It's not just, "Hey, I think I found something", but let's take a click or two and investigate what it is so we can make a decision, does this require immediate action or not. Now, for certain sort of detections, we can actually take an automated response, but there are a variety of detections where you probably want to investigate a little more. >> Yeah. >> I also noticed the Purdue Pharma case study on your website, and looking at some of the bottom line impacts that your technology is making where they saved, reduced their data center footprint by 70% and increased app response times by 70%. We're talking about pharmaceutical data. You guys are also very big in the healthcare space, so we're talking about literally potentially life-saving situations that need to be acted on immediately. >> Certainly that can be true. Healthcare, there can be life-and-death situations, and timely access to medical records, to medical data, whether it's a workstation inside an exam room or an iPad or something like that can be absolutely critical. You often see a lot of desktop and application virtualization in the healthcare environment, primarily due to the protection of PHI, personal health information, and HIPPA constraints, so very common deployments in those environments. If the logins are slow or if there's an inability to access these records, it can be devastating. We have a large number of customers who are essentially care providers, hospital chains, and such that use ExtraHop to ensure that they have timely access to these records. That's more on the performance side. We also have healthcare customers that have used our ability to detect ransomware infections. Ransomware is just a bit of a plague within healthcare. Unfortunately, that industry vertical's been hit quite hard with those infections. The ability to detect a ransomware infection and perform some sort of immediate quarantining is extremely important. This is where I think micro-segmentation comes into play, because as these environments are more and more virtualized, natural micro-segmentation can help limit damage to ransomware, but, more often than not, these systems and workstations do have access to something like a network drive or a share. What I like about micro-segmentation is the flexibility to configure the policies, so when a ransomware infection is detected, we have the ability to quarantine it and shut it down. Keep in mind that there's defense in depth, it's kind of a security strategy that we've been employing for decades. You know, literally multiple layers of protection, so there are always protections at your gateway, and your firewall, at the perimeter, your NGFW, and there are protections at the endpoint, but if these were 100% effective, we wouldn't have ransomware infections. Unfortunately, they're not, and we always require that last, and maybe a last line of defense where we examine what's going on in the east-west corridor, and we look for those potential threats and that sort of suspicious activity or even known behaviors that are known to be bad. >> Well, Jesse, thanks so much for stopping by theCUBE and sharing with us what ExtraHop is doing, and what differentiates you in the market. We appreciate your time. >> My pleasure, Lisa, Justin. Thank you so much for having me. >> And we want to thank you for watching theCUBE. I'm Lisa Martin with Justin Warren. Stick around, we'll be back. Day three of the VMworld 2018 coverage in just a moment. (pulsing music)

>> Announcer: Live from midtown Manhattan, it's theCUBE. Covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Okay welcome back everyone, live here in New York City it's theCUBE's coverage of Big Data NYC. It's our event we've been doing for five years in conjunction with Strata Hadoop now called Strata Data right around the corner, separate place. Every year we get the best voices tech. Thought leaders, CEO's, executives, entrepreneurs anyone who's bringing the signal, we share that with you. I'm John Furrier, the co-host of theCUBE. Eight years covering Big Data, since 2010, the original Hadoop world. I'm here with Amit Walia, who's the Executive Vice President, Chief Product Officer for Informatica. Welcome back, good to see you. >> Good to be here John. >> theCUBE alumni, always great to have you on. Love product we had everyone on from Hortonworks. >> I just saw that. >> Product guys are great, can share the road map and kind of connect the dots. As Chief Product Officer, you have to have a 20 mile stare into the future. You got to know what the landscape is today, where it's going to be tomorrow. So I got to ask you, where's it going to be tomorrow? It seems that the rubber's hit the road, real value has to be produced. The hype of AI is out there, which I love by the way. People can see through that but they get it's good. Where's the value today? That's what customers want to know. I got hybrid cloud on the table, I got a lot of security concerns. Governance is a huge problem. The European regulations are coming over the top. I don't have time to do IoT and these other things, or do I? I mean this is a lot of challenges but how do you see it playing out? >> I think, to be candid, it's the best of times. The changing times are the best of times because people can experiment. I would say if you step back and take a look, we've been talking for such a long time. If there was any time, where forget the technology jargon of infrastructure, cloud, IoT, data has become the currency for every enterprise right? Everybody wants data. I say like you know, business users want today's data yesterday to make a decision tomorrow. IT has always been in the business of data, everybody wants more data. But the point you're making is that while that has become more relevant to an enterprise, it brings into the lot of other things, GDPR, it brings governance, it brings security issues, I mean hybrid clouds, some data on-prem, some data on cloud but in essence, what I think every company has realized that they will live and die by how well do they predict the future with the data they have on all their customers, products, whatever it is, and that's the new normal. >> Well hate to say it, admit pat myself on the back, but we in theCUBE team and Wikibon saw this early. You guys did too, and I want to bring up a comment we've talked about a couple of years ago. One, you guys were in the data business, Informatica. You guys went private but that was an early indicator of the trend that everyone's going private now. And that's a signal. For the first time, private equity finance have had trumped bigger venture capital asset class financing. Which is a signal that the waves are coming. We're surfing these little waves right now, we think they're big but they big ones are coming. The indicator is everyone's retrenching. Private equity's a sign of undervaluation. They want to actually also transform maybe some of the product engineering side of it or go to market. Basically get the new surfboard. >> Yeah. >> For the big waves. >> I mean that was the premise for us too because we saw as we were chatting right. We knew the new world, which was going towards predictive analytics or AI. See data is the richest thing for AI to be applied to but the thing is that it requires some heavy lifting. In fact that was our thesis, that as we went private, look we can double down on things like cloud. Invest truly for the next four years which being in public markets sometimes is hard. So we step back and look where we are as you were acting from my cover today. Our big believers look, there's so much data, so many varying architecture, so many different places. People are in Azure, or AWS, on-prem, by the way, still on mainframe. That hasn't gone away, you go back to the large customers. But ultimately when you talk about the biggest, I would say the new normal, which is AI, which clearly has been overtalked about but in my opinion has been barely touched because the biggest application of machine learning is on data. And that predicts things, whether you want to predict forecasting, or you predict something will come down or you can predict, and that's what we believe is where the world is going to go and that's what we double down on with our Claire technology. Just go deep, bring AI to data across the enterprise. >> We got to give you guys props, you guys are right on the line. I got to say as a product person myself, I see you guys executing great strategy, you've been very complimentary to your team, think you're doing a great job. Let's get back to AI. I think if you look at the hype cycles of things, IoT certainly has, still think there's a lot more hype to have there, there's so much more to do there. Cloud was overhyped, remember cloud washing? Pexus back in 2010-11, oh they're just cloud washing. Well that's a sign that ended up becoming what everyone was kind of hyping up. It did turn out. AI thinks the same thing. And I think it's real because you can almost connect the dots and be there but the reality is, is that it's just getting started. And so we had Rob Thomas from IBM on theCUBE and, you know we were talking. He made a comment, I want to get your reaction to, he said, "You can't have AI without IA." Information architecture. And you're in the information Informatica business you guys have been laying out an architecture specifically around governance. You guys kind of saw that early too. You can't just do AI, AI needs to be trained as data models. There's a lot of data involved that feeds AI. Who trains the machines that are doing the learning? So, you know, all these things come into play back to data. So what is the preferred information architecture, IA, that can power AI, artificial intelligence? >> I think it's a great question. I think of what typically, we recommend and we see large companies do look in the current complex architectures the companies are in. Hybrid cloud, multicloud, old architecture. By the way mainframe, client server, big data, you pick your favorite archit, everything exists for any enterprise right. People are not, companies are not going to move magically, everything to one place, to just start putting data in one place and start running some kind of AI on it. Our belief is that that will get organized around metadata. Metadata is data about data right? The organizing principle for any enterprise has to be around metadata. Leave your data wherever it is, organize your metadata, which is a much lighter footprint and then, that layer becomes the true central nervous system for your new next gen information architecture. That's the layer on which you apply machine learning too. So a great example is look, take GDPR. I mean GDPR is, if I'm a distributor, large companies have their GDPR. I mean who's touching my data? Where is my data coming from? Which database has sensitive data? All of these things are such complex problems. You will not move everything magically to one place. You will apply metadata approach to it and then machine learning starts to telling you gee I some anomaly detection. You see I'm seeing some data which does not have access to leave the geographical boundaries, of lets say Germany, going to, let's say UK. Those are kind of things that become a lot easier to solve once you go organize yourself at the metadata layer and that's the layer on which you apply AI. To me, that's the simplest way to describe as the organizing principle of what I call the data architecture or the information architecture for the next ten years. >> And that metadata, you guys saw that earlier, but how does that relate to these new things coming in because you know, one would argue that the ideal preferred infrastructure would be one that says hey no matter what next GDPR thing will happen, there'll be another Equifax that's going to happen, there'll be some sort of state sponsor cyber attack to the US, all these things are happening. I mean hell, all securities attacks are going up-- >> Security's a great example of that. We saw it four years ago you know, and we worked on a metadata driven approach to security. Look I've been on the security business however that's semantic myself. Security's a classic example of where it was all at the infrastructure layer, network, database, server. But the problem is that, it doesn't matter. Where is your database? In the cloud. Where is your network? I mean, do you run a data center anymore right? If I may, figuratively you don't. Ultimately, it's all about the data. The way at which we are going and we want more users like you and me access to data. So security has to be applied at the data layer. So in that context, I just talked about the whole metadata driven approach. Once you have the context of your data, you can apply governance to your data, you can apply security to your data, and as you keep adding new architectures, you do not have to create a paddle architecture you have to just append your metadata. So security, governance, hybrid cloud, all of those things become a lot easier for you, versus clearing one new architecture after another which you can never get to. >> Well people will be afraid of malware and these malicious attacks so auditing becomes now a big thing. If you look at the Equifax, it might take on, I have some data on that show that there was other action, they were fleeced out for weeks and months before the hack was even noticed. >> All this happens. >> I mean, they were ten times phished over even before it was discovered. They were inside, so audit trail would be interesting. >> Absolutely, I'll give you, typically, if you read any external report this is nothing tied to Equifax. It takes any enterprise three months minimum to figure out they're under attack. And now if a sophisticated attacker always goes to right away when they enter your enterprise, they're finding the weakest link. You're as secure as your weakest link in security. And they will go to some data trail that was left behind by some business user who moved onto the next big thing. But data was still flowing through that pipe. Or by the way, the biggest issue is inside our attack right? You will have somebody hack your or my credentials and they don't download like Snowden, a big fat document one day. They'll go drip by drip by drip by drip. You won't even know that. That again is an anomaly detection thing. >> Well it's going to get down to the firmware level. I mean look at the sophisticated hacks in China, they run their own DNS. They have certificates, they hack the iPhones. They make the phones and stuff, so you got to assume packing. But now, it's knowing what's going on and this is really the dynamic nature. So we're in the same page here. I'd love to do a security feature, come into the studio in our office at Palo Alto, think that's worthy. I just had a great cyber chat with Vidder, CTO of Vidder. Junaid is awesome, did some work with the government. But this brings up the question around big data. The landscape that we're in is fast and furious right now. You have big data being impacted by cloud because you have now unlimited compute, low latency storage, unlimited power source in that engine. Then you got the security paradigm. You could argue that that's going to slow things down maybe a little bit, but it also is going to change the face of big data. What is your reaction to the impact to security and cloud to big data? Because even though AI is the big talk of the show, what's really happening here at Strata Data is it's no longer a data show, it's a cloud and security show in my opinion. >> I mean cloud to me is everywhere. It was the, when Hadoop started it was on-prem but it's pretty much in the cloud and look at AWS and Azure, everyone runs natively there, so you're exactly right. To me what has happened is that, you're right, companies look at things two ways. If I'm experimenting, then I can look at it in a way where I'm not, I'm in dev mode. But you're right. As things are getting more operational and production then you have to worry about security and governance. So I don't think it's a matter of slowing down, it's a nature of the business where you can be fast and experiment on one side, but as you go prod, as you go real operational, you have to worry about controls, compliance and governance. By the way in that case-- >> And by the way you got to know what's going on, you got to know the flows. A data lake is a data lake, but you got the Niagara falls >> That's right. >> streaming content. >> Every, every customer of ours who's gone production they always want to understand full governance and lineage in the data flow. Because when I go talk to a regulator or I got talk to my CEO, you may have hundred people going at the data lake. I want to know who has access to it, if it's a production data lake, what are they doing, and by the way, what data is going in. The other one is, I mean walk around here. How much has changed? The world of big data or the wild wild west. Look at the amount of consolidation that has happened. I mean you see around the big distribution right? To me it's going to continue to happen because it's a nature of any new industry. I mean you looked at securities, cyber security big data, AI, you know, massive investment happens and then as customers want to truly go to scale they say look I can only bet on a few that can not only scale, but had the governance and compliance of what a large company wants. >> The waves are coming, there's no doubt about it. Okay so, let me get your reaction to end this segment. What's Informatica doing right now? I mean I've seen a whole lot 'cause we've cover you guys with the show and also we keep in touch, but I want you to spend a minute to talk about why you guys are better than what's out there on the floor. You have a different approach, why are customers working with you and if the folks aren't working with you yet, why should they work with Informatica? >> Our approach in a way has changed but not changed. We believe we operate in what we call the enterprise cloud data management. Our thing is look, we embrace open source. Open source, parks, parkstreaming, Kafka, you know, Hive, MapReduce, we support them all. To us, that's not where customers are spending their time. They're spending their time, once I got all that stuff, what can I do with it? If I'm truly building next gen predictive analytics platform I need some level of able to manage batch and streaming together. I want to make sure that it can scale. I want to make sure it has security, it has governance, it has compliance. So customers work with us to make sure that they can run a hybrid architecture. Whether it is cloud on-prem, whether it is traditional or big data or IoT, all in once place, it is scale-able and it has governance and compliance bricked into it. And then they also look for somebody that can provide true things like, not only data integration, quality, cataloging, all of those things, so when we working with large or small customers, whether you are in dev or prod, but ultimately helping you, what I call take you from an experiment stage to a large scale operational stage. You know, without batting an eyelid. That's the business we are in and in that case-- >> So you are in the business of operationalizing data for customers who want to add scale. >> Our belief is, we want to help our customers succeed. And customers will only succeed, not just by experimenting, but taking their experiments to production. So we have to think of the entire lifecycle of a customer. We cannot stop and say great for experiments, sorry don't go operational with us. >> So we've had a theme here in theCUBE this week called, I'm calling it, don't be a tool, and too many tools are out there right now. We call it the tool shed phenomenon. The tool shed phenomenon is customers aren't, they're tired of having too many tools and they bought a hammer a couple years ago that wants to try to be a lawn mower now and so you got to understand the nature of having great tooling, which you need which defines the work, but don't confuse a tool with a platform. And this is a huge issue because a lot of these companies that are flowing by wayside are groping for platforms. >> So there are customers tell us the same thing, which is why we-- >> But tools have to work in context. >> That's exactly, so that's why you heard, we talked about that for the last couple, it was the intelligent data platform. Customers don't buy a platform but all of our products, like are there microservices on our platform. Customers want to build the next gen data management platform, which is the intelligent data platform. A lot of little things are features or tools along the way but if I am a large bank, if I'm a large airline, and I want to go at scale operational, I can't stitch hundred tools and expect to run my IT shop from there. >> Yeah >> I can't I will never be able to do it. >> There's good tools out there that have a nice business model, lifestyle business or cashflow business, or even tools that are just highly focused and that's all they do and that's great. It's the guys who try to become something that they're not. It's hard, it's just too difficult. >> I think you have to-- >> The tool shed phenomenon is real. >> I think companies have to realize whether they are a feature. I always say are you a feature or are you a product? You have to realize the difference between the two and in between sits our tool. (John laughing) >> Well that quote came, the tool comment came from one of our chief data officers, that was kind of sparked the conversation but people buy a hammer, everything looks like a nail and you don't want to mow your lawn with a hammer, get a lawn mower right? Do the right tool for the job. But you have to platform, the data has to have a holistic view. >> That's exactly right. The intelligent data platform, that's what we call it. >> What's new with Informatica, what's going on? Give us a quick update, we'll end the segment with a quick update on Informatica. What do you got going on, what events are coming up? >> Well we just came off a very big release, we call it 10-2 which had lot of big data, hybrid cloud, AI and catalog and security and governance, all five of them. Big release, just came out and basically customers are adopting it. Which obviously was all centered around the things we talked in Informatica. Again, single platform, cloud, hybrid, big data, streaming and governance and compliance. And then right now, we are basically in the middle, after Informatica, we go on as barrage of tours across multiple cities across the globe so customers can meet us there. Paris is coming up, I was in London a few weeks ago. And then separately we're getting up for coming up, I will probably see you there at Amazon re:Invent. I mean we are obviously all-in partner for-- >> Do you have anything in China? >> China is a- >> Alibaba? >> We're working with them, I'll leave it there. >> We'll be in Alibaba in two weeks for their cloud event. >> Excellent. >> So theCUBE is breaking into China, CUBE China. We need some translators so if anyone out there wants to help us with our China blog. >> We'll be at Dreamforce. We were obviously, so you'll see us there. We were at Amazon Ignite, obviously very close to- >> re:Invent will be great. >> Yeah we will be there and Amazon obviously is a great partner and by the way a great customer of ours. >> Well congratulations, you guys are doing great, Informatica. Great to see the success. We'll see you at re:Invent and keep in touch. Amit Walia, the Executive Vice President, EVP, Chief Product Officer, Informatica. They get the platform game, they get the data game, check em out. It's theCUBE ending day two coverage. We've got a big event tonight. We're going to be streaming live our research that we are going to be rolling out here at Big Data NYC, our even that we're running in conjunction with Strata Data. They run their event, we run our event. Thanks for watching and stay tuned, stay with us. At five o'clock, live Wikibon coverage of their new research and then Party at Seven, which will not be filmed, that's when we're going to have some cocktails. I'm John Furrier, thanks for watching. Stay tuned. (techno music)

>> Narrator: It's The Cube. Covering NAB 2017. Brought to you buy HGST. >> Hey welcome back everybody, Jeff Frick here with The Cube. We're at NAB 2017. It's not only 100,000, it's 102,000 people according to the official press release talking about the media and entertainment and technology. That theme is actually met as the technology is so intimately to media entertainment that you can't separate them out anymore. We're really excited for our next guest. He is right in the heart of it. He's in his happy place. He's leading the whole contingent here. It's Eric Weaver. He's the global director of media, entertainment, and market development for HGST. Eric, welcome. >> Thank you so much. Glad to be here today. >> So first impressions of the show. I'm sure you've been here a 1000 times. It's crazy. >> Yeah, no, it's really amazing. It's always a wonderful show. There's so many great people here really trying to get an understanding of what's coming up, what's going to solve their problems that they're facing right now. >> And the problems keep getting bigger because people want more. I mean it's amazing you walk around the level of gear and equipment. Some of the green screen setups here, they look like professional studios. And now we've gone from HD to 4K to AK to ultra HD. We've got 360 cameras. Little commercial ones by Samsung and professional grade ones. That's only going to increase the complexity of trying to manage all this stuff. >> Absolutely, it's really becoming a reality now that 4K and UHD are coming down the pipe. I think I heard some number that 56% of all sets will be that by 2020. And it's really great because you'll see the creative community starting to embrace HDR or UHD because they have never seen it before and until they go into the color suites and see the difference, they're absolutely blown away. So you're going to have a drive here. You're going to have a drive between the director saying this is what I want, and this is my look, and the camera or the tv set saying, this is what we can produce in theaters and what we can produce. >> Right, we didn't even talk about VR or AI. >> And VR and AI absolutely are some of the hottest topics out there right now. Trying to comprehend. You're also seeing a big shift from 360 video to photogrammetry and computational photography and these things. Volumetric capture. And those things are really going to be taking over in the next couple years and they are huge in understanding how they work for everyone. >> Okay, so you dropped a couple new vocabulary words. I have to have you dig in a little deeper. >> Alright, so volumetric. >> Photogemetric first? >> Photogrammetry. Photogrammetry. So what photogrammetry is is recreating a room with photographs by stitching them together. So for example, I worked on a piece called Wonder Buffalo and in Wonder Buffalo we basically took 956 photographs of a room and then stitched them together at 50 megapixels each and created this whole new room environment. You combine that with what's called volumetric capture. So instead of 12-24 cameras pointing out where you're stuck in a locked position which is a traditional 360 video. You're now doing 36 cameras in and those 36 cameras doing an almost hologram. The big difference here is now all of a sudden you feed it into a gaming engine, like Unity and you can walk around and explore the entire scene. So it's the closest you've ever seen to the Holodeck by maybe Star Trek or something. >> Right. >> It's really quite an amazing experience. >> Now on the other side of the equation, on the simpler side, you know you've got a lot of independent film makers now have YouTube and Vimeo and all these distribution platforms and you know, I'm a huge Casey Neistat fan. You know, he's got his little $2000 camera and he's out shooting and getting tremendous views so the focus on audience and story telling and sort of the democratization of distribution is another huge trend. >> Absolutely. Really big. YouTube is, what's fascinating about something like YouTube is YouTube wasn't possible a couple years ago. Something like the Cloud made YouTube possible. If you historically look back, you'll see something like the electricity juxtaposition, and until Niagara Falls was there, we didn't have the ability to have electricity in such volumes. And so some of the breakthrough cases might have been like Upcoa, who produced aluminum. They were burning, tearing down whole forests to put together furnaces that could burn hot enough to make it. Now that they have cost effective aluminum, or electricity, they could do this. The same situation was like someone like YouTube. They can scale at a level that we've never seen before and was never possible. >> Right. >> So it opens up whole new opportunities of democratization of video. >> Right. >> Absolutely amazing new tools. >> And then obviously cloud, right? Cloud is changing the world. The big cloud providers like Amazon and Google and Microsoft and a ton of second tier service providers. But they're not kind of on the cloud for big assets is speed of light is too damn slow, you know, getting stuff up and down is a pain. And also you know that's where you really wanted a big machine with local horsepower. >> So. >> But now you've got rendering, all this huge stuff that you need massive scale that you're little machine can't do anymore. >> So a big confusion a lot of people have in cloud is they think about taking their current data center and lifting and shifting it to the cloud. That doesn't work. You have to reimagine how the whole structure works. What do you put up there? Why do you put it up there? Are you using a proxy? Are you using some kind of hybrid workflow to maximize and benefit? Because if you're just dumping something up there and expecting to bounce it back and forth, you're right, speed of light and other things are going to kill you. >> Right. >> But there's other ways out there to leverage that. Principles such as IOA. Inner Oriented Connected Architectures. So placing your storage or your centralized data link at an Equinox or some kind of colo facility, where you can centrally leverage it and then working off proxies, most people don't know that when you're working in your color suite, almost all the time you're still working off proxies because you cannot see all those bits or we cannot get all the bits to the monitors. >> Right, right. >> That we have. So learning how to create the proper workflow there is absolutely critical, and will save you a fortune if you know what you're doing. >> Right. >> Or go to the right people to show you how to do that properly. >> So it's really use the best attributes of both as much as you can. >> Yes, you have to figure out how to use the best attributes of both. >> So the other kind of knock on too much tech in this business is sometimes the storytelling gets lost. And I know because I have a personal pet peeve on a lot of these big huge cinematic explosions that they could still have a story. >> Yes, yes. >> So, you know, I think that having a narrative is still so important. Is that lost? Is that enhanced? How do you see that integrating with the tech? >> So, I think it's absolutely critical. I saw Spielberg speaking at USC a little while back and he was like story, story, story. Tech is simply there to empower the story. And if you lose sight of that, you're absolutely lost. It really is the truth. So for example, I have two shorts out right now and one's at Tribeca one's at South by South West but we focused on the story. Although it's an R and D research project, you have to have a story. >> Right, right. >> That's the only way to move this thing forward. And if you don't have that, everything else is lost. >> Right. Now the other great thing that's happened with cloud and keeper storage and all these advanced infrastructure components is now you can keep everything. >> Yes. >> Data is no longer a liability that is expensive to hold and manage and you got to figure out what you're going to throw away because it's too expensive. Now people finally understand, it is an asset. So it opens up all types of opportunities to store it and do things with it. >> And you're seeing a lot of this shift from tape to object and other things like that because they want to monetize this content. There's so many new mechanisms to monetize content between the Netflix and the other distributors Amazon, and everyone else, that they are realizing this is not just an asset for the closet that you might someday use or sell in some broad agreement to some secondary station in Europe, or somewhere else. These are things that you can monetize on a regular basis. But that actually brings you the next problem. Understanding what you have. >> Right, right. >> People get very confused. They assume that there is one film. There's not one film. There's about 120 versions of the films that are released. Between the versioning such as culturally sensitive areas like the Middle East, to different language titles, to different ad pieces or other inserted parts, there are a lot of different versions to run a film. >> Right. >> And so people don't always understand that. >> And that's interesting but the other account of not gone film or video traditionally, from a metadata point of view in a search and a consumption and discovery point of view, is if I search for a picture and I find the one that I'm looking for, I immediately know that's the one that I want. But if I want to find something that's seven minutes in to an hour long video, how do I find it? How do I consume it? How do I share it. That's an age old problem with this media type. >> So, part of the problem there is that we have not broke down metadata tagging in each of these pictures and these pieces. This is coming. I actually help with ABC help build a tool that created x-ray like Amazon has for production sites, so they could scour and tag all these pieces and begin to say this is an action scene with this character in it, at this point in the movie. That is coming probably a year to a year and a half out. But all of those things will begin to evolve very very soon. >> Right. Certainly a great application for AI. >> Yeah, AI is absolutely hot as well and this is what the studios are trying to get their hands on right now. >> Right. >> People like Netflix have really pioneered some of this work and it originally was to understand how to find content or what people like content like so they could begin to produce content that was relatable to their audience. They've now moved it into things like QC'ing because they are the largest studio in the world at this point. Over 1000 hours. >> Are they the largest studio in the world? >> Netflix is the largest studio in the world right now. >> Wow, I didn't know that. >> So they're doing over 1000 hours I think a season, at this point. >> Amazing. >> But the studios are really trying to, are really doing a lot of work to get their hands on some of this and so there's a lot of really great, high level, private meetings going on that's bringing these industry leaders together. ETC is a wonder place to see that. They talk about these innovations. >> So you're in the middle of it all. You've been doing this for a long time. What are some of your priorities for 2017 and what are some of the things that still just get you up in the morning right now that you're excited about? >> So, absolutely my priorities is going to be cloud. Over the last about a year, 18 months, it's been a massive shift. It was before it was all before no, no, no. And I actually heard this exact quote from somebody at one of the major studios. He said, "It used to be no, no, no, you better have a darn good reason, to now yes, yes, yes, you better have a darn good reason not to." >> Right, to say no. >> Number one, very hot, very on board. The next one again, is VRAR, understanding how VRAR is going to begin to change our lives and produce things. I wasn't originally a big fan of that, I thought of it as kind of 3D, but then I went to USC's VR LA meeting, and there was over 600 students in this group and every single school was represented. Medical, architectural, journalism. These students understand that this is going to touch everybody. I don't know if you ever really got into genuine good content. Someone like a Nonny de la Pena does stuff that touches on more towards journalistic. For example, she did a meeting in San Diego and it's a very terrible rendering but the audio is good and you see a man being beaten from the police and people are calling out saying, "Stop, stop, stop." And you've never felt it so emotionally in your life. This is like bam. It hits you. >> The VR part of it or just that she had great content? >> The VR part of it and the context. >> Okay. >> Of telling a story and what's going wrong with the story. This is going to affect us in a different way and it might not just be they clip pieces for TV shows but it's going to be touching us in a lot of different ways. >> Right. Right. >> Very powerful stuff. >> We talk a lot about the AR. I think the AR piece from a commercial point of view is tremendous too. >> It's absolutely a bigger market. So what's really going to be biggest is mixed reality or MR. MR is going to come in and it's going to fade you between the two things. So, that is really where it's going to meet in the middle. >> You distinctly called out the differentiation between VR and 360. >> Yes. >> How do you split those? >> So when you look at it, if you're looking at 360 video that's a camera rigged stuck in one particular location, it's got 12, 24, 36 cameras all pointing outward, and when you're watching that, you're stuck in a location. You're hostage in more of a traditional film way to what within that 360 scope they want you to kind of be from one spot. When you look at volumetric capture, volumetric capture is the opposite. It allows you to walk around, choose your own point of view, be wherever you want to be within that scene. So, it's where we're going to be going, it's going to be much more like the Holodeck from Star Trek. >> Right. >> Very amazing stuff. >> Alright, well Eric, thank you for taking a few minutes. Congrats. I'm sure you're going to be busy, busy, busy for the next three days so, >> I know. >> So thank you for taking a few minutes with us on The Cube. >> No problem, thank you so much. >> Alright, he's Eric, I'm Jeff Frick. You're watching The Cube from NAB 2017 and we'll be back after this short break. Thanks for watching. (upbeat techno music)