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

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