>>Yeah, yeah. >>Welcome back to session, too. Thoughts about everywhere. Unlock new revenue streams with embedded search and I Today we're joined by our senior director of Global Oh am Rick Dimel, along with speakers from our thoughts about customer Hayes to discuss how thought spot is open for everyone by unlocking unprecedented value through data search in A I, you'll see how thoughts about compound analytics in your applications and hear how industry leaders are creating new revenue streams with embedded search and a I. You'll also learn how to increase app stickiness on how to create an autonomous this experience for your end users. I'm delighted to introduce our senior director of Global OPM from Phillips Spot, Rick DeMARE on then British Ramesh, chief technology officer, and Leon Roof, director of product management, both from Hayes over to you. Rick, >>Thank you so much. I appreciate it. Hi, everybody. We're here to talk to you about Fox Spot everywhere are branded version of our embedded analytics application. It really our analytics application is all about user experience. And in today's world, user experience could mean a lot of things in ux design methodologies. We want to talk about the things that make our product different from an embedded perspective. If you take a look at what product managers and product design people and engineers are doing in this space, they're looking at a couple of key themes when they design applications for us to consume. One of the key things in the marketplace today is about product led growth, where the product is actually the best marketing tool for the business, not even the sales portion or the marketing department. The product, by the word of mouth, is expanding and getting more people onto the system. Why is that important? It's important because within the first few days of any application, regardless of what it is being used binding users, 70% of those users will lose. Interest will stop coming back. Why do they stop coming back? Because there's no ah ha moment through them. To get engaged within the technology, today's technologies need to create a direct relationship with the user. There can't be a gatekeeper between the user and the products, such as marketing or sales or information. In our case. Week to to make this work, we have toe leverage learning models in leverage learning as it's called Thio. Get the user is engaged, and what that means is we have to give them capabilities they already know how to use and understand. There are too many applications on the marketplace today for for users to figure out. So if we can leverage the best of what other APS have, we can increase the usage of our systems. Because in today's world, what we don't want to do from a product perspective is lead the user to a dead end or from a product methodology. Our perspective. It's called an empty state, and in our world we do that all the time. In the embedded market place. If you look at at the embedded marketplace, it's all visualizations and dashboards, or what I call check engine lights in your application's Well, guess what happens when you hit a check engine life. You've got to call the dealer to get more information about what just took place. The same thing happens in the analytic space where we provide visualizations to users. They get an indicator, but they have to go through your gatekeepers to get access to the real value of that data. What am I looking at? Why is it important the best user experiences out on the marketplace today? They are autonomous. If we wanna leverage the true value of digital transformation, we have to allow our developers to develop, not have them, the gatekeepers to the rial, content to users want. And in today's world, with data growing at much larger and faster levels than we've ever seen. And with that shelf life or value of that data being much shorter and that data itself being much more fragmented, there's no developer or analysts that can create enough visualizations or dashboards in the world to keep the consumption or desire for these users to get access to information up to speed. Clients today require the ability to sift through this information on their own to customize their own content. And if we don't support this methodology, our users are gonna end up feeling powerless and frustrated and coming back to us. The gatekeepers of that information for more information. Loyalty, conversely, can be created when we give the users the ability toe access this information on their own. That is what product like growth is all about in thought spot, as you know we're all about search. It's simple. It's guided as we type. It gives a super fast responses, but it's also smart on the back end handling complexities, and it's really safe from a governance and as well as who gets access to what perspective it's unknown learned environment. Equally important in that learned environment is this expectation that it's not just search on music. It's actually gonna recommend content to me on the fly instantly as I try content I might not even thought of before. Just the way Spotify recommends music to us or Netflix recommends a movie. This is a expected learned behavior, and we don't want to support that so that they can get benefit and get to the ah ha moments much quicker. In the end, which consumption layer do you want to use, the one that leads you to the Dead End Street or the one that gets you to the ah ha moment quickly and easily and does it in an autonomous fashion. Needless to say, the benefits of autonomous user access are well documented today. Natural language search is the wave of the future. It is today. By 2004 75% of organizations are going to be using it. The dashboard is dead. It's no longer going to be utilized through search today, I if we can improve customer satisfaction and customer productivity, we're going to increase pretensions of our retention of our applications. And if we do that just a little bit, it's gonna have a tremendous impact to our bottom line. The way we deploy hotspots. As you know, from today's conversations in the cloud, it could be a manage class, not offering or could be software that runs in your own VPC. We've talked about that at length at this conference. We've also talked about the transformation of application delivery from a Cloud Analytics perspective at length here it beyond. But we apply those same principles to your product development. The benefits are astronomical because not only do you get architectural flexibility to scale up and scale down and right size, but your engineers will increase their productivity because their offerings, because their time and effort is not going to be spent on delivering analytics but delivering their offerings. The speed of innovation isn't gonna be released twice a year or four times a year. It's gonna It can happen on a weekly basis, so your time to market in your margins should increase significantly. At this point, I want a hand. The microphone over to Revert. Tesche was going to tell you a little bit about what they're doing. It hes for cash. >>Thanks, Rick. I just want to introduce myself to the audience. My name is Rotational. Mention the CTO Europe ace. I'm joined my today by my colleague Gillian Ruffles or doctor of product management will be demoing what we have built with thoughts about, >>um but >>just to my introduction, I'm going to talk about five key things. Talk about what we do. What hes, uh we have Really, um what we went through the select that spot with other competitors What we have built with that spot very quickly and last but not least, some lessons learned during the implementation. So just to start with what we do, uh, we're age. We are health care compliance and revenue integrity platform were a saas platform voter on AWS were very short of l A. That's it. Use it on these around 1 50 customers across the U. S. On these include large academic Medical Insight on. We have been in the compliant space for the last 30 plus years, and we were traditionally consulting company. But very recently we have people did more towards software platform model, uh, in terms off why we chose that spot. There were three business problems that I faced when I took this job last year. At age number one is, uh, should be really rapidly deliver new functionality, nor platform, and he agile because some of our product development cycles are in weeks and not months. Hey had a lot of data, which we collected traditionally from the SAS platform, and all should be really create inside stretch experience for our customers. And then the third Big one is what we saw Waas large for customers but really demanding self service capabilities. But they were really not going for the static dash boats and and curated content, but instead they wanted to really use the cell service capabilities. Thio mind the data and get some interesting answers during their questions. So they elevated around three products around these problems statements, and there were 14 reasons why we just start spot number one wars off course. The performance and speed to insights. Uh, we had around 800 to a billion robot of data and we wanted to really kind of mind the data and set up the data in seconds on not minutes and hours. We had a lot of out of the box capabilities with that spot, be it natural language search, predictive algorithms. And also the interactive visualization, which, which was which, Which gave us the agility Thio deliver these products very quickly. And then, uh, the end user experience. We just wanted to make sure that I would users can use this interface s so that they can very quickly, um, do some discovery of data and get some insights very quickly. On last but not least, talksport add a lot of robust AP ice around the platform which helped us embed tot spot into are offering. But those are the four key reasons which we went for thoughts part which we thought was, uh, missing in in the other products we evaluated performance and search, uh, the interactive visualization, the end user experience, and last but not least flexible AP ice, which we could customize into our platform in terms of what we built. We were trying to solve to $50 billion problem in health care, which is around denials. Um so every year, around 2, 50 to $300 billion are denied by players thes air claims which are submitted by providers. And we built offering, which we called it US revenue optimizer. But in plain English, what revenue optimizer does is it gives the capability tow our customers to mind that denials data s so that they can really understand why the claims were being denied. And under what category? Recent reasons. We're all the providers and quarters who are responsible for these claims, Um, that were dryland denials, how they could really do some, uh, prediction off. It is trending based on their historical denial reasons. And then last but not least, we also build some functionality in the platform where we could close the loop between insights, action and outcome that Leon will be showing where we could detect some compliance and revenue risks in the platform. On more importantly, we could, uh, take those risks, put it in a I would say, shopping card and and push it to the stakeholders to take corrective action so the revenue optimizer is something which we built in three months from concept to lunch and and that that pretty much prove the value proposition of thoughts. But while we could kind of take it the market within a short period of time Next leopard >>in terms >>off lessons learned during the implementation thes air, some of the things that came to my mind asses, we're going through this journey. The first one is, uh, focus on the use case formulation, outcomes and wishful story boarding. And that is something that hot spot that's really balance. Now you can you can focus on your business problem formulation and not really focus on your custom dash boarding and technology track, etcetera. So I think it really helped our team to focus on the versus problem, to focus on the outcomes from the problem and more importantly, really spend some time on visualizing What story are we say? Are we trying to say to our customers through revenue optimizer The second lesson learned first When we started this implementation, we did not dualistic data volume and capacity planning exercise and we learned it our way. When we are we loaded a lot of our data sets into that spot. And then Aziz were doing performance optimization. XYZ. We figured out that we had to go back and shot the infrastructure because the data volumes are growing exponentially and we did not account for it. So the biggest lesson learned This is part of your architectural er planning, exercise, always future proof your infrastructure and make sure that you work very closely with the transport engineering team. Um, to make sure that the platform can scale. Uh, the last two points are passport as a robust set of AP Ice and we were able to plug into those AP ice to seamlessly ended the top spot software into a platform. And last but not least, one thing I would like to closest as we start these projects, it's very common that the solution design we run into a lot of surprises. The one thing I should say is, along those 12 weeks, we very closely work with the thoughts, part architecture and accounting, and they were a great partner to work with us to really understand our business problem, and they were along the way to kind of government suggested, recommends and workarounds and more importantly, also, helpers put some other features and functionality which you requested in their engineering roadmap. So it's been a very successful partnership. Um, So I think the biggest take of it is please make sure that you set up your project and operating model value ember thoughts what resources and your team to make sure that they can help you as you. It's some obstacles in the projects so that you can meet your time ones. Uh, those are the key lessons learned from the implementation. And with that, I would pass this to my colleague Leon Rough was going to show you a demo off what we go. >>Thanks for Tesh. So when we were looking Thio provide this to our customer base, we knew that not everyone needed do you access or have available to them the same types of information or at the same particular level of information. And we do have different roles within RMD auto Enterprise platform. So we did, uh, minimize some roles to certain information. We drew upon a persona centric approach because we knew that those different personas had different goals and different reasons for wanting to drive into these insights, and those different personas were on three different levels. So we're looking at the executive level, which is more on the C suite. Chief Compliance Officer. We have a denial trending analyses pin board, which is more for the upper, uh, managers and also exact relatives if they're interested. And then really, um, the targeted denial analysis is more for the day to day analysts, um, the usage so that they could go in and they can really see where the trends are going and how they need to take action and launch into the auditing workflow so within the executive or review, Um, and not to mention that we were integrating and implementing this when everyone was we were focused on co vid. So as you can imagine, just without covert in the picture, our customers are concentrated on denials, and that's why they utilize our platform so they could minimize those risks and then throw in the covert factor. Um, you know, those denial dollars increase substantially over the course of spring and the summer, and we wanted to be able to give them ah, good view of the denials in aggregate as well as's we focus some curated pin boards specific to those areas that were accounting for those high developed denials. So on the Executive Overview Board, we created some banner tiles. The banner tiles are pretty much a blast of information for executives thes air, particular areas where there concentrating and their look looking at those numbers consistently so it provides them away to take a good look at that and have that quick snapshot. Um, more importantly, we did offer as I mentioned some curated pin boards so that it would give customers this turnkey access. They wouldn't necessarily have to wonder, You know, what should I be doing now on Day one, but the day one that we're providing to them these curated insights leads the curiosity and increases that curiosity so that they can go in and start creating their own. But the base curated set is a good overview of their denial dollars and those risks, and we used, um, a subject matter expert within our organization who worked in the field. So it's important to know you know what you're targeting and why you're targeting it and what's important to these personas. Um, not everyone is necessarily interests in all the same information, and you want to really hit on those critical key point to draw them and, um, and allowed them that quick access and answer those questions they may have. So in this particular example, the curated insight that we created was a monthly denial amount by functional area. And as I was mentioning being uber focused on co vid, you know, a lot of scrutiny goes back to those organizations, especially those coding and H i M departments, um, to ensure that their coding correctly, making sure that players aren't sitting on, um, those payments or denying those payments. So if I were in executive and I came in here and this was interesting to me and I want to drill down a little bit, I might say, You know, let me focus more on the functional area than I know probably is our main concern. And that's coating and h i M. And because of it hit in about the early winter. I know that those claims came in and they weren't getting paid until springtime. So that's where I start to see a spike. And what's nice is that the executive can drill down, they may have a hunch, or they can utilize any of the data attributes we made available to them from the Remittance file. So all of these data, um, attributes are related to what's being sent on the 8 35 fear familiar with the anti 8 35 file. So in particular, if I was curious and had a suspicion that these were co vid related or just want to concentrate in that area, um, we have particular flag set up. So the confirmed and suspected cases are pulling in certain diagnosis and procedure codes. And I might say 1.27 million is pretty high. Um, toe look at for that particular month, and then they have the ability to drill down even further. Maybe they want to look at a facility level or where that where that's coming from. Furthermore, on the executive level, we did take advantage of Let me stop here where, um also provided some lagged a so leg. This is important to organizations in this area because they wanna know how long does it take before they re submit a claim that was originally denied before they get paid industry benchmark is about 10 days of 10 days is a fairly good, good, um, basis to look at. And then, obviously anything over that they're going to take a little bit more scrutiny on and want to drill in and understand why that is. And again, they have that capabilities in order to drill down and really get it. Those answers that they're looking for, we also for this particular pin board. And these users thought it would be helpful to utilize the time Siri's forecasting that's made available. So again, thes executives need thio need to keep track and forecast where they're trends were going or what those numbers may look like in the future. And we thought by providing the prediction pins and we have a few prediction pins, um would give them that capability to take a look at that and be able to drill down and use that within, um, certain reporting and such for their organization. Another person, a level that I will go to is, um, Mawr on the analyst side, where those folks are utilizing, um, are auditing workflow and being in our platform, creating audits, completing audits, we have it segregated by two different areas. And this is by claim types so professional or institutional, I'm going to jump in here. And then I am going to go to present mode. So in this particular, um, in this particular view or insight, we're providing that analysts view with something that's really key and critical in their organization is denials related Thio HCC s andi. That's a condition category that kind of forecast, the risk of treatment. And, you know, if that particular patient is probably going to be seen again and have more conditions and higher costs, higher health care spending. So in this example, we're looking at the top 15 attending providers that had those HCC denials. And this is, um, critical because at this point, it really peaks in analyst curiosity. Especially, You know, they'll see providers here and then see the top 15 on the top is generating Ah, hide denial rate. Hi, denial. The dollars for those HCC's and that's a that's a real risk to the organization, because if that behavior continues, um, then those those dollars won't go down. That number won't go down so that analysts then can go in and they can drill down um, I'm going to drill down on diagnosis and then look at the diagnosis name because I have a suspicion, but I'm not exactly sure. And what's great is that they can easily do this. Change the view. Um, you know, it's showing a lot of diagnoses, but what's important is the first one is sepsis and substance is a big one. Substances something that those organizations see a lot of. And if they hover, they can see that 49.57 million, um, is attributed to that. So they may want to look further into that. They'd probably be interested in closing that loop and creating an audit. And so what allowed us to be able to do that for them is we're launching directly into our auditing workflow. So they noticed something in the carried insight. It sparked some investigation, and then they don't have to leave that insight to be able to jump into the auditing workflow and complete that. Answer that question. Okay, so now they're at the point where we've pulled back all the cases that attributed to that dollar amount that we saw on the Insight and the users launching into their auditing workflow. They have the ability Thio select be selective about what cases they wanna pull into the audit or if they were looking, um, as we saw with sepsis, they could pull in their 1600 rose, but they could take a sampling size, which is primarily what they would do. They went audit all 1600 cases, and then from this point in they're into, they're auditing workflow and they'd continue down the path. Looking at those cases they just pulled in and being able Thio finalized the audit and determine, you know, if further, um, education with that provider is needed. So that concludes the demo of how we integrated thought spot into our platform. >>Thank you, LeAnn. And thank you. Re test for taking the time to walk us through. Not only your company, but how Thought spot is helping you Power analytics for your clients. At this point, we want to open this up for a little Q and A, but we want to leave you with the fact that thought spot everywhere. Specifically, it cannot only do this for Hayes, but could do it for any company anywhere they need. Analytical applications providing these applications for their customers, their partners, providers or anybody within their network for more about this, you can see that the website attached below >>Thanks, Rick and thanks for tests and Leon that I find it just fascinating hearing what our customers are doing with our technology. And I certainly have learned 100% more about sepsis than I ever knew before this session. So thank you so much for sharing that it's really is great to see how you're taking our software and putting it into your application. So that's it for this session. But do stay tuned for the next session, which is all about getting the most out of your data and amplifying your insights. With the help of A, I will be joined by two thought spot leaders who will share their first hand experiences. So take a quick breather and come right back

>>welcome to our session on security titled Securing Your Cloud. Everywhere With Me is Brian Langston, senior solutions engineer from Miranda's, who leads security initiatives from Renta's most security conscious customers. Our topic today is security, and we're setting the bar high by talking in some depth about the requirements of the most highly regulated industries. So, Brian four Regulated industries What do you perceive as the benefits of evolution from classic infra za service to container orchestration? >>Yeah, the adoption of container orchestration has given rise to five key benefits. The first is accountability. Think about the evolution of Dev ops and the security focused version of that team. Deb. SEC ops. These two competencies have emerged to provide, among other things, accountability for the processes they oversee. The outputs that they enable. The second benefit is audit ability. Logging has always been around, but the pervasiveness of logging data within container or container environments allows for the definition of audit trails in new and interesting ways. The third area is transparency organizations that have well developed container orchestration pipelines are much more likely to have a higher degree of transparency in their processes. This helps development teams move faster. It helped operations teams operations teams identify and resolve issues easier and help simplify the observation and certification of security operations by security organizations. Next is quality. Several decades ago, Toyota revolutionized the manufacturing industry when they implemented the philosophy of continuous improvement. Included within that philosophy was this dependency and trust in the process as the process was improved so that the quality of the output Similarly, the refinement of the process of container orchestration yields ah, higher quality output. The four things have mentioned ultimately points to a natural outcome, which is speed when you don't have to spend so much time wondering who does what or who did what. When you have the clear visibility to your processes and because you can continuously improve the quality of your work, you aren't wasting time in a process that produces defects or spending time and wasteful rework phases. You can move much faster than we've seen this to be the case with our customers. >>So what is it specifically about? Container orchestration that gives these benefits, I guess. I guess I'm really asking why are these benefits emerging now around these technologies? What's enabling them, >>right? So I think it boils down to four things related to the orchestration pipelines that are also critical components. Two successful security programs for our customers and related industry. The first one is policy. One of the core concepts and container orchestration is this idea of declaring what you want to happen or declaring the way you want things done? One place where declarations air made our policies. So as long as we can define what we want to happen, it's much easier to do complementary activities like enforcement, which is our second enabler. Um, tools that allow you to define a policy typically have a way to enforce that policy. Where this isn't the case, you need to have a way of enforcing and validating the policies objectives. Miranda's tools allow custom policies to be written and also enforce those policies. The third enabler is the idea of a baseline. Having a well documented set of policies and processes allows you to establish a baseline. Um, it allows you to know what's normal. Having a baseline allows you to measure against it as a way of evaluating whether or not you're achieving your objectives with container orchestration. The fourth enabler of benefits is continuous assessment, which is about measuring constantly back to what I said a few minutes ago. With the toilet away measuring constantly helps you see whether your processes and your target and state are being delivered as your output deviates from that baseline, your adjustments can be made more quickly. So these four concepts, I think, could really make or break your compliance status. >>It's a really way interesting way of thinking about compliance. I had thought previously back compliance, mostly as a as a matter of legally declaring and then trying to do something. But at this point, we have methods beyond legal boilerplate for asserting what we wanna happen, as you say, and and this is actually opening up new ways to detect, deviation and and enforce failure to comply. That's really exciting. Um, so you've you've touched on the benefits of container orchestration here, and you've provided some thoughts on what the drivers on enablers are. So what does Miranda's fit in all this? How does how are we helping enable these benefits, >>right? Well, our goal and more antis is ultimately to make the world's most compliant distribution. We we understand what our customers need, and we have developed our product around those needs, and I could describe a few key security aspects about our product. Um, so Miranda's promotes this idea of building and enabling a secure software supply chain. The simplified version of that that pertains directly to our product follows a build ship run model. So at the build stage is doctor trusted registry. This is where images are stored following numerous security best practices. Image scanning is an optional but highly recommended feature to enable within D T R. Image tags can be regularly pruned so that you have the most current validated images available to your developers. And the second or middle stage is the ship stage, where Miranda's enforces policies that also follow industry best practices, as well as custom image promotion policies that our customers can write and align to their own internal security requirements. The third and final stages to run stage. And at this stage, we're talking about the engine itself. Docker Engine Enterprise is the Onley container, run time with 51 40 dash to cryptography and has many other security features built in communications across the cluster across the container platform are all secure by default. So this build ship stage model is one way of how our products help support this idea of a secure supply chain. There are other aspects of the security supply chain that arm or customer specific that I won't go into. But that's kind of how we could help our product. The second big area eso I just touched on the secure supply chain. The second big area is in a Stig certification. Um, a stick is basically an implementation or configuration guide, but it's published by the U. S government for products used by the US government. It's not exclusive to them, but for customers that value security highly, especially in a regulated industry, will understand the significance and value that the Stig certification brings. So in achieving the certification, we've demonstrated compliance or alignment with a very rigid set of guidelines. Our fifth validation, the cryptography and the Stig certification our third party at two stations that our product is secure, whether you're using our product as a government customer, whether you're a customer in a regulated industry or something else, >>I did not understand what the Stig really Waas. It's helpful because this is not something that I think people in the industry by and large talk about. I suspect because these things are hard to get and time consuming to get s so they don't tend to bubble up to the top of marketing speak the way glitzy new features do that may or may not >>be secure. >>The, uh so then moving on, how has container orchestration changed? How your customers approach compliance assessment and reporting. >>Yeah, This has been an interesting experience and observation as we've worked with some of our customers in these areas. Eso I'll call out three areas. One is the integration of assessment tooling into the overall development process. The second is assessment frequency and then the third is how results are being reported, which includes what data is needed to go into the reporting. There are very likely others that could be addressed. But those are three things that I have noticed personally and working with customers. >>What do you mean exactly? By integration of assessment tooling. >>Yeah. So our customers all generally have some form of a development pipeline and process eso with various third party and open source tools that can be inserted at various phases of the pipeline to do things like status static source would analysis or host scanning or image scanning and other activities. What's not very well established in some cases is how everything fits within the overall pipeline framework. Eso fit too many customers, ends up having a conversation with us about what commands need should be run with what permissions? Where in the environment should things run? How does code get there that does this scanning? Where does the day to go? Once the out once the scan is done and how will I consume it? Thies Real things where we can help our customers understand? Um, you know what? Integration? What? Integration of assessment. Tooling really means. >>It is fascinating to hear this on, baby. We can come back to it at the end. But what I'm picking out of this Ah, this the way you speak about this and this conversation is this kind of re emergence of these Japanese innovations in product productivity in in factory floor productivity. Um, like, just in time delivery and the, you know, the Toyota Miracle and, uh, and that kind of stuff. Fundamentally, it's someone Yesterday, Anders Wahlgren from cloud bees, of course. The C I. C D expert told me, um, that one of the things he likes to tell his, uh consult ease and customers is to put a GoPro on the head of your code and figure out where it's going and how it's spending its time, which is very reminiscent of these 19 fifties time and motion studies, isn't it that that that people, you know pioneered accelerating the factory floor in the industrial America of the mid century? The idea that we should be coming back around to this and doing it at light speed with code now is quite fascinating. >>Yeah, it's funny how many of those same principles are really transferrable from 50 60 70 years ago to today. Yeah, quite fascinating. >>So getting back to what you were just talking about integrating, assessment, tooling, it sounds like that's very challenging. And you mentioned assessment frequency and and reporting. What is it about those areas that that's required? Adaptation >>Eso eso assessment frequency? Um, you know, in legacy environments, if we think about what those look like not too long ago, uh, compliance assessment used to be relatively infrequent activity in the form of some kind of an audit, whether it be a friendly peer review or intercompany audit. Formal third party assessments, whatever. In many cases, these were big, lengthy reviews full of interview questions, Um, it's requests for information, periods of data collection and then the actual review itself. One of the big drawbacks to this lengthy engagement is an infrequent engagement is that vulnerabilities would sometimes go unnoticed or unmitigated until these reviews at it. But in this era of container orchestration, with the decomposition of everything in the software supply chain and with clearer visibility of the various inputs to the build life cycle, our customers can now focus on what tooling and processes can be assembled together in the form of a pipeline that allows constant inspection of a continuous flow of code from start to finish. And they're asking how our product can integrate into their pipeline into their Q A frameworks to help simplify this continuous assessment framework. Eso that's that kind of addresses the frequency, uh, challenge now regarding reporting, our customers have had to reevaluate how results are being reported and the data that's needed in the reporting. The root of this change is in the fact that security has multiple stakeholder groups and I'll just focus on two of them. One is development, and their primary focus, if you think about it, is really about finding and fixing defects. That's all they're focused on, really, is there is there pushing code? The other group, though, is the Security Project Management Office, or PMO. This group is interested in what security controls are at risk due to those defects. So the data that you need for these two stakeholder groups is very different. But because it's also related, it requires a different approach to how the data is expressed, formatted and ultimately integrated with sometimes different data sources to be able to appease both use cases. >>Mhm. So how does Miranda's help improve the rate of compliance assessment? Aziz? Well, as this question of the need for differential data presentation, >>right, So we've developed on exposed a P I S that helped report the compliance status of our product as it's implemented in our customers on environment. So through these AP eyes, we express the data and industry standard formats using plastic out Oscar is a relatively new project out of the mist organization. It's really all about standardizing a set of standards instead of formats that expresses control information. So in this way our customers can get machine and human readable information related to compliance, and that data can then be massaged into other tools or downstream processes that our customers might have. And what I mean by downstream processes is if you're a development team and you have the inspection tools, the process is to gather findings defects related to your code. A downstream process might be the ticketing system with the era that might log a formal defect or that finding. But it all starts with having a common, standard way of expressing thes scan output. And the findings such that both development teams and and the security PMO groups can both benefit from the data. So essentially we've been following this philosophy of transparency, insecurity. What we mean by that is security isn't or should not be a black box of information on Lee, accessible and consumable by security professionals. Assessment is happening proactively in our product, and it's happening automatically. We're bringing security out of obscurity by exposing the aspects of our product that ultimately have a bearing on your compliance status and then making that information available to you in very user friendly ways. >>It's fascinating. Uh uh. I have been excited about Oscar's since, uh, since first hearing about it, Um, it seems extraordinarily important to have what is, in effect, a ah query capability. Um, that that let's that that lets different people for different reasons formalize and ask questions of a system that is constantly in flux, very, very powerful. So regarding security, what do you see is the basic requirements for container infrastructure and tools for use in production by the industries that you are working with, >>right? So obviously, you know, the tools and infrastructure is going to vary widely across customers. But Thio generalize it. I would refer back to the concept I mentioned earlier of a secure software supply chain. There are several guiding principles behind us that are worth mentioning. The first is toe have a strategy for ensuring code quality. What this means is being able to do static source code analysis, static source code analysis tools are largely language specific, so there may be a few different tools that you'll need to have to be able to manage that, um, second point is to have a framework for doing regular testing or even slightly more formal security assessments. There are plenty of tools that can help get a company started doing this. Some of these tools are scanning engines like open ESCAP that's also a product of n'est open. ESCAP can use CS benchmarks as inputs, and these tools do a very good job of summarizing and visualizing output, um, along the same family or idea of CS benchmarks. There's many, many benchmarks that are published. And if you look at your own container environment, um, there are very likely to be many benchmarks that can form the core platform, the building blocks of your container environment. There's benchmarks for being too, for kubernetes, for Dr and and it's always growing. In fact, Mirante is, uh, editing the benchmark for container D, so that will be a formal CSCE benchmark coming up very shortly. Um, next item would be defining security policies that line with your organization's requirements. There are a lot of things that come out of box that comes standard that comes default in various products, including ours, but we also give you through our product. The ability to write your own policies that align with your own organization's requirements, uh, minimizing your tax surface. It's another key area. What that means is only deploying what's necessary. Pretty common sense. But sometimes it's overlooked. What this means is really enabling required ports and services and nothing more. Um, and it's related to this concept of least privilege, which is the next thing I would suggest focusing on these privileges related to minimizing your tax service. It's, uh, it's about only allowing permissions to those people or groups that excuse me that are absolutely necessary. Um, within the container environment, you'll likely have heard this deny all approach. This denial approach is recommended here, which means deny everything first and then explicitly allow only what you need. Eso. That's a very common, uh uh, common thing that sometimes overlooked in some of our customer environments. Andi, finally, the idea of defense and death, which is about minimizing your plast radius by implementing multiple layers of defense that also are in line with your own risk management strategy. Eso following these basic principles, adapting them to your own use cases and requirements, uh, in our experience with our customers, they could go a long way and having a secure software supply chain. >>Thank you very much, Brian. That was pretty eye opening. Um, and I had the privilege of listening to it from the perspective of someone who has been working behind the scenes on the launch pad 2020 event. So I'd like to use that privilege to recommend that our listeners, if you're interested in this stuff certainly if you work within one of these regulated industries in a development role, um, that you may want to check out, which will be easy for you to do today, since everything is available once it's been presented. Matt Bentley's live presentation on secure Supply Chain, where he demonstrates one possible example of a secure supply chain that permits image. Signing him, Scanning on content Trust. Um, you may want to check out the session that I conducted with Andres Falcon at Cloud Bees who talks about thes um, these industrial efficiency factory floor time and motion models for for assessing where software is in order to understand what policies can and should be applied to it. Um, and you will probably want to frequent the tutorial sessions in that track, uh, to see about how Dr Enterprise Container Cloud implements many of these concentric security policies. Um, in order to provide, you know, as you say, defense in depth. There's a lot going on in there, and, uh, and it's ah, fascinating Thio to see it all expressed. Brian. Thanks again. This has been really, really educational. >>My pleasure. Thank you. >>Have a good afternoon. >>Thank you too. Bye.

>> Narrator: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. >> Welcome back to IBM's Machine Learning Everywhere. Build your ladder to AI, along with Dave Vellante, John Walls here, wrapping up here in New York City. Just about done with the programming here in Midtown. Dave, let's just take a step back. We've heard a lot, seen a lot, talked to a lot of folks today. First off, tell me, AI. We've heard some optimistic outlooks, some, I wouldn't say pessimistic, but some folks saying, "Eh, hold off." Not as daunting as some might think. So just your take on the artificial intelligence conversation we've heard so far today. >> I think generally, John, that people don't realize what's coming. I think the industry, in general, our industry, technology industry, the consumers of technology, the businesses that are out there, they're steeped in the past, that's what they know. They know what they've done, they know the history and they're looking at that as past equals prologue. Everybody knows that's not the case, but I think it's hard for people to envision what's coming, and what the potential of AI is. Having said that, Jennifer Shin is a near-term pessimist on the potential for AI, and rightly so. There are a lot of implementation challenges. But as we said at the open, I'm very convinced that we are now entering a new era. The Hadoop big data industry is going to pale in comparison to what we're seeing. And we're already seeing very clear glimpses of it. The obvious things are Airbnb and Uber, and the disruptions that are going on with Netflix and over-the-top programming, and how Google has changed advertising, and how Amazon is changing and has changed retail. But what you can see, and again, the best examples are Apple getting into financial services, moving into healthcare, trying to solve that problem. Amazon buying a grocer. The rumor that I heard about Amazon potentially buying Nordstrom, which my wife said is a horrible idea. (John laughs) But think about the fact that they can do that is a function of, that they are a digital-first company. Are built around data, and they can take those data models and they can apply it to different places. Who would have thought, for example, that Alexa would be so successful? That Siri is not so great? >> Alexa's become our best friend. >> And it came out of the blue. And it seems like Google has a pretty competitive piece there, but I can almost guarantee that doing this with our thumbs is not the way in which we're going to communicate in the future. It's going to be some kind of natural language interface that's going to rely on artificial intelligence and machine learning and the like. And so, I think it's hard for people to envision what's coming, other than fast forward where machines take over the world and Stephen Hawking and Elon Musk say, "Hey, we should be concerned." Maybe they're right, not in the next 10 years. >> You mentioned Jennifer, we were talking about her and the influencer panel, and we've heard from others as well, it's a combination of human intelligence and artificial intelligence. That combination's more powerful than just artificial intelligence, and so, there is a human component to this. So, for those who might be on the edge of their seat a little bit, or looking at this from a slightly more concerning perspective, maybe not the case. Maybe not necessary, is what you're thinking. >> I guess at the end of the day, the question is, "Is the world going to be a better place with all this AI? "Are we going to be more prosperous, more productive, "healthier, safer on the roads?" I am an optimist, I come down on the side of yes. I would not want to go back to the days where I didn't have GPS. That's worth it to me. >> Can you imagine, right? If you did that now, you go back five years, just five years from where we are now, back to where we were. Waze was nowhere, right? >> All the downside of these things, I feel is offset by that. And I do think it's incumbent upon the industry to try to deal with the problem, especially with young people, the blue light problem. >> John: The addictive issue. >> That's right. But I feel like those downsides are manageable, and the upsides are of enough value that society is going to continue to move forward. And I do think that humans and machines are going to continue to coexist, at least in the near- to mid- reasonable long-term. But the question is, "What can machines "do that humans can't do?" And "What can humans do that machines can't do?" And the answer to that changes every year. It's like I said earlier, not too long ago, machines couldn't climb stairs. They can now, robots can climb stairs. Can they negotiate? Can they identify cats? Who would've imagined that all these cats on the Internet would've led to facial recognition technology. It's improving very, very rapidly. So, I guess my point is that that is changing very rapidly, and there's no question it's going to have an impact on society and an impact on jobs, and all those other negative things that people talk about. To me, the key is, how do we embrace that and turn it into an opportunity? And it's about education, it's about creativity, it's about having multi-talented disciplines that you can tap. So we talked about this earlier, not just being an expert in marketing, but being an expert in marketing with digital as an understanding in your toolbox. So it's that two-tool star that I think is going to emerge. And maybe it's more than two tools. So that's how I see it shaping up. And the last thing is disruption, we talked a lot about disruption. I don't think there's any industry that's safe. Colin was saying, "Well, certain industries "that are highly regulated-" In some respects, I can see those taking longer. But I see those as the most ripe for disruption. Financial services, healthcare. Can't we solve the HIPAA challenge? We can't get access to our own healthcare information. Well, things like artificial intelligence and blockchain, we were talking off-camera about blockchain, those things, I think, can help solve the challenge of, maybe I can carry around my health profile, my medical records. I don't have access to them, it's hard to get them. So can things like artificial intelligence improve our lives? I think there's no question about it. >> What about, on the other side of the coin, if you will, the misuse concerns? There are a lot of great applications. There are a lot of great services. As you pointed out, a lot of positive, a lot of upside here. But as opportunities become available and technology develops, that you run the risk of somebody crossing the line for nefarious means. And there's a lot more at stake now because there's a lot more of us out there, if you will. So, how do you balance that? >> There's no question that's going to happen. And it has to be managed. But even if you could stop it, I would say you shouldn't because the benefits are going to outweigh the risks. And again, the question we asked the panelists, "How far can we take machines? "How far can we go?" That's question number one, number two is, "How far should we go?" We're not even close to the "should we go" yet. We're still on the, "How far can we go?" Jennifer was pointing out, I can't get my password reset 'cause I got to call somebody. That problem will be solved. >> So, you're saying it's more of a practical consideration now than an ethical one, right now? >> Right now. Moreso, and there's certainly still ethical considerations, don't get me wrong, but I see light at the end of the privacy tunnel, I see artificial intelligence as, well, analytics is helping us solve credit card fraud and things of that nature. Autonomous vehicles are just fascinating, right? Both culturally, we talked about that, you know, we learned how to drive a stick shift. (both laugh) It's a funny story you told me. >> Not going to worry about that anymore, right? >> But it was an exciting time in our lives, so there's a cultural downside of that. I don't know what the highway death toll number is, but it's enormous. If cell phones caused that many deaths, we wouldn't be using them. So that's a problem that I think things like artificial intelligence and machine intelligence can solve. And then the other big thing that we talked about is, I see a huge gap between traditional companies and these born-in-the-cloud, born-data-oriented companies. We talked about the top five companies by market cap. Microsoft, Amazon, Facebook, Alphabet, which is Google, who am I missing? >> John: Apple. >> Apple, right. And those are pretty much very much data companies. Apple's got the data from the phones, Google, we know where they get their data, et cetera, et cetera. Traditional companies, however, their data resides in silos. Jennifer talked about this, Craig, as well as Colin. Data resides in silos, it's hard to get to. It's a very human-driven business and the data is bolted on. With the companies that we just talked about, it's a data-driven business, and the humans have expertise to exploit that data, which is very important. So there's a giant skills gap in existing companies. There's data silos. The other thing we touched on this is, where does innovation come from? Innovation drives value drives disruption. So the innovation comes from data. He or she who has the best data wins. It comes from artificial intelligence, and the ability to apply artificial intelligence and machine learning. And I think something that we take for granted a lot, but it's cloud economics. And it's more than just, and somebody, one of the folks mentioned this on the interview, it's more than just putting stuff in the cloud. It's certainly managed services, that's part of it. But it's also economies of scale. It's marginal economics that are essentially zero. It's speed, it's low latency. It's, and again, global scale. You combine those things, data, artificial intelligence, and cloud economics, that's where the innovation is going to come from. And if you think about what Uber's done, what Airbnb have done, where Waze came from, they were picking and choosing from the best digital services out there, and then developing their own software from this, what I say my colleague Dave Misheloff calls this matrix. And, just to repeat, that matrix is, the vertical matrix is industries. The horizontal matrix are technology platforms, cloud, data, mobile, social, security, et cetera. They're building companies on top of that matrix. So, it's how you leverage the matrix is going to determine your future. Whether or not you get disrupted, whether your the disruptor or the disruptee. It's not just about, we talked about this at the open. Cloud, SaaS, mobile, social, big data. They're kind of yesterday's news. It's now new artificial intelligence, machine intelligence, deep learning, machine learning, cognitive. We're still trying to figure out the parlance. You could feel the changes coming. I think this matrix idea is very powerful, and how that gets leveraged in organizations ultimately will determine the levels of disruption. But every single industry is at risk. Because every single industry is going digital, digital allows you to traverse industries. We've said it many times today. Amazon went from bookseller to content producer to grocer- >> John: To grocer now, right? >> To maybe high-end retailer. Content company, Apple with Apple Pay and companies getting into healthcare, trying to solve healthcare problems. The future of warfare, you live in the Beltway. The future of warfare and cybersecurity are just coming together. One of the biggest issues I think we face as a country is we have fake news, we're seeing the weaponization of social media, as James Scott said on theCUBE. So, all these things are coming together that I think are going to make the last 10 years look tame. >> Let's just switch over to the currency of AI, data. And we've talked to, Sam Lightstone today was talking about the database querying that they've developed with the Plex product. Some fascinating capabilities now that make it a lot richer, a lot more meaningful, a lot more relevant. And that seems to be, really, an integral step to making that stuff come alive and really making it applicable to improving your business. Because they've come up with some fantastic new ways to squeeze data that's relevant out, and get it out to the user. >> Well, if you think about what I was saying earlier about data as a foundational core and human expertise around it, versus what most companies are, is human expertise with data bolted on or data in silos. What was interesting about Queryplex, I think they called it, is it essentially virtualizes the data. Well, what does that mean? That means i can have data in place, but I can have access to that data, I can democratize that data, make it accessible to people so that they can become data-driven, data is the core. Now, what I don't know, and I don't know enough, just heard about it today, I missed that announcement, I think they announced it a year ago. He mentioned DB2, he mentioned Netezza. Most of the world is not on DB2 and Netezza even though IBM customers are. I think they can get to Hadoop data stores and other data stores, I just don't know how wide that goes, what the standards look like. He joked about the standards as, the great thing about standards is- >> There are a lot of 'em. (laughs) >> There's always another one you can pick if this one fails. And he's right about that. So, that was very interesting. And so, this is again, the question, can traditional companies close that machine learning, machine intelligence, AI gap? Close being, close the gap that the big five have created. And even the small guys, small guys like Uber and Airbnb, and so forth, but even those guys are getting disrupted. The Airbnbs and the Ubers, right? Again, blockchain comes in and you say, "Why do I need a trusted third party called Uber? "Why can't I do this on the blockchain?" I predict you're going to see even those guys get disrupted. And I'll say something else, it's hard to imagine that a Google or a Facebook can be unseated. But I feel like we may be entering an era where this is their peak. Could be wrong, I'm an Apple customer. I don't know, I'm not as enthralled as I used to be. They got trillions in the bank. But is it possible that opensource and blockchain and the citizen developer, the weekend and nighttime developers, can actually attack that engine of growth for the last 10 years, 20 years, and really break that monopoly? The Internet has basically become an oligopoly where five companies, six companies, whatever, 10 companies kind of control things. Is it possible that opensource software, AI, cryptography, all this activity could challenge the status quo? Being in this business as long as I have, things never stay the same. Leaders come, leaders go. >> I just want to say, never say never. You don't know. >> So, it brings it back to IBM, which is interesting to me. It was funny, I was asking Rob Thomas a question about disruption, and I think he misinterpreted it. I think he was thinking that I was saying, "Hey, you're going to get disrupted by all these little guys." IBM's been getting disrupted for years. They know how to reinvent. A lot of people criticize IBM, how many quarters they haven't had growth, blah, blah, blah, but IBM's made some big, big bets on the future. People criticizing Watson, but it's going to be really interesting to see how all this investment that IBM has made is going to pay off. They were early on. People in the Valley like to say, "Well, the Facebooks, and even Amazon, "Google, they got the best AI. "IBM is not there with them." But think about what IBM is trying to do versus what Google is doing. They're very consumer-oriented, solving consumer problems. Consumers have really led the consumerization of IT, that's true, but none of those guys are trying to solve cancer. So IBM is talking about some big, hairy, audacious goals. And I'm not as pessimistic as some others you've seen in the trade press, it's popular to do. So, bringing it back to IBM, I saw IBM as trying to disrupt itself. The challenge IBM has, is it's got a lot of legacy software products that have purchased over the years. And it's got to figure out how to get through those. So, things like Queryplex allow them to create abstraction layers. Things like Bluemix allow them to bring together their hundreds and hundreds and hundreds of SaaS applications. That takes time, but I do see IBM making some big investments to disrupt themselves. They've got a huge analytics business. We've been covering them for quite some time now. They're a leader, if not the leader, in that business. So, their challenge is, "Okay, how do we now "apply all these technologies to help "our customers create innovation?" What I like about the IBM story is they're not out saying, "We're going to go disrupt industries." Silicon Valley has a bifurcated disruption agenda. On the one hand, they're trying to, cloud, and SaaS, and mobile, and social, very disruptive technologies. On the other hand, is Silicon Valley going to disrupt financial services, healthcare, government, education? I think they have plans to do so. Are they going to be able to execute that dual disruption agenda? Or are the consumers of AI and the doers of AI going to be the ones who actually do the disrupting? We'll see, I mean, Uber's obviously disrupted taxis, Silicon Valley company. Is that too much to ask Silicon Valley to do? That's going to be interesting to see. So, my point is, IBM is not trying to disrupt its customers' businesses, and it can point to Amazon trying to do that. Rather, it's saying, "We're going to enable you." So it could be really interesting to see what happens. You're down in DC, Jeff Bezos spent a lot of time there at the Washington Post. >> We just want the headquarters, that's all we want. We just want the headquarters. >> Well, to the point, if you've got such a growing company monopoly, maybe you should set up an HQ2 in DC. >> Three of the 20, right, for a DC base? >> Yeah, he was saying the other day that, maybe we should think about enhancing, he didn't call it social security, but the government, essentially, helping people plan for retirement and the like. I heard that and said, "Whoa, is he basically "telling us he's going to put us all out of jobs?" (both laugh) So, that, if I'm a customer of Amazon's, I'm kind of scary. So, one of the things they should absolutely do is spin out AWS, I think that helps solve that problem. But, back to IBM, Ginni Rometty was very clear at the World of Watson conference, the inaugural one, that we are not out trying to compete with our customers. I would think that resonates to a lot of people. >> Well, to be continued, right? Next month, back with IBM again? Right, three days? >> Yeah, I think third week in March. Monday, Tuesday, Wednesday, theCUBE's going to be there. Next week we're in the Bahamas. This week, actually. >> Not as a group taking vacation. Actually a working expedition. >> No, it's that blockchain conference. Actually, it's this week, what am I saying next week? >> Although I'm happy to volunteer to grip on that shoot, by the way. >> Flying out tomorrow, it's happening fast. >> Well, enjoyed this, always good to spend time with you. And good to spend time with you as well. So, you've been watching theCUBE, machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Have a good one. (techno music)

>> Announcer: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Welcome back to New York City. Along with Dave Vellante, I'm John Walls. We continue our coverage here on theCUBE of machine learning everywhere. Build your ladder to AI, IBM our host here today. We put together, occasionally at these events, a panel of esteemed experts with deep perspectives on a particular subject. Today our influencer panel is comprised of three well-known and respected authorities in this space. Glad to have Colin Sumpter here with us. He's the man with the mic, by the way. He's going to talk first. But, Colin is an IT architect with CrowdMole. Thank you for being with us, Colin. Jennifer Shin, those of you on theCUBE, you're very familiar with Jennifer, a long time Cuber. Founded 8 Path Solutions, on the faculty at NYU and Cal Berkeley, and also with us is Craig Brown, a big data consultant. And a home game for all of you guys, right, more or less here we are in the city. So, thanks for having us, we appreciate the time. First off, let's just talk about the title of the event, Build Your Path... Or Your Ladder, excuse me, to AI. What are those steps on that ladder, Colin? The fundamental steps that you've got to jump on, or step on, in order to get to that true AI environment? >> In order to get to that true AI environment, John, is a matter of mastering or organizing your information well enough to perform analytics. That'll give you two choices to do either linear regression or supervised classification, and then you actually have enough organized data to talk to your team and organize your team around that data to begin that ladder to successively benefit from your data science program. >> Want to take a stab at it, Jennifer? >> So, I would say, compute, right? You need to have the right processing, or at least the ability to scale out to be able to process the algorithm fast enough to be able to find value in your data. I think the other thing is, of course, the data source itself. Do you have right data to answer the questions you want to answer? So, I think, without those two things, you'll either have a lot of great data that you can't process in time, or you'll have a great process or a great algorithm that has no real information, so your output is useless. I think those are the fundamental things you really do need to have any sort of AI solution built. >> I'll take a stab at it from the business side. They have to adopt it first. They have to believe that this is going to benefit them and that the effort that's necessary in order to build into the various aspects of algorithms and data subjects is there, so I think adopting the concept of machine learning and the development aspects that it takes to do that is a key component to building the ladder. >> So this just isn't toe in the water, right? You got to dive in the deep end, right? >> Craig: Right. >> It gets to culture. If you look at most organizations, not the big five market capped companies, but most organizations, data is not at their core. Humans are at their core, human expertise and data is sort of bolted on, but that has to change, or they're going to get disrupted. Data has to be at the core, maybe the human expertise leverages that data. What do you guys seeing with end customers in terms of their readiness for this transformation? >> What I'm seeing customers spending time right now is getting out of the silos. So, when you speak culture, that's primarily what the culture surrounds. They develop applications with functionality as a silo, and data specific to that functionality is the component in which they look at data. They have to get out of that mindset and look at the data holistically, and ultimately, in these events, looking at it as an asset. >> The data is a shared resource. >> Craig: Right, correct. >> Okay, and again, with the exception of the... Whether it's Google, Facebook, obviously, but the Ubers, the AirBNB's, etc... With the exception of those guys, most customers aren't there. Still, the data is in silos, they've got myriad infrastructure. Your thoughts, Jennifer? >> I'm also seeing sort of a disconnect between the operationalizing team, the team that runs these codes, or has a real business need for it, and sometimes you'll see corporations with research teams, and there's sort of a disconnect between what the researchers do and what these operations, or marketing, whatever domain it is, what they're doing in terms of a day to day operation. So, for instance, a researcher will look really deep into these algorithms, and may know a lot about deep learning in theory, in theoretical world, and might publish a paper that's really interesting. But, that application part where they're actually being used every day, there's this difference there, where you really shouldn't have that difference. There should be more alignment. I think actually aligning those resources... I think companies are struggling with that. >> So, Colin, we were talking off camera about RPA, Robotic Process Automation. Where's the play for machine intelligence and RPA? Maybe, first of all, you could explain RPA. >> David, RPA stands for Robotic Process Automation. That's going to enable you to grow and scale a digital workforce. Typically, it's done in the cloud. The way RPA and Robotic Process Automation plays into machine learning and data science, is that it allows you to outsource business processes to compensate for the lack of human expertise that's available in the marketplace, because you need competency to enable the technology to take advantage of these new benefits coming in the market. And, when you start automating some of these processes, you can keep pace with the innovation in the marketplace and allow the human expertise to gradually grow into these new data science technologies. >> So, I was mentioning some of the big guys before. Top five market capped companies: Google, Amazon, Apple, Facebook, Microsoft, all digital. Microsoft you can argue, but still, pretty digital, pretty data oriented. My question is about closing that gap. In your view, can companies close that gap? How can they close that gap? Are you guys helping companies close that gap? It's a wide chasm, it seems. Thoughts? >> The thought on closing the chasm is... presenting the technology to the decision-makers. What we've learned is that... you don't know what you don't know, so it's impossible to find the new technologies if you don't have the vocabulary to just begin a simple research of these new technologies. And, to close that gap, it really comes down to the awareness, events like theCUBE, webinars, different educational opportunities that are available to line of business owners, directors, VP's of systems and services, to begin that awareness process, finding consultants... begin that pipeline enablement to begin allowing the business to take advantage and harness data science, machine learning and what's coming. >> One of the things I've noticed is that there's a lot of information out there, like everyone a webinar, everyone has tutorials, but there's a lot of overlap. There aren't that many very sophisticated documents you can find about how to implement it in real world conditions. They all tend to use the same core data set, a lot of these machine learning tutorials you'll find, which is hilarious because the data set's actually very small. And I know where it comes from, just from having the expertise, but it's not something I'd ever use in the real world. The level of skill you need to be able to do any of these methodologies. But that's what's out there. So, there's a lot of information, but they're kind of at a rudimentary level. They're not really at that sophisticated level where you're going to learn enough to deploy in real world conditions. One of the things I'm noticing is, with the technical teams, with the data science team, machine learning teams, they're kind of using the same methodologies I used maybe 10 years ago. Because the management who manage these teams are not technical enough. They're business people, so they don't understand how to guide them, how to explain hey maybe you shouldn't do that with your code, because that's actually going to cause a problem. You should use parallel code, you should make sure everything is running in parallel so compute's faster. But, if these younger teams are actually learning for the first time, they make the same mistakes you made 10 years ago. So, I think, what I'm noticing is that lack of leadership is partly one of the reasons, and also the assumption that a non-technical person can lead the technical team. >> So, it's just not skillset on the worker level, if you will. It's also knowledge base on the decision-maker level. That's a bad place to be, right? So, how do you get into the door to a business like that? Obviously, and we've talked about this a little bit today, that some companies say, "We're not data companies, we're not digital companies, we sell widgets." Well, yeah but you sell widgets and you need this to sell more widgets. And so, how do you get into the door and talk about this problem that Jennifer just cited? You're signing the checks, man. You're going to have to get up to speed on this otherwise you're not going to have checks to sign in three to five years, you're done! >> I think that speaks to use cases. I think that, and what I'm actually saying at customers, is that there's a disconnect and an understanding from the executive teams and the low-level technical teams on what the use case actually means to the business. Some of the use cases are operational in nature. Some of the use cases are data in nature. There's no real conformity on what does the use case mean across the organization, and that understanding isn't there. And so, the CIO's, the CEO's, the CTO's think that, "Okay, we're going to achieve a certain level of capability if we do a variety of technological things," and the business is looking to effectively improve some or bring some efficiency to business processes. At each level within the organization, the understanding is at the level at which the discussions are being made. And so, I'm in these meetings with senior executives and we have lots of ideas on how we can bring efficiencies and some operational productivity with technology. And then we get in a meeting with the data stewards and "What are these guys talking about? They don't understand what's going on at the data level and what data we have." And then that's where the data quality challenges come into the conversation, so I think that, to close that cataclysm, we have to figure out who needs to be in the room to effectively help us build the right understanding around the use cases and then bring the technology to those use cases then actually see within the organization how we're affecting that. >> So, to change the questioning here... I want you guys to think about how capable can we make machines in the near term, let's talk next decade near term. Let's say next decade. How capable can we make machines and are there limits to what we should do? >> That's a tough one. Although you want to go next decade, we're still faced with some of the challenges today in terms of, again, that adoption, the use case scenarios, and then what my colleagues are saying here about the various data challenges and dev ops and things. So, there's a number of things that we have to overcome, but if we can get past those areas in the next decade, I don't think there's going to be much of a limit, in my opinion, as to what the technology can do and what we can ask the machines to produce for us. As Colin mentioned, with RPA, I think that the capability is there, right? But, can we also ultimately, as humans, leverage that capability effectively? >> I get this question a lot. People are really worried about AI and robots taking over, and all of that. And I go... Well, let's think about the example. We've all been online, probably over the weekend, maybe it's 3 or 4 AM, checking your bank account, and you get an error message your password is wrong. And we swear... And I've been there where I'm like, "No, no my password's right." And it keeps saying that the password is wrong. Of course, then I change it, and it's still wrong. Then, the next day when I login, I can login, same password, because they didn't put a great error message there. They just defaulted to wrong password when it's probably a server that's down. So, there are these basics or processes that we could be improving which no one's improving. So you think in that example, how many customer service reps are going to be contacted to try to address that? How many IT teams? So, for every one of these bad technologies that are out there, or technologies that are not being run efficiently or run in a way that makes sense, you actually have maybe three people that are going to be contacted to try to resolve an issue that actually maybe could have been avoided to begin with. I feel like it's optimistic to say that robots are going to take over, because you're probably going to need more people to put band-aids on bad technology and bad engineering, frankly. And I think that's the reality of it. If we had hoverboards, that would be great, you know? For a while, we thought we did, right? But we found out, oh it's not quite hoverboards. I feel like that might be what happens with AI. We might think we have it, and then go oh wait, it's not really what we thought it was. >> So there are real limits, certainly in the near to mid to maybe even long term, that are imposed. But you're an optimist. >> Yeah. Well, not so much with AI but everything else, sure. (laughing) AI, I'm a little bit like, "Well, it would be great, but I'd like basic things to be taken care of every day." So, I think the usefulness of technology is not something anyone's talking about. They're talking about this advancement, that advancement, things people don't understand, don't know even how to use in their life. Great, great is an idea. But, what about useful things we can actually use in our real life? >> So block and tackle first, and then put some reverses in later, if you will, to switch over to football. We were talking about it earlier, just about basics. Fundamentals, get your fundamentals right and then you can complement on that with supplementary technologies. Craig, Colin? >> Jen made some really good points and brought up some very good points, and so has... >> John: Craig. >> Craig, I'm sorry. (laughing) >> Craig: It's alright. >> 10 years out, Jen and Craig spoke to false positives. And false positives create a lot of inefficiency in businesses. So, when you start using machine learning and AI 10 years from now, maybe there's reduced false positives that have been scored in real time, allowing teams not to have their time consumed and their business resources consumed trying to resolve false positives. These false positives have a business value that, today, some businesses might not be able to record. In financial services, banks count money not lended. But, in every day business, a lot of businesses aren't counting the monetary consequences of false positives and the drag it has on their operational ability and capacity. >> I want to ask you guys about disruption. If you look at where the disruption, the digital disruptions, have taken place, obviously retail, certainly advertising, certainly content businesses... There are some industries that haven't been highly disruptive: financial services, insurance, we were talking earlier about aerospace, defense rather. Is any business, any industry, safe from digital disruption? >> There are. Certain industries are just highly regulated: healthcare, financial services, real estate, transactional law... These are very extremely regulated technologies, or businesses, that are... I don't want to say susceptible to technology, but they can be disrupted at a basic level, operational efficiency, to make these things happen, these business processes happen more rapidly, more accurately. >> So you guys buy that? There's some... I'd like to get a little debate going here. >> So, I work with the government, and the government's trying to change things. I feel like that's kind of a sign because they tend to be a little bit slower than, say, other private industries, or private companies. They have data, they're trying to actually put it into a system, meaning like if they have files... I think that, at some point, I got contacted about putting files that they found, like birth records, right, marriage records, that they found from 100-plus years ago and trying to put that into the system. By the way, I did look into it, there was no way to use AI for that, because there was no standardization across these files, so they have half a million files, but someone's probably going to manually have to enter that in. The reality is, I think because there's a demand for having things be digital, we aren't likely to see a decrease in that. We're not going to have one industry that goes, "Oh, your files aren't digital." Probably because they also want to be digital. The companies themselves, the employees themselves, want to see that change. So, I think there's going to be this continuous move toward it, but there's the question of, "Are we doing it better?" It is better than, say, having it on paper sometimes? Because sometimes I just feel like it's easier on paper than to have to look through my phone, look through the app. There's so many apps now! >> (laughing) I got my index cards cards still, Jennifer! Dave's got his notebook! >> I'm not sure I want my ledger to be on paper... >> Right! So I think that's going to be an interesting thing when people take a step back and go like, "Is this really better? Is this actually an improvement?" Because I don't think all things are better digital. >> That's a great question. Will the world be a better, more prosperous place... Uncertain. Your thoughts? >> I think the competition is probably the driver as to who has to this now, who's not safe. The organizations that are heavily regulated or compliance-driven can actually use that as the reasoning for not jumping into the barrel right now, and letting it happen in other areas first, watching the technology mature-- >> Dave: Let's wait. >> Yeah, let's wait, because that's traditionally how they-- >> Dave: Good strategy in your opinion? >> It depends on the entity but I think there's nothing wrong with being safe. There's nothing wrong with waiting for a variety of innovations to mature. What level of maturity, I think, is the perspective that probably is another discussion for another day, but I think that it's okay. I don't think that everyone should jump in. Get some lessons learned, watch how the other guys do it. I think that safety is in the eyes of the beholder, right? But some organizations are just competition fierce and they need a competitive edge and this is where they get it. >> When you say safety, do you mean safety in making decisions, or do you mean safety in protecting data? How are you defining safety? >> Safety in terms of when they need to launch, and look into these new technologies as a basis for change within the organization. >> What about the other side of that point? There's so much more data about it, so much more behavior about it, so many more attitudes, so on and so forth. And there is privacy issues and security issues and all that... Those are real challenges for any company, and becoming exponentially more important as more is at stake. So, how do companies address that? That's got to be absolutely part of their equation, as they decide what these future deployments are, because they're going to have great, vast reams of data, but that's a lot of vulnerability too, isn't it? >> It's as vulnerable as they... So, from an organizational standpoint, they're accustomed to these... These challenges aren't new, right? We still see data breaches. >> They're bigger now, right? >> They're bigger, but we still see occasionally data breaches in organizations where we don't expect to see them. I think that, from that perspective, it's the experiences of the organizations that determine the risks they want to take on, to a certain degree. And then, based on those risks, and how they handle adversity within those risks, from an experience standpoint they know ultimately how to handle it, and get themselves to a place where they can figure out what happened and then fix the issues. And then the others watch while these risk-takers take on these types of scenarios. >> I want to underscore this whole disruption thing and ask... We don't have much time, I know we're going a little over. I want to ask you to pull out your Hubble telescopes. Let's make a 20 to 30 year view, so we're safe, because we know we're going to be wrong. I want a sort of scale of 1 to 10, high likelihood being 10, low being 1. Maybe sort of rapid fire. Do you think large retail stores are going to mostly disappear? What do you guys think? >> I think the way that they are structured, the way that they interact with their customers might change, but you're still going to need them because there are going to be times where you need to buy something. >> So, six, seven, something like that? Is that kind of consensus, or do you feel differently Colin? >> I feel retail's going to be around, especially fashion because certain people, and myself included, I need to try my clothes on. So, you need a location to go to, a physical location to actually feel the material, experience the material. >> Alright, so we kind of have a consensus there. It's probably no. How about driving-- >> I was going to say, Amazon opened a book store. Just saying, it's kind of funny because they got... And they opened the book store, so you know, I think what happens is people forget over time, they go, "It's a new idea." It's not so much a new idea. >> I heard a rumor the other day that their next big acquisition was going to be, not Neiman Marcus. What's the other high end retailer? >> Nordstrom? >> Nordstrom, yeah. And my wife said, "Bad idea, they'll ruin it." Will driving and owning your own car become an exception? >> Driving and owning your own car... >> Dave: 30 years now, we're talking. >> 30 years... Sure, I think the concept is there. I think that we're looking at that. IOT is moving us in that direction. 5G is around the corner. So, I think the makings of it is there. So, since I can dare to be wrong, yeah I think-- >> We'll be on 10G by then anyway, so-- >> Automobiles really haven't been disrupted, the car industry. But you're forecasting, I would tend to agree. Do you guys agree or no, or do you think that culturally I want to drive my own car? >> Yeah, I think people, I think a couple of things. How well engineered is it? Because if it's badly engineered, people are not going to want to use it. For instance, there are people who could take public transportation. It's the same idea, right? Everything's autonomous, you'd have to follow in line. There's going to be some system, some order to it. And you might go-- >> Dave: Good example, yeah. >> You might go, "Oh, I want it to be faster. I don't want to be in line with that autonomous vehicle. I want to get there faster, get there sooner." And there are people who want to have that control over their lives, but they're not subject to things like schedules all the time and that's their constraint. So, I think if the engineering is bad, you're going to have more problems and people are probably going to go away from wanting to be autonomous. >> Alright, Colin, one for you. Will robots and maybe 3D printing, for example RPA, will it reverse the trend toward offshore manufacturing? >> 30 years from now, yes. I think robotic process engineering, eventually you're going to be at your cubicle or your desk, or whatever it is, and you're going to be able to print office supplies. >> Do you guys think machines will make better diagnoses than doctors? Ohhhhh. >> I'll take that one. >> Alright, alright. >> I think yes, to a certain degree, because if you look at the... problems with diagnosis, right now they miss it and I don't know how people, even 30 years from now, will be different from that perspective, where machines can look at quite a bit of data about a patient in split seconds and say, "Hey, the likelihood of you recurring this disease is nil to none, because here's what I'm basing it on." I don't think doctors will be able to do that. Now, again, daring to be wrong! (laughing) >> Jennifer: Yeah so--6 >> Don't tell your own doctor either. (laughing) >> That's true. If anything happens, we know, we all know. I think it depends. So maybe 80%, some middle percentage might be the case. I think extreme outliers, maybe not so much. You think about anything that's programmed into an algorithm, someone probably identified that disease, a human being identified that as a disease, made that connection, and then it gets put into the algorithm. I think what w6ll happen is that, for the 20% that isn't being done well by machine, you'll have people who are more specialized being able to identify the outlier cases from, say, the standard. Normally, if you have certain symptoms, you have a cold, those are kind of standard ones. If you have this weird sort of thing where there's n6w variables, environmental variables for instance, your environment can actually lead to you having cancer. So, there's othe6 factors other than just your body and your health that's going to actually be important to think about wh6n diagnosing someone. >> John: Colin, go ahead. >> I think machines aren't going to out-decision doctors. I think doctors are going to work well the machine learning. For instance, there's a published document of Watson doing the research of a team of four in 10 minutes, when it normally takes a month. So, those doctors,6to bring up Jen and Craig's point, are going to have more time to focus in on what the actual symptoms are, to resolve the outcome of patient care and patient services in a way that benefits humanity. >> I just wish that, Dave, that you would have picked a shorter horizon that... 30 years, 20 I feel good about our chances of seeing that. 30 I'm just not so sure, I mean... For the two old guys on the panel here. >> The consensus is 20 years, not so much. But beyond 10 years, a lot's going to change. >> Well, thank you all for joining this. I always enjoy the discussions. Craig, Jennifer and Colin, thanks for being here with us here on theCUBE, we appreciate the time. Back with more here from New York right after this. You're watching theCUBE. (upbeat digital music)

>> [Narrator] Live from New York, it's theCube, covering Machine Learning Everywhere. Build your ladder to AI, brought to you by IBM. >> Welcome back here to New York City as we continue at IBM's Machine Learning Everywhere, build your ladder to AI, along with Dave Vellante, I'm John Walls. It is now a great honor of ours to have I think probably and arguably the greatest chess player of all time, Garry Kasparov now joins us. He's currently the chairman of the Human Rights Foundation, political activist in Russia as well some time ago. Thank you for joining us, we really appreciate the time, sir. >> Thank you for inviting me. >> We've been looking forward to this. Let's just, if you would, set the stage for us. Artificial Intelligence obviously quite a hot topic. The maybe not conflict, the complementary nature of human intelligence. There are people on both sides of the camp. But you see them as being very complementary to one another. >> I think that's natural development in this industry that will bring together humans and machines. Because this collaboration will produce the best results. Our abilities are complementary. The humans will bring creativity and intuition and other typical human qualities like human judgment and strategic vision while machines will add calculation, memory, and many other abilities that they have been acquiring quickly. >> So there's room for both, right? >> Yes, I think it's inevitable because no machine will ever reach 100% perfection. Machines will be coming closer and closer, 90%, 92, 94, 95. But there's still room for humans because at the end of the day even with this massive power you have guide it. You have to evaluate the results and at the end of the day the machine will never understand when it reaches the territory of diminishing returns. It's very important for humans actually to identify. So what is the task? I think it's a mistake that is made by many pundits that they automatically transfer the machine's expertise for the closed systems into the open-ended systems. Because in every closed system, whether it's the game of chess, the game of gall, video games like daughter, or anything else where humans already define the parameters of the problem, machines will perform phenomenally. But if it's an open-ended system then machine will never identify what is the sort of the right question to be asked. >> Don't hate me for this question, but it's been reported, now I don't know if it's true or not, that at one point you said that you would never lose to a machine. My question is how capable can we make machines? First of all, is that true? Did you maybe underestimate the power of computers? How capable to you think we can actually make machines? >> Look, in the 80s when the question was asked I was much more optimistic because we saw very little at that time from machines that could make me, world champion at the time, worry about machines' capability of defeating me in the real chess game. I underestimated the pace it was developing. I could see something was happening, was cooking, but I thought it would take longer for machines to catch up. As I said in my talk here is that we should simply recognize the fact that everything we do while knowing how we do that, machines will do better. Any particular task that human perform, machine will eventually surpass us. >> What I love about your story, I was telling you off-camera about when we had Erik Brynjolfsson and Andrew McAfee on, you're the opposite of Samuel P. Langley to me. You know who Samuel P. Langley is? >> No, please. >> Samuel P. Langley, do you know who Samuel P. Langley is? He was the gentleman that, you guys will love this, that the government paid. I think it was $50,000 at the time, to create a flying machine. But the Wright Brothers beat him to it, so what did Samuel P. Langley do after the Wright Brothers succeeded? He quit. But after you lost to the machine you said you know what? I can beat the machine with other humans, and created what is now the best chess player in the world, is my understanding. It's not a machine, but it's a combination of machines and humans. Is that accurate? >> Yes, in chess actually, we could demonstrate how the collaboration can work. Now in many areas people rely on the lessons that have been revealed, learned from what I call advanced chess. That in this team, human plus machine, the most important element of success is not the strengths of the human expert. It's not the speed of the machine, but it's a process. It's an interface, so how you actually make them work together. In the future I think that will be the key of success because we have very powerful machine, those AIs, intelligent algorithms. All of them will require very special treatment. That's why also I use this analogy with the right fuel for Ferrari. We will have expert operators, I call them the shepherds, that will have to know exactly what are the requirements of this machine or that machine, or that group of algorithms to guarantee that we'll be able by our human input to compensate for their deficiencies. Not the other way around. >> What let you to that response? Was it your competitiveness? Was it your vision of machines and humans working together? >> I thought I could last longer as the undefeated world champion. Ironically, 1997 when you just look at the game and the quality of the game and try to evaluate the Deep Blue real strengths, I think I was objective, I was stronger. Because today you can analyze these games with much more powerful computers. I mean any chess app on your laptop. I mean you cannot really compare with Deep Blue. That's natural progress. But as I said, it's not about solving the game, it's not about objective strengths. It's about your ability to actually perform at the board. I just realized while we could compete with machines for few more years, and that's great, it did take place. I played two more matches in 2003 with German program. Not as publicized as IBM match. Both ended as a tie and I think they were probably stronger than Deep Blue, but I knew it would just be over, maybe a decade. How can we make chess relevant? For me it was very natural. I could see this immense power of calculations, brute force. On the other side I could see us having qualities that machines will never acquire. How about bringing together and using chess as a laboratory to find the most productive ways for human-machine collaboration? >> What was the difference in, I guess, processing power basically, or processing capabilities? You played the match, this is 1997. You played the match on standard time controls which allow you or a player a certain amount of time. How much time did Deep Blue, did the machine take? Or did it take its full time to make considerations as opposed to what you exercised? >> Well it's the standard time control. I think you should explain to your audience at that time it was seven hours game. It's what we call classical chess. We have rapid chess that is under one hour. Then you have blitz chess which is five to ten minutes. That was a normal time control. It's worth mentioning that other computers they were beating human players, myself included, in blitz chess. In the very fast chess. We still thought that more time was more time we could have sort of a bigger comfort zone just to contemplate the machine's plans and actually to create real problems that machine would not be able to solve. Again, more time helps humans but at the end of the day it's still about your ability not to crack under pressure because there's so many things that could take you off your balance, and machine doesn't care about it. At the end of the day machine has a steady hand, and steady hand wins. >> Emotion doesn't come into play. >> It's not about apps and strength, but it's about guaranteeing that it will play at a certain level for the entire game. While human game maybe at one point it could go a bit higher. But at the end of the day when you look at average it's still lower. I played many world championship matches and I analyze the games, games played at the highest level. I can tell you that even the best games played by humans at the highest level, they include not necessarily big mistakes, but inaccuracies that are irrelevant when humans facing humans because I make a mistake, tiny mistake, then I can expect you to return the favor. Against the machine it's just that's it. Humans cannot play at the same level throughout the whole game. The concentration, the vigilance are now required when humans face humans. Psychologically when you have a strong machine, machine's good enough to play with a steady hand, the game's over. >> I want to point out too, just so we get the record straight for people who might not be intimately familiar with your record, you were ranked number one in the world from 1986 to 2005 for all but three months. Three months, that's three decades. >> Two decades. >> Well 80s, 90s, and naughts, I'll give you that. (laughing) That's unheard of, that's phenomenal. >> Just going back to your previous question about why I just look for some new form of chess. It's one of the key lessons I learned from my childhood thanks to my mother who spent her live just helping me to become who I am, who I was after my father died when I was seven. It's about always trying to make the difference. It's not just about winning, it's about making a difference. It led me to kind of a new motto in my professional life. That is it's all about my own quality of the game. As long as I'm challenging my own excellence I will never be short of opponents. For me the defeat was just a kick, a push. So let's come up with something new. Let's find a new challenge. Let's find a way to turn this defeat, the lessons from this defeat into something more practical. >> Love it, I mean I think in your book I think, was it John Henry, the famous example. (all men speaking at once) >> He won, but he lost. >> Motivation wasn't competition, it was advancing society and creativity, so I love it. Another thing I just want, a quick aside, you mentioned performing under pressure. I think it was in the 1980s, it might have been in the opening of your book. You talked about playing multiple computers. >> [Garry] Yeah, in 1985. >> In 1985 and you were winning all of them. There was one close match, but the computer's name was Kasparov and you said I've got to beat this one because people will think that it's rigged or I'm getting paid to do this. So well done. >> It's I always mention this exhibition I played in 1985 against 32 chess-playing computers because it's not the importance of this event was not just I won all the games, but nobody was surprised. I have to admit that the fact that I could win all the games against these 32 chess-playing computers they're only chess-playing machine so they did nothing else. Probably boosted my confidence that I would never be defeated even by more powerful machines. >> Well I love it, that's why I asked the question how far can we take machines? We don't know, like you said. >> Why should we bother? I see so many new challenges that we will be able to take and challenges that we abandoned like space exploration or deep ocean exploration because they were too risky. We couldn't actually calculate all the odds. Great, now we have AI. It's all about increasing our risk because we could actually measure against this phenomenal power of AI that will help us to find the right pass. >> I want to follow up on some other commentary. Brynjolfsson and McAfee basically put forth the premise, look machines have always replaced humans. But this is the first time in history that they have replaced humans in the terms of cognitive tasks. They also posited look, there's no question that it's affecting jobs. But they put forth the prescription which I think as an optimist you would agree with, that it's about finding new opportunities. It's about bringing creativity in, complementing the machines and creating new value. As an optimist, I presume you would agree with that. >> Absolutely, I'm always saying jobs do not disappear, they evolve. It's an inevitable part of the technological progress. We come up with new ideas and every disruptive technology destroys some industries but creates new jobs. So basically we see jobs shifting from one industry to another. Like from agriculture, manufacture, from manufacture to other sectors, cognitive tasks. But now there will be something else. I think the market will change, the job market will change quite dramatically. Again I believe that we will have to look for riskier jobs. We will have to start doing things that we abandoned 30, 40 years ago because we thought they were too risky. >> Back to the book you were talking about, deep thinking or machine learning, or machine intelligence ends and human intelligence begins, you talked about courage. We need fail safes in place, but you also need that human element of courage like you said, to accept risk and take risk. >> Now it probably will be easier, but also as I said the machine's wheel will force a lot of talent actually to move into other areas that were not as attractive because there were other opportunities. There's so many what I call raw cognitive tasks that are still financially attractive. I hope and I will close many loops. We'll see talent moving into areas where we just have to open new horizons. I think it's very important just to remember it's the technological progress especially when you're talking about disruptive technology. It's more about unintended consequences. The fly to the moon was just psychologically it's important, the Space Race, the Cold War. But it was about also GPS, about so many side effects that in the 60s were not yet appreciated but eventually created the world we have now. I don't know what the consequences of us flying to Mars. Maybe something will happen, one of the asteroids will just find sort of a new substance that will replace fossil fuel. What I know, it will happen because when you look at the human history there's all this great exploration. They ended up with unintended consequences as the main result. Not what was originally planned as the number one goal. >> We've been talking about where innovation comes from today. It's a combination of a by-product out there. A combination of data plus being able to apply artificial intelligence. And of course there's cloud economics as well. Essentially, well is that reasonable? I think about something you said, I believe, in the past that you didn't have the advantage of seeing Deep Blue's moves, but it had the advantage of studying your moves. You didn't have all the data, it had the data. How does data fit into the future? >> Data is vital, data is fuel. That's why I think we need to find some of the most effective ways of collaboration between humans and machines. Machines can mine the data. For instance, it's a breakthrough in instantly mining data and human language. Now we could see even more effective tools to help us to mine the data. But at the end of the day it's why are we doing that? What's the purpose? What does matter to us, so why do we want to mine this data? Why do we want to do here and not there? It seems at first sight that the human responsibilities are shrinking. I think it's the opposite. We don't have to move too much but by the tiny shift, just you know percentage of a degree of an angle could actually make huge difference when this bullet reaches the target. The same with AI. More power actually offers opportunities to start just making tiny adjustments that could have massive consequences. >> Open up a big, that's why you like augmented intelligence. >> I think artificial is sci-fi. >> What's artificial about it, I don't understand. >> Artificial, it's an easy sell because it's sci-fi. But augmented is what it is because our intelligent machines are making us smarter. Same way as the technology in the past made us stronger and faster. >> It's not artificial horsepower. >> It's created from something. >> Exactly, it's created from something. Even if the machines can adjust their own code, fine. It still will be confined within the parameters of the tasks. They cannot go beyond that because again they can only answer questions. They can only give you answers. We provide the questions so it's very important to recognize that it is we will be in the leading role. That's why I use the term shepherds. >> How do you spend your time these days? You're obviously writing, you're speaking. >> Writing, speaking, traveling around the world because I have to show up at many conferences. The AI now is a very hot topic. Also as you mentioned I'm the Chairman of Human Rights Foundation. My responsibilities to help people who are just dissidents around the world who are fighting for their principles and for freedom. Our organization runs the largest dissident gathering in the world. It's called the Freedom Forum. We have the tenth anniversary, tenth event this May. >> It has been a pleasure. Garry Kasparov, live on theCube. Back with more from New York City right after this. (lively instrumental music)

>> Announcer: Live from New York, it's theCUBE covering Machine Learning Everywhere, Build Your Ladder To AI, brought to you by IBM. (techy music playing) >> Welcome back to New York City as we continue here at IBM's Machine Learning Everywhere, Build Your Ladder To AI bringing it to you here on theCUBE, of course the rights to the broadcast of SiliconANGLE Media and Dave Vellante joins me here. Dave, good morning once again to you, sir. >> Hey, John, good to see you. >> And we're joined by Madhu Kochar, who is the Vice President of Analytics Development and Client Success at IBM, I like that, client success. Good to see you this morning, thanks for joining us. >> Yeah, thank you. >> Yeah, so let's bring up a four letter / ten letter word, governance, that some people just cringe, right, right away, but that's very much in your wheelhouse. Let's talk about that in terms of what you're having to be aware of today with data and all of a sudden these great possibilities, right, but also on the other side, you've got to be careful, and I know there's some clouds over in Europe as well, but let's just talk about your perspective on governance and how it's important to get it all under one umbrella. >> Yeah, so I lead product development for IBM analytics, governance, and integration, and like you said, right, governance has... Every time you talk that, people cringe and you think it's a dirty word, but it's not anymore, right. Especially when you want to tie your AI ladder story, right, there is no AI without information architecture, no AI without IA, and if you think about IA, what does that really mean? It means the foundation of that is data and analytics. Now, let's look deeper, what does that really mean, what is data analytics? Data is coming at us from everywhere, right, and there's records... The data shows there's about 2.5 quintillion bytes of data getting generated every single day, raw data from everywhere. How are we going to make sense out of it, right, and from that perspective it is just so important that you understand this type of data, what is the type of data, what's the classification of this means in a business. You know, when you are running your business, there's a lot of cryptic fields out there, what is the business terms assigned to it and what's the lineage of it, where did it come from. If you do have to do any analytics, if data scientists have to do any analytics on it they need to understand where did it actually originated from, can I even trust this data. Trust is really, really important here, right, and is the data clean, what is the quality of this data. The data is coming at us all raw formats from IOT sensors and such. What is the quality of this data? To me, that is the real definition of governance. Right, it's not just about what we used to think about compliance, yes, that's-- >> John: Like rolling a rag. >> Right, right. >> But it's all about being appropriate with all the data you have coming in. >> Exactly, I call it governance 2.0 or governance for insights, because that's what it needs to be all about. Right, compliance, yes indeed, with GDPR and other things coming at us it's important, but I think the most critical is that we have to change the term of governance into, like, this is that foundation for your AI ladder that is going to help us really drive the right insights, that's my perspective. >> I want to double click on that because you're right, I mean, it is kind of governance 2.0. It used to be, you know, Enron forced a lot of, you know, governance and the Federal Rules of Civil Procedure forced a lot of sort of even some artificial governance, and then I think organization, especially public companies and large organizations said, "You know what, we can't just do "this as a band-aid every time." You know, now GDPR, many companies are not ready for GDPR, we know that. Having said that, because it is, went through governance 1.0, many companies are not panicked. I mean, they're kind of panicking because May is coming, (laughs) but they've been through this before. >> Madhu: Mm-hm. >> Do you agree with that premise, that they've got at least the skillsets and the professionals to, if they focus, they can get there pretty quickly? >> Yeah, no, I agree with that, but I think our technology and tools needs to change big time here, right, because regulations are coming at us from all different angles. Everybody's looking to cut costs, right? >> Dave: Right. >> You're not going to hire more people to sit there and classify the data and say, "Hey, is this data ready for GDPR," or for Basel or for POPI, like in South Africa. I mean, there's just >> Dave: Yeah. >> Tons of things, right, so I do think the technology needs to change, and that's why, you know, in our governance portfolio, in IBM information server, we have infused machine learning in it, right, >> Dave: Hm. >> Where it's automatically you have machine learning algorithms and models understanding your data, classifying the data. You know, you don't need humans to sit there and assign terms, the business terms to it. We have compliance built into our... It's running actually on machine learning. You can feed in taxonomy for GDPR. It would automatically tag your data in your catalog and say, "Hey, this is personal data, "this is sensitive data, or this data "is needed for these type of compliance," and that's the aspect which I think we need to go focus on >> Dave: Mm-hm. >> So the companies, to your point, don't shrug every time they hear regulations, that it's kind of built in-- >> Right. >> In the DNA, but technologies have to change, the tools have to change. >> So, to me that's good news, if you're saying the technology and the tools is the gap. You know, we always talk about people, process, and technology the bromide is, but it's true, people and process are the really-- >> Madhu: Mm-hm. >> Hard pieces of it. >> Madhu: Mm-hm. >> Technology comes and goes >> Madhu: Mm-hm. >> And people kind of generally get used to that. So, I'm inferring from your comments that you feel as though governance, there's a value component of governance now >> Yeah, yeah. >> It's not just a negative risk avoidance. It can be a contributor to value. You mentioned the example of classification, which I presume is auto-classification >> Madhu: Yes. >> At the point of use or creation-- >> Madhu: Yes. >> Which has been a real nagging problem for decades, especially after FRCP, Federal Rules of Civil Procedure, where it was like, "Ugh, we can't figure "this out, we'll do email archiving." >> Madhu: Mm-hm. >> You can't do this manually, it's just too much data-- >> Yeah. >> To your point, so I wonder if you could talk a little bit about governance and its contribution to value. >> Yeah, so this is good question. I was just recently visiting some large banks, right, >> Dave: Mm-hm. >> And normally, the governance and compliance has always been an IT job, right? >> Dave: Right. >> And they figure out bunch of products, you know, you can download opensource and do other things to quickly deliver data or insights to their business groups, right, and for business to further figure out new business models and such, right. So, recently what has happened is by doing machine learning into governance, you're making your IT guys the heroes because now they can deliver stuff very quickly, and the business guys are starting to get those insights and their thoughts on data is changing, you know, and recently I was talking with these banks where they're like, "Can you come and talk to "our CFOs because I think the policies," the cultural change you referred to then, maybe the data needs to be owned by businesses. >> Dave: Hm. >> No longer an IT thing, right? So, governance I feel like, you know, governance and integration I feel like is a glue which is helping us drive that cultural change in the organizations, bringing IT and the business groups together to further drive the insights. >> So, for years we've been talking about information as a liability or an asset, and for decades it was really viewed as a liability, get rid of it if you can. You have to keep it for seven years, then get rid of it, you know. That started to change, you know, with the big data movement, >> Madhu: Yeah. >> But there was still sort of... It was hard, right, but what I'm hearing now is increasingly, especially of the businesses sort of owning the data, it's becoming viewed as an asset. >> Madhu: Yes. >> You've got to manage the liabilities, we got that, but now how do we use it to drive business value. >> Yeah, yeah, no, exactly, and that's where I think our focus in IBM analytics, with machine learning and automation, and truly driving that insights out of the data. I mean, you know, people... We've been saying data is a natural resource. >> Dave: Mm-hm. >> It's our bloodline, it's this and that. It truly is, you know, and talking to the large enterprises, everybody is in their mode of digital transformation or transforming, right? We in IBM are doing the same things. Right, we're eating our own, drinking our own champagne (laughs). >> John: Not the Kool-Aid. >> You know, yeah, yeah. >> John: Go right to the dog. >> Madhu: Yeah, exactly. >> Dave: No dog smoothie. (laughs) >> Drinking our own champagne, and truly we're seeing transformation in how we're running our own business as well. >> Now what, there are always surprises. There are always some, you know, accidents kind of waiting to happen, but in terms of the IOT, you know, have got these millions, right, of sensors-- >> Madhu: Mm-hm. >> You know, feeding data in, and what, from a governance perspective, is maybe a concern about, you know, an unexpected source or an unexpected problem or something where yeah, you have great capabilities, but with those capabilities might come a surprise or two in terms of protecting data and a machine might provide perhaps a little more insight than you might've expected. So, I mean, just looking down the road from your perspective, you know, is there anything along those lines that you're putting up flags for just to keep an eye on to see what new inputs might create new problems for you? >> Yeah, no, for sure, I mean, we're always looking at how do we further do innovation, how do we disrupt ourselves and make sure that data doesn't become our enemy, right, I mean it's... You know, as we are talking about AI, people are starting to ask a lot of questions about ethics and other things, too, right. So, very critical, so obviously when you focus on governance, the point of that is let's take the manual stuff out, make it much faster, but part of the governance is that we're protecting you, right. That's part of that security and understanding of the data, it's all about that you don't end up in jail. Right, that's the real focus in terms of our technology in terms of the way we're looking at. >> So, maybe help our audience a little bit. So, I described at our open AI is sort of the umbrella and machine learning is the math and the algorithms-- >> Madhu: Yeah. >> That you apply to train systems to do things maybe better than, maybe better than humans can do and then there's deep learning, which is, you know, neural nets and so forth, but am I understanding that you've essentially... First of all, is that sort of, I know it's rudimentary, but is it reasonable, and then it sounds like you've infused ML into your software. >> Madho: Yes. >> And so I wonder if you could comment on that and then describe from the client's standpoint what skills they need to take advantage of that, if any. >> Oh, yeah, no, so embedding ML into a software, like a packaged software which gets delivered to our client, people don't understand actually how powerful that is, because your data, your catalog, is learning. It's continuously learning from the system itself, from the data itself, right, and that's very exciting. The value to the clients really is it cuts them their cost big time. Let me give you an example, in a large organization today for example, if they have, like, maybe 22,000 some terms, normally it would take them close to six months for one application with a team of 20 to sit there and assign the terms, the right business glossary for their business to get data. (laughs) So, by now doing machine learning in our software, we can do this in days, even in hours, obviously depending on what's the quantity of the data in the organization. That's the value, so the value to the clients is cutting down that. They can take those folks and go focus on some, you know, bigger value add applications and others and take advantage of that data. >> The other huge value that I see is as the business changes, the machine can help you adapt. >> Madhu: Yeah. >> I mean, taxonomies are like cement in data classification, and while we can't, you know, move the business forward because we have this classification, can your machines adapt, you know, in real time and can they change at the speed of my business, is my question. >> Right, right, no, it is, right, and clients are not able to move on their transformation journey because they don't have data classified done right. >> Dave: Mm-hm. >> They don't, and you can't put humans to it. You're going to need the technology, you're going to need the machine learning algorithms and the AI built into your software to get that, and that will lead to, really, success of every kind. >> Broader question, one of the good things about things like GDPR is it forces, it puts a deadline on there and we all know, "Give me a deadline and I'll hit it," so it sort of forces action. >> Madhu: Mm-hm. >> And that's good, we've talked about the value that you can bring to an organization from a data perspective, but there's a whole non-governance component of data orientation. How do you see that going, can the governance initiatives catalyze sort of what I would call a... You know, people talk about a data driven organization. Most companies, they may say they are data driven but they're really not foundational. >> Mm-hm. >> Can governance initiatives catalyze that transformation to a data driven organization, and if so, how? >> Yeah, no, absolutely, right. So, the example I was sharing earlier with talking to some of the large financial institutes, where the business guys, you know, outside of IT are talking about how important it is for them to get the data really real time, right, and self-service. They don't want to be dependent on either opening a work ticket for somebody in IT to produce data for them and god forbid if somebody's out on vacation they can never get that. >> Dave: Right. >> We don't live in that world anymore, right. It's online, it's real time, it's all, you know, self-service type of aspects, which the business, the data scientists building new analytic models are looking for that. So, for that, data is the key, key core foundation in governance. The way I explained it earlier, it's not just about compliance. That is going to lead to that transformation for every client, it's the core. They will not be successful without that. >> And the attributes are changing. Not only is it self-service, it's pervasive-- >> Madhu: Yeah. >> It's embedded, it's aware, it's anticipatory. Am I overstating that? >> Madhu: No. >> I mean, is the data going to find me? >> Yeah, you know, (laughs) that's a good way to put it, you know, so no, you're at the, I think you got it. This is absolutely the right focus, and the companies and the enterprises who understand this and use the right technology to fix it that they'll win. >> So, if you have a partner that maybe, if it is contextual, I mean... >> Dave: Yeah. >> So, also make it relevant-- >> Madhu: Yes. >> To me and help me understand its relevance-- >> Madhu: Yes. >> Because maybe as a, I hate to say as a human-- >> Madhu: Yes. >> That maybe just don't have that kind of prism, but can that, does that happen as well, too? >> Madhu: Yeah, no. >> John: It can put up these white flags and say, "Yeah, this is what you need." >> Yeah, no, absolutely, so like the focus we have on our natural language processing, for example, right. If you're looking for something you don't have to always know what your SQL is going to be for a query to do it. You just type in, "Hey, I'm looking for "some customer retention data," you know, and it will go out and figure it out and say, "Hey, are you looking for churn analysis "or are you looking to do some more promotions?" It will learn, you know, and that's where this whole aspect of machine learning and natural language processing is going to give you that contextual aspect of it, because that's how the self-service models will work. >> Right, what about skills, John asked me at the open about skillsets and I want to ask a general question, but then specifically about governance. I would make the assertion that most employees don't have the multidimensional digital skills and domain expertise skills today. >> Yeah. >> Some companies they do, the big data companies, but in governance, because it's 2.0, do you feel like the skills are largely there to take advantage of the innovations that IBM is coming out with? >> I think I generally, my personal opinion is the way the technology's moving, the way we are getting driven by a lot of disruptions, which are happening around us, I think we don't have the right skills out there, right. We all have to retool, I'm sure all of us in our career have done this all the time. You know, so (laughs) to me, I don't think we have it. So, building the right tools, the right technologies and enabling the resources that the teams out there to retool themselves so they can actually focus on innovation in their own enterprises is going to be critical, and that's why I really think more burn I can take off from the IT groups, more we can make them smarter and have them do their work faster. It will help give that time to go see hey, what's their next big disruption in their organization. >> Is it fair to say that traditionally governance has been a very people-intensive activity? >> Mm-hm. >> Will governance, you know, in the next, let's say decade, become essentially automated? >> That's my desire, and with the product-- >> Dave: That's your job. >> That's my job, and I'm actually really proud of what we have done thus far and where we are heading. So, next time when we meet we will be talking maybe governance 3.0, I don't know, right. (laughs) Yeah, that's the thing, right? I mean, I think you hit it on the nail, that this is, we got to take a lot of human-intensive stuff out of our products and more automation we can do, more smarts we can build in. I coined this term like, hey, we've got to build smarter metadata, right? >> Dave: Right. >> Data needs to, metadata is all about data of your data, right? That needs to become smarter, think about having a universe where you don't have to sit there and connect the dots and say, "I want to move from here to there." System already knows it, they understand certain behaviors, they know what your applications is going to do and it kind of automatically does it for you. No more science fake, I think it can happen. (laughs) >> Do you think we'll ever have more metadata than data... (laughs) >> Actually, somebody did ask me that question, will we be figuring out here we're building data lakes, what do we do about metadata. No, I think we will not have that problem for a while, we'll make it smarter. >> Dave: Going too fast, right. >> You're right. >> But it is, it's like working within your workforce and you're telling people, you know, "You're a treasure hunter and we're going to give you a better map." >> Madhu: Yeah. >> So, governance is your better map, so trust me. >> Madhu: Hey, I like that, maybe I'll use it next time. >> Yeah, but it's true, it's like are you saying governance is your friend here-- >> Madhu: Yes. >> And we're going to fine-tune your search, we're going to make you a more efficient employee, we're going to make you a smarter person and you're going to be able to contribute in a much better way, but it's almost enforced, but let it be your friend, not your foe. >> Yes, yeah, be your differentiator, right. >> But my takeaway is it's fundamental, it's embedded. You know, you're doing this now with less thinking. Security's got to get to the same play, but for years security, "Ugh, it slows me down," but now people are like, "Help me," right, >> Madhu: Mm-hm. >> And I think the same dynamic is true here, embedded governance in my business. Not a bolt on, not an afterthought. It's fundamental and foundational to my organization. >> Madhu: Yeah, absolutely. >> Well, Madhu, thank you for the time. We mentioned on the outset by the interview if you want to say hi to your kids that's your camera right there. Do you want to say hi to your kids real quick? >> Yeah, hi Mohed, Kepa, I love you so much. (laughs) >> All right. >> Thank you. >> So, they know where mom is. (laughs) New York City at IBM's Machine Learning Everywhere, Build Your Ladder To AI. Thank you for joining us, Madhu Kochar. >> Thank you, thank you. >> Back with more here from New York in just a bit, you're watching theCUBE. (techy music playing)

>> Announcer: Live from New York, it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI, brought to you by IBM. >> Welcome back to New York City. theCUBE continue our coverage here at IBM's event, Machine Learning Everywhere: Build Your Ladder to AI. And with us now is Rob Thomas, who is the vice president of, or general manager, rather, of IBM analytics. Sorry about that, Rob. Good to have you with us this morning. Good to see you, sir. >> Great to see you John. Dave, great to see you as well. >> Great to see you. >> Well let's just talk about the event first. Great lineup of guests. We're looking forward to visiting with several of them here on theCUBE today. But let's talk about, first off, general theme with what you're trying to communicate and where you sit in terms of that ladder to success in the AI world. >> So, maybe start by stepping back to, we saw you guys a few times last year. Once in Munich, I recall, another one in New York, and the theme of both of those events was, data science renaissance. We started to see data science picking up steam in organizations. We also talked about machine learning. The great news is that, in that timeframe, machine learning has really become a real thing in terms of actually being implemented into organizations, and changing how companies run. And that's what today is about, is basically showcasing a bunch of examples, not only from our clients, but also from within IBM, how we're using machine learning to run our own business. And the thing I always remind clients when I talk to them is, machine learning is not going to replace managers, but I think machine learning, managers that use machine learning will replace managers that do not. And what you see today is a bunch of examples of how that's true because it gives you superpowers. If you've automated a lot of the insight, data collection, decision making, it makes you a more powerful manager, and that's going to change a lot of enterprises. >> It seems like a no-brainer, right? I mean, or a must-have. >> I think there's a, there's always that, sometimes there's a fear factor. There is a culture piece that holds people back. We're trying to make it really simple in terms of how we talk about the day, and the examples that we show, to get people comfortable, to kind of take a step onto that ladder back to the company. >> It's conceptually a no-brainer, but it's a challenge. You wrote a blog and it was really interesting. It was, one of the clients said to you, "I'm so glad I'm not in the technology industry." And you went, "Uh, hello?" (laughs) "I've got news for you, you are in the technology industry." So a lot of customers that I talk to feel like, meh, you know, in our industry, it's really not getting disrupted. That's kind of taxis and retail. We're in banking and, you know, but, digital is disrupting every industry and every industry is going to have to adopt ML, AI, whatever you want to call it. Can traditional companies close that gap? What's your take? >> I think they can, but, I'll go back to the word I used before, it starts with culture. Am I accepting that I'm a technology company, even if traditionally I've made tractors, as an example? Or if traditionally I've just been you know, selling shirts and shoes, have I embraced the role, my role as a technology company? Because if you set that culture from the top, everything else flows from there. It can't be, IT is something that we do on the side. It has to be a culture of, it's fundamental to what we do as a company. There was an MIT study that said, data-driven cultures drive productivity gains of six to 10 percent better than their competition. You can't, that stuff compounds, too. So if your competitors are doing that and you're not, not only do you fall behind in the short term but you fall woefully behind in the medium term. And so, I think companies are starting to get there but it takes a constant push to get them focused on that. >> So if you're a tractor company, you've got human expertise around making tractors and messaging and marketing tractors, and then, and data is kind of there, sort of a bolt-on, because everybody's got to be data-driven, but if you look at the top companies by market cap, you know, we were talking about it earlier. Data is foundational. It's at their core, so, that seems to me to be the hard part, Rob, I'd like you to comment in terms of that cultural shift. How do you go from sort of data in silos and, you know, not having cloud economics and, that are fundamental, to having that dynamic, and how does IBM help? >> You know, I think, to give companies credit, I think most organizations have developed some type of data practice or discipline over the last, call it five years. But most of that's historical, meaning, yeah, we'll take snapshots of history. We'll use that to guide decision making. You fast-forward to what we're talking about today, just so we're on the same page, machine learning is about, you build a model, you train a model with data, and then as new data flows in, your model is constantly updating. So your ability to make decisions improves over time. That's very different from, we're doing historical reporting on data. And so I think it's encouraging that companies have kind of embraced that data discipline in the last five years, but what we're talking about today is a big next step and what we're trying to break it down to what I call the building blocks, so, back to the point on an AI ladder, what I mean by an AI ladder is, you can't do AI without machine learning. You can't do machine learning without analytics. You can't do analytics without the right data architecture. So those become the building blocks of how you get towards a future of AI. And so what I encourage companies is, if you're not ready for that AI leading edge use case, that's okay, but you can be preparing for that future now. That's what the building blocks are about. >> You know, I think we're, I know we're ahead of, you know, Jeremiah Owyang on a little bit later, but I was reading something that he had written about gut and instinct, from the C-Suite, and how, that's how companies were run, right? You had your CEO, your president, they made decisions based on their guts or their instincts. And now, you've got this whole new objective tool out there that's gold, and it's kind of taking some of the gut and instinct out of it, in a way, and maybe there are people who still can't quite grasp that, that maybe their guts and their instincts, you know, what their gut tells them, you know, is one thing, but there's pretty objective data that might indicate something else. >> Moneyball for business. >> A little bit of a clash, I mean, is there a little bit of a clash in that respect? >> I think you'd be surprise by how much decision making is still pure opinion. I mean, I see that everywhere. But we're heading more towards what you described for sure. One of the clients talking here today, AMC Networks, think it's a great example of a company that you wouldn't think of as a technology company, primarily a content producer, they make great shows, but they've kind of gone that extra step to say, we can integrate data sources from third parties, our own data about viewer habits, we can do that to change our relationship with advertisers. Like, that's a company that's really embraced this idea of being a technology company, and you can see it in their results, and so, results are not coincidence in this world anymore. It's about a practice applied to data, leveraging machine learning, on a path towards AI. If companies are doing that, they're going to be successful. >> And we're going to have the tally from AMC on, but so there's a situation where they have embraced it, that they've dealt with that culture, and data has become foundational. Now, I'm interested as to what their journey look like. What are you seeing with clients? How they break this down, the silos of data that have been built up over decades. >> I think, so they get almost like a maturity curve. You've got, and the rule I talk about is 40-40-20, where 40% of organizations are really using data just to optimize costs right now. That's okay, but that's on the lower end of the maturity curve. 40% are saying, all right, I'm starting to get into data science. I'm starting to think about how I extend to new products, new services, using data. And then 20% are on the leading edge. And that's where I'd put AMC Networks, by the way, because they've done unique things with integrating data sets and building models so that they've automated a lot of what used to be painstakingly long processes, internal processes to do it. So you've got this 40-40-20 of organizations in terms of their maturity on this. If you're not on that curve right now, you have a problem. But I'd say most are somewhere on that curve. If you're in the first 40% and you're, right now data for you is just about optimizing cost, you're going to be behind. If you're not right now, you're going to be behind in the next year, that's a problem. So I'd kind of encourage people to think about what it takes to be in the next 40%. Ultimately you want to be in the 20% that's actually leading this transformation. >> So change it to 40-20-40. That's where you want it to go, right? You want to flip that paradigm. >> I want to ask you a question. You've done a lot of M and A in the past. You spent a lot of time in Silicon Valley and Silicon Valley obviously very, very disruptive, you know, cultures and organizations and it's always been a sort of technology disruption. It seems like there's a ... another disruption going on, not just horizontal technologies, you know, cloud or mobile or social, whatever it is, but within industries. Some industries, as we've been talking, radically disrupted. Retail, taxis, certainly advertising, et cetera et cetera. Some have not yet, the client that you talked to. Do you see, technology companies generally, Silicon Valley companies specifically, as being able to pull off a sort of disruption of not only technologies but also industries and where does IBM play there? You've made a sort of, Ginni in particular has made a deal about, hey, we're not going to compete with our customers. So talking about this sort of dual disruption agenda, one on the technology side, one within industries that Apple's getting into financial services and, you know, Amazon getting into grocery, what's your take on that and where does IBM fit in that world? >> So, I mean, IBM has been in Silicon Valley for a long time, I would say probably longer than 99.9% of the companies in Silicon Valley, so, we've got a big lab there. We do a lot of innovation out of there. So love it, I mean, the culture of the valley is great for the world because it's all about being the challenger, it's about innovation, and that's tremendous. >> No fear. >> Yeah, absolutely. So, look, we work with a lot of different partners, some who are, you know, purely based in the valley. I think they challenge us. We can learn from them, and that's great. I think the one, the one misnomer that I see right now, is there's a undertone that innovation is happening in Silicon Valley and only in Silicon Valley. And I think that's a myth. Give you an example, we just, in December, we released something called Event Store which is basically our stab at reinventing the database business that's been pretty much the same for the last 30 to 40 years. And we're now ingesting millions of rows of data a second. We're doing it in a Parquet format using a Spark engine. Like, this is an amazing innovation that will change how any type of IOT use case can manage data. Now ... people don't think of IBM when they think about innovations like that because it's not the only thing we talk about. We don't have, the IBM website isn't dedicated to that single product because IBM is a much bigger company than that. But we're innovating like crazy. A lot of that is out of what we're doing in Silicon Valley and our labs around the world and so, I'm very optimistic on what we're doing in terms of innovation. >> Yeah, in fact, I think, rephrase my question. I was, you know, you're right. I mean people think of IBM as getting disrupted. I wasn't posing it, I think of you as a disruptor. I know that may sound weird to some people but in the sense that you guys made some huge bets with things like Watson on solving some of the biggest, world's problems. And so I see you as disrupting sort of, maybe yourselves. Okay, frame that. But I don't see IBM as saying, okay, we are going to now disrupt healthcare, disrupt financial services, rather we are going to help our, like some of your comp... I don't know if you'd call them competitors. Amazon, as they say, getting into content and buying grocery, you know, food stores. You guys seems to have a different philosophy. That's what I'm trying to get to is, we're going to disrupt ourselves, okay, fine. But we're not going to go hard into healthcare, hard into financial services, other than selling technology and services to those organizations, does that make sense? >> Yeah, I mean, look, our mission is to make our clients ... better at what they do. That's our mission, we want to be essential in terms of their journey to be successful in their industry. So frankly, I love it every time I see an announcement about Amazon entering another vertical space, because all of those companies just became my clients. Because they're not going to work with Amazon when they're competing with them head to head, day in, day out, so I love that. So us working with these companies to make them better through things like Watson Health, what we're doing in healthcare, it's about making companies who have built their business in healthcare, more effective at how they perform, how they drive results, revenue, ROI for their investors. That's what we do, that's what IBM has always done. >> Yeah, so it's an interesting discussion. I mean, I tend to agree. I think Silicon Valley maybe should focus on those technology disruptions. I think that they'll have a hard time pulling off that dual disruption and maybe if you broadly define Silicon Valley as Seattle and so forth, but, but it seems like that formula has worked for decades, and will continue to work. Other thoughts on sort of the progression of ML, how it gets into organizations. You know, where you see this going, again, I was saying earlier, the parlance is changing. Big data is kind of, you know, mm. Okay, Hadoop, well, that's fine. We seem to be entering this new world that's pervasive, it's embedded, it's intelligent, it's autonomous, it's self-healing, it's all these things that, you know, we aspire to. We're now back in the early innings. We're late innings of big data, that's kind of ... But early innings of this new era, what are your thoughts on that? >> You know, I'd say the biggest restriction right now I see, we talked before about somehow, sometimes companies don't have the desire, so we have to help create the desire, create the culture to go do this. Even for the companies that have a burning desire, the issue quickly becomes a skill gap. And so we're doing a lot to try to help bridge that skill gap. Let's take data science as an example. There's two worlds of data science that I would describe. There's clickers, and there's coders. Clickers want to do drag and drop. They will use traditional tools like SPSS, which we're modernizing, that's great. We want to support them if that's how they want to work and build models and deploy models. There's also this world of coders. This is people that want to do all their data science in ML, and Python, and Scala, and R, like, that's what they want to do. And so we're supporting them through things like Data Science Experience, which is built on Apache Jupiter. It's all open source tooling, it'd designed for coders. The reason I think that's important, it goes back to the point on skill sets. There is a skill gap in most companies. So if you walk in and you say, this is the only way to do this thing, you kind of excluded half the companies because they say, I can't play in that world. So we are intentionally going after a strategy that says, there's a segmentation in skill types. In places there's a gap, we can help you fill that gap. That's how we're thinking about them. >> And who does that bode well for? If you say that you were trying to close a gap, does that bode well for, we talked about the Millennial crowd coming in and so they, you know, do they have a different approach or different mental outlook on this, or is it to the mid-range employee, you know, who is open minded, I mean, but, who is the net sweet spot, you think, that say, oh, this is a great opportunity right now? >> So just take data science as an example. The clicker coder comment I made, I would put the clicker audience as mostly people that are 20 years into their career. They've been around a while. The coder audience is all the Millennials. It's all the new audience. I think the greatest beneficiary is the people that find themselves kind of stuck in the middle, which is they're kind of interested in this ... >> That straddle both sides of the line yeah? >> But they've got the skill set and the desire to do some of the new tooling and new approaches. So I think this kind of creates an opportunity for that group in the middle to say, you know, what am I going to adopt as a platform for how I go forward and how I provide leadership in my company? >> So your advice, then, as you're talking to your clients, I mean you're also talking to their workforce. In a sense, then, your advice to them is, you know, join, jump in the wave, right? You've got your, you can't straddle, you've got to go. >> And you've got to experiment, you've got to try things. Ultimately, organizations are going to gravitate to things that they like using in terms of an approach or a methodology or a tool. But that comes with experimentation, so people need to get out there and try something. >> Maybe we could talk about developers a little bit. We were talking to Dinesh earlier and you guys of course have focused on data scientists, data engineers, obviously developers. And Dinesh was saying, look, many, if not most, of the 10 million Java developers out there, they're not, like, focused around the data. That's really the data scientist's job. But then, my colleague John Furrier says, hey, data is the new development kit. You know, somebody said recently, you know, Andreessen's comment, "software is eating the world." Well, data is eating software. So if Furrier is right and that comment is right, it seems like developers increasingly have to become more data aware, fundamentally. Blockchain developers clearly are more data focused. What's your take on the developer community, where they fit into this whole AI, machine learning space? >> I was just in Las Vegas yesterday and I did a session with a bunch of our business partners. ISVs, so software companies, mostly a developer audience, and the discussion I had with them was around, you're doing, you're building great products, you're building great applications. But your product is only as good as the data and the intelligence that you embed in your product. Because you're still putting too much of a burden on the user, as opposed to having everything happen magically, if you will. So that discussion was around, how do you embed data, embed AI, into your products and do that at the forefront versus, you deliver a product and the client has to say, all right, now I need to get my data out of this application and move it somewhere else so I can do the data science that I want to do. That's what I see happening with developers. It's kind of ... getting them to think about data as opposed to just thinking about the application development framework, because that's where most of them tend to focus. >> Mm, right. >> Well, we've talked about, well, earlier on about the governance, so just curious, with Madhu, which I'll, we'll have that interview in just a little bit here. I'm kind of curious about your take on that, is that it's a little kinder, gentler, friendlier than maybe some might look at it nowadays because of some organization that it causes, within your group and some value that's being derived from that, that more efficiency, more contextual information that's, you know, more relevant, whatever. When you talk to your clients about meeting rules, regs, GDPR, all these things, how do you get them to see that it's not a black veil of doom and gloom but it really is, really more of an opportunity for them to cash in? >> You know, my favorite question to ask when I go visit clients is I say, I say, just show of hands, how many people have all the data they need to do their job? To date, nobody has ever raised their hand. >> Not too many hands up. >> The reason I phrased it that way is, that's fundamentally a governance challenge. And so, when you think about governance, I think everybody immediately thinks about compliance, GDPR, types of things you mentioned, and that's great. But there's two use cases for governance. One is compliance, the other one is self service analytics. Because if you've done data governance, then you can make your data available to everybody in the organization because you know you've got the right rules, the right permissions set up. That will change how people do their jobs and I think sometimes governance gets painted into a compliance corner, when organizations need to think about it as, this is about making data accessible to my entire workforce. That's a big change. I don't think anybody has that today. Except for the clients that we're working with, where I think we've made good strides in that. >> What's your sort of number one, two, and three, or pick one, advice for those companies that as you blogged about, don't realize yet that they're in the software business and the technology business? For them to close the ... machine intelligence, machine learning, AI gap, where should they start? >> I do think it can be basic steps. And the reason I say that is, if you go to a company that hasn't really viewed themselves as a technology company, and you start talking about machine intelligence, AI, like, everybody like, runs away scared, like it's not interesting. So I bring it back to building blocks. For a client to be great in data, and to become a technology company, you really need three platforms for how you think about data. You need a platform for how you manage your data, so think of it as data management. You need a platform for unified governance and integration, and you need a platform for data science and business analytics. And to some extent, I don't care where you start, but you've got to start with one of those. And if you do that, you know, you'll start to create a flywheel of momentum where you'll get some small successes. Then you can go in the other area, and so I just encourage everybody, start down that path. Pick one of the three. Or you may already have something going in one of them, so then pick one where you don't have something going. Just start down the path, because, those building blocks, once you have those in place, you'll be able to scale AI and ML in the future in your organization. But without that, you're going to always be limited to kind of a use case at a time. >> Yeah, and I would add, this is, you talked about it a couple times today, is that cultural aspect, that realization that in order to be data driven, you know, buzzword, you have to embrace that and drive that through the culture. Right? >> That starts at the top, right? Which is, it's not, you know, it's not normal to have a culture of, we're going to experiment, we're going to try things, half of them may not work. And so, it starts at the top in terms of how you set the tone and set that culture. >> IBM Think, we're less than a month away. CUBE is going to be there, very excited about that. First time that you guys have done Think. You've consolidated all your big, big events. What can we expect from you guys? >> I think it's going to be an amazing show. To your point, we thought about this for a while, consolidating to a single IBM event. There's no question just based on the response and the enrollment we have so far, that was the right answer. We'll have people from all over the world. A bunch of clients, we've got some great announcements that will come out that week. And for clients that are thinking about coming, honestly the best thing about it is all the education and training. We basically build a curriculum, and think of it as a curriculum around, how do we make our clients more effective at competing with the Amazons of the world, back to the other point. And so I think we build a great curriculum and it will be a great week. >> Well, if I've heard anything today, it's about, don't be afraid to dive in at the deep end, just dive, right? Get after it and, looking forward to the rest of the day. Rob, thank you for joining us here and we'll see you in about a month! >> Sounds great. >> Right around the corner. >> All right, Rob Thomas joining us here from IBM Analytics, the GM at IBM Analytics. Back with more here on theCUBE. (upbeat music)

>> Voiceover: Live from New York it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. (upbeat techno music) >> Welcome back to New York City as theCUBE continues our coverage here at IBM's Machine Learning Everywhere: Build Your Ladder to AI. Along with Dave Vellante, I'm John Walls. We're now joined by Vitaly Tsivan who is Executive Vice President at AMC Networks. And Vitaly, thanks for joining us here this morning. >> Thank you. >> I don't know how this interview is going to go, frankly. Because we've got a die-hard Yankee fan in our guest, and a Red Sox fans who bleeds Red Sox Nation. Can you guys get along for about 15 minutes? >> Dave: Maybe about 15. >> I'm glad there's a bit of space between us. >> Dave: It's given us the off-season and the Yankees have done so well. I'll be humble. Okay? (John laughs) We'll wait and see. >> All right. Just in case, I'm ready to jump in if we have to separate here. But it is good to have you here with us this morning. Thanks for making the time. First off, talk about AMC Networks a little bit. So, five U.S. networks. You said multiple international networks and great presence there. But you've had to make this transition to becoming a data company, in essence. You have content and you're making this merger in the data. How has that gone for you? And how have you done that? >> First of all, you make me happy when you say that AMC Networks have made a transition to be a data company. So, we haven't. We are using data to help our primary business, which is obviously broadcasting our content to our viewers. But yes, we use data to help to tune our business, to follow the lead that viewers are giving us. As you can imagine, in the last so many years, viewers have actually dictating how they want to watch. Whether it's streaming video rather than just turning their satellite boxes or TV boxes on, and pretty much dictating what content they want to watch. So, we have to follow, we have to adjust and be at the cutting edge all for our business. And this is where data come into play. >> How did you get there? You must have done a lot of testing, right? I mean, I remember when binge watching didn't even exist, and then all of a sudden now everybody drops 10 episodes at once. Was that a lot of A-B testing? Just analyzing data? How does a company like yours come to that realization? Or is it just, wow, the competition is doing it, we should too. Explain how -- >> Vitaly: Interesting. So, when I speak to executives, I always tell them that business intelligence and data analytics for any company is almost like an iceberg. So, you can actually see the top of it, and you enjoy it very much but there's so much underwater. So, that's what you're referring to which is that in order to be able to deliver that premium thing that's the tip of the iceberg is that we have to have state of the art data management platforms. We have to curate our own first by data. We have to acquire meaningful third party data. We have to mingle it all together. We have to employ optimization predictive algorithms on top of that. We have to employ statistics, and arm business with data-driven decisions. And then it all comes to fruition. >> Now, your company's been around for awhile. You've got an application -- You're a developer. You're an application development executive. So, you've sort of made your personal journey. I'm curious as to how the company made its journey. How did you close that gap between the data platforms that we all know, the Googles, the Facebooks, etc., which data is the central part of their organization, to where you used to be? Which probably was building, looking back doing a lot of business intelligence, decision support, and a lot of sort of asynchronous activities. How did you get from there to where you are today? >> Makes sense. So, I've been with AMC Networks for four years. Prior to that I'd been with Disney, ABC, ESPN four, six years, doing roughly the same thing. So, number one, we're utilizing ever rapidly changing technologies to get us to the right place. Number two is during those four years with AMC, we've employed various tactics. Some of them are called data democratization. So, that's actually not only get the right data sources not only process them correctly, but actually arm everyone in the company with immediate, easy access to this data. Because the entire business, data business, is all about insights. So, the insights -- And if you think of the business, if you for a minute separate business and business intelligence, then business doesn't want to know too much about business intelligence. What they want insights on a silver plate that will tell them what to do next. Now, that's the hardest thing, you can imagine, right? And so the search and drive for those insights has to come from every business person in the organization. Now, obviously, you don't expect them to build their own statistical algorithms and see the results in employee and machine learning. But if you arm them with that data at the tip of their fingers, they'll make many better decisions on a daily basis which means that they're actually coming up with their own small insights. So, there are small insights, big insights, and they're all extremely valuable. >> A big part of that is cultural as well, that mindset. Many companies that I work with, they're data is very siloed. I don't know if that was the case with your firm, maybe less prior to your joining. I'd be curious as to how you've achieved that cultural mindset shift. Cause a lot of times, people try to keep their own data. They don't want to share it. They want to keep it in a silo, gain political power. How did you address that? >> Vitaly: Absolutely. One of my conversations with the president, we were discussing the fact that if we were to go make recordings of how people talk about data in their organization today and go back in time and show them what they will be doing three years from now, they would be shocked. They wouldn't believe that. So, absolutely. So, culturally, educationally, bringing everyone into the place where they can understand data. They can take advantage of the data. It's an undertaking. But we are successful in doing that. >> Help me out here. Maybe I just have never acquired a little translation here, or simplification. So, you think about AMC. You've got programming. You've got your line up. I come on, I click, I go, I watch a movie and I enjoy it or watch my program, whatever. So, now in this new world of viewer habits changing, my behaviors are changing. What have you done? What have you looked for in terms of data and telling you about me that has now allowed you to modify your business and adapt to that. So, I mean, health data shouldn't drive that on a day to day basis in terms of how I access your programming. >> So, good example to that would be something we called TV everywhere. So, you said it yourself, obviously users or viewers are used to watching television as when the shows were provided via television. So, with new technologies, with streaming opportunities, today, they want to watch when they want to watch, and what they want to watch. So, one of the ways we accommodate them with that is that we don't just television, so we are on every available platform today and we are allowing viewers to watch our content on demand, digitally, when they want to watch it. So, that is one of the ways how we are reacting to it. And so, that puts us in the position as one of the B to C type of businesses, where we're now speaking directly to our consumers not via just the television. So, we're broadcasting, their watching which means that we understand how they watch and we try to react accordingly to that. Which is something that Netflix is bragging about is that they know the patterns, they actually kind of promote their business so we on that business too. >> Can you describe your innovation formula, if you will? How do you go about innovating? Obviously, there's data, there's technology. Presumably, there's infrastructure that scales. You have to be able to scale and have massive speed and infrastructure that heals itself. All those other things. But what's your innovation formula? How would you describe it? So, informally simple. It starts with business. I'm fortunate that business has desire to innovate. So, formulating goals is something that drives us to respond to it. So, we don't just walk around the thing, and look around and say, "Let's innovate." So, we follow the business goals with innovation. A good example is when we promote our shows. So, the major portion of our marketing campaigns falls on our own air. So, we promote our shows to our AMC viewers or WE tv viewers. When we do that, we try to optimize our campaigns to the highest level possible, to get the most out of ROI out of that. And so, we've succeeded and we managed today to get about 30% ROI on that and either just do better with our promotional campaigns or reallocate that time for other businesses. >> You were saying that after the first question, or during responding to the first question, about you saying we're really not ... We're a content company still. And we have incorporated data, but you really aren't, Dave and I have talked about this a lot, everybody's a data company now, in a way. Because you have to be. Cause you've got this hugely competitive landscape that you're operating in, right? In terms of getting more odd calls. >> That's right. >> So, it's got to be no longer just a part of what you do or a section of what you do. It's got to be embedded in what you do. Does it not? Oh, it absolutely is. I still think that it's a bit premature to call AMC Networks a data company. But to a degree, every company today is a data company. And with the culture change over the years, if I used to solicit requests and go about implementing them, today it's more of a prioritization of work because every department in the company got educated to the degree that they all want to get better. And they all want those insights from the data. They want their parts of the business to be improved. And we're venturing into new businesses. And it's quite a bit in demand. >> So, is it your aspiration to become a data company? Or is it more data-driven sort of TV network? How would you sort of view that? >> I'd like to say data-driven TV network. Of course. >> Dave: Okay. >> It's more in tune with reality. >> And so, talk about aligning with the business goals. That's kind of your starting point. You were talking earlier about a gut feel. We were joking about baseball. Moneyball for business. So, you're a data person. The data doesn't lie, etc. But insights sometimes are hard. They don't just pop out. Is that true? Do you see that changing as the time to insight, from insight to decision going to compress? What do you see there? >> The search for insights will never stop. And the more dense we are in that journey the better we are going to be as a company. The data business is so much depends on technologies. So, that when technologies matures, and we manage to employ them in a timely basis, so we simply get better from that. So, good example is machine learning. There are a ton of optimizations, optimization algorithms, forecasting algorithms that we put in place. So, for awhile it was a pinnacle of our deliveries. Now, with machine learning maturing today. We are able or trying to be in tune with the audience that is changing their behavior. So, the patterns that we would be looking for manually in the past, machine is now looking for those patterns. So, that's the perfect example for our strength to catch up with the reality. What I'm hoping for, and that's where the future is, is that one day we won't be just reacting utilizing machine learning to the change in patterns in behavior. We are actually going to be ahead of those patterns and anticipate those changes to come, and react properly. >> I was going to say, yeah, what is the next step? Because you said that you are reacting. >> Vitaly: I was ahead of your question. >> Yeah, you were. (laughter) So, I'm going to go ahead and re-ask it. >> Dave: Data guy. (laughter) >> But you've got to get to that next step of not just anticipating but almost creating, right, in your way. Creating new opportunities, creating news data to develop these insights into almost shaping viewer behavior, right? >> Vitaly: Totally. So, like I said, optimization is one avenue that we pursue and continue to pursue. Forecasting is another. But I'm talking about true predictability. I mean, something goes beyond just to say how our show will do. Even beyond, which show would do better. >> John: Can you do that? Even to the point and say these are the elements that have been successful for this genre and for this size of audience, and therefore as we develop programming, whether it's in script and casting, whatever. I mean, take it all the way down to that micro-level to developing almost these ideals, these optimal programs that are going to be better received by your audience. >> Look, it's not a big secret. Every company that is in the content business is trying to get as many The Walking Deads as they can in their portfolio. Is there a direct path to success? Probably not, otherwise everyone would have been-- >> John: Over do it. >> Yeah, would be doing that. But yeah, so those are the most critical and difficult insights to get ahold of and we're working toward that. >> Are you finding that your predictive capabilities are getting meaningfully better? Maybe you could talk about that a little bit in terms of predicting those types of successes. Or is it still a lot of trial and error? >> I'd like to say they are meaningfully better. (laughter) Look, we do, there are obviously interesting findings. There are sometimes setbacks and we learn from it, and we move forward. >> Okay, as good as the weather or better? Or worse? (laughs) >> Depends on the morning and the season. (laughter) >> Vitaly, how have your success or have your success measurements changed as we enter this world of digital and machine learning and artificial intelligence? And if so, how? >> Well, they become more and more challenging and complex. Like, I gave an example for data democratization. It was such an interesting and telling company-wide initiative. And at the time, it felt as a true achievement when everybody get access to their data on their desktops and laptops. When we look back now a few years, it was a walk in the park to achieve. So, the more complex data and objectives we set in front of ourselves, the more educated people in the company become, the more challenging it is to deliver and take the next step. And we strive to do that. >> I wonder if I can ask you a question from a developers perspective. You obviously understand the developer mindset. We were talking to Dennis earlier. He's like, "Yeah, you know, it's really the data scientists that are loving the data, taking a bath in it. The data engineers and so forth." And I was kind of pushing on that saying, "Well, but eventually the developers have to be data-oriented. Data is the new development kit. What's your take? I mean, granted the 10 million Java developers most of them are not focused on the data per se. Will that change? Is that changing? >> So, first of all, I want separate the classical IT that you just referred to, which are developers. Because this discipline has been well established whether it's Waterfall or Agile. So, every company has those departments and they serve companies well. Business intelligence is a different animal. So, most of the work, if not all of the work we do is more of an R&D type of work. It is impossible to say, in three months I'll arrive with the model that will transform this business. So, we're driving there. That's the major distinction between the two. Is it the right path for some of the data-oriented developers to move on from, let's say, IT disciplines and into BI disciplines? I would highly encourage that because the job is so much more challenging, so interesting. There's very little routine as we said. It's actually challenge, challenge, and challenge. And, you know, you look at the news the way I do, and you see that data scientists becomes the number one desired job in America. I hope that there will be more and more people in that space because as every other department was struggling to find good people, right people for the space, and even within that space, you have as you mentioned, data engineers. You have data scientists or statisticians. And now it's maturing to the point that you have people who are above and beyond that. Those who actually can envision models not to execute on them. >> Are you investigating blockchain and playing around with that at all? Is there an application in your business? >> It hasn't matured fully yet in our hands but we're looking into it. >> And the reason I ask is that there seems to me that blockchain developers are data-oriented. And those two worlds, in my view, are coming together. But it's earlier days. >> Look, I mean, we are in R&D space. And like I said, we don't know exactly, we can't fully commit to a delivery. But it's always a balance between being practical and dreaming. So, if I were to say, you know, let me jump into a blockchain right now and be ahead of the game. Maybe. But then my commitments are going to be sort of farther ahead and I'm trying to be pragmatic. >> Before we let you go, I got to give you 30 seconds on your Yankees. How do you feel about the season coming up? >> As for with every season, I'm super-excited. And I can't wait until the season starts. >> We're always excited when pitchers and catchers show up. >> That's right. (laughter) >> If I were a Yankee fan, I'd be excited too. I must admit. >> Nobody's lost a game. >> That's right. >> Vitaly, thank you for being with us here. We appreciate it. And continued success at AMC Networks. Thank you for having me. >> Back with more on theCUBE right after this. (upbeat techno music)

>> Narrator: Live from New York, it's the Cube. Covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. >> And welcome back here to New York City. We're at IBM's Machine Learning Everywhere: Build Your Ladder to AI, along with Dave Vellante, John Walls, and we're now joined by Sam Lightstone, who is an IBM fellow in analytics. And Sam, good morning. Thanks for joining us here once again on the Cube. >> Yeah, thanks a lot. Great to be back. >> Yeah, great. Yeah, good to have you here on kind of a moldy New York day here in late February. So we're talking, obviously data is the new norm, is what certainly, have heard a lot about here today and of late here from IBM. Talk to me about, in your terms, of just when you look at data and evolution and to where it's now become so central to what every enterprise is doing and must do. I mean, how do you do it? Give me a 30,000-foot level right now from your prism. >> Sure, I mean, from a super, if you just stand back, like way far back, and look at what data means to us today, it's really the thing that is separating companies one from the other. How much data do they have and can they make excellent use of it to achieve competitive advantage? And so many companies today are about data and only data. I mean, I'll give you some like really striking, disruptive examples of companies that are tremendously successful household names and it's all about the data. So the world's largest transportation company, or personal taxi, can't call it taxi, but (laughs) but, you know, Uber-- >> Yeah, right. >> Owns no cars, right? The world's largest accommodation company, Airbnb, owns no hotels, right? The world's largest distributor of motion pictures owns no movie theaters. So these companies are disrupting because they're focused on data, not on the material stuff. Material stuff is important, obviously. Somebody needs to own a car, somebody needs to own a way to view a motion picture, and so on. But data is what differentiates companies more than anything else today. And can they tap into the data, can they make sense of it for competitive advantage? And that's not only true for companies that are, you know, cloud companies. That's true for every company, whether you're a bricks and mortars organization or not. Now, one level of that data is to simply look at the data and ask questions of the data, the kinds of data that you already have in your mind. Generating reports, understanding who your customers are, and so on. That's sort of a fundamental level. But the deeper level, the exciting transformation that's going on right now, is the transformation from reporting and what we'll call business intelligence, the ability to take those reports and that insight on data and to visualize it in the way that human beings can understand it, and go much deeper into machine learning and AI, cognitive computing where we can start to learn from this data and learn at the pace of machines, and to drill into the data in a way that a human being cannot because we can't look at bajillions of bytes of data on our own, but machines can do that and they're very good at doing that. So it is a huge, that's one level. The other level is, there's so much more data now than there ever was because there's so many more devices that are now collecting data. And all of us, you know, every one of our phones is collecting data right now. Your cars are collecting data. I think there's something like 60 sensors on every car that rolls of the manufacturing line today. 60. So it's just a wild time and a very exciting time because there's so much untapped potential. And that's what we're here about today, you know. Machine learning, tapping into that unbelievable potential that's there in that data. >> So you're absolutely right on. I mean the data is foundational, or must be foundational in order to succeed in this sort of data-driven world. But it's not necessarily the center of the universe for a lot of companies. I mean, it is for the big data, you know, guys that we all know. You know, the top market cap companies. But so many organizations, they're sort of, human expertise is at the center of their universe, and data is sort of, oh yeah, bolt on, and like you say, reporting. >> Right. >> So how do they deal with that? Do they get one big giant DB2 instance and stuff all the data in there, and infuse it with MI? Is that even practical? How do they solve this problem? >> Yeah, that's a great question. And there's, again, there's a multi-layered answer to that. But let me start with the most, you know, one of the big changes, one of the massive shifts that's been going on over the last decade is the shift to cloud. And people think of the shift to cloud as, well, I don't have to own the server. Someone else will own the server. That's actually not the right way to look at it. I mean, that is one element of cloud computing, but it's not, for me, the most transformative. The big thing about the cloud is the introduction of fully-managed services. It's not just you don't own the server. You don't have to install, configure, or tune anything. Now that's directly related to the topic that you just raised, because people have expertise, domains of expertise in their business. Maybe you're a manufacturer and you have expertise in manufacturing. If you're a bank, you have expertise in banking. You may not be a high-tech expert. You may not have deep skills in tech. So one of the great elements of the cloud is that now you can use these fully managed services and you don't have to be a database expert anymore. You don't have to be an expert in tuning SQL or JSON, or yadda yadda. Someone else takes care of that for you, and that's the elegance of a fully managed service, not just that someone else has got the hardware, but they're taking care of all the complexity. And that's huge. The other thing that I would say is, you know, the companies that are really like the big data houses, they got lots of data, they've spent the last 20 years working so hard to converge their data into larger and larger data lakes. And some have been more successful than others. But everybody has found that that's quite hard to do. Data is coming in many places, in many different repositories, and trying to consolidate, you know, rip the data out, constantly ripping it out and replicating into some data lake where you, or data warehouse where you can do your analytics, is complicated. And it means in some ways you're multiplying your costs because you have the data in its original location and now you're copying it into yet another location. You've got to pay for that, too. So you're multiplying costs. So one of the things I'm very excited about at IBM is we've been working on this new technology that we've now branded it as IBM Queryplex. And that gives us the ability to query data across all of these myriad sources as if they are in one place. As if they are a single consolidated data lake, and make it all look like (snaps) one repository. And not only to the application appear as one repository, but actually tap into the processing power of every one of those data sources. So if you have 1,000 of them, we'll bring to bear the power 1,000 data sources and all that computing and all that memory on these analytics problems. >> Well, give me an example why that matters, of what would be a real-world application of that. >> Oh, sure, so there, you know, there's a couple of examples. I'll give you two extremes, two different extremes. One extreme would be what I'll call enterprise, enterprise data consolidation or virtualization, where you're a large institution and you have several of these repositories. Maybe you got some IBM repositories like DB2. Maybe you've got a little bit of Oracle and a little bit of SQL Server. Maybe you've got some open source stuff like Postgres or MySQL. You got a bunch of these and different departments use different things, and it develops over decades and to some extent you can't even control it, (laughs) right? And now you just want to get analytics on that. You just, what's this data telling me? And as long as all that data is sitting in these, you know, dozens or hundreds of different repositories, you can't tell, unless you copy it all out into a big data lake, which is expensive and complicated. So Queryplex will solve that problem. >> So it's sort of a virtual data store. >> Yeah, and one of the terms, many different terms that are used, but one of the terms that's used in the industry is data virtualization. So that would be a suitable terminology here as well. To make all that data in hundreds, thousands, even millions of possible data sources, appear as one thing, it has to tap into the processing power of all of them at once. Now, that's one extreme. Let's take another extreme, which is even more extreme, which is the IoT scenario, Internet of Things, right? Internet of Things. Imagine you've, have devices, you know, shipping containers and smart meters on buildings. You could literally have 100,000 of these or a million of these things. They're usually small; they don't usually have a lot of data on them. But they can store, usually, couple of months of data. And what's fascinating about that is that most analytics today are really on the most recent you know, 48 hours or four weeks, maybe. And that time is getting shorter and shorter, because people are doing analytics more regularly and they're interested in, just tell me what's going on recently. >> I got to geek out here, for a second. >> Please, well thanks for the warning. (laughs) >> And I know you know things, but I'm not a, I'm not a technical person, but I've been a molt. I've been around a long time. A lot of questions on data virtualization, but let me start with Queryplex. The name is really interesting to me. When I, and you're a database expert, so I'm going to tap your expertise. When I read the Google Spanner paper, I called up my colleague David Floyer, who's an ex-IBM, I said, "This is like global Sysplex. "It's a global distributed thing," And he goes, "Yeah, kind of." And I got very excited. And then my eyes started bleeding when I read the paper, but the name, Queryplex, is it a play on Sysplex? Is there-- >> It's actually, there's a long story. I don't think I can say the story on-air, but we, suffice it to say we wanted to get a name that was legally usable and also descriptive. >> Dave: Okay. >> And we went through literally hundreds and hundreds of permutations of words and we finally landed on Queryplex. But, you know, you mentioned Google Spanner. I probably should spend a moment to differentiate how what we're doing is-- >> Great, if you would. >> A different kind of thing. You know, on Google Spanner, you put data into Google Spanner. With Queryplex, you don't put data into it. >> Dave: Don't have to move it. >> You don't have to move it. You leave it where it is. You can have your data in DB2, you can have it in Oracle, you can have it in a flat file, you can have an Excel spreadsheet, and you know, think about that. An Excel spreadsheet, a collection of text files, comma delimited text files, SQL Server, Oracle, DB2, Netezza, all these things suddenly appear as one database. So that's the transformation. It's not about we'll take your data and copy it into our system, this is about leave your data where it is, and we're going to tap into your (snaps) existing systems for you and help you see them in a unified way. So it's a very different paradigm than what others have done. Part of the reason why we're so excited about it is we're, as far as we know, nobody else is really doing anything quite like this. >> And is that what gets people to the 21st century, basically, is that they have all these legacy systems and yet the conversion is much simpler, much more economical for them? >> Yeah, exactly. It's economical, it's fast. (snaps) You can deploy this in, you know, a very small amount of time. And we're here today talking about machine learning and it's a very good segue to point out in order to get to high-quality AI, you need to have a really strong foundation of an information architecture. And for the industry to show up, as some have done over the past decade, and keep telling people to re-architect their data infrastructure, keep modifying their databases and creating new databases and data lakes and warehouses, you know, it's just not realistic. And so we want to provide a different path. A path that says we're going to make it possible for you to have superb machine learning, cognitive computing, artificial intelligence, and you don't have to rebuild your information architecture. We're going to make it possible for you to leverage what you have and do something special. >> This is exciting. I wasn't aware of this capability. And we were talking earlier about the cloud and the managed service component of that as a major driver of lowering cost and complexity. There's another factor here, which is, we talked about moving data-- >> Right. >> And that's one of the most expensive components of any infrastructure. If I got to move data and the transmission costs and the latency, it's virtually impossible. Speed of light's still up. I know you guys are working on speed of light, but (Sam laughs) you'll eventually get there. >> Right. >> Maybe. But the other thing about cloud economics, and this relates to sort of Queryplex. There's this API economy. You've got virtually zero marginal costs. When you were talking, I was writing these down. You got global scale, it's never down, you've got this network effect working for you. Are you able to, are the standards there? Are you able to replicate those sort of cloud economics the APIs, the standards, that scale, even though you're not in control of this, there's not a single point of control? Can you explain sort of how that magic works? >> Yeah, well I think the API economy is for real and it's very important for us. And it's very important that, you know, we talk about API standards. There's a beautiful quote I once heard. The beautiful thing about standards is there's so many to choose from. (All laugh) And the reality is that, you know, you have standards that are official standards, and then you have the de facto standards because something just catches on and nobody blessed it. It just got popular. So that's a big part of what we're doing at IBM is being at the forefront of adopting the standards that matter. We made a big, a big investment in being Spark compatible, and, in fact, even with Queryplex. You can issue Spark SQL against Queryplex even though it's not a Spark engine, per se, but we make it look and feel like it can be Spark SQL. Another critical point here, when we talk about the API economy, and the speed of light, and movement to the cloud, and these topics you just raised, the friction of the Internet is an unbelievable friction. (John laughs) It's unbelievable. I mean, you know, when you go and watch a movie over the Internet, your home connection is just barely keeping up. I mean, you're pushing it, man. So a gigabyte, you know, a gigabyte an hour or something like that, right? Okay, and if you're a big company, maybe you have a fatter pipe. But not a lot fatter. I mean, not orders of, you're talking incredible friction. And what that means is that it is difficult for people, for companies, to en masse, move everything to the cloud. It's just not happening overnight. And, again, in the interest of doing the best possible service to our customers, that's why we've made it a fundamental element of our strategy in IBM to be a hybrid, what we call hybrid data management company, so that the APIs that we use on the cloud, they are compatible with the APIs that we use on premises. And whether that's software or private cloud. You've got software, you've got private cloud, you've got public cloud. And our APIs are going to be consistent across, and applications that you code for one will run on the other. And you can, that makes it a lot easier to migrate at your leisure when you're ready. >> Makes a lot of sense. That way you can bring cloud economics and the cloud operating model to your data, wherever the data exists. Listening to you speak, Sam, it reminds me, do you remember when Bob Metcalfe who I used to work with at IDG, predicted the collapse of the Internet? He predicted that year after year after year, in speech after speech, that it was so fragile, and you're bringing back that point of, guys, it's still, you know, a lot of friction. So that's very interesting, (laughs) as an architect. >> You think Bob's going to be happy that you brought up that he predicted the Internet was going to be its own demise? (Sam laughs) >> Well, he did it in-- >> I'm just saying. >> I'm staying out of it, man. >> He did it as a lightning rod. >> As a talking-- >> To get the industry to respond, and he had a big enough voice so he could do that. >> That it worked, right. But so I want to get back to Queryplex and the secret sauce. Somehow you're creating this data virtualization capability. What's the secret sauce behind it? >> Yeah, so I think, we're not the first to try, by the way. Actually this problem-- >> Hard problem. >> Of all these data sources all over the place, you try to make them look like one thing. People have been trying to figure out how to do that since like the '70s, okay, so, but-- >> Dave: Really hasn't worked. >> And it hasn't worked. And really, the reason why it hasn't worked is that there's been two fundamental strategies. One strategy is, you have a central coordinator that tries to speak to each of these data sources. So I've got, let's say, 10,000 data sources. I want to have one coordinator tap into each of them and have a dialogue. And what happens is that that coordinator, a server, an agent somewhere, becomes a network bottleneck. You were talking about the friction of the Internet. This is a great example of friction. One coordinator trying to speak to, you know, and collaborators becomes a point of friction. And it also becomes a point of friction not only in the Internet, but also in the computation, because he ends up doing too much of the work. There's too many things that cannot be done at the, at these edge repositories, aggregations, and joins, and so on. So all the aggregations and joins get done by this one sucker who can't keep up. >> Dave: The queue. >> Yeah, so there's a big queue, right. So that's one strategy that didn't work. The other strategy that people tried was sort of an end squared topology where every data source tries to speak to every other data source. And that doesn't scale as well. So what we've done in Queryplex is something that we think is unique and much more organic where we try to organize the universe or constellation of these data sources so that every data source speaks to a small number of peers but not a large number of peers. And that way no single source is a bottleneck, either in network or in computation. That's one trick. And the second trick is we've designed algorithms that can truly be distributed. So you can do joins in a distributed manner. You can do aggregation in a distributed manner. These are things, you know, when I say aggregation, I'm talking about simple things like a sum or an average or a median. These are super popular in, in analytic queries. Everybody wants to do a sum or an average or a median, right? But in the past, those things were hard to do in a distributed manner, getting all the participants in this universe to do some small incremental piece of the computation. So it's really these two things. Number one, this organic, dynamically forming constellation of devices. Dynamically forming a way that is latency aware. So if I'm a, if I represent a data source that's joining this universe or constellation, I'm going to try to find peers who I have a fast connection with. If all the universe of peers were out there, I'll try to find ones that are fast. And the second is having algorithms that we can all collaborate on. Those two things change the game. >> We're getting the two minute sign, and this is fascinating stuff. But so, how do you deal with the data consistency problem? You hear about eventual consistency and people using atomic clocks and-- Right, so Queryplex, you know, there's a reason we call it Queryplex not Dataplex. Queryplex is really a read-only operation. >> Dave: Oh, there you go. >> You've got all these-- >> Problem solved. (laughs) >> Problem solved. You've got all these data sources. They're already doing their, they already have data's coming in how it's coming in. >> Dave: Simple and brilliant. >> Right, and we're not changing any of that. All we're saying is, if you want to query them as one, you can query them as one. I should say a few words about the machine learning that we're doing here at the conference. We've talked about the importance of an information architecture and how that lays a foundation for machine learning. But one of the things that we're showing and demonstrating at the conference today, or at the showcase today, is how we're actually putting machine learning into the database. Create databases that learn and improve over time, learn from experience. In 1952, Arthur Samuel was a researcher at IBM who first, had one of the most fundamental breakthroughs in machine learning when he created a machine learning algorithm that will play checkers. And he programmed this checker playing game of his so it would learn over time. And then he had a great idea. He programmed it so it would play itself, thousands and thousands and thousands of times over, so it would actually learn from its own mistakes. And, you know, the evolution since then. Deep Blue playing chess and so on. The Watson Jeopardy game. We've seen tremendous potential in machine learning. We're putting into the database so databases can be smarter, faster, more consistent, and really just out of the box (snaps) performing. >> I'm glad you brought that up. I was going to ask you, because the legend Steve Mills once said to me, I had asked him a question about in-memory databases. He said ever databases have been around, in-memory databases have been around. But ML-infused databases are new. >> Sam: That's right, something totally new. >> Dave: Yeah, great. >> Well, you mentioned Deep Blue. Looking forward to having Garry Kasparov on a little bit later on here. And I know he's speaking as well. But fascinating stuff that you've covered here, Sam. We appreciate the time here. >> Thank you, thanks for having me. >> And wish you continued success, as well. >> Thank you very much. >> Sam Lightstone, IBM fellow joining us here live on the Cube. We're back with more here from New York City right after this. (electronic music)

>> Announcer: Live from New York, it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. >> Welcome back to Midtown, New York. We are at Machine Learning Everywhere: Build Your Ladder to AI being put on by IBM here in late February in the Big Apple. Along with Dave Vellante, I'm John Walls. We're now joined by Dinesh Nirmal, who is the Vice President of Analytics Development and Site Executive at the IBM Silicon Valley lab, soon. Dinesh, good to see you, this morning, sir. >> Thank you, John. >> Fresh from California. You look great. >> Thanks. >> Alright, you've talked about this, and it's really your world: data, the new normal. Explain that. When you say it's the new normal, what exactly... How is it transforming, and what are people having to adjust to in terms of the new normal. >> So, if you look at data, I would say each and every one of us has become a living data set. Our age, our race, our salary. What our likes or dislikes, every business is collecting every second. I mean, every time you use your phone, that data is transmitted somewhere, stored somewhere. And, airlines for example, is looking, you know, what do you like? Do you like an aisle seat? Do you like to get home early? You know, all those data. >> All of the above, right? >> And petabytes and zettabytes of data is being generated. So now, businesses' challenge is that how do you take that data and make insights out of it to serve you as a better customer. That's where I've come to, but the biggest challenge is that, how do you deal with this tremendous amount of data? That is the challenge. And creating sites out of it. >> That's interesting. I mean, that means the definition of identity is really... For decades it's been the same, and what you just described is a whole new persona, identity of an individual. >> And now, you take the data, and you add some compliance or provisioning like GDPR on top of it, all of a sudden how do-- >> John: What is GDPR? For those who might not be familiar with it. >> Dinesh: That's the regulatory term that's used by EU to make sure that, >> In the EU. >> If me as a customer come to an enterprise, say, I don't want any of my data stored, it's up to you to go delete that data completely, right? That's the term that's being used. And that goes into effect in May. How do you make sure that that data gets completely deleted by that time the customer has... How do you get that consent from the customer to go do all those... So there's a whole lot of challenges, as data multiplies, how do you deal with the data, how do you create insights to the data, how do you create consent on the data, how do you be compliant on that data, how do you create the policies that's needed to generate that data? All those things need to be... Those are the challenges that enterprises face. >> You bring up GDPR, which, for those who are not familiar with it, actually went into effect last year but the fines go into effect this year, and the fines are onerous, like 4% of turnover, I mean it's just hideous, and the question I have for you is, this is really scary for companies because they've been trying to catch up to the big data world, and so they're just throwing big data projects all over the place, which is collecting data, oftentimes private information, and now the EU is coming down and saying, "Hey you have to be able to, if requested, delete that." A lot of times they don't even know where it is, so big challenge. Are you guys, can you help? >> Yeah, I mean, today if you look at it, the data exists all over the place. I mean, whether it's in your relational database or in your Hadoop, unstructured data, whereas you know, optics store, it exists everywhere. And you have to have a way to say where the data is and the customer has to consent given to go, for you to look at the data, for you to delete the data, all those things. We have tools that we have built and we have been in the business for a very long time for example our governance catalog where you can see all the data sources, the policies that's associated with it, the compliance, all those things. So for you to look through that catalog, and you can see which data is GDPR compliant, which data is not, which data you can delete, which data you cannot. >> We were just talking in the open, Dave made the point that many companies, you need all-stars, not just somebody who has a specialty in one particular area, but maybe somebody who's in a particular regiment and they've got to wear about five different hats. So how do you democratize data to the point that you can make these all-stars? Across all kinds of different business units or different focuses within a company, because all of a sudden people have access to great reams of information. I've never had to worry about this before. But now, you've got to spread that wealth out and make everybody valuable. >> Right, really good question. Like I said, the data is existing everywhere, and most enterprises don't want to move the data. Because it's a tremendous effort to move from an existing place to another one and make sure the applications work and all those things. We are building a data virtualization layer, a federation layer, whereby which if you are, let's say you're a business unit. You want to get access to that data. Now you can use that federational data virtualization layer without moving data, to go and grab that small piece of data, if you're a data scientist, let's say, you want only a very small piece of data that exists in your enterprise. You can go after, without moving the data, just pick that data, do your work, and build a model, for example, based on that data. So that data virtualization layer really helps because it's basically an SQL statement, if I were to simplify it. That can go after the data that exists, whether it's at relational or non-relational place, and then bring it back, have your work done, and then put that data back into work. >> I don't want to be a pessimist, because I am an optimist, but it's scary times for companies. If they're a 20th century organization, they're really built around human expertise. How to make something, how to transact something, or how to serve somebody, or consult, whatever it is. The 21st century organization, data is foundational. It's at the core, and if my data is all over the place, I wasn't born data-driven, born in the cloud, all those buzzwords, how do traditional organizations catch up? What's the starting point for them? >> Most, if not all, enterprises are moving into a data-driven economy, because it's all going to be driven by data. Now it's not just data, you have to change your applications also. Because your applications are the ones that's accessing the data. One, how do you make an application adaptable to the amount of data that's coming in? How do you make accuracy? I mean, if you're building a model, having an accurate model, generating accuracy, is key. How do you make it performant, or govern and self-secure? That's another challenge. How do you make it measurable, monitor all those things? If you take three or four core tenets, that's what the 21st century's going to be about, because as we augment our humans, or developers, with AI and machine learning, it becomes more and more critical how do you bring these three or four core tenets into the data so that, as the data grows, the applications can also scale. >> Big task. If you look at the industries that have been disrupted, taxis, hotels, books, advertising. >> Dinesh: Retail. >> Retail, thank you. Maybe less now, and you haven't seen that disruption yet in banks, insurance companies, certainly parts of government, defense, you haven't seen a big disruption yet, but it's coming. If you've got the data all over the place, you said earlier that virtually every company has to be data-driven, but a lot of companies that I talk to say, "Well, our industry is kind of insulated," or "Yeah, we're going to wait and see." That seems to me to be the recipe for disaster, what are your thoughts on that? >> I think the disruption will come from three angles. One, AI. Definitely that will change the way, blockchain, another one. When you say, we haven't seen in the financial side, blockchain is going to change that. Third is quantum computing. The way we do compute is completely going to change by quantum computing. So I think the disruption is coming. Those are the three, if I have to predict into the 21st century, that will change the way we work. I mean, AI is already doing a tremendous amount of work. Now a machine can basically look at an image and say what it is, right? We have Watson for cancer oncology, we have 400 to 500,000 patients being treated by Watson. So AI is changing, not just from an enterprise perspective, but from a socio-economic perspective and a from a human perspective, so Watson is a great example for that. But yeah, disruption is happening as we speak. >> And do you agree that foundational to AI is the data? >> Oh yeah. >> And so, with your clients, like you said, you described it, they've got data all over the place, it's all in silos, not all, but much of it is in silos. How does IBM help them be a silo-buster? >> Few things, right? One, data exists everywhere. How do you make sure you get access to the data without moving the data, that's one. But if you look at the whole lifecycle, it's about ingesting the data, bringing the data, cleaning the data, because like you said, data becomes the core. Garbage in, garbage out. You cannot get good models unless the data is clean. So there's that whole process, I would say if you're a data scientist, probably 70% of your time is spent on cleaning the data, making the data ready for building a model or for a model to consume. And then once you build that model, how do you make sure that the model gets retrained on a regular basis, how do you monitor the model, how do you govern the model, so that whole aspect goes in. And then the last piece is visualizational reporting. How do you make sure, once the model or the application is built, how do you create a report that you want to generate or you want to visualize that data. The data becomes the base layer, but then there's a whole lifecycle on top of it based on that data. >> So the formula for future innovation, then, starts with data. You add in AI, I would think that cloud economics, however we define that, is also a part of that. My sense is most companies aren't ready, what's your take? >> For the cloud, or? >> I'm talking about innovation. If we agree that innovation comes from the data plus AI plus you've got to have... By cloud economics I mean it's an API economy, you've got massive scale, those kinds of, to compete. If you look at the disruptions in taxis and retail, it's got cloud economics underneath it. So most customers don't really have... They haven't yet even mastered cloud economics, yet alone the data and the AI component. So there's a big gap. >> It's a huge challenge. How do we take the data and create insights out of the data? And not just existing data, right? The data is multiplying by the second. Every second, petabytes or zettabytes of data are being generated. So you're not thinking about the data that exists within your enterprise right now, but now the data is coming from several different places. Unstructured data, structured data, semi-structured data, how do you make sense of all of that? That is the challenge the customers face, and, if you have existing data, on top of the newcoming data, how do you predict what do you want to come out of that. >> It's really a pretty tough conundrum that some companies are in, because if you're behind the curve right now, you got a lot of catching up to do. So you think that we have to be in this space, but keeping up with this space, because the change happens so quickly, is really hard, so we have to pedal twice as fast just to get in the game. So talk about the challenge, how do you address it? How do you get somebody there to say, "Yep, here's your roadmap. "I know it's going to be hard, "but once you get there you're going to be okay, "or at least you're going to be on a level playing field." >> I look at the three D's. There's the data, there's the development of the models or the applications, and then the deployment of those models or applications into your existing enterprise infrastructure. Not only the data is changing, but that development of the models, the tools that you use to develop are also changing. If you look at just the predictive piece, I mean look from the 80's to now. You look at vanilla machine learning versus deep learning, I mean there's so many tools available. How do you bring it all together to make sense which one would you use? I think, Dave, you mentioned Hadoop was the term from a decade ago, now it's about object store and how do you make sure that data is there or JSON and all those things. Everything is changing, so how do you bring, as an enterprise, you keep up, afloat, on not only the data piece, but all the core infrastructure piece, the applications piece, the development of those models piece, and then the biggest challenge comes when you have to deploy it. Because now you have a model that you have to take and deploy in your current infrastructure, which is not easy. Because you're infusing machine learning into your legacy applications, your third-party software, software that was written in the 60's and 70's, it's not an easy task. I was at a major bank in Europe, and the CTO mentioned to me that, "Dinesh, we built our model in three weeks. "It has been 11 months, we still haven't deployed it." And that's the reality. >> There's a cultural aspect too, I think. I think it was Rob Thomas, I was reading a blog that he wrote, and he said that he was talking to a customer saying, "Thank god I'm not in the technology industry, "things change so fast I could never, "so glad I'm not a software company." And Rob's reaction was, "Uh, hang on. (laughs) "You are in the technology business, "you are a software company." And so there's that cultural mindset. And you saw it with GE, Jeffrey Immelt said, "I went to bed an industrial giant, "woke up a software company." But look at the challenges that industrial giant has had transforming, so... They need partners, they need people that have done this before, they need expertise and obviously technology, but it's people and process that always hold it up. >> I mean technology is one piece, and that's where I think companies like IBM make a huge difference. You understand enterprise. Because you bring that wealth of knowledge of working with them for decades and they understand your infrastructure, and that is a core element, like I said the last piece is the deployment piece, how do you bring that model into your existing infrastructure and make sure that it fits into that architecture. And that involves a tremendous amount of work, skills, and knowledge. >> Job security. (all laugh) >> Dinesh, thanks for being with us this morning, we appreciate that and good luck with the rest of the event, here in New York City. Back with more here on theCUBE, right after this. (calming techno music)

>> Announcer: Live from New York, it's theCUBE! Covering Machine Learning Everywhere: Build Your Ladder To AI. Brought to you by IBM. >> Well, good morning! Welcome here on theCUBE. Along with Dave Vellante, I'm John Walls. We're in Midtown New York for IBM's Machine Learning Everywhere: Build Your Ladder To AI. Great lineup of guests we have for you today, looking forward to bringing them to you, including world champion chess master Garry Kasparov a little bit later on. It's going to be fascinating. Dave, glad you're here. Dave, good to see you, sir. >> John, always a pleasure. >> How you been? >> Up from DC, you know, I was in your area last week doing some stuff with John Furrier, but I've been great. >> Stopped by the White House, drop in? >> You know, I didn't this time. No? >> No. >> Dave: My son, as you know, goes to school down there, so when I go by my hotel, I always walk by the White House, I wave. >> Just in case, right? >> No reciprocity. >> Same deal, we're in the same boat. Let's talk about what we have coming up here today. We're talking about this digital transformation that's going on within multiple industries. But you have an interesting take on it that it's a different wave, and it's a bigger wave, and it's an exciting wave right now, that digital is creating. >> Look at me, I've been around for a long time. I think we're entering a new era. You know, the great thing about theCUBE is you go to all these events, you hear the innovations, and we started theCUBE in 2010. The Big Data theme was just coming in, and it appeared, everybody was very excited. Still excited, obviously, about the data-driven concept. But we're now entering a new era. It's like every 10 years, the parlance in our industry changes. It was cloud, Big Data, SaaS, mobile, social. It just feels like, okay, we're here. We're doing that now. That's sort of a daily ritual. We used to talk about how it's early innings. It's not anymore. It's the late innings for those. I think the industry is changing. The describers of what we're entering are autonomous, pervasive, self-healing, intelligent. When you infuse artificial intelligence, I'm not crazy about that name, but when you infuse that throughout the landscape, things start to change. Data is at the center of it, but I think, John, we're going to see the parlance change. IBM, for example, uses cognitive. People use artificial intelligence. I like machine intelligence. We're trying to still figure out the names. To me, it's an indicator that things are changing. It's early innings now. What we're seeing is a whole new set of opportunities emerging, and if you think about it, it's based on this notion of digital services, where data is at the center. That's something that I want to poke at with the folks at IBM and our guests today. How are people going to build new companies? You're certainly seeing it with the likes of Uber, Airbnb, Waze. It's built on these existing cloud and security, off-the-shelf, if you will, horizontal technologies. How are new companies going to be built, what industries are going to be disruptive? Hint, every industry. But really, the key is, how will existing companies keep pace? That's what I really want to understand. >> You said, every industry's going to be disrupted, which is certainly, I think, an exciting prospect in some respects, but a little scary to some, too, right? Because they think, "No, we're fat and happy "and things are going well right now in our space, "and we know our space better than anybody." Some of those leaders might be thinking that. But as you point out, digital technology has transformed to the extent now that there's nobody safe, because you just slap this application in, you put this technology in, and I'm going to change your business overnight. >> That's right. Digital means data, data is at the center of this transformation. A colleague of mine, David Moschella, has come up with this concept of the matrix, and what the matrix is is a set of horizontal technology services. Think about cloud, or SaaS, or security, or mobile, social, all the way up the stack through data services. But when you look at the companies like Airbnb and Uber and, certainly, what Google is doing, and Facebook, and others, they're building services on top of this matrix. The matrix is comprised of vertical slices by industry and horizontal slices of technology. Disruptors are cobbling together through software and data new sets of services that are disrupting industries. The key to this, John, in my view, anyway, is that, historically, within healthcare or financial services, or insurance, or manufacturing, or education, those were very siloed. But digital and data allows companies and disruptors to traverse silos like never before. Think about it. Amazon buying Whole Foods. Apple getting into healthcare and financial services. You're seeing these big giants disrupt all of these different industries, and even smaller guys, there's certainly room for startups. But it's all around the data and the digital transformation. >> You spoke about traditional companies needing to convert, right? Needing to get caught up, perhaps, or to catch up with what's going on in that space. What do you do with your workforce in that case? You've got a bunch of great, hardworking people, embedded legacy. You feel good about where you are. And now you're coming to that workforce and saying, "Here's a new hat." >> I think that's a great question. I think the concern that one would have for traditional companies is, data is not foundational for most companies. It's not at their core. The vast majority of companies, the core are the people. You hear it all the time. "The people are our greatest asset." That, I hate to say it, but it's somewhat changing. If you look at the top five companies by market cap, their greatest asset is their data, and the people are surrounding that data. They're very, very important because they know how to leverage that data. But if you look at most traditional companies, people are at their core. Data is kind of, "Oh, we got this bolt-on," or it's in a bunch of different silos. The big question is, how do they close that gap? You're absolutely right. The key is skillsets, and the skills have to be, you know, we talk about five-tool baseball players. You're a baseball fan, as am I. Well, you need multi-tool players, those that understand not only the domain of whether it's marketing or sales or operational expertise or finance, but they also require digital expertise. They know, for example, if you're a marketing professional, they know how to do hypertargeting. They know how to leverage social. They know how to do SEO, all these digital skills, and they know how to get information that's relevant and messaging out into the marketplace and permeate that. And so, we're entering, again, this whole new world that's highly scalable, highly intelligent, pervasive, autonomous. We're going to talk about that today with a lot of their guests, with a lot of our guests, that really are kind of futurists and have thought through, I think, the changes that are coming. >> You can't have a DH anymore, right, that's what you're saying? You need a guy that can play the field. >> Not only play the field, not only a utility player, but somebody who's a utility player, but great. Best of breed at all these different skillsets. >> Machine learning, we haven't talked much about that, and another term, right, that certainly has different definitions, but certainly real specific applications to what's going on today. We'll talk a lot about ML today. Your thoughts about that, and how that squares into the artificial intelligence picture, and what we're doing with all those machines out there that are churning 24/7. >> Yeah, so, real quick, I know we're tight on time here. Artificial intelligence to me is the umbrella. Machine learning is the application of math and algorithms to solve a particular problem or answer a particular question. And then there's deep learning, which is highly focused neural networks that go deeper and deeper and deeper, and become auto-didactic, self-learning, in a manner. Those are just the very quick and rudimentary description. Machine learning to me is the starting point, and that's really where organizations really want to start to learn and begin to close the gap. >> A lot of ground to cover, and we're going to do that for you right here on theCUBE as we continue our coverage of Machine Learning Everywhere: Your Ladder To AI, coming up here, IBM hosting us in Midtown, New York. Back with more here on theCUBE in just a bit. (fast electronic music)

(bright music) >> Hello everyone, welcome today's session for the "AWS Startup Showcase" presented by theCUBE, featuring ThoughtSpot for this track and data and analytics. I'm John Furrier, your host. Today, we're joined by Victor Chang, VP of ThoughtSpot Everywhere and Corporate Development for ThoughtSpot. Victor, thanks for coming on and thanks for presenting. Talking about this building interactive data apps through ThoughtSpot Everywhere. Thanks for coming on. >> Thank you, it's my pleasure to be here. >> So digital transformation is reality. We're seeing it large-scale. More and more reports are being told fast. People are moving with modern application development and if you don't have AI, you don't have automation, you don't have the analytics, you're going to get slowed down by other forces and even inside companies. So data is driving everything, data is everywhere. What's the pitch to customers that you guys are doing as everyone realizes, "I got to go faster, I got to be more secure," (laughs) "And I don't want to get slowed down." What's the- >> Yeah, thank you John. No, it's true. I think with digital transformation, what we're seeing basically is everything is done in the cloud, everything gets done in applications, and everything has a lot of data. So basically what we're seeing is if you look at companies today, whether you are a SaaS emerging growth startup, or if you're a traditional company, the way you engage with your customers, first impression is usually through some kind of an application, right? And the application collects a lot of data from the users and the users have to engage with that. So for most of the companies out there, one of the key things that really have to do is find a way to make sense and get value for the users out of their data and create a delightful and engaging experience. And usually, that's pretty difficult these days. You know, if you are an application company, whether it doesn't really matter what you do, if you're hotel management, you're productivity application, analytics is not typically your strong suit, and where ThoughtSpot Everywhere comes in is instead of you having to build your own analytics and interactivity experience with a data, ThoughtSpot Everywhere helps deliver a really self-service interactive experience and transform your application into a data application. And with digital transformation these days, all applications have to engage, all applications have to delight, and all applications have to be self-service. And with analytics, ThoughtSpot Everywhere brings that for you to your customers and your users. >> So a lot of the mainstream enterprises and even businesses from SMB, small businesses that are in the cloud are scaling up, they're seeing the benefits. What's the problem that you guys are targeting? What's the use case? When does a potential customer or customer know they get that ThoughtSpot is needed to be called in and to work with? Is it that they want low code, no code? Is it more democratization? What's the problem statement and how do you guys turn that problem being solved into an opportunity and benefit? >> I think the key problem we're trying to solve is that most applications today, when they try to deliver analytics, really when they're delivering, is usually a static representation of some data, some answers, and some insights that are created by someone else. So usually the company would present, you know, if you think about it, if you go to your banking application, they usually show some pretty charts for you and then it sparks your curiosity about your credit card transactions or your banking transactions over the last month. Naturally, usually for me, I would then want to click in and ask the next question, which transactions fall into this category, what time, you know, change the categories a bit, usually you're stuck. So what happens with most applications? The challenge is because someone else is asking the questions and then the user is just consuming static insights, you wet their appetite and you don't satisfy it. So application users typically get stunted, they're not satisfied, and then leave application. Where ThoughtSpot comes in, ThoughtSpots through differentiation is our ability to create an interactive curiosity journey with the user. So ThoughtSpot in general, if you buy a standalone, that's the experience that we really stand by, now you can deliberate your application where the user, any user, business user, untrained, without the help of an analyst can ask their own questions. So if you see, going back to my example, if it's in your banking app, you see some kind of visualization around expense actions, you can dig in. What about last month? What about last week? Which transactions? Which merchant? You know, all those things you can continue your curiosity journey so that the business user and the app user ask their questions instead of an analyst who's sitting in the company behind a desk kind of asking your questions for you. >> And that's the outcome that everyone wants. I totally see that and everyone kind of acknowledges that, but I got to then ask you, okay, how do you make that happen? Because you've got the developers who have essentially make that happen and so, the cloud is essentially SaaS, right? So you got a SaaS kind of marketplace here. The apps can be deployed very quickly, but in order to do that, you kind of need self-service and you got to have good analytics, right? So self-service, you guys have that. Now on the analytics side, most people have to build their own or use an existing tool and tools become specialists, you know what I'm saying? So you're in this kind of like weird cycle of, "Okay, I got to deploy and spend resource to build my own, which could be long and tiresome." >> Yeah. >> "And or rely on other tools that could be good, but then I have too many tools but that creates specialism kind of silos." These seems to be trends. Do you agree with that? And if customers have this situation, you guys come in, can you help there? >> Absolutely, absolutely. So, you know, if you think about the two options that you just laid out, that you could either roll your own, kind of build your own, and that's really hard. If you think about analyst industry, where 20, $30 billion industry with a lot of companies that specialize in building analytics so it's a really tough thing to do. So it doesn't really matter how big of a company you are, even if you're a Microsoft or an Amazon, it's really hard for them to actually build analytics internally. So for a company to try to do it on their own, hire the talent and also to come up with that interactive experience, most companies fail. So what ends up happening is you deliver the budget and the time to market ends up taking much longer, and then the experience is engaging for the users and they still end up leaving your app, having a bad impression. Now you can also buy something. They are our competitors who offer embedded analytics options as well, but the mainstream paradigm today with analytics is delivering. We talked about earlier static visualizations of insights that are created by someone else. So that certainly is an option. You know, where ThoughtSpot Everywhere really stands out above everything else is our technology is fundamentally built for search and interactive and cloud-scale data kind of an experience that the static visualizations today can't really deliver. So you could deliver a static dashboard purchase from one of our competitors, or if you really want to engage your users again, today is all about self-service, it's all about interactivity, and only ThoughtSpot's architecture can deliver that embedded in a data app for you. >> You know, one of the things I'm really impressed with you guys at ThoughtSpot is that you see data as I see strategic advantage for companies and people say that it's kind of a cliche but, or a punchline, and some sort of like business statement. But when you start getting into new kinds of workflows, that's the intellectual property. If you can enable people to essentially with very little low-code, no-code, or just roll their own analysis and insights from a platform, you're then creating intellectual property for the company. So this is kind of a new paradigm. And so a lot of CIO's that I talked to, or even CSOs on the security side of like, they kind of want this but maybe can't get there overnight. So if I'm a CIO, Victor, who do I, how do I point to on my team to engage with you guys? Like, okay, you sold me on it, I love the vision. This is definitely where we want to go. Who do I bring into the meeting? >> I think that in any application, in any company actually, there's usually product leaders and developers that create applications. So, you know, if you are a SaaS company, obviously your core product, your core product team would be the right team we want to talk to. If you're a traditional enterprise, you'd be surprised actually, how many traditional enterprises that been around for 50, 100 years, you might think of them selling a different product but actually, they have a lot of visual applications and product teams within their company as well. For example, you know, we have customers like a big tractor company. You can probably imagine who they might be. They actually have visual applications that they use ThoughtSpot to offer to the dealers so that they can look at their businesses with the tractors. We also have a big telecom company, for example, that you would think about telecom as a whole service but they have a building application that they offer to their merchants to track their billing. So what I'm saying is really, whether you're a software company where that's your core product, or you're a traditional enterprise that has visual applications underneath to support your core product, there's usually product teams, product leaders, and developers. Those are the ones that we want to talk to and we can help them realize a better vision for the product that they're responsible for. >> I mean, the reality is all applications need analytics, right, at some level. >> Yes. >> Full instrumentation at a minimum log everything and then the ability to roll that up, that's where I see people always telling me like that's where the challenge seems to be. Okay, I can log everything, but now how do I have a... And then after the fact that they say, "Give me a report, what's happening?" >> That's right. >> They get stuck. >> They get stuck 'cause you get that report and you know, someone else asked that question for you and you're probably a curious person. I'm a curious person. You always have that next question, and then usually if you're in a company, let's just say, you're a CIO. You're probably used to having a team of analysts at your fingertip so at least if you have a question, you don't like the report, you can find two people, five people they'll respond to your request. But if you're a business application user, you're sitting there, I don't know about you, but I don't remember the last time I actually went through and really found a support ticket in my application, or I really read a detailed documentation describing features in application. Users like to be self-taught, self-service and they like to explore it on their own. And there's no analyst there, there's no IT guy that they can lean on so if they get a static report of the data, they'll naturally always want to ask more questions, then they're stuck. So it's that kind of unsatisfying where, "I have some curiosity, you sparked by questions, I can't answer them." That's where I think a lot of companies struggle with. That's why a lot of applications, they're data intensive but they don't deliver any insights. >> It's interesting and I like this anywhere idea because you think about like what you guys do, applications can be, they always start small, right? I mean, applications got to be built. So you guys, your solution really fits for small startups and business all the way up to large enterprises which in a large enterprise, they could have hundreds and thousands of applications which look like small startups. >> Absolutely, absolutely. You know, that's a great thing about the sort of ThoughtSpot Everywhere which takes the engine around ThoughtSpot that we built over the last eight or nine years and could deliver in any kind of a context. 'Cause nowadays, as opposed to 10, 15, 20 years ago, everything does run in applications these days. We talk about visual transformation at the beginning of the call. That's really what it means is today, the workflows of business are conducted in applications no matter who you're interacting with. And so we have all these applications. A lot of times, yes, if you have big analytical problems, you can take the data and put into a different context like ThoughtSpot's own UI and do a lot of analytics, but we also understand that a lot of times customers and users, they like to analyze in the context the workflow of the application they're actually working in. And so with that situation, actually having the analytics embedded within right next to their workflow is something that I think a lot of, especially business users that are less trained, they'd like to do that right in the context of their business productivity workflow. And so that's where ThoughtSpot Everywhere, I know the terminology is a little self-serving, but ThoughtSpot Everywhere, we think ThoughtSpot could actually be everywhere in your business workflow. >> That's great value proposition. I'm going to put my skeptic hat on challenge you and say, Okay, I don't want to... Prove it to me, what's in it for me? And how much is it going to cost me, how do I engage? So, you know- >> Yeah. >> What's in it for me as the buyer? If people want to buy this, I want to use it, I'm going to get engaged with ThoughtSpot and how much does it cost and what's the engagements look like? >> So, what's in it for you is easy. So if you have data in the cloud and you have an application, you should use ThoughtSpot Everywhere to deliver a much more valuable, interactive experience for your user's data. So that's clear. How do you engage? So we have a very flexible pricing models. If your data's in the cloud, we can either, you can purchase with us, we'll land small and then grow with your consumption. You know, that's always the kind of thing, "Hey, allow us to prove it to you, right?" We start, and then if a user starts to consume, you don't really have to pay a big bill until we see the consumption increase. So we have consumption and data capacity-based types of pricing models. And you know, one of the real advantages that we have for cloud applications is if you're a developer, often, even in the past for ThoughtSpot, we haven't always made that development experience very easy. You have to embed a relatively heavy product but the beauty for ThoughtSpot is from the beginning, we were designed with a modern API-based kind of architecture. Now, a lot of our BI competitors were designed and developed in the desktop server kind of era where everything you embed is very monolithic. But because we have an API driven architecture, we invest a lot of time now to wrap a seamless developer SDK, plus very easy to use REST APIs, plus an interactive kind of a portal to make that development experience also really simple. So if you're a developer, now you really can get from zero to an easy app for ThoughtSpot embedded in your data app in just often in less than 60 minutes. >> John: Yeah. >> So that's also a very great proposition where modern leaders is your data's in the cloud, you've got developers with an SDK, it can get you into an app very quickly. >> All right so bottom line, if you're in the cloud, you got to get the data embed in the apps, data everywhere with ThoughtSpot. >> Yes. >> All right, so let's unpack it a little bit because I think you just highlighted I think what I think is the critical factor for companies as they evaluate their plethora of tools that they have and figuring out how to streamline and be cloud native in scale. You mentioned static and old BI competitors to the cloud. They also have a team of analysts as well that just can make the executives feel like the all of the reports are dynamic but they're not, they're just static. But look at, I know you guys have a relation with Snowflake, and not to kind of bring them into this but to highlight this, Snowflake disrupted the data warehouse. >> Yes. >> Because they're in the cloud and then they refactored leveraging cloud scale to provide a really easy, fast type of value for their product and then the rest is history. They're public, they're worth a lot of money. That's kind of an example of what's coming for every category of companies. There's going to be that. In fact, Jerry Chen, who was just given the keynote here at the event, had just had a big talk called "Castles In The Cloud", you can build a moat in the cloud with your application if you have the right architecture. >> Absolutely. >> So this is kind of a new, this is a new thing and it's almost like beachfront property, whoever gets there first wins the category. >> Exactly, exactly. And we think the timing is right now. You know, Snowflake, and even earlier, obviously we had the best conference with Redshift, which really started the whole cloud data warehouse wave, and now you're seeing Databricks even with their Delta Lake and trying to get into that kind of swim lane as well. Right now, all of a sudden, all these things that have been brewing in the background in the data architecture has to becoming mainstream. We're now seeing even large financial institutions starting to always have to test and think about moving their data into cloud data warehouse. But once you're in the cloud data warehouse, all the benefits of its elasticity, performance, that can really get realized at the analytics layer. And what ThoughtSpot really can bring to the table is we've always, because we're a search-based paradigm and when you think about search. Search is all about, doesn't really matter what kind of search you're doing, it's about digging really deep into a lot of data and delivering interactive performance. Those things have always... Doesn't really matter what data architecture we sit on, I've always been really fundamental to how we build our product. And that translates extremely well when you have your data in a Snowflake or Redshift have billions of rows in the cloud. We're the only company, we think, that can deliver interactive performance on all the data you have in a cloud data warehouse. >> Well, I want to congratulate you, guys. I'm really a big fan of the company. I think a lot of companies are misunderstood until they become big and there was, "Why didn't everyone else do that search? Well, I thought they were a search engine?" Being search centric is an architectural philosophy. I know as a North Star for your company but that creates value, right? So if you look at like say, Snowflake, Redshift and Databricks, you mentioned a few of those, you have kind of a couple of things going on. You have multiple personas kind of living well together and the developers like the data people. Normally, they hated each other, right? (giggles) Or maybe they didn't hate each other but there's conflict, there's always cultural tension between the data people and the developers. Now, you have developers who are becoming data native, if you will, just by embedding that in. So what Snowflake, these guys, are doing is interesting. You can be a developer and program and get great results and have great performance. The developers love Snowflake, they love Databricks, they love Redshift. >> Absolutely. >> And it's not that hard and the results are powerful. This is a new dynamic. What's your reaction to that? >> Yeah, no, I absolutely believe that. I think, part of the beauty of the cloud is I like your kind of analogy of bringing people together. So being in the cloud, first of all, the data is accessible by everyone, everywhere. You just need a browser and the right permissions, you can get your data, and also different kind of roles. They all kind of come together. Things best of breed tools get blended together through APIs. Everything just becomes a lot more accessible and collaborative and I know that sounds kind of little kumbaya, but the great thing about the cloud is it does blur the lines between goals. Everyone can do a little bit of everything and everyone can access a little bit more of their data and get more value out of it. >> Yeah. >> So all of that, I think that's the... If you talk about digital transformation, you know, that's really at the crux of it. >> Yeah, and I think at the end of the day, speed and high quality applications is a result and I think, the speed game if automation being built in on data plays a big role in that, it's super valuable and people will get slowed down. People get kind of angry. Like I don't want to get, I want to go faster, because automations and AI is going to make things go faster on the dev side, certainly with DevOps, clouds proven that. But if you're like an old school IT department (giggles) or data department, you're talking to weeks not minutes for results. >> Yes. >> I mean, that's the powerful scale we're talking about here. >> Absolutely. And you know, if you think about it, you know, if it's days to minutes, it sounds like a lot but if you think about like also each question, 'cause usually when you're thinking about questions, they come in minutes. Every minute you have a new question and if each one then adds days to your journey, that over time is just amplified, it's just not sustainable. >> Okay- >> So now in the cloud world, you need to have things delivered on demand as you think about it. >> Yeah, and of course you need the data from a security standpoint as well and build that in. Chances is people shift left. I got to ask you if I'm a customer, I want to just run this by you. You mentioned you have an SDK and obviously talking to developers. So I'm working with ThoughtSpot, I'm the leader of the organization. I'm like, "Okay, what's the headroom? What's going to happen as a bridge, the future gets built so I'm going to ride with ThoughtSpot." You mentioned SDK, how much more can I do to build and wrap around ThoughtSpot? Because obviously, this kind of value proposition is enabling value. >> Yes. >> So I want to build around it. How do I get started and where does it go? >> Yeah, well, you can get started as easy as starting with our free trial and just play around with it. And you know, the beauty of SDK and when I talk about how ThoughtSpot is built with API-driven architecture is, hey, there's a lot of magic and features built into ThoughtSpot core pod. You could embed all of that into an application if you would like or you could also use our SDK and our APIs to say, "I just want to embed a couple of visualizations," start with that and allow the users to take into that. You could also embed the whole search feature and allow users to ask repetitive questions, or you can have different role-based kind of experiences. So all of that is very flexible and very dynamic and with SDK, it's low-code in the sense where it creates a JavaScript portal for you and even for me who's haven't coded in a long time. I can just copy and paste some JavaScript code and I can see my applications reflecting in real time. So it's really kind of a modern experience that developers in today's world appreciate, and because all the data's in the cloud and in the cloud, applications are built as services connected through APIs, we really think that this is the modern way that developers would get started. And analysts, even analysts who don't have strong developer training can get started with our developer portal. So really, it's a very easy experience and you can customize it in whichever way you want that suits your application's needs. >> Yeah, I think it's, you don't have to be a developer to really understand the basic value of reuse and discovery of services. I think that's one of these we hear from developers all the time, "I had no idea that Victor did that code. Why do I have to rewrite that?" So you see, reuse come up a lot around automation where code is building with code, right? So you have this new vibe and you need data to discover that search paradigm mindset. How prevalent is that on the minds of customers? Are they just trying to like hold on and survive through the pandemic? (giggles) >> Well, customers are definitely thinking about it. You know, the challenge is change is always hard, you know? So it takes time for people to see the possibilities and then have to go through especially in larger organizations, but even in smaller organizations, people think about, "Well, how do I change my workflow?" and then, "How do I change my data pipeline?" You know, those are the kinds of things where, you know, it takes time, and that's why Redshift has been around since 2012 or I believe, but it took years before enterprises really are now saying, "The benefits are so profound that we really have to change the workflows, change the data pipelines to make it work because we can't hold on to the old ways." So it takes time but when the benefits are so clear, it's really kind of a snowball effect, you know? Once you change a data warehouse, you got to think about, "Do I need to change my application architecture?" Then, "Do I need to change the analytics layer?" And then, "Do I need to change the workflow?" And then you start seeing new possibilities because it's all more flexible that you can add more features to your application and it's just kind of a virtuous cycle, but it starts with taking that first step to your point of considering migrating your data into the cloud and we're seeing that across all kinds of industries now. I think nobody's holding back anymore. It just takes time, sometimes some are slower and some are faster. >> Well, all apps or data apps and it's interesting, I wrote a blog post in 2017 called, "Data Is The New Developer Kit" meaning it was just like a vision statement around data will be part of how apps, like software, it'll be data as code. And you guys are doing that. You're allowing data to be a key ingredient for interactivity with analytics. This is really important. Can you just give us a use case example of how someone builds an interactive data app with ThoughtSpot Everywhere? >> Yeah, absolutely. So I think there are certain applications that when naturally things relates to data, you know, I talk about bending or those kinds of things. Like when you use it, you just kind of inherently know, "Hey, there's tons of data and then can I get some?" But a lot of times we're seeing, you know, for example, one of our customers is a very small company that provides software for personal trainers and small fitness studios. You know, you would think like, "Oh well, these are small businesses. They don't have a ton of data. A lot of them would probably just run on QuickBooks or Excel and all of that." But they could see the value is kind of, once a personal trainer conducts his business on a cloud software, then he'll realize, "Oh, I don't need to download any more data. I don't need to run Excel anymore, the data is already there in a software." And hey, on top of that, wouldn't it be great if you have an analytics layer that can analyze how your clients paid you, where your appointments are, and so forth? And that's even just for, again like I said, no disrespect to personal trainers, but even for one or two personal trainers, hey, they can be an analytics and they could be an analyst on their business data. >> Yeah, why not? Everyone's got a Fitbits and watches and they could have that built into their studio APIs for the trainers. They can get collaboration. >> That's right. So there's no application you can think that's too simple or you might think too traditional or whatnot for analytics. Every application now can become a very engaging data application. >> Well Victor, it's great to have you on. Obviously, great conversation around ThoughtSpot anywhere. And as someone who runs corp dev for ThoughtSpot, for the folks watching that aren't customers yet for ThoughtSpot, what should they know about you guys as a company that they might not know about or they should know about? And what are people talking about ThoughtsSpot, what are they saying about it? So what should they know that know that's not being talked about or they may not understand? And what are other people saying about ThoughtSpot? >> So a couple of things. One is there's a lot of fun out there. I think about search in general, search is generally a very broad term but I think it, you know, I go back to what I was saying earlier is really what differentiates ThoughtSpot is not just that we have a search bar that's put on some kind of analytics UI. Really, it's the fundamental technical architecture underlying that is from the ground up built for search large data, granular, and detailed exploration of your data. That makes us truly unique and nobody else can really do search if you're not built with a technical foundation. The second thing is, we're very much a cloud first company now, and a ton of our over the past few years because of the growth of these highly performing data warehouses like Snowflake and Redshift, we're able to really focus on what we do best which is the search and the query processing performance on the front end and we're fully engaged with cloud platforms now. So if you have data in the cloud, we are the best analytics front end for that. >> Awesome, well, thanks for coming on. Great the feature you guys here in the "Startup Showcase", great conversation, ThoughtSpot leading company, hot startup. We did their event with them with theCUBE a couple of months ago. Congratulations on all your success. Victor Chang, VP of ThoughtSpot Everywhere and Corporate Development here on theCUBE and "AWS Startup Showcase". Go to awsstartups.com and be part of the community, we're doing these quarterly featuring the hottest startups in the cloud. I'm John Furrier, thanks for watching. >> Victor: Thank you so much. (bright music)

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hey, welcome back, everybody. Jeff Frick with theCUBE. We're in our Palo Alto Studios on this kind of continuing leadership series that we've put together. Reaching out to the community for tips and tricks on kind of getting through what is, this kind of ongoing COVID crisis and situation as it continues to go weeks and weeks and weeks. And I'm really excited to have one of my favorite members of our community, is Nick Mehta, the CEO of Gainsight. Had the real pleasure of interviewing him a couple times and had to get him on. So Nick, thanks for taking some time out of your very busy day to join us. >> Jeff, honored to be here, thank you. >> Pleasure, so let's just jump into it. One of the reasons I wanted to get you on, is that Gainsight has been a distributed company from the beginning, and so I think the COVID, suddenly everyone got this work from home order, there was no prep, there was no planning, it's like this light switch digital transformation moment. So love to hear from someone who's been doing it for awhile. What are some of the lessons? How should people think about running a distributed company? >> Yeah, it's really interesting, Jeff, 'cause we are just by happenstance, from the beginning, distributed where we have, our first two offices were St. Louis and Hyderabad, India. So two places you cannot get there through one flight. So, you have to figure out how to collaborate asynchronously and then over time, we have offices in the Bay Area. We have tons of people that work from home. And so we try to tell people we don't have a headquarters. The headquarters is wherever you are, wherever you live and wherever you want to work. And so we've always been super flexible about come in to the office if you want, don't come in, et cetera. So different than some companies in that respect. And because of that, pre-COVID, we always a very heavy video culture, lots of video conferencing. Even if some people were in an office, there's always somebody else dialing in. One benefit we got from that is you never had to miss your kids' stuff or your family things. I would go to my daughter's performance in the middle of the day and know I can just dial into a call on the way there. And so we always had that. But what's amazing is now we're all on a level playing field, there's nobody in our office. And I got to say, this is, in some ways, even better 'cause I feel like when you're the person dialed in, and a lot of people are in a room, you probably had that experience, and it feels like you're kind of not on the same playing field, right? Hard to hear the jokes or the comments and you might not feel like you're totally in crowd, so to speak, right? But now everyone's just at their computer, sitting there in a chair all day doing these Zooms and it does feel like it's equalizing a little bit. And what it's caused us to do is say, hey, what are ways we can all recreate that community from home? So as an example, every 7:45 a.m. every day, we have a Zoom call that's just pure joy and fun. Trivia, pets, kids. The employees' kids announce people's birthdays and the weather. And so these ways we've been able to integrate our home and our work that we never could before, it's really powerful. It's a tough situation overall, and we feel for all the people affected. But even in tough situations, there are silver linings, and we're finding 'em. >> Yeah, it's funny, we just had Darren Murph on the other day. I don't know if you know Darren. He is the head of Remote Work at GitLab, and he-- >> Oh, yeah. >> And he talked about kind of the social norms. And one of the instances that he brought up was, back in the day when you had some people in the office, some people joining via remote, that it is this kind of disharmony because they're very different situations. So one of his suggestions was have everybody join via their laptop, even if they're sitting at their desk, right? So, as you said, you get kind of this level playing field. And the other thing which dovetails off what you just said is he always wanted executives to have a forcing function to work from home for an extended period of time, so they got to understand what it's all about. And it's not only looking through a little laptop or this or that, but it's also the distractions of the kids and the dogs and whatever else is happening around the house. So it is wild how this forcing function has really driven it. And his kind of takeaway is, as we, like say, move from can we get it into cloud to cloud first? And does it work on mobile to mobile first? >> Now it's really-- >> Yeah. >> It's really remote first. And if you-- >> Remote first. >> A remote first attitude about it and kind of turn it on it's head, it's why shouldn't it be remote versus can it be remote? It really changes the conversation and the dynamic of the whole situation. >> I love that. And just, GitLab, by the way, has been a true inspiration 'cause they are the most remote, remote company. And they share so much, I love what you said. As just two examples of reacting to what you said, pre-COVID, we always wanted to keep a level playing field. So we actually moved our all-hands meetings to be instead of being broadcast from one room, and you're kind of seeing this small screen with all these people, we all just were at computers presenting. And so everyone's on a level playing field. So I thought what GitLab said is great. And then the other point, I think post-COVID we have learned is the kids and the dogs aren't distractions, they're part of our life. And so embracing those and saying, hey, I see that kid in the background, bring them onto the screen. Even during work meetings, even customer meetings, you know? And I'm seeing, I'm on a customer meeting and the customer's bringing their kids onto the screen and it's kind of breaking this artificial wall between who we are at home and who we are at work 'cause we're human beings all throughout. At Gainsight, we talk about a human first approach to business and we've never been more human as a world than we are right now. >> Love it, love it. So another, get your thoughts on, is this whole idea of measurement and productivity at home. And it's really, I have to say, disturbing to see some of the new product announcements that are coming out in terms of people basically snoopin' on people. Whether it's trackin' how many hours of Zoom calls they're on, or how often are they in the VPN, or having their camera flip on every so many minutes or something. We had Marten Mickos on, who's now the CEO of HackerOne. He was CEO at MySQL years ago before it went to Sun and he had the great line, he said, it's so easy to fake it at the office, but when you're at home and you're only output is your deliverable, it makes it a lot easier. So I wonder if you can share some of your thoughts in terms of kind of managing output, setting expectations, to get people to get their work done. And then, as you see some of these new tools for people that are just entering this thing, it's just not right (chuckles). >> Yeah, I agree with you and Marten. I'm a huge fan of Marten, as well, I totally agree with both of it. And I think there's an older approach to work, which is more like a factory. It's like you got to see how many widgets you're processing and you got to micromanage and you got to monitoring and inspecting. Look, I don't run a factory, so maybe there are places where that model makes sense. So I'm not going to speak for every leader, but I could say if you're in a world where your job is information, services, software, where the value is the people and their knowledge, managing them that way is a losing battle. I go back to, some folks probably know, this famous TED Talk by Dan Pink on basically what motivates people. And in these knowledge worker jobs, it's autonomy, mastery and purpose. So autonomy, we have the freedom to do what we want. Mastery, we feel like we're getting better at jobs. And purpose, which is I have a why behind what I do. And I think, take that time you spend on your micromanagement and your Zoom, analyzing the Zoom sessions, and spend it on inspiring your team, on the purpose. Spend it on enabling your team in terms of mastery. Spend it on taking away barriers so they have more autonomy. I think you'll get way more out of your team. >> Yeah, I agree. I think it's, as Darren said, again, he's like, well, would you trust your people if you're on the fourth floor and they're on the sixth? So just-- >> Yeah, exactly. >> If you don't trust your people, you got to bigger issue than worrying about how many hours they're on Zoom, which is not the most productive use of time. >> People waste so much time in the office, and getting to the office. And by the way, I'm not saying that it's wrong, it's fine too. But it's not like the office is just unfettered productivity all the time, that's a total myth. >> Yes, so let's shift gears a little bit and talk about events. So, obviously, the CUBE's in the event business. We've had to flip completely 'cause all the events are, well, they're all going digital for sure, and/or postponing it or canceling. So we've had to flip and do all dial-ins and there's a whole lot of stuff about asynchronous. But for you, I think it's interesting because as a distributed company, you had Gainsight Pulse as that moment to bring people together physically. You're in the same boat as everybody else, physical is not an option this year. So how are you approaching Gainsight Pulse, both because it's a switch from what you've done in the past, but you at least had the benefit of being in a distributed world? So you probably have a lot of advantages over people that have never done this before. >> Yeah, that's a really interesting, insightful observation. So just for a context, Pulse is an event we do every year to bring together the customer success community. 'Cause, as you observed, there is value in coming together. And so this is not just for our employees, this is for all the customer success people, and actually increasingly product management people out there, coming together around this common goal of driving success for your customers. And it started in 2013 with 300 people, and last year, we had 5,000 people at our event in San Francisco. We had similar events in London and Sydney. And so it's a big deal. And there's a lot of value to coming together physically. But obviously, that's not possible now, nor is it advisable. And we said, okay, how do we convert this and not lose what's special about Pulse? And leverage, like you said, Jeff, the fact that we're good at distributed stuff in general. And so we created what we call Pulse Everywhere. We didn't want to call it Pulse Virtual or something like that, Pulse Webinar, because we didn't want to set the bar as just like, oh, my virtual event, my webinar. This is something different. And we called it Everywhere, 'cause it's Pulse wherever you are. And we joke, it's in your house, it's in your backyard, it's on the peloton, it's walking the dog. You could be wherever you are and join Pulse this year, May 13th and 14th. And what's amazing is last year we had 5,000 people in person, this year we already have 13,000 people registered as of the end of April. And so we'll probably have more than three times the number of people at Pulse Everywhere. And we're really bringing that physical event concept into the virtual, literally with, instead of a puppy pit, where you're in a physical event, you'll bring puppies often, we have a puppy cam where you can see the puppies. We're not giving up on all of our silly music videos and jokes and we actually ship cameras and high-end equipment to all the speakers' houses. So they're going to have a very nice digital experience, our attendees are. It's not going to be like watching a video conference call. It's going to be like watching a TV show, one much like what you try to do here, right? And so we have this amazing experience for all of our presenters and then for the audience. And we're really trying to say how do we make it so it feels like you're in this really connected community? You just happen to not be able to shake people's hands. So it's coming up in a few weeks. It's a big experiment, but we're excited about it. >> There's so many conversations, and we jumped in right away, when this was all going down, what defines a digital event? And like you, I don't like the word virtual. There's nothing fake or virtual. To me, virtual's second to life. And kind of-- >> Yeah. >> Video game world. And like you, we did, it can't be a webinar, right? And so, if you really kind of get into the attributes of what is a webinar? It's generally a one-way communication for a significant portion of the allocated time and you kind of get your questions in and hopefully they take 'em, right? It's not a truly kind of engaged process. That said, as you said, to have the opportunity to separate creation, distribution and consumption of the content, now opens up all types of opportunity. And that's before you get into the benefits of the democratization, as you said, we're seeing that with a lot of the clients we work with. Their registration numbers are giant. >> Totally. >> Because-- >> You're not traveling to spend money, yeah. >> It'll be curious to see what the conversion is and I don't know we have a lot of data there. But, such a democratizing opportunity. And then, you have people that are trying to force, as Ben Nelson said on, you know Ben from Minerva, right? A car is not a mechanical horse, they're trying to force this new thing into this old paradigm and have people sit for, I saw one today, 24 hours, in front of their laptop. It's like a challenge. And it's like, no, no, no. Have your rally moment, have your fun stuff, have your kind of your one-to-many, but really there's so much opportunity for many-to-many. >> Many-to-many. >> Make all the content out there, yeah. >> We've created this concept in this Pulse Everywhere event called Tribes. And the idea is that when you go to an event, the goal is actually partially content, but a lot of times it's connection. And so in any given big event, there's lots of little communities out there and you want to meet people "like you". Might be people in a similar phase of their career, a similar type of company, in our case, it could be companies in certain industry. And so these Tribes in our kind of Pulse Everywhere experience, let people break out into their own tribes, and then kind of basically chat with each other throughout the event. And so it's not the exact same thing as having a drink with people, but at least a little bit more of that serendipitous conversation. >> Right, no, it's different and I think that's really the message, right? It's different, it's not the same. But there's a lot of stuff you can do that you can't do in the physical way, so quit focusing on what you can't do and embrace what you can. So that's great. And good luck on the event. Again, give the plug for it. >> Yeah, it's May 13th and 14th. If you go to gainsightpulse.com you can sign up, and it's basically anything related to driving better success for your customers, better retention, less churn, and better product experience. It's a great event to learn. >> Awesome, so I want to shift gears one more time and really talk about leadership. That's really kind of the focus of this series that we've been doing. And tough times call for great leadership. And it's really an opportunity for great leaders to show their stuff and let the rest of us learn. You have a really fantastic style. You know I'm a huge fan, we're social media buddies. But you're very personable and you're very, kind of human, I guess, is really the best word, in your communications. You've got ton of frequency, ton of variety. But really, most of it has kind of this human thread. I wonder if you can share kind of your philosophy behind social, 'cause I think a lot of leaders are afraid of it. I think they're afraid that there is reward for saying something stupid is not worth the benefit of saying okay things. And I think also a lot of leaders are afraid of showing some frailty, showing some emotion. Maybe you're a little bit scared, maybe we don't have all the answers. And yet you've really, you're not afraid at all. And I think it's really shines in the leadership activities and behaviors and things you do day in and day out. So how do you think about it? What's your strategy? >> Yeah, it's really interesting you ask, Jeff, because I'm in a group of CEOs that get together on a regular basis, and I'm going to be leading a session on social media for CEOs. And honestly, when I was putting it together, I was like, it's 2020, does that still need to exist? But somehow, there is this barrier. And I'll talk more about it, but I think the barrier isn't just about social media, it's just about how a CEO wants to present herself or himself into the world. And I think, to me, the three things to ask yourself are, first of all, why? Why do you want to be on social media? Why do you want to communicate to the outside? You should have a why. Hopefully you enjoy it, but also you're connecting from a business perspective with your customers. And for us, it's been a huge benefit to really be able to connect with our customers. And then, who are you targeting? So, I actually think an important thing to think about is it's okay to have a micro-audience. I don't have millions of Twitter followers like Lady Gaga, but within the world of SaaS and customer success and retention, I probably have a decent number. And that means I can really connect with my own specific audience. And then, what. So, the what is really interesting 'cause I think there's a lot of non-obvious things about, it's not just about your business. So I can tweet about customer success or retention and I do, but also the, what, about you as an individual, what's happening in your family? What's happening in the broader industry, in my case of SaaS? What's happening in the world of leading through COVID-19? All the questions you've asked, Jeff, are in this lens. And then that gets you to the final which is the, how. And I think the, how, is the most important. It's basically whether you can embrace the idea of being vulnerable. There's a famous TED Talk by Brene Brown. She talks about vulnerability is the greatest superpower for leaders. I think the reason a lot of people have a hard time on social media, is they have a hard time really being vulnerable. And just saying, look, I'm just a human being just like all of you. I'm a privileged human being. I have a lot of things that luckily kind of came my way, but I'm just a human being. I get scared, I get anxious, I get lonely, all those things. Just like all of you, you know. And really being able to take off your armor of, oh, I'm a CEO. And then when you do that, you are more human. And it's like, this goes back to this concept of human first business. There's no work persona and home persona, there's just you. And I think it's surprising when you start doing it, and I started maybe seven, eight, nine years ago, it's like, wow, the world wants more human leaders. They want you to just be yourself, to talk about your challenges. I had the kids, when we got to 13,000 registrations for Pulse Everywhere, they pied me in the face. And the world wants to see CEOs being pied in the face. Probably that one, for sure, that's a guaranteed crowd pleaser. CEOs being pied in the face. But they want to see what you're into outside of work and the pop culture you're into. And they want to see the silly things that you're doing. They want you to be human. And so I think if you're willing to be vulnerable, which takes some bravery, it can really, really pay off for your business, but I think also for you as a person. >> Yeah, yeah. I think it's so insightful. And I think people are afraid of it for the wrong reasons, 'cause it is actually going to help people, it's going to help your own employees, as well, get to know you better. >> Totally, they love it. >> And you touched on another concept that I think is so important that I think a lot of people miss as we go from kind of the old broadcast world to more narrow casting, which is touching your audience and developing your relationship with your audience. So we have a concept here at theCUBE that one is greater than 1% of 100. Why go with the old broadcast model and just spray and you hope you have these really ridiculously low conversion rates to get to that person that you're trying to get to, versus just identifying that person and reaching out directly to those people, and having a direct engagement and a relative conversation within the people that care. And it's not everybody, but, as you said, within the population that cares about it it's meaningful and they get some value out of it. So it's a really kind of different strategy. So-- >> I love that. >> You're always get a lot of stuff out, but you are super prolific. So you got a bunch of projects that are just hitting today. So as we're getting ready to sit down, I see you just have a book came out. So tell us a little bit about the book that just came out. >> Sure, yeah, it's funny. I need to get my physical copy too at my home. I've got so a few, just for context. Five years ago, we released this first book on "Customer Success" which you can kind of see here. It's surprising really, really popular in this world of SaaS and customer success and it ties, Jeff, to what you just said which is, you don't need to be the book that everyone in the world reads, you need to be the book that everyone in your world reads. And so this book turned out to be that. Thousands of company management teams and CEOs in software and SaaS read it. And so, originally when this came out, it was just kind of an introduction to what we call customer success. Basically, how do you retain your customers for the long-term? How do you get them more value? And how do you get them to use more of what they've bought and eventually spend more money with you? And that's a mega-trend that's happening. We decided that we needed an update. So this second book is called "Customer Success Economy." It just came out, literally today. And it's available on Amazon. And it's about the idea that customer success started in tech companies, but it's now gone into many, many industries, like healthcare, manufacturing, services. And it started with a specific team called the customer success management team. But now it's affecting how companies build products, how they sell, how they market. So it's sort of this book is kind of a handbook for management teams on how to apply customer success to your whole business and we call it "Customer Success Economy" 'cause we do think the future of the economy isn't about marketing and selling transactional products, but it's about making sure what your customers are buying is actually delivering value for them, right? That's better for the world, but it's also just necessary 'cause your customers have the power now. You and I have the power to decide how to transport ourselves, whether it's buying a car or rideshare, in the old world when we could leave our house. And we have the power to decide how we're going to stay in a city, whether it's a hotel or Airbnb or whatever. And so customers have the power now, and if you're not driving success, you're not going to be able to keep those customers. And so "Customer Success Economy" is all about that. >> Yeah, and for people that aren't familiar with Gainsight, obviously, there's lots of resources that they can go. They should go to the show in a couple weeks, but also, I think, the interview that we did at PagerDuty, I think you really laid out kind of a great definition of what customer success is. And it's not CRM, it has nothing to do with CRM. CRM is tracking leads and tracking ops. It's not customer success. So, people can also check that. But I want to shift gears again a little bit because one, you also have your blog, MehtaPhysical, that came out. And you just came out again recently with a new post. I don't know when you, you must have a army of helper writers, but you talk about something that is really top of mind right now. And everyone that we get on theCUBE, especially big companies that have the benefit of a balance sheet with a few bucks in it, say we want to help our customers, we want to help our people be safe, obviously, that's first. But we also want to help our customers. But nobody ever really says what exactly does that mean? And it's pretty interesting. You lay out a bunch of things that are happening in the SaaS world, but I jumped on, I think it's number 10 of your list, which is how to think about helping your customers. And you give some real specific kind of guidance and guidelines and definitions, if you will, of how do you help our customers through these tough times. >> Yeah, so I'll summarize for the folks listening. One of the things we observed is, in this terrible tough times right now, your customers are in very different situations. And for simplicity, we thought about three categories. So the companies that we call category one, which are unfortunately, adversely affected by this terrible crisis, but also by the shutdown itself, and that's hotels, restaurants, airlines, and you can put other folks in that example. What do those customers need? Well, they probably need some financial relief. And you have to figure out what you're going to do there and that's a hard decision. And they also just need empathy. It's not easy and the stress level they have is massive. Then you've got, on the other extremes, a small number of your customers might be doing great despite this crisis or maybe even because of it, because they make video conferencing technology or remote work technology, or they make stuff for virtual or telemedicine. And those folks actually are likely to be super busy because they're just trying to keep up with the demand. So what they need from you is time and help. And then you got the people in between. Most companies, right, where there may be a mix of some things going well, some don't. And so what we recommended is think about your strategy, not just inside out, what you want, but outside in, what those clients need. And so as an example, you might think about in that first category, financial relief. The second category, the companies in the middle, they may need, they may not be willing to spend more money, but they may want to do more stuff. So maybe you unlock your product, make it available, so they can use everything in your suite for a while. And maybe in that third category, they're wiling to spend money, but they're just really busy. So maybe you offer services for them or things to help them as they scale. >> Yeah, so before I let you go, I just want to get your reaction to one more great leader. And as you can tell, I love great leaders and studying great leaders. Back when I was in business school we had Dave Pottruck, who at that time was the CEO of Schwab, come and speak and he's a phenomenal speaker and if you ever get a chance to see him speak. And at that point in time, Schwab had to reinvent their business with online trading and basically kill their call-in brokerage for online brokerage, and I think that they had a fixed price of 19.99, whatever it was. This was back in the late 90s. But he was a phenomenal speaker. And we finished and he had a small dinner with a group of people, and we just said, David, you are a phenomenal speaker, why, how, why're you so good? And he goes, you know, it's really pretty simple. As a CEO, I have one job. It's to communicate. And I have three constituencies. I kind of have the street and the market, I have my internal people, and then I have my customers and my ecosystem. And so he said, I, and he's a wrestler, he said, you know I treated it like wrestling. I hired a coach, I practiced my moves, I did it over and over, and I embraced it as a skill and it just showed so brightly. And it's such a contrast to people that get wrapped around the axle with their ego, or whatever. And I think you're such a shiny example of someone who over communicates, arguably, in terms of getting the message out, getting people on board, and letting people know what you're all about, what the priorities are, and where you're going. And it's such a sheer, or such a bright contrast to the people that don't do that that I think is so refreshing. And you do it in a fun and novel and in your own personal way. >> That's awesome to hear that story. He's a inspirational leader, and I've studied him, for sure. But I hadn't heard this specific story, and I totally agree with you. Communication is not something you're born with. Honestly, you might know this, Jeff, or not, as a kid, I was super lonely. I didn't really have any friends and I was one of those kids who just didn't fit in. So I was not the one they would pick to be on stage in front of thousands of people or anything else. But you just do it over and over again and you try to get better and you find, I think a big thing is finding your own voice, your own style. I'm not a super formal style, I try to be very human and authentic. And so finding your style that works for you, I agree, it's completely learnable. >> Yeah, well, Nick, thank you. Thanks for taking a few minutes. I'm sure you're super, super busy getting ready for the show in a couple weeks. But it's always great to catch up and really appreciate you taking some time to share your thoughts and insights with us. >> Thank you, Jeff, it's an honor. >> All right, he's Nick Mehta, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (soft music)

>> Is and who we are today as as a country, as a universe. >> Narrator: Congratulations Reggie Jackson, (inspirational music) you are a CUBE alumni. (upbeat music) >> Announcer: Live from Las Vegas it's theCUBE covering Splunk.Conf19. Brought to you by Splunk. >> Okay, welcome back everyone it's theCUBE's live coverage here in Las Vegas for Splunk.Conf19. I am John Furrier host of theCUBE. It's the 10th Anniversary of Splunk's .Conf user conference. Our 7th year covering it. It's been quite a ride, what a wave. Splunk keeps getting stronger and better, adding more features, and has really become a powerhouse from a third party security standpoint. We got a C-SO in theCUBE on theCUBE today. Chief Information Security, John Frushour Deputy Chief (mumbles) New York-Presbyterian The Award Winner from the Data to Everywhere Award winner, welcome by theCube. >> Thank you, thank you. >> So first of all, what is the award that you won? I missed the keynotes, I was working on a story this morning. >> Frushour: Sure, sure. >> What's the award? >> Yeah, the Data Everything award is really celebrating using Splunk kind of outside its traditional use case, you know I'm a security professional. We use Splunk. We're a Splunk Enterprise Security customer. That's kind of our daily duty. That's our primary use case for Splunk, but you know, New York Presbyterian developed the system to track narcotic diversion. We call it our medication analytics platform and we're using Splunk to track opioid diversion, slash narcotic diversions, same term, across our enterprise. So, looking for improper prescription usage, over prescription, under prescription, prescribing for deceased patients, prescribing for patients that you've never seen before, superman problems like taking one pill out of the drawer every time for the last thirty times to build up a stash. You know, not resupplying a cabinet when you should have thirty pills and you only see fifteen. What happened there? Everything's data. It's data everything. And so we use this data to try to solve this problem. >> So that's (mumbles) that's great usage we'll find the drugs, I'm going to work hard for it. But that's just an insider threat kind of concept. >> Frushour: Absolutely. >> As a C-SO, you know, security's obviously paramount. What's changed the most? 'Cause look at, I mean, just looking at Splunk over the past seven years, log files, now you got cloud native tracing, all the KPI's, >> Frushour: Sure. >> You now have massive volumes of data coming in. You got core business operations with IOT things all instrumental. >> Sure, sure. >> As a security offer, that's a pretty big surface area. >> Yeah. >> How do you look at that? What's your philosophy on that? >> You know, a lot of what we do, and my boss, the C-SO (mumbles) we look at is endpoint protection and really driving down to that smaller element of what we complete and control. I mean, ten, fifteen years ago information security was all about perimeter control, so you've got firewalls, defense and depth models. I have a firewall, I have a proxy, I have an endpoint solution, I have an AV, I have some type of data redaction capability, data masking, data labeling capability, and I think we've seen.. I don't think security's changed. I hear a lot of people say, "Oh, well, information security's so much different nowadays." No, you know, I'm a military guy. I don't think anything's changed, I think the target changed. And I think the target moved from the perimeter to the endpoint. And so we're very focused on user behavior. We're very focused on endpoint agents and what people are doing on their individual machines that could cause a risk. We're entitling and providing privilege to end users today that twenty years ago we would've never granted. You know, there was a few people with the keys to the kingdom, and inside the castle keep. Nowadays everybody's got an admin account and everybody's got some level of privilege. And it's the endpoint, it's the individual that we're most focused on, making sure that they're safe and they can operate effectively in hospitals. >> Interviewer: What are some of the tactical things that have changed? Obviously, the endpoint obviously shifted, so some tactics have to change probably again. Operationally, you still got to solve the same problem: attacks, insider threats, etc. >> Frushour: Yeah. >> What are the tactics? What new tactics have emerged that are critical to you guys? >> Yeah, that's a tough question, I mean has really anything changed? Is the game really the game? Is the con really the same con? You look at, you know, titans of security and think about guys like Kevin Mitnick that pioneered, you know, social engineering and this sort of stuff, and really... It's really just convincing a human to do something that they shouldn't do, right? >> Interviewer: Yeah. >> I mean you can read all these books about phone freaking and going in and convincing the administrative assistant that you're just late for meeting and you need to get in through that special door to get in that special room, and bingo. Then you're in a Telco closet, and you know, you've got access. Nowadays, you don't have to walk into that same administrative assistant's desk and convince 'em that you're just late for the meeting. You can send a phishing email. So the tactics, I think, have changed to be more personal and more direct. The phishing emails, the spear phishing emails, I mean, we're a large healthcare institution. We get hit with those types of target attacks every day. They come via mobile device, They come via the phishing emails. Look at the Google Play store. Just, I think, in the last month has had two apps that have had some type of backdoor or malicious content in them that got through the app store and got onto people's phones. We had to pull that off people's phones, which wasn't pretty. >> Interviewer: Yeah. >> But I think it's the same game. It's the same kind to convince humans to do stuff that they're not supposed to do. But the delivery mechanism, the tactical delivery's changed. >> Interviewer: How is Splunk involved? Cause I've always been a big fan of Splunk. People who know me know that I've pretty much been a fan boy. The way they handle large amounts of data, log files, (mumbles) >> Frushour: Sure. >> and then expand out into other areas. People love to use Splunk to bring in their data, and to bring it into, I hate to use the word data leg but I mean, Just getting... >> Yeah >> the control of the data. How is data used now in your world? Because you got a lot of things going on. You got healthcare, IOT, people. >> Frushour: Sure, sure. >> I mean lives are on the line. >> Frushour: Lives are on the line, yeah. >> And there's things you got to be aware of and data's key. What is your approach? >> Well first I'm going to shamelessly plug a quote I heard from (mumbles) this week, who leads the security practice. She said that data is the oxygen of AI, and I just, I love that quote. I think that's just a fantastic line. Data's the oxygen of AI. I wish I'd come up with it myself, but now I owe her a royalty fee. I think you could probably extend that and say data is the lifeline of Splunk. So, if you think about a use case like our medication analytics platform, we're bringing in data sources from our time clock system, our multi-factor authentication system, our remote access desktop system. Logs from our electronic medical records system, Logs from the cabinets that hold the narcotics that every time you open the door, you know, a log then is created. So, we're bringing in kind of everything that you would need to see. Aside from doing something with actual video cameras and tracking people in some augmented reality matrix whatever, we've got all the data sources to really pin down all the data that we need to pin down, "Okay, Nurse Sally, you know, you opened that cabinet on that day on your shift after you authenticated and pulled out this much Oxy and distributed it to this patient." I mean, we have a full picture and chain of everything. >> Full supply chain of everything. >> We can see everything that happens and with every new data source that's out there, the beauty of Splunk is you just add it to Splunk. I mean, the Splunk handles structured and unstructured data. Splunk handles cis log fees and JSON fees, and there's, I mean there's just, it doesn't matter You can just add that stream to Splunk, enrich those events that were reported today. We have another solution which we call the privacy platform. Really built for our privacy team. And in that scenario, kind of the same data sets. We're looking at time cards, we're looking at authentication, we're looking at access and you visited this website via this proxy on this day, but the information from the EMR is very critical because we're watching for people that open patient records when they're not supposed to. We're the number five hospital in the country. We're the number one hospital in the state of New York. We have a large (mumbles) of very important people that are our patients and people want to see those records. And so the privacy platform is designed to get audit trails for looking at all that stuff and saying, "Hey, Nurse Sally, we just saw that you looked at patient Billy's record. That's not good. Let's investigate." We have about thirty use cases for privacy. >> Interviewer: So it's not in context of what she's doing, that's where the data come in? >> That's where the data come in, I mean, it's advanced. Nurse Sally opens up the EMR and looks at patient Billy's record, maybe patient Billy wasn't on the chart, or patient Billy is a VIP, or patient Billy is, for whatever reason, not supposed to be on that docket for that nurse, on that schedule for that nurse, we're going to get an alarm. The privacy team's going to go, "Oh, well, were they supposed to look at that record?" I'm just giving you, kind of, like two or three uses cases, but there's about thirty of them. >> Yeah, sure, I mean, celebrities whether it's Donald Trump who probably went there at some point. Everyone wants to get his taxes and records to just general patient care. >> Just general patient care. Yeah, exactly, and the privacy of our patients is paramount. I mean, especially in this digital age where, like we talked about earlier, everyone's going after making a human do something silly, right? We want to ensure that our humans, our nurses, our best in class patient care professionals are not doing something with your record that they're not supposed to. >> Interviewer: Well John, I want to hear your thoughts on this story I did a couple weeks ago called the Industrial IOT Apocalypse: Now or Later? And the provocative story was simply trying to raise awareness that malware and spear phishing is just tactics for that. Endpoint is critical, obviously. >> Sure. >> You pointed that out, everyone kind of knows that . >> Sure. >> But until someone dies, until there's a catastrophe where you can take over physical equipment, whether it's a self-driving bus, >> Frushour: Yeah. >> Or go into a hospital and not just do ransom ware, >> Frushour: Absolutely. >> Actually using industrial equipment to kill people. >> Sure. >> Interviewer: To cause a lot of harm. >> Right. >> This is an industrial, kind of the hacking kind of mindset. There's a lot of conversations going on, not enough mainstream conversations, but some of the top people are talking about this. This is kind of a concern. What's your view on this? Is it something that needs to be talked about more of? Is it just BS? Should it be... Is there any signal there that's worth talking about around protecting the physical things that are attached to them? >> Oh, absolutely, I mean this is a huge, huge area of interest for us. Medical device security at New York Presbyterian, we have anywhere from about eighty to ninety thousand endpoints across the enterprise. Every ICU room in our organization has about seven to ten connected devices in the ICU room. From infusion pumps to intubation machines to heart rate monitors and SPO2 monitors, all this stuff. >> Interviewer: All IP and connected. >> All connected, right. The policy or the medium in which they're connected changes. Some are ZP and Bluetooth and hard line and WiFi, and we've got all these different protocols that they use to connect. We buy biomedical devices at volume, right? And biomedical devices have a long path towards FDA certification, so a lot of the time they're designed years before they're fielded. And when they're fielded, they come out and the device manufacturer says, "Alright, we've got this new widget. It's going to, you know, save lives, it's a great widget. It uses this protocol called TLS 1.0." And as a security professional I'm sitting there going, "Really?" Like, I'm not buying that but that's kind of the only game, that's the only widget that I can buy because that's the only widget that does that particular function and, you know, it was made. So, this is a huge problem for us is endpoint device security, ensuring there's no vulnerabilities, ensuring we're not increasing our risk profile by adding these devices to our network and endangering our patients. So it's a huge area. >> And also compatible to what you guys are thinking. Like I could imagine, like, why would you want a multi-threaded processor on a light bulb? >> Frushour: Yeah. >> I mean, scope it down, turn it on, turn it off. >> Frushour: Scope it down for its intended purpose, yeah, I mean, FDA certification is all about if the device performs its intended function. But, so we've, you know, we really leaned forward, our CSO has really leaned forward with initiatives like the S bomb. He's working closely with the FDA to develop kind of a set of baseline standards. Ports and protocols, software and services. It uses these libraries, It talks to these servers in this country. And then we have this portfolio that a security professional would say, "Okay, I accept that risk. That's okay, I'll put that on my network moving on." But this is absolutely a huge area of concern for us, and as we get more connected we are very, very leaning forward on telehealth and delivering a great patient experience from a mobile device, a phone, a tablet. That type of delivery mechanism spawns all kinds of privacy concerns, and inter-operability concerns with protocol. >> What's protected. >> Exactly. >> That's good, I love to follow up with you on that. Something we can double down on. But while we're here this morning I want to get back to data. >> Frushour: Sure. >> Thank you, by the way, for sharing that insight. Something I think's really important, industrial IOT protection. Diverse data is really feeds a lot of great machine learning. You're only as good as your next blind spot, right? And when you're doing pattern recognition by using data. >> Frushour: Absolutely. >> So data is data, right? You know, telecraft, other data. Mixing data could actually be a good thing. >> Frushour: Sure, sure. >> Most professionals would agree to that. How do you look at diverse data? Because in healthcare there's two schools of thought. There's the old, HIPAA. "We don't share anything." That client privacy, you mentioned that, to full sharing to get the maximum out of the AI or machine learning. >> Sure. >> How are you guys looking at that data, diverse data, the sharing? Cause in security sharing's good too, right? >> Sure, sure, sure. >> What's your thoughts on sharing data? >> I mean sharing data across our institutions, which we have great relationships with, in New York is very fluid at New York Presbyterian. We're a large healthcare conglomerate with a lot of disparate hospitals that came as a result of partnership and acquisition. They don't all use the same electronic health record system. I think right now we have seven in play and we're converging down to one. But that's a lot of data sharing that we have to focus on between seven different HR's. A patient could move from one institution to the next for a specialty procedure, and you got to make sure that their data goes with them. >> Yeah. >> So I think we're pretty, we're pretty decent at sharing the data when it needs to be shared. It's the other part of your question about artificial intelligence, really I go back to like dedication analytics. A large part of the medication analytics platform that we designed does a lot of anomaly detections, anomaly detection on diversion. So if we see that, let's say you're, you know, a physician and you do knee surgeries. I'm just making this up. I am not a clinician, so we're going to hear a lot of stupidity here, but bare with me. So you do knee surgeries, and you do knee surgeries once a day, every day, Monday through Friday, right? And after that knee surgery, which you do every day in cyclical form, you prescribe two thousand milligrams of Vicodin. That's your standard. And doctors, you know, they're humans. Humans are built on patterns. That's your pattern. Two thousand milligrams. That's worked for you; that's what you prescribe. But all of the sudden on Saturday, a day that you've never done a knee surgery in your life for the last twenty years, you all of a sudden perform a very invasive knee surgery procedure that apparently had a lot of complications because the duration of the procedure was way outside the bounds of all the other procedures. And if you're kind of a math geek right now you're probably thinking, "I see where he's going with this." >> Interviewer: Yeah. >> Because you just become an anomaly. And then maybe you prescribe ten thousand milligrams of Vicodin on that day. A procedure outside of your schedule with a prescription history that we've never seen before, that's the beauty of funneling this data into Splunk's ML Toolkit. And then visualizing that. I love the 3D visualization, right? Because anybody can see like, "Okay, all this stuff, the school of phish here is safe, but these I've got to focus on." >> Interviewer: Yeah. >> Right? And so we put that into the ML Toolkit and then we can see, "Okay, Dr. X.." We have ten thousand, a little over ten thousand physicians across New York Presbyterian. Doctor X right over here, that does not look like a normal prescriptive scenario as the rest of their baseline. And we can tweak this and we can change precision and we can change accuracy. We can move all this stuff around and say, "Well, let's just look on medical record number, Let's just focus on procedure type, Let's focus on campus location. What did they prescribe from a different campus?" That's anomalous. So that is huge for us, using the ML Toolkit to look at those anomalies and then drive the privacy team, the risk teams, the pharmacy analytics teams to say, "Oh, I need to go investigate." >> So, that's a lot of heavy lifting for ya? Let you guys look at data that you need to look at. >> Absolutely. >> Give ya a (mumbles). Final question, Splunk, in general, you're happy with these guys? Obviously, they do a big part of your data. What should people know about Splunk 2019, this year? And are you happy with them? >> Oh, I mean Splunk has been a great partner to New York Presbyterian. We've done so much incredible development work with them, and really, what I like to talk about is Splunk for healthcare. You know, we've created, we saw some really important problems in our space, in this article. But, we're looking, we're leaning really far forward into things like risk based analysis, peri-op services. We've got a microbial stewardship program, that we're looking at developing into Splunk, so we can watch that. That's a huge, I wouldn't say as big of a crisis as the opioid epidemic, but an equally important crisis to medical professionals across this country. And, these are all solvable problems, this is just data. Right? These are just events that happen in different systems. If we can get that into Splunk, we can cease the archaic practice of looking at spreadsheets, and look up tables and people spending days to find one thing to investigate. Splunk's been a great partner to us. The tool it has been fantastic in helping us in our journey to provide best in-class patient care. >> Well, congratulations, John Frushour, Deputy Chief Information Security Officer, New York Presbyterian. Thanks for that insight. >> You're welcome. >> Great (mumbles) healthcare and your challenge and your opportunity. >> Congratulations for the award winner Data to Everything award winner, got to get that slogan. Get used to that, it's two everything. Getting things done, he's a doer. I'm John Furrier, here on theCube doing the Cube action all day for three days. We're on day two, we'll be back with more coverage, after this short break. (upbeat music)

[Narrator] Live from Las Vegas, it's theCUBE. Covering Vmworld 2018. Brought to you by VMware, and its ecosystem Partners. >> Welcome back to theCUBE. We are live at Vmworld 2018, Mandalay Bay in Las Vegas. I'm Lisa Martin with Dave Vellante, hey Dave. >> Hey Lisa, how's it going? >> Great, this morning started off with tremendous amount of momentum from Pat Gelsinger, including a new tattoo that he debuted. 20th anniversary of VMware, 20th anniversary of the Rackspace, DellEMC partnership, please welcome to theCUBE a veteran and alumni, Peter Fitzgibbon, the VP and GM of the VMware practice at Rackspace. Peter it's great to have you back. >> It's great to be back here at Vmworld. >> And we're excited to welcome David Trigg to theCUBE, the Global Vice President of Market Development and Service Providers from DellEMC, welcome. >> Thank you, glad to but be here. So happy 20th anniversary to Rackspace and DellEMC. >> Thank you. >> Longstanding partnership, what's going on? A lot of momentum at Dell Tech World just, what, four months ago? What's some of the momentum that you guys have seen in your joint customer space this summer? >> Yeah, so at Dell Technologies World we launched our Rackspace private cloud, R by VMware, our Everywhere edition, as we're referring to it, which is extending Rackspace private clouds into customer data centers and colos. And since that announcement back at Dell Technologies World, we've seen fantastic adoption from both our existing installed base that's interested, and knows the Rackspace brand, and our fanatical experience, as well as new customers that know now we can service them in new locations. >> And then David, for you, Dell Technologies World was all about IT transformation, digital transformation, security transformation, and making it real. How is DellEMC working with Rackspace to help customers make these transformations a successful reality? >> Yeah, well one of the fist things, in my opinion, to highlight is the length of time that we have worked together, and through that length of time, Rackspace has made incredible investments in their skill set, their ability to manage infrastructure, you know, there's a lot of a deep knowledge there, so customers can feel very confident about the ability to provide the services. And as customers go through transformation, customers have more choices now, and more things, as we talk about the edge, and the core, and the cloud, they have to manage infrastructure in more places than they've ever had to manage before. So we're very proud of the relationship that we've had, the investments that they've made, because our customers are needing help in managing through, not only the transformations, but all of the choices that they have to make on where's the best place to put an application? Where's the best place to put a workload, and how do they manage the migrations and the modernization? So yeah, it plays very, very close into our transformation message, and quite frankly, we couldn't do it without partners like Rackspace. >> Let's talk a little bit more about that, because you're talking about more than just a storage partnership, right? Is that, >> Oh, yeah. >> A lot more to its, it's much more comprehensive, >> Absolutely. >> Sets of integration, practices, and areas of expertise, so let's double click on that a little bit. >> Yeah, there's a lot of skill sets that are required to even just do assessments, where I'm really understanding where do the applications go. Really then making sure that they understand, how do you support the infrastructure? How do you monitor the infrastructure and how do you make sure that it's running a lot? And again, Rackspace has made a lot of investments, is one of the best in the world in being able to do a lot of this. >> Let's talk a little bit more about that. Why Rackspace? >> Well, we are offering customers strategic flexibility, really. Whether they want to deploy in a Rackspace data center, a customer data center, get access to our deep expertise, not just a DellEMC, but our 150 plus VMware certified experts that our customers can now tap into, because this world gets more and more complex. And you saw the evening announcements this morning. It was like, how do our customers get the best value from those technologies, and not simply have shelf ware? Tap into Rackspace, with our partnerships with DellEMC and VMware to get the real value out of that expensive technology. >> So from a customer's standpoint, help us understand what's really going on. We asked the question a lot this week, is things like the AWS VMware partnership, is it a one-way trip to the cloud, or is it boom for the data center? And a lot of people are saying the latter. What are customers saying? What do they really want to do? >> Listen, customers are going to be living their data center for a long time yet to come. We've got legacy applications, they've got mainframes, we got client server applications, and then we have direct cloud-native applications, but there's a slew of applications in the middle where customers are kind of unsure about where to go, and they lean on a trusted partner like Rackspace, who really is cloud agnostic to help them figure out should they go public cloud? Should they be private cloud? Or are they in a hybrid cloud journey like everybody is on? So we want to be the Switzerland, where we can help people determine where they should go, and really offer unbiased expertise. >> So you guys announced, kind of along the lines of being Switzerland, at Dell Technologies World, Rackspace Private Cloud Everywhere, powered by VMware, Everywhere. I know you've got, what, five data centers in five continents. Talk about that Everywhere. How does it help customers to embrace the reality of multi-cloud, and to actually do so in a way that allows them to understand, working with you guys, where different applications should be placed at different times in the year? >> Yeah so, Everywhere is a natural evolution of what we've offered in our own data centers over time. So now deploying out in customer data centers and colos, well later this year, we hope to launch a formal VMware on AWS software as well. So Everywhere constitutes three parts, really, Rackspace data centers, customer data centers, to get as close to their data as needs be, and VMware on AWS as our product matures, as you saw from a number of announcements this morning. >> And to add on to John's question about the promise of the cloud, I think the original promise, and maybe the threat of the cloud was everything was going at the cloud. Well as we're learning through IoT and other new, emerging trends, that's not realistic. Customers really have to think about the edge, their own data center, because their own data centers are not going away. They have to think about the SLAs that they're providing to their end users, to their employees, and that's where you have to place the application, the workload in the right place to enable the best customer experience for their customers and their employees. And that's were a company like Rackspace, that can really get to the edge, the core, the cloud, by managing that infrastructure regardless. Obviously, the investments that VMware's making to help enable that as well, and being supported by a lot of the DellEMC stuff. It's an exciting time, I think. >> I Want to follow up on that, because Peter, off camera said cloud migration doesn't mean leaving your data center. >> Absolutely. >> This Gartner analyst came out, not that recently, but I think it was last year, and said that 80% of data centers will shut down by 2025, so that caused a lot of, right? Both eye rolling and no way, and et cetera. The Wikibon crew, which is affiliated with theCUBE, a sister company, sister division, just came out with a report that said true private cloud is going to be a $32 billion market this year. So that means on-prem cloud. >> Yep. >> So you have all these countervailing messages going on. Then you see, of course, the epitome of Andy Jassy up on stage today with Pat Gelsinger talking about hybrid cloud. What do you guys make of all of this? What's really happening and going to happen? >> I think customer data centers are going to live for some time to come, as people figure out where should the workload actually go? What can they do with that specific workload? Can they refactor it and rebuild it and go cloud-native? Great. Can they move to a hosted private cloud model with Rackspace rolling racks into a customer data center? Or is it a legacy application that really needs to be kept and maintained over time until the next disruption happens, where they really have to refactor it? >> Yeah, really, in that case there may be no business case. Why lift in and shift it, for what? Just to say, >> Exactly, they get it. >> Hey I'm in the cloud. >> Exactly, I think with cloud migrations, does not mean leaving your data center. I think that's going to continue for some time, where people can get the benefits from Rackspace, moving from a CapEX to an OpEx model with managed services, with industry leading SLAs, but still in their own data center, because they have applications running that cannot be moved. >> Well it's interesting, David to see this equilibrium that's kind of being reached, you know? A few short years ago, there was sort of antagonism between VMware and the AWS. You know, the whole book seller comment. Andy Jassy was like, pfft, on-prem cloud, there's no such thing, and now you see those worlds coming together, underscoring the reality that you can't just shove your business into the public cloud. You can't just move all your data there, and there may not be a business case, or an advantage of doing that. >> Right, right. >> What do you think? >> Well a lot of times the answer to the question in the, one, I'm not an analyst, so it's not my job to really predict where it's going to go. I mean, obviously we watch trends and look where it's going. You know, my job and our job is to help customers deal with the realities that they're dealing with right now, right? And they have data centers. They are thinking about the cloud. They are having to take care of the edge, right? And in time, we've seen some of those shifts, right? There was a lot of the, where are we going with the cloud? Where's it going to go? Are they going to shut down all their data centers? Regardless of that, we will adjust to the market and make sure that we're adjusting the market. But more importantly, we're going to do what's right for our customers, to help enable them to those journeys, and it's still yet to be proven. There's a lot of Predictions out there. Will they shut down all data centers? I'm sure there'll be some consolidation of it, but yeah, it's getting more complex. >> Okay, so VMware, Rackspace, DellEMC, you're not screwed, check. (David laughs) what about the edge? Help us unpack that a little bit, you know, whether VMware at the edge, Rackspace, DellEMC, what do you guys see evolving there? >> I think there's many definitions of the edge, and when you talk about it, everything's IoT initially, but even just deploying smaller data centers in customer locations in partnerships with these guys, to kind of meet customers where they are, and get smaller, roll in racks into different locations is continuing to be something that customers are looking for. >> So there's the hinterland edge, which is a bunch of devices, you know IP cameras, they're going to be instrument, most of the data's going to stay there anyway, but then I think you guys call it, I don't know, the core. There's a aggregation point, >> Well the core, >> if you will. >> which is typically what we refer to as kind of the customer data center, and then there's the cloud, right? So kind of the two different, customer data center versus the cloud, and then, truly, the edge, capturing, And it started, and we referred to everything from laptops, phones, as well as, really a lot of the sensors that are going to be out there, and your ability to have to process and analyze and react real time at the edge. And so a lot of use cases, public safety use cases, where, you know, when an event happens, that connection back to a place where you would analyze it. Obviously the autonomous cars, right? They can't have to connect to a data center every time it wants to make a left turn. So a lot of that ability has to be pushed out to the edge, but yet, then also being able to bring that data back, be able to manage that, and be able to update those computers, or those data centers. I mean, an autonomous car is basically a mini data center. Someone's got to manage that, patch that, make sure it's running, and manage that. So yeah, to your point, the edge is beginning to mean a lot of different things. There are the hinterlands, I think was the word you used, and some of those things, but then there are the more traditional work cases, and even just running a phone app is now considered an app, versus, you know, and that's were people start to really look at, is how do you deliver that experience on a phone, and that's an application. >> A lot of data. Well, I like to follow the data, you know? A lot of data at the edge. There's a lot of data, like I say, at the aggregation point, and then if you want to do some hardcore modeling, go to the cloud, and that cloud can be your own, on-prem data center. >> Yeah. >> Right. >> Yeah, there's just so much data being generated, and data is power, I think was one of the key taglines of DellEMC Worlds. And I was like, it truly is. Where the data is, is where the power is. So some has to be transferred back to the core. Some may be pushed up to the cloud for deep processing with AI and ML type processing, but there'll be data at all these different points. >> Well that's the other point, is it's just like the innovation engine no longer is Moore's Law in this industry. it's the data, applying machine intelligence, and then cloud scale. And then, you got to, as suppliers in this business, you better be playing in some way, shape, or form, in all three of those, right? >> Absolutely. >> So how, and speaking of that, I think Pat Gelsinger talked about it this morning in the context of superpowers. He talked about autonomous vehicles, AI machine learning, advanced analytics, IoT. How is DellEMC and their technology, Peter, helping to enable Rackspace to optimize your offerings to be able to take advantage of machine learning AI? To be able to deliver on customer expectations? >> Yeah, we're deeply partnered with these guys from those announcements you head earlier this year, that we're already investigating the different capabilities they're having from an AIML perspective, and really seeing what sort of technologies are they launching that we can then put into our private cloud practice, and offer to our customers. So it's our deep partnership allows us to kind of get a front seat at that and working closely to investigate and to do a lot of R&D with the new capabilities they're coming out with so. >> What superpower does that give Rackspace? In terms of differentiation? >> Uh, you've stumped me on that one. (laughs) >> Well customers have, we talked about, everybody wants flexibility. They also have choice. >> Yeah. >> What are some things that this 20 year partnership infuses into Rackspace to give you those differentiation points? >> Yeah, it's the deep partnership, and knowing, working so long together that we know who to pick up the phone to solve some of these complex problems. >> Yeah, and of us, from my perspective, we always start with out joint customers in mind first. So it's our job to bring the advance technologies, the advanced capabilities that we're making big investments in, and make sure that Rackspace is able to support and leverage those within their business so that we'd provide a better experience for the end customer, but then also making sure that we show Rackspace how they make money on that, and how they can run a business on that, that's really, is differentiated to your point. Because a lot of, you know, you painted a very pretty vision of what the world might look like. Most customers aren't there yet. Most customers aren't taking advantage of AI and deep learning. They're still dealing with some very traditional legacy issues, and it's that gap that becomes very, we love talking about the cool, new, exciting stuff, but for a lot of customers, they're stuck somewhere in the middle, and that's were partnerships like this, because you can not only help them with the legacy, old stuff. How do you migrate, and then how do you take advantage of the really new stuff? Or how do start at least thinking about that and exploring that and looking. A lot of the original IoT use cases, the ROI wasn't known. They're setting up projects, then they hoped they'd get a benefit out of it, right? And that's continuing to emerge and evolve as time goes on. >> Well it's hard, too. I mean, everybody's afraid of getting Uberized and disrupted, et cetera, et cetera, but they, at the same time, if you over rotate, to a new, you can spend bunch of money and not get any return. >> Yeah. >> Everybody's trying to get digital right, it seems, but it's unclear what that means. So they look to partners like you to help them figure that out. >> Well, it's a scary journey to your point, because they obviously have existing revenue streams. It's the inventor's dilemma, right? It's they have existing revenue streams, but how do they digitize their business? How do they reach customers in a different way? And so they don't become Uberized or Airbnbed, or whatever, what term you want to use. Every CIO, every executive is thinking about that. IT for a long time was about taking cost out of the business which, after a while, that's no fun, because that usually means head count reductions, that usually, I mean, that's not a fun conversation to have every single day. Now with the digital transformations mode, how do you generate new revenue streams? How do you, in a way, a lot of companies never, one of the most older industries, being taxis, a little bit not that exciting. It's gotten reinvigorated through some of these things. So it's kind of cool. >> Yeah, and you said digital transformation, right? What does that really mean? Cloud transformation, security transformation, app transformation, so there's many different factors. And companies like Rackspace can offer expertise in all those different areas, where some of our competitors may only hit on one of those. They're only a security company, or only a VMware shop, or only an AWS shop. >> Helping customers really glean the power from that data, because if they can't, it's not powerful. Gentlemen, thank you so much for stopping by theCUBE and talking with Dave and me. We appreciate hearing what's going on with Rackspace and DellEMC. >> Thanks guys. >> Thank you so much, I appreciate it. >> Thanks very much. I appreciate the time. >> Thank you. >> We want to thank you for watching theCUBE. For Dave Vellante, I am Lisa Martin. We're at VMworld day one. Stick around, we'll be back after break. (upbeat electronic music)