>>Good evening everyone and welcome back to Sparkly Sin City, Las Vegas, Nevada, where we are here with the cube covering AWS Reinvent for the 10th year in a row. John Furrier has been here for all 10. John, we are in our last session of day one. How does it compare? >>I just graduated high school 10 years ago. It's exciting to be, here's been a long time. We've gotten a lot older. My >>Got your brain is complex. You've been a lot in there. So fast. >>Graduated eight in high school. You know how it's No. All good. This is what's going on. This next segment, wrapping up day one, which is like the the kickoff. The Mondays great year. I mean Tuesdays coming tomorrow big days. The announcements are all around the kind of next gen and you're starting to see partnering and integration is a huge part of this next wave cuz API's at the cloud, next gen cloud's gonna be deep engineering integration and you're gonna start to see business relationships and business transformation scale a horizontally, not only across applications but companies. This has been going on for a while, covering it. This next segment is gonna be one of those things that we're gonna look at as something that's gonna happen more and more on >>Yeah, I think so. It's what we've been talking about all day. Without further ado, I would like to welcome our very exciting guest for this final segment, trust from single store. Thank you for being here. And we also have him on from IBM Data and ai. Y'all are partners. Been partners for about a year. I'm gonna go out on a limb only because their legacy and suspect that a few people, a few more people might know what IBM does versus what a single store does. So why don't you just give us a little bit of background so everybody knows what's going on. >>Yeah, so single store is a relational database. It's a foundational relational systems, but the thing that we do the best is what we call us realtime analytics. So we have these systems that are legacy, which which do operations or analytics. And if you wanted to bring them together, like most of the applications want to, it's really a big hassle. You have to build an ETL pipeline, you'd have to duplicate the data. It's really faulty systems all over the place and you won't get the insights really quickly. Single store is trying to solve that problem elegantly by having an architecture that brings both operational and analytics in one place. >>Brilliant. >>You guys had a big funding now expanding men. Sequel, single store databases, 46 billion again, databases. We've been saying this in the queue for 12 years have been great and recently not one database will rule the world. We know that. That's, everyone knows that databases, data code, cloud scale, this is the convergence now of all that coming together where data, this reinvent is the theme. Everyone will be talking about end to end data, new kinds of specialized services, faster performance, new kinds of application development. This is the big part of why you guys are working together. Explain the relationship, how you guys are partnering and engineering together. >>Yeah, absolutely. I think so ibm, right? I think we are mainly into hybrid cloud and ai and one of the things we are looking at is expanding our ecosystem, right? Because we have gaps and as opposed to building everything organically, we want to partner with the likes of single store, which have unique capabilities that complement what we have. Because at the end of the day, customers are looking for an end to end solution that's also business problems. And they are very good at real time data analytics and hit staff, right? Because we have transactional databases, analytical databases, data lakes, but head staff is a gap that we currently have. And by partnering with them we can essentially address the needs of our customers and also what we plan to do is try to integrate our products and solutions with that so that when we can deliver a solution to our customers, >>This is why I was saying earlier, I think this is a a tell sign of what's coming from a lot of use cases where people are partnering right now you got the clouds, a bunch of building blocks. If you put it together yourself, you can build a durable system, very stable if you want out of the box solution, you can get that pre-built, but you really can't optimize. It breaks, you gotta replace it. High level engineering systems together is a little bit different, not just buying something out of the box. You guys are working together. This is kind of an end to end dynamic that we're gonna hear a lot more about at reinvent from the CEO ofs. But you guys are doing it across companies, not just with aws. Can you guys share this new engineering business model use case? Do you agree with what I'm saying? Do you think that's No, exactly. Do you think John's crazy, crazy? I mean I all discourse, you got out of the box, engineer it yourself, but then now you're, when people do joint engineering project, right? They're different. Yeah, >>Yeah. No, I mean, you know, I think our partnership is a, is a testament to what you just said, right? When you think about how to achieve realtime insights, the data comes into the system and, and the customers and new applications want insights as soon as the data comes into the system. So what we have done is basically build an architecture that enables that we have our own storage and query engine indexing, et cetera. And so we've innovated in our indexing in our database engine, but we wanna go further than that. We wanna be able to exploit the innovation that's happening at ibm. A very good example is, for instance, we have a native connector with Cognos, their BI dashboards right? To reason data very natively. So we build a hyper efficient system that moves the data very efficiently. A very other good example is embedded ai. >>So IBM of course has built AI chip and they have basically advanced quite a bit into the embedded ai, custom ai. So what we have done is, is as a true marriage between the engineering teams here, we make sure that the data in single store can natively exploit that kind of goodness. So we have taken their libraries. So if you have have data in single store, like let's imagine if you have Twitter data, if you wanna do sentiment analysis, you don't have to move the data out model, drain the model outside, et cetera. We just have the pre-built embedded AI libraries already. So it's a, it's a pure engineering manage there that kind of opens up a lot more insights than just simple analytics and >>Cost by the way too. Moving data around >>Another big theme. Yeah. >>And latency and speed is everything about single store and you know, it couldn't have happened without this kind of a partnership. >>So you've been at IBM for almost two decades, don't look it, but at nearly 17 years in how has, and maybe it hasn't, so feel free to educate us. How has, how has IBM's approach to AI and ML evolved as well as looking to involve partnerships in the ecosystem as a, as a collaborative raise the water level together force? >>Yeah, absolutely. So I think when we initially started ai, right? I think we are, if you recollect Watson was the forefront of ai. We started the whole journey. I think our focus was more on end solutions, both horizontal and vertical. Watson Health, which is more vertically focused. We were also looking at Watson Assistant and Watson Discovery, which were more horizontally focused. I think it it, that whole strategy of the world period of time. Now we are trying to be more open. For example, this whole embedable AI that CICE was talking about. Yeah, it's essentially making the guts of our AI libraries, making them available for partners and ISVs to build their own applications and solutions. We've been using it historically within our own products the past few years, but now we are making it available. So that, how >>Big of a shift is that? Do, do you think we're seeing a more open and collaborative ecosystem in the space in general? >>Absolutely. Because I mean if you think about it, in my opinion, everybody is moving towards AI and that's the future. And you have two option. Either you build it on your own, which is gonna require significant amount of time, effort, investment, research, or you partner with the likes of ibm, which has been doing it for a while, right? And it has the ability to scale to the requirements of all the enterprises and partners. So you have that option and some companies are picking to do it on their own, but I believe that there's a huge amount of opportunity where people are looking to partner and source what's already available as opposed to investing from the scratch >>Classic buy versus build analysis for them to figure out, yeah, to get into the game >>And, and, and why reinvent the wheel when we're all trying to do things at, at not just scale but orders of magnitude faster and and more efficiently than we were before. It, it makes sense to share, but it's, it is, it does feel like a bit of a shift almost paradigm shift in, in the culture of competition versus how we're gonna creatively solve these problems. There's room for a lot of players here, I think. And yeah, it's, I don't >>Know, it's really, I wanted to ask if you don't mind me jumping in on that. So, okay, I get that people buy a bill I'm gonna use existing or build my own. The decision point on that is, to your point about the path of getting the path of AI is do I have the core competency skills, gap's a big issue. So, okay, the cube, if you had ai, we'd take it cuz we don't have any AI engineers around yet to build out on all the linguistic data we have. So we might use your ai but I might say this to then and we want to have a core competency. How do companies get that core competency going while using and partnering with, with ai? What you guys, what do you guys see as a way for them to get going? Because I think some people probably want to have core competency of >>Ai. Yeah, so I think, again, I think I, I wanna distinguish between a solution which requires core competency. You need expertise on the use case and you need expertise on your industry vertical and your customers versus the foundational components of ai, which are like, which are agnostic to the core competency, right? Because you take the foundational piece and then you further train it and define it for your specific use case. So we are not saying that we are experts in all the industry verticals. What we are good at is like foundational components, which is what we wanna provide. Got it. >>Yeah, that's the hard deep yes. Heavy lift. >>Yeah. And I can, I can give a color to that question from our perspective, right? When we think about what is our core competency, it's about databases, right? But there's a, some biotic relationship between data and ai, you know, they sort of like really move each other, right? You >>Need, they kind of can't have one without the other. You can, >>Right? And so the, the question is how do we make sure that we expand that, that that relationship where our customers can operationalize their AI applications closer to the data, not move the data somewhere else and do the modeling and then training somewhere else and dealing with multiple systems, et cetera. And this is where this kind of a cross engineering relationship helps. >>Awesome. Awesome. Great. And then I think companies are gonna want to have that baseline foundation and then start hiring in learning. It's like driving the car. You get the keys when you're ready to go. >>Yeah, >>Yeah. Think I'll give you a simple example, right? >>I want that turnkey lifestyle. We all do. Yeah, >>Yeah. Let me, let me just give you a quick analogy, right? For example, you can, you can basically make the engines and the car on your own or you can source the engine and you can make the car. So it's, it's basically an option that you can decide. The same thing with airplanes as well, right? Whether you wanna make the whole thing or whether you wanna source from someone who is already good at doing that piece, right? So that's, >>Or even create a new alloy for that matter. I mean you can take it all the way down in that analogy, >>Right? Is there a structural change and how companies are laying out their architecture in this modern era as we start to see this next let gen cloud emerge, teams, security teams becoming much more focused data teams. Its building into the DevOps into the developer pipeline, seeing that trend. What do you guys see in the modern data stack kind of evolution? Is there a data solutions architect coming? Do they exist yet? Is that what we're gonna see? Is it data as code automation? How do you guys see this landscape of the evolving persona? >>I mean if you look at the modern data stack as it is defined today, it is too detailed, it's too OSes and there are way too many layers, right? There are at least five different layers. You gotta have like a storage you replicate to do real time insights and then there's a query layer, visualization and then ai, right? So you have too many ETL pipelines in between, too many services, too many choke points, too many failures, >>Right? Etl, that's the dirty three letter word. >>Say no to ETL >>Adam Celeste, that's his quote, not mine. We hear that. >>Yeah. I mean there are different names to it. They don't call it etl, we call it replication, whatnot. But the point is hassle >>Data is getting more hassle. More >>Hassle. Yeah. The data is ultimately getting replicated in the modern data stack, right? And that's kind of one of our thesis at single store, which is that you'd have to converge not hyper specialize and conversation and convergence is possible in certain areas, right? When you think about operational analytics as two different aspects of the data pipeline, it is possible to bring them together. And we have done it, we have a lot of proof points to it, our customer stories speak to it and that is one area of convergence. We need to see more of it. The relationship with IBM is sort of another step of convergence wherein the, the final phases, the operation analytics is coming together and can we take analytics visualization with reports and dashboards and AI together. This is where Cognos and embedded AI comes into together, right? So we believe in single store, which is really conversions >>One single path. >>A shocking, a shocking tie >>Back there. So, so obviously, you know one of the things we love to joke about in the cube cuz we like to goof on the old enterprise is they solve complexity by adding more complexity. That's old. Old thinking. The new thinking is put it under the covers, abstract the way the complexities and make it easier. That's right. So how do you guys see that? Because this end to end story is not getting less complicated. It's actually, I believe increasing and complication complexity. However there's opportunities doing >>It >>More faster to put it under the covers or put it under the hood. What do you guys think about the how, how this new complexity gets managed or in this new data world we're gonna be coming in? >>Yeah, so I think you're absolutely right. It's the world is becoming more complex, technology is becoming more complex and I think there is a real need and it's not just from coming from us, it's also coming from the customers to simplify things. So our approach around AI is exactly that because we are essentially providing libraries, just like you have Python libraries, there are libraries now you have AI libraries that you can go infuse and embed deeply within applications and solutions. So it becomes integrated and simplistic for the customer point of view. From a user point of view, it's, it's very simple to consume, right? So that's what we are doing and I think single store is doing that with data, simplifying data and we are trying to do that with the rest of the portfolio, specifically ai. >>It's no wonder there's a lot of synergy between the two companies. John, do you think they're ready for the Instagram >>Challenge? Yes, they're ready. Uhoh >>Think they're ready. So we're doing a bit of a challenge. A little 32nd off the cuff. What's the most important takeaway? This could be your, think of it as your thought leadership sound bite from AWS >>2023 on Instagram reel. I'm scrolling. That's the Instagram, it's >>Your moment to stand out. Yeah, exactly. Stress. You look like you're ready to rock. Let's go for it. You've got that smile, I'm gonna let you go. Oh >>Goodness. You know, there is, there's this quote from astrophysics, space moves matter, a matter tells space how to curve. They have that kind of a relationship. I see the same between AI and data, right? They need to move together. And so AI is possible only with right data and, and data is meaningless without good insights through ai. They really have that kind of relationship and you would see a lot more of that happening in the future. The future of data and AI are combined and that's gonna happen. Accelerate a lot faster. >>Sures, well done. Wow. Thank you. I am very impressed. It's tough hacks to follow. You ready for it though? Let's go. Absolutely. >>Yeah. So just, just to add what is said, right, I think there's a quote from Rob Thomas, one of our leaders at ibm. There's no AI without ia. Essentially there's no AI without information architecture, which essentially data. But I wanna add one more thing. There's a lot of buzz around ai. I mean we are talking about simplicity here. AI in my opinion is three things and three things only. Either you use AI to predict future for forecasting, use AI to automate things. It could be simple, mundane task, it would be complex tasks depending on how exactly you want to use it. And third is to optimize. So predict, automate, optimize. Anything else is buzz. >>Okay. >>Brilliantly said. Honestly, I think you both probably hit the 32nd time mark that we gave you there. And the enthusiasm loved your hunger on that. You were born ready for that kind of pitch. I think they both nailed it for the, >>They nailed it. Nailed it. Well done. >>I I think that about sums it up for us. One last closing note and opportunity for you. You have a V 8.0 product coming out soon, December 13th if I'm not mistaken. You wanna give us a quick 15 second preview of that? >>Super excited about this. This is one of the, one of our major releases. So we are evolving the system on multiple dimensions on enterprise and governance and programmability. So there are certain features that some of our customers are aware of. We have made huge performance gains in our JSON access. We made it easy for people to consume, blossom on OnPrem and hybrid architectures. There are multiple other things that we're gonna put out on, on our site. So it's coming out on December 13th. It's, it's a major next phase of our >>System. And real quick, wasm is the web assembly moment. Correct. And the new >>About, we have pioneers in that we, we be wasm inside the engine. So you could run complex modules that are written in, could be C, could be rushed, could be Python. Instead of writing the the sequel and SQL as a store procedure, you could now run those modules inside. I >>Wanted to get that out there because at coupon we covered that >>Savannah Bay hot topic. Like, >>Like a blanket. We covered it like a blanket. >>Wow. >>On that glowing note, Dre, thank you so much for being here with us on the show. We hope to have both single store and IBM back on plenty more times in the future. Thank all of you for tuning in to our coverage here from Las Vegas in Nevada at AWS Reinvent 2022 with John Furrier. My name is Savannah Peterson. You're watching the Cube, the leader in high tech coverage. We'll see you tomorrow.

>>Good morning fellow nerds and welcome back to AWS Reinvent. We are live from the show floor here in Las Vegas, Nevada. My name is Savannah Peterson, joined with my fabulous co-host John Furrier. Day two keynotes are rolling. >>Yeah. What do you thinking this? This is the day where everything comes, so the core gets popped off the bottle, all the announcements start flowing out tomorrow. You hear machine learning from swee lot more in depth around AI probably. And then developers with Verner Vos, the CTO who wrote the seminal paper in in early two thousands around web service that becames. So again, just another great year of next level cloud. Big discussion of data in the keynote bulk of the time was talking about data and business intelligence, business transformation easier. Is that what people want? They want the easy button and we're gonna talk a lot about that in this segment. I'm really looking forward to this interview. >>Easy button. We all want the >>Easy, we want the easy button. >>I love that you brought up champagne. It really feels like a champagne moment for the AWS community as a whole. Being here on the floor feels a bit like the before times. I don't want to jinx it. Our next guest, Scott Castle, from Si Sense. Thank you so much for joining us. How are you feeling? How's the show for you going so far? Oh, >>This is exciting. It's really great to see the changes that are coming in aws. It's great to see the, the excitement and the activity around how we can do so much more with data, with compute, with visualization, with reporting. It's fun. >>It is very fun. I just got a note. I think you have the coolest last name of anyone we've had on the show so far, castle. Oh, thank you. I'm here for it. I'm sure no one's ever said that before, but I'm so just in case our audience isn't familiar, tell us about >>Soy Sense is an embedded analytics platform. So we're used to take the queries and the analysis that you can power off of Aurora and Redshift and everything else and bring it to the end user in the applications they already know how to use. So it's all about embedding insights into tools. >>Embedded has been a, a real theme. Nobody wants to, it's I, I keep using the analogy of multiple tabs. Nobody wants to have to leave where they are. They want it all to come in there. Yep. Now this space is older than I think everyone at this table bis been around since 1958. Yep. How do you see Siente playing a role in the evolution there of we're in a different generation of analytics? >>Yeah, I mean, BI started, as you said, 58 with Peter Lu's paper that he wrote for IBM kind of get became popular in the late eighties and early nineties. And that was Gen one bi, that was Cognos and Business Objects and Lotus 1 23 think like green and black screen days. And the way things worked back then is if you ran a business and you wanted to get insights about that business, you went to it with a big check in your hand and said, Hey, can I have a report? And they'd come back and here's a report. And it wasn't quite right. You'd go back and cycle, cycle, cycle and eventually you'd get something. And it wasn't great. It wasn't all that accurate, but it's what we had. And then that whole thing changed in about two, 2004 when self-service BI became a thing. And the whole idea was instead of going to it with a big check in your hand, how about you make your own charts? >>And that was totally transformative. Everybody started doing this and it was great. And it was all built on semantic modeling and having very fast databases and data warehouses. Here's the problem, the tools to get to those insights needed to serve both business users like you and me and also power users who could do a lot more complex analysis and transformation. And as the tools got more complicated, the barrier to entry for everyday users got higher and higher and higher to the point where now you look, look at Gartner and Forester and IDC this year. They're all reporting in the same statistic. Between 10 and 20% of knowledge workers have learned business intelligence and everybody else is just waiting in line for a data analyst or a BI analyst to get a report for them. And that's why the focus on embedded is suddenly showing up so strong because little startups have been putting analytics into their products. People are seeing, oh my, this doesn't have to be hard. It can be easy, it can be intuitive, it can be native. Well why don't I have that for my whole business? So suddenly there's a lot of focus on how do we embed analytics seamlessly? How do we embed the investments people make in machine learning in data science? How do we bring those back to the users who can actually operationalize that? Yeah. And that's what Tysons does. Yeah. >>Yeah. It's interesting. Savannah, you know, data processing used to be what the IT department used to be called back in the day data processing. Now data processing is what everyone wants to do. There's a ton of data we got, we saw the keynote this morning at Adam Lesky. There was almost a standing of vision, big applause for his announcement around ML powered forecasting with Quick Site Cube. My point is people want automation. They want to have this embedded semantic layer in where they are not having all the process of ETL or all the muck that goes on with aligning the data. All this like a lot of stuff that goes on. How do you make it easier? >>Well, to be honest, I, I would argue that they don't want that. I think they, they think they want that, cuz that feels easier. But what users actually want is they want the insight, right? When they are about to make a decision. If you have a, you have an ML powered forecast, Andy Sense has had that built in for years, now you have an ML powered forecast. You don't need it two weeks before or a week after in a report somewhere. You need it when you're about to decide do I hire more salespeople or do I put a hundred grand into a marketing program? It's putting that insight at the point of decision that's important. And you don't wanna be waiting to dig through a lot of infrastructure to find it. You just want it when you need it. What's >>The alternative from a time standpoint? So real time insight, which is what you're saying. Yep. What's the alternative? If they don't have that, what's >>The alternative? Is what we are currently seeing in the market. You hire a bunch of BI analysts and data analysts to do the work for you and you hire enough that your business users can ask questions and get answers in a timely fashion. And by the way, if you're paying attention, there's not enough data analysts in the whole world to do that. Good luck. I am >>Time to get it. I really empathize with when I, I used to work for a 3D printing startup and I can, I have just, I mean, I would call it PTSD flashbacks of standing behind our BI guy with my list of queries and things that I wanted to learn more about our e-commerce platform in our, in our marketplace and community. And it would take weeks and I mean this was only in 2012. We're not talking 1958 here. We're talking, we're talking, well, a decade in, in startup years is, is a hundred years in the rest of the world life. But I think it's really interesting. So talk to us a little bit about infused and composable analytics. Sure. And how does this relate to embedded? Yeah. >>So embedded analytics for a long time was I want to take a dashboard I built in a BI environment. I wanna lift it and shift it into some other application so it's close to the user and that is the right direction to go. But going back to that statistic about how, hey, 10 to 20% of users know how to do something with that dashboard. Well how do you reach the rest of users? Yeah. When you think about breaking that up and making it more personalized so that instead of getting a dashboard embedded in a tool, you get individual insights, you get data visualizations, you get controls, maybe it's not even actually a visualization at all. Maybe it's just a query result that influences the ordering of a list. So like if you're a csm, you have a list of accounts in your book of business, you wanna rank those by who's priorities the most likely to churn. >>Yeah. You get that. How do you get that most likely to churn? You get it from your BI system. So how, but then the question is, how do I insert that back into the application that CSM is using? So that's what we talk about when we talk about Infusion. And SI started the infusion term about two years ago and now it's being used everywhere. We see it in marketing from Click and Tableau and from Looker just recently did a whole launch on infusion. The idea is you break this up into very small digestible pieces. You put those pieces into user experiences where they're relevant and when you need them. And to do that, you need a set of APIs, SDKs, to program it. But you also need a lot of very solid building blocks so that you're not building this from scratch, you're, you're assembling it from big pieces. >>And so what we do aty sense is we've got machine learning built in. We have an LQ built in. We have a whole bunch of AI powered features, including a knowledge graph that helps users find what else they need to know. And we, we provide those to our customers as building blocks so that they can put those into their own products, make them look and feel native and get that experience. In fact, one of the things that was most interesting this last couple of couple of quarters is that we built a technology demo. We integrated SI sensee with Office 365 with Google apps for business with Slack and MS teams. We literally just threw an Nlq box into Excel and now users can go in and say, Hey, which of my sales people in the northwest region are on track to meet their quota? And they just get the table back in Excel. They can build charts of it and PowerPoint. And then when they go to their q do their QBR next week or week after that, they just hit refresh to get live data. It makes it so much more digestible. And that's the whole point of infusion. It's bigger than just, yeah. The iframe based embedding or the JavaScript embedding we used to talk about four or five years >>Ago. APIs are very key. You brought that up. That's gonna be more of the integration piece. How does embedable and composable work as more people start getting on board? It's kind of like a Yeah. A flywheel. Yes. What, how do you guys see that progression? Cause everyone's copying you. We see that, but this is a, this means it's standard. People want this. Yeah. What's next? What's the, what's that next flywheel benefit that you guys coming out with >>Composability, fundamentally, if you read the Gartner analysis, right, they, when they talk about composable, they're talking about building pre-built analytics pieces in different business units for, for different purposes. And being able to plug those together. Think of like containers and services that can, that can talk to each other. You have a composition platform that can pull it into a presentation layer. Well, the presentation layer is where I focus. And so the, so for us, composable means I'm gonna have formulas and queries and widgets and charts and everything else that my, that my end users are gonna wanna say almost minority report style. If I'm not dating myself with that, I can put this card here, I can put that chart here. I can set these filters here and I get my own personalized view. But based on all the investments my organization's made in data and governance and quality so that all that infrastructure is supporting me without me worrying much about it. >>Well that's productivity on the user side. Talk about the software angle development. Yeah. Is your low code, no code? Is there coding involved? APIs are certainly the connective tissue. What's the impact to Yeah, the >>Developer. Oh. So if you were working on a traditional legacy BI platform, it's virtually impossible because this is an architectural thing that you have to be able to do. Every single tool that can make a chart has an API to embed that chart somewhere. But that's not the point. You need the life cycle automation to create models, to modify models, to create new dashboards and charts and queries on the fly. And be able to manage the whole life cycle of that. So that in your composable application, when you say, well I want chart and I want it to go here and I want it to do this and I want it to be filtered this way you can interact with the underlying platform. And most importantly, when you want to use big pieces like, Hey, I wanna forecast revenue for the next six months. You don't want it popping down into Python and writing that yourself. >>You wanna be able to say, okay, here's my forecasting algorithm. Here are the inputs, here's the dimensions, and then go and just put it somewhere for me. And so that's what you get withy sense. And there aren't any other analytics platforms that were built to do that. We were built that way because of our architecture. We're an API first product. But more importantly, most of the legacy BI tools are legacy. They're coming from that desktop single user, self-service, BI environment. And it's a small use case for them to go embedding. And so composable is kind of out of reach without a complete rebuild. Right? But with SI senses, because our bread and butter has always been embedding, it's all architected to be API first. It's integrated for software developers with gi, but it also has all those low code and no code capabilities for business users to do the minority report style thing. And it's assemble endless components into a workable digital workspace application. >>Talk about the strategy with aws. You're here at the ecosystem, you're in the ecosystem, you're leading product and they have a strategy. We know their strategy, they have some stuff, but then the ecosystem goes faster and ends up making a better product in most of the cases. If you compare, I know they'll take me to school on that, but I, that's pretty much what we report on. Mongo's doing a great job. They have databases. So you kind of see this balance. How are you guys playing in the ecosystem? What's the, what's the feedback? What's it like? What's going on? >>AWS is actually really our best partner. And the reason why is because AWS has been clear for many, many years. They build componentry, they build services, they build infrastructure, they build Redshift, they build all these different things, but they need, they need vendors to pull it all together into something usable. And fundamentally, that's what Cient does. I mean, we didn't invent sequel, right? We didn't invent jackal or dle. These are not, these are underlying analytics technologies, but we're taking the bricks out of the briefcase. We're assembling it into something that users can actually deploy for their use cases. And so for us, AWS is perfect because they focus on the hard bits. The the underlying technologies we assemble those make them usable for customers. And we get the distribution. And of course AWS loves that. Cause it drives more compute and it drives more, more consumption. >>How much do they pay you to say that >>Keynote, >>That was a wonderful pitch. That's >>Absolutely, we always say, hey, they got a lot of, they got a lot of great goodness in the cloud, but they're not always the best at the solutions and that they're trying to bring out, and you guys are making these solutions for customers. Yeah. That resonates with what they got with Amazon. For >>Example, we, last year we did a, a technology demo with Comprehend where we put comprehend inside of a semantic model and we would compile it and then send it back to Redshift. And it takes comprehend, which is a very cool service, but you kind of gotta be a coder to use it. >>I've been hear a lot of hype about the semantic layer. What is, what is going on with that >>Semantec layer is what connects the actual data, the tables in your database with how they're connected and what they mean so that a user like you or me who's saying I wanna bar chart with revenue over time can just work with revenue and time. And the semantic layer translates between what we did and what the database knows >>About. So it speaks English and then they converts it to data language. It's >>Exactly >>Right. >>Yeah. It's facilitating the exchange of information. And, and I love this. So I like that you actually talked about it in the beginning, the knowledge map and helping people figure out what they might not know. Yeah. I, I am not a bi analyst by trade and I, I don't always know what's possible to know. Yeah. And I think it's really great that you're doing that education piece. I'm sure, especially working with AWS companies, depending on their scale, that's gotta be a big part of it. How much is the community play a role in your product development? >>It's huge because I'll tell you, one of the challenges in embedding is someone who sees an amazing experience in outreach or in seismic. And to say, I want that. And I want it to be exactly the way my product is built, but I don't wanna learn a lot. And so you, what you want do is you want to have a community of people who have already built things who can help lead the way. And our community, we launched a new version of the SES community in early 2022 and we've seen a 450% growth in the c in that community. And we've gone from an average of one response, >>450%. I just wanna put a little exclamation point on that. Yeah, yeah. That's awesome. We, >>We've tripled our organic activity. So now if you post this Tysons community, it used to be, you'd get one response maybe from us, maybe from from a customer. Now it's up to three. And it's continuing to trend up. So we're, it's >>Amazing how much people are willing to help each other. If you just get in the platform, >>Do it. It's great. I mean, business is so >>Competitive. I think it's time for the, it's time. I think it's time. Instagram challenge. The reels on John. So we have a new thing. We're gonna run by you. Okay. We just call it the bumper sticker for reinvent. Instead of calling it the Instagram reels. If we're gonna do an Instagram reel for 30 seconds, what would be your take on what's going on this year at Reinvent? What you guys are doing? What's the most important story that you would share with folks on Instagram? >>You know, I think it's really what, what's been interesting to me is the, the story with Redshift composable, sorry. No, composable, Redshift Serverless. Yeah. One of the things I've been >>Seeing, we know you're thinking about composable a lot. Yes. Right? It's, it's just, it's in there, it's in your mouth. Yeah. >>So the fact that Redshift Serverless is now kind becoming the defacto standard, it changes something for, for my customers. Cuz one of the challenges with Redshift that I've seen in, in production is if as people use it more, you gotta get more boxes. You have to manage that. The fact that serverless is now available, it's, it's the default means it now people are just seeing Redshift as a very fast, very responsive repository. And that plays right into the story I'm telling cuz I'm telling them it's not that hard to put some analysis on top of things. So for me it's, it's a, maybe it's a narrow Instagram reel, but it's an >>Important one. Yeah. And that makes it better for you because you get to embed that. Yeah. And you get access to better data. Faster data. Yeah. Higher quality, relevant, updated. >>Yep. Awesome. As it goes into that 80% of knowledge workers, they have a consumer great expectation of experience. They're expecting that five ms response time. They're not waiting 2, 3, 4, 5, 10 seconds. They're not trained on theola expectations. And so it's, it matters a lot. >>Final question for you. Five years out from now, if things progress the way they're going with more innovation around data, this front end being very usable, semantic layer kicks in, you got the Lambda and you got serverless kind of coming in, helping out along the way. What's the experience gonna look like for a user? What's it in your mind's eye? What's that user look like? What's their experience? >>I, I think it shifts almost every role in a business towards being a quantitative one. Talking about, Hey, this is what I saw. This is my hypothesis and this is what came out of it. So here's what we should do next. I, I'm really excited to see that sort of scientific method move into more functions in the business. Cuz for decades it's been the domain of a few people like me doing strategy, but now I'm seeing it in CSMs, in support people and sales engineers and line engineers. That's gonna be a big shift. Awesome. >>Thank >>You Scott. Thank you so much. This has been a fantastic session. We wish you the best at si sense. John, always pleasure to share the, the stage with you. Thank you to everybody who's attuning in, tell us your thoughts. We're always eager to hear what, what features have got you most excited. And as you know, we will be live here from Las Vegas at reinvent from the show floor 10 to six all week except for Friday. We'll give you Friday off with John Furrier. My name's Savannah Peterson. We're the cube, the the, the leader in high tech coverage.

>>Welcome back. You're watching the cubes coverage of AWS 2021. We're here in the Venetian, formerly the sands convention center in Las Vegas. My name is Dave Volante. Really excited to have Bob bright tell here. He's the director of SAP global alliances at IBM and Manu.. I'm going to try that again. Pro boxcar, is that correct? Rebecca Head of Linux and IBM alliances at AWS Manu. I'm sorry for bashing your name, but at least I got it right, guys. Great to see you. Thanks for coming on. >>And I'm actually now AWS partnership. I had SAP before, so it's great. I first, my first reinvest, >>I have a old DNA title. That's great. That's why I was asking you about Philly before you don't have the accent though. Bob, >>I'm not a Philly native, so cowboy >>Because you have the SAP connection there. IBM, AWS. It's like, whoa, what's going on here? >>Well, maybe I'll start and then have my new, my new, make some comments. And I'll just start by just, uh, we're real excited to be here. IBM's a diamond sponsor at, at re-invent and it's great to be in person and really appreciate AWS being able to put this event on this week and get us back in person. It really makes a difference. And I know there's a lot of people virtually as well, but, um, IBM and AWS have worked together for a number of years. Uh, maybe we could characterize it more opportunistically, um, prior, but in the last 12, 18 months, I think there's been a lot of developments that have really made us come together strategically as partners. I know we'll talk a little bit about red hat during the course of the conversation, but with IBM's >>You say opportunities like you mean in the field and the more strategic >>Relationship or strategic and with IBM's open hybrid cloud strategy. And, uh, with so many of our clients preferring AWS is their cloud. Um, we are working together now to meet clients where they're at to help them get the value of the cloud. And we're talking a little bit about coming out of the pandemic before this. Um, and one of the things that we're seeing with our clients that IBM is a lot of that low hanging fruit. The cloud was achieved, maybe the lift and shift or doing some SAS based applications, but now it's even more important to rapidly adopt hybrid cloud and cloud technologies to provide your business with flexible innovation transformation, all of those things. So that's why it has been important for us to, to partner with AWS strategically. Um, our clients are telling us that when they do move those heavier workloads to the cloud and do it in a hybrid model, they see about two and a half times the value. >>So with that, our partnership is multi-dimensional, we're doing a lot with IBM consulting. My new we'll talk a little bit about IBM software and red hat. Just one example, Dave, with IBM consulting, we now are up to almost 10,000 certifications and 10 plus AWS competencies. So that competency chart that shows we're knocking them all out on the, on the checkerboard there to get them to IBM consulting competencies. And we just had the energy one announced this week. So IBM consulting is in area software's big too. In my news, been helping us with that part of the partner. >>Well, it's, you know, to your point, you can't pick whatever cliche you want. You can't fight fashion. The trend is your friend. You have a lot of, a lot of people want to be on AWS. So rather than fighting, oh, we have our own cloud. No, you've got to meet customers where they are, right >>David, this is where this takes us. You know, the analogy we use between Bob and I, IBM boss spoke about IBM consulting, which we know has been a strategic partnership for the last two, two and a half years. I think I'm going to share the best kept secret in the cloud phase right now. IBM software and AWS now are working together. The analogy we use is IBM software and AWS. I like peanut butter and jelly better together. And over the last 12 months, the two companies have accelerated working together around three key dimensions. Number one, around product, number two around making sure our customers, joint customers successful. And number three, around building a robust ecosystem of partners. One thing that we have realized is just helping customers modernize. Migrate is challenging. And on the product side, now we have about 15 products on AWS marketplace. >>I think about trusty or verify insecurity, cloud Pak for data, uh, uh, Cognos data DataStage over the next 12 months, we plan to land all of the cloud packs. These are containerized version of IBM software on AWS and the marketplace. In addition, many of our customers are now using the managed red hat, OpenShift servers. We launched it earlier in April. This year, we are seeing tremendous customer feedback, tremendous, uh, growth there that is also informing that customers really like the open shirt model managed services one-click deployment. And so our goal is over the next 12 months, launched many more IBM software as a managed service offering. So that's kind of like what we're doing on the product side, on the customer success. A great example is somebody is helping a big oil and gas customers managed with this energy transition that we're working through. Um, Schlumberger software around simulation runs on OpenShift on Amazon in a hybrid environment, especially critical as we have a lot of oil and gas data that needs to have maybe sit on premises, uh, because of data residency requirements. >>I think the third piece is around building an ecosystem of partners for our red hat OpenShift services, which we launched April. We already have 30 partners that are helping customers not only to modernize, but to migrate on AWS. We know modernization is challenging, moving to containers is difficult. So we need this robust ecosystem of partners and Bob and I, and you know, the IBM and AWS team are investing heavily. We have cash credit to do financial incentives plus also technical content so that our customers so that our partners can help customers to be successful. Yeah, >>So the cloud packs are cool. That makes a lot of sense. And now the acquisition of red hat makes it easier. It's a catalyst gets IBM, much more closely aligned to developers and it makes it easier for things like cloud packs to be migrated to the cloud and being running cloud native. How did that acquisition affect from your standpoint menu and Bob I'd love your thoughts and your relationship. >>The red hat acquisition by IBM is a net positive red hat. And AWS have been working together for 14 years now. And we have tens of thousands of customers that are running mission critical workloads, such as SAP, Oracle databases. And there's a lot of trust that is engendered by working in the field for 14 years, uh, supporting mission critical customers, mission critical workloads. And so that relationship has provided a lot of tailwinds to our partnership with IBM software. I think a lot of the stuff we spoke about a lot of the progress you've made in the last six to eight to 12 months, a big function is that the trust that we have engendered working together with red hat. >>Yeah. I'll add Dave that, um, I, I agree with my new comments on the red hat. Red hat really is the epitome of openness right. Of open source software and the history that Manu described with AWS, there has been excellent adoption of red hat on AWS, red hat, enterprise Linux, and then most recently, um, red hat OpenShift on AWS. And just to give another example to the ecosystem point, just this morning, red hat with IBM, with a major ISV named Solonus announced that Solonus will be running one of their key, uh, applications and releasing it on Rossa on AWS. And all this means for our clients is faster adoption and acceleration and being able to innovate, um, in a hybrid way. So that's really the value that red hat is helping, um, to bring to the table in our cloud packs are available on open shift and rose as an option as well. So we're excited about the red hat partnership. It's really essential to our partnership into our, our hybrid cloud strategy. >>You mentioned up front, you know, happy that AWS decided to have this show. Of course, a lot of people watching online and you can get massive scale online, but there's nothing like the live event, you know, and when you make announcements at a live event, there's a little buzz going on and you get feedback. So are you making any hard news here? What, what announcements can you >>Share? Yeah, well, the one we had, um, on, um, uh, Solonus earlier with red hat and to do roasts on top of red hat was one and there's just an advance of, um, of re-invent. Um, we announced something in the data and AI space. So that's another big area of our partnership is data and AI. So we're in, we announced that in the oil industry and in the, um, uh, in that area that we are partnering together with AWS to be able to get insights on data so that we could get clean and reusable energy solutions out there. And there's so much untapped data. We know data is such an important resource, that that's an area that we're going to partner on with our cloud Pak for data on AWS. And of course underlying everything is open shifts. So that's one big announcement. We're also doing a lot in security for IBM and my news has been working closely with this. So my new, I, I know you're close to the integrations we're doing with AWS. So I'll let you comment maybe on some of the things in security. >>I mean, everybody's a security company these days, right? I mean, >>And then we continue to work and making sure that a lot of the IBM security products are integrating with our native services. So the customers have a seamless experience. And as he you'll see a lot of the same investments happening over 2022 as we grow the >>Partnership. So what like a QRadar or something like that >>Are, for example, integrating with security hub. That would be great example. >>I mean, it's the, it's the number one topic for CEO's that has been for a while and still will be okay. So give us a little roadmap, you know, maybe Bob, you could start, where do you want to see this relationship go? Um, what can we expect in the, in the coming 12 months? Yeah, well, >>Again, we're super excited about our partnership with AWS. I think we're just scratching the surface of how we're going to add value to our clients on this, on this hybrid cloud journey that they're all going through. And IBM, and this has been in our financial reports and in our earnings and everything, we're investing over a billion dollars in the ecosystem. And so partners like AWS are critical to provide that platform of growth for our clients and innovation for our clients. So all of the things that I talked about in money talked about today, whether it be our IBM consulting capabilities or our IBM software, our red hat, we're going to continue to invest. We talked about the red hat acquisition. IBM has made a few other acquisitions that help drive this partnership and drive value to our clients for adoption, from Instana to Turbonomic X, to some really innovative cloud consulting companies like Knorr cloud in towels. So we're going to continue to make investments. And I think we're just on the tip of the iceberg and we invite everybody at re-invent, either in person, which is exciting or virtually to learn more about our partnership and how we can help you and my new, any additional comments to that. >>Thanks, Bob V have a golden child hair with red hat OpenShift on Amazon. That'd be launched in April. We are seeing tremendous customer adoption. So we suspect that in next year, we'll continue to see solid adoption around red hat OpenShift. That VocaliD is also informing how customers want a more native experience for IBM software on AWS. And so we, um, we are targeting to, to launch many more IBM software in a native format on edema. So that would be the big team for next year. Uh, in addition, again, I'll call to action to our partner community. There's a huge opportunity to help our joint customers to modernize and migrate on AWS via both IBM, AWS are leaning in, we have cash credit to give financial incentives to partners, to help our customers, to migrate and modernize as well as we are also creating a lot of technical content that is not freely available so that a lot of our partners can start this. IBM focus on AWS practice >>Guys. Thanks so much for coming on the cube. Congratulations, and look at, you know, I often say the next 10 years is not going to be like the last 10 years. The cloud is expanding is a really good example. So thank you for your time. Appreciate your time. All right. You're watching the cube, the leader in high tech coverage at AWS reinvent 2021

(upbeat music) >> Hey, welcome back everyone to theCube's coverage of AWS re:Invent 2021. I'm John Furrier, host of theCube, wall-to-wall coverage in-person and hybrid. The two great guests here, Manu Parbhakar, worldwide Leader, Linux and IBM Software Partnership at AWS, and Mike Evans, Vice President of Technical Business Development at Red Hat. Gentlemen, thanks for coming on theCube. Love this conversation, bringing Red Hat and AWS together. Two great companies, great technologies. It really is about software in the cloud, Cloud-Scale. Thanks for coming on. >> Thanks John. >> So get us into the partnership. Okay. This is super important. Red Hat, well known open source as cloud needs to become clear, doing an amazing work. Amazon, Cloud-Scale, Data is a big part of it. Modern software. Tell us about the partnership. >> Thanks John. Super excited to share about our partnership. As we have been partnering for almost 14 years together. We started in the very early days of AWS. And now we have tens of thousands of customers that are running RHEL on EC2. If you look at over the last three years, the pace of innovation for our joint partnership has only increased. It has manifested in three key formats. The first one is the pace at which RHEL supports new EC2 instances like Arm, Graviton. You know, think a lot of features like Nitro. The second is just the portfolio of new RHEL offerings that we have launched over the last three years. We started with RHEL for sequel, RHEL high availability, RHEL for SAP, and then only last month, we've launched the support for knowledge base for RHEL customers. Mike, you want to talk about what you're doing with OpenShift and Ansible as well? >> Yeah, it's good to be here. It's fascinating to me cause I've been at Red Hat for 21 years now. And vividly remember the start of working with AWS back in 2008, when the cloud was kind of a wild idea with a whole bunch of doubters. And it's been an interesting time, but I feel the next 14 years are going to be exciting in a different way. We now have a very large customer base from almost every industry in the world built on RHEL, and running on AWS. And our goal now is to continue to add additional elements to our offerings, to build upon that and extend it. The largest addition which we're going to be talking a lot about here at the re:Invent show was the partnership in April this year when we launched the Red Hat OpenShift service on AWS as a managed version of OpenShift for containers based workloads. And we're seeing a lot of the customers that have standardized on RHEL on EC2, or ones that are using OpenShift on-premise deployments, as the early adopters of ROSA, but we're also seeing a huge number of new customers who never purchased anything from Red Hat. So, in addition to the customers, we're getting great feedback from systems integrators and ISV partners who are looking to have a software application run both on-premise and in AWS, and with OpenShift being one of the pioneers in enabling both container and harnessing Kubernetes where ROSA is just a really exciting area for us to track and continue to advance together with AWS. >> It's very interesting. Before I get to ROSA, I want to just get the update on Red Hat and IBM, obviously the acquisition part of IBM, how is that impacting the partnership? You can just quickly touch on that. >> Sure. I'll start off and, I mean, Red Hat went from a company that was about 15,000 employees competing with a lot of really large technology companies and we added more than 100,000 field oriented people when IBM acquired Red Hat to help magnify the Red Hat solutions, and the global scale and coverage of IBM is incredible. I like to give two simple examples of people. One is, I remember our salesforce in EMEA telling me they got a $4 million order from a country in Africa theydidn't even know existed. And IBM had 100 people in it, or AT&T is one of Red Hat's largest accounts, and I think at one point we had seven full-time people on it and AT&T is one of IBM's largest accounts and they had two seven storey buildings full of people working with AT&T. So RHELative to AWS, we now also see IBM embracing AWS more with both software, and services, in the magnification of Red Hat based solutions, combined with that embrace should be, create some great growth. And I think IBM is pretty excited about being able to sell Red Hat software as well. >> Yeah, go ahead. >> And Manu I think you have, yeah. >> Yeah. I think there's also, it is definitely very positive John. >> Yeah. >> You know, just the joint work that Red Hat and AWS have done for the last 14 years, working in the trenches supporting our end customers is now also providing lot of Tailwinds for the IBM software partnership. We have done some incredible work over the last 12 months around three broad categories. The first one is around product, what we're doing around customer success, and then what we're doing around sales and marketing. So on the product side, we have listed about 15 products on Marketplace over the course of the last 12 to 15 months. And our goal is to launch all of the IBM Cloud Paks. These are containerized versions of IBM software on Marketplace by the first half of next year. The other feedback that we are getting from our customers is that, hey, we love IBM software running at Amazon, but we like to have a cloud native SaaS version of the software. So there's a lot of work that's going on right now, to make sure that many of these offerings are available in a cloud-native manner. And you're not talking with Db2 Cognos, Maximo, (indistinct), on EC2. The second thing that we're doing is making sure that many of these large enterprise customers are running IBM software, are successful. So our technical teams are attached to the hip, working on the ground floor in making customers like Delta successful in running IBM software on them. I think the third piece around sales and marketing just filing up a vibrant ecosystem, rather how do we modernize and migrate this IBM software on Cloud Paks on AWS? So there's a huge push going on here. So (indistinct), you know, the Red Hat partnership is providing a lot of Tailwinds to accelerate our partnership with IBM software. >> You know, I always, I've been saying all this year in Red Hat summit, as well as Ansible Fest that, distributed computing is coming to large scale. And that's really the, what's happening. I mean, you looking at what you guys are doing cause it's amazing. ROSA Red Hat OpenShift on AWS, very notable to use the term on AWS, which actually means something in the partnership as we learned over the years. How is that going Mike because you launched on theCube in April, ROSA, it had great traction going in. It's in the Marketplace. You've got some integration. It's really a hand in glove situation with Cloud-Scale. Take us through what's the update? >> Yeah, let me, let me let Manu speak first to his AWS view and then I'll add the Red Hat picture. >> Thanks Mike. John for ROSA is part of an entire container portfolio. So if you look at it, so we have ECS, EKS, the managed Kubernetes service. We have the serverless containers with Fargate. We launched ECS case anywhere. And then ROSA is part of an entire portfolio of container services. As you know, two thirds of all container workloads run on AWS. And a big function of that is because we (indistinct) from our customer and then sold them what the requirements are. There are two sets of key customers that are driving the demand and the early adoption of ROSA. The first set of customers that have standardized on OpenShift on-premises. They love the fact that everything that comes out of the box and they would love to use it on Arm. So that's the first (indistinct). The second set of customers are, you know, the large RHEL users on EC2. The tens of thousands of customers that we've talked about that want to move from VM to containers, and want to do DevOps. So it's this set of two customers that are informing our roadmap, as well as our investments around ROSA. We are seeing solid adoption, both in terms of adoption by a customer, as well as the partners and helping, and how our partners are helping our customers in modernizing from VMs to containers. So it's a, it's a huge, it's a huge priority for our container service. And over the next few years, we continue to see, to increase our investment on the product road map here. >> Yeah, from my perspective, first off at the high level in mind, my one of the most interesting parts of ROSA is being integrated in the AWS console and not just for the, you know, where it shows up on the screen, but also all the work behind what that took to get there and why we did it. And we did it because customers were asking both of us, we're saying, look, OpenShift is a platform. We're going to be building and deploying serious applications at incredible scale on it. And it's really got to have joint high-quality support, joint high-quality engineering. It's got to be rock solid. And so we came to agreement with AWS. That was the best way to do that, was to build it in the console, you know, integrated in, into the core of an AWS engineering team with Red Hat engineers, Arm and Arms. So that's, that's a very unique service and it's not like a high level SaaS application that runs above everything, it's down in the bowels and, and really is, needs to be rock solid. So we're seeing, we're seeing great interest, both from end users, as I mentioned, existing customers, new customers, the partner base, you know, how the systems integrators are coming on board. There's lots of business and money to be made in modernizing applications as well as building new cloud native applications. People can, you know, between Red Hat and AWS, we've got some, some models around supporting POCs and customer migrations. We've got some joint investments. it's a really ripe area. >> Yeah. That's good stuff. Real quick. what do you think of ROSA versus EKS and ECS? What's, how should people think about that Mike? (indistinct) >> You got to go for it Manu. Your job is to position all these (indistinct). (indistinct) >> John, ROSA is part of our container portfolio services along with EKS, ECS, Fargate, and any (indistinct) services that we just launched earlier this year. There are, you know, set of customers both that are running OpenShift on-premises that are standardized on ROSA. And then there are large set of RHEL customers that are running RHEL on EC2, that want to use the ROSA service. So, you know, both AWS and Red Hat are now continuing to invest in accelerating the roadmap of the service on our platform. You know, we are working on improving the console experience. Also one of the things we just launched recently is the Amazon controller to Kubernetes, or what , you know, service operators for S3. So over the next few years you will see, you know, significant investment from both Red Hat and AWS in this joint service. And this is an integral part of our overall container portfolio. >> And great stuff to get in the console. That's great, great integration. That's the future. I got to ask about the graviton instances. It's been one of the most biggest success stories, I think we believe in Amazon history in the acquisition of Annapurna, has really created great differentiation. And anyone who's in the software knows if you have good chips powering apps, they go faster. And if the chips are good, they're less expensive. And that's the innovation. We saw that RHEL now supports graviton instances. Tell us more about the Red Hat strategy with graviton and Arms specifically, has that impact your (indistinct) development, and what does it mean for customers? >> Sure. Yeah, it's pretty, it's a pretty fascinating area for me. As I said, I've been a Red Hat for 21 years and my job is actually looking at new markets and new technologies now for Red Hat and work with our largest partners. So, I've been tracking the Arm dynamics for awhile, and we've been working with AWS for over two years, supporting graviton. And it's, I'm seeing more enthusiasm now in terms of developers and, especially for very horizontal, large scale applications. And we're excited to be working with AWS directly on it. And I think it's going to be a fascinating next two years on Arm, personally. >> Many of the specialized processors for training and instances, all that stuff, can be applied to web services and automation like cloud native services, right? Is that, it sounds like a good direction. Take us through that. >> John, on our partnership with Red Hat, we are continuing to iterate, as Mike mentioned, the stuff that we've done around graviton, both the last two years is pretty incredible. And the pace at which we are innovating is improving. Around the (indistinct) and the inferential instances, we are continuing to work with Red Hat and, you know, the support for RHEL should come shortly, very soon. >> Well, my prediction is that the graviton success was going to be applied to every single category. You can get that kind of innovation with this on the software side, just really kind of just, that's the magical, that's the, that's the proven form of software, right? We've been there. Good software powering with some great performance. Manu, Mike, thank you for coming on and sharing the, the news and the partnership update. Congratulations on the partnership. Really good. Thank you. >> Excellent John. Incredible (indistinct). >> Yeah, this is the future software as we see, it's all coming together. Here on theCube, we're bringing all the action, software being powered by chips, is theCube coverage of AWS re:invent 2021. I'm John Furrier, your host. Thanks for watching. (upbeat music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi Bri welcome back this is Dave Volante and welcome to this special presentation made possible by IBM we're talking about data op data ops in Acton Steve Lucas here he's the senior vice president and director of data management at Associated Bank be great to see how are things going and in Wisconsin all safe we're doing well we're staying safe staying healthy thanks for having me Dave yeah you're very welcome so Associated Bank and regional bank Midwest to cover a lot of the territories not just Wisconsin but another number of other states around there retail commercial lending real estate offices stuff I think the largest bank in in Wisconsin but tell us a little bit about your business in your specific role sure yeah no it's a good intro we're definitely largest bank at Corvis concen and then we have branches in the in the Upper Midwest area so Minnesota Illinois Wisconsin our primary locations my role at associated I'm director data management so been with the bank a couple of years now and really just focused on defining our data strategy as an overall everything from data ingestion through consumption of data and analytics all the way through and then I'm also the data governance components and keeping the controls and the rails in place around all of our data in its usage so financial services obviously one of the more cutting-edge industries in terms of their use of technology not only are you good negotiators but you you often are early adopters you guys were on the Big Data bandwagon early a lot of financial services firms we're kind of early on in Hadoop but I wonder if you could tell us a little bit about sort of the business drivers and and where's the poor the pressure point that are informing your digital strategy your your data and data op strategy sure yeah I think that one of the key areas for us is that we're trying to shift from more of a reactive mode into more of a predictive prescriptive mode from a data and analytics perspective and using our data to infuse and drive more business decisions but also to infuse it in actual applications and customer experience etc so we have a wealth of data at our fingertips we're really focused on starting to build out that data link style strategy make sure that we're kind of ahead of the curve as far as trying to predict what our end users are going to need and some of the advanced use cases we're going to have before we even know that they actually exist right so it's really trying to prepare us for the future and what's next and and then abling and empowering the business to be able to pivot when we need to without having everything perfect that they prescribed and and ready for what if we could talk about a little bit about the data journey I know it's kind of a buzzword but in my career as a independent observer and analyst I've kind of watched the promise of whether it was decision support systems or enterprise data warehouse you know give that 360 degree view of the business the the real-time nature the the customer intimacy all that in and up until sort of the recent digital you know meme I feel as though the industry hasn't lived up to that promise so I wonder if you could take us through the journey and tell us sort of where you came from and where you are today and I really want to sort of understand some of the successes they've had sure no that's a that's a great point nice I feel like as an industry I think we're at a point now where the the people process technology have sort of all caught up to each other right I feel that that real-time streaming analytics the data service mentality just leveraging web services and API is more throughout our organization in our industry as a whole I feel like that's really starting to take shape right now and and all the pieces of that puzzle have come together so kind of where we started from a journey perspective it was it was very much if your your legacy reporting data warehouse mindset of tell me tell me the data elements that you think you're going to need we'll figure out how do we map those in and form them we'll figure out how to get those prepared for you and that whole lifecycle that waterfall mentality of how do we get this through the funnel and get it to users quality was usually there the the enablement was still there but it was missing that that rapid turnaround it was also missing the the what's next right than what you haven't thought of and almost to a point of just discouraging people from asking for too many things because it got too expensive it got too hard to maintain there was some difficulty in that space so some of the things that we're trying to do now is build that that enablement mentality of encouraging people to ask for everything so when we bring out new systems - the bank is no longer an option as far as how much data they're going to send to us right we're getting all of the data we're going to we're going to bring that all together for people and then really starting to figure out how can this data now be used and and we almost have to push that out and infuse it within our organization as opposed to waiting for it to be asked for so I think that all of the the concepts so that bringing that people process and then now the tools and capabilities together has really started to make a move for us and in the industry I mean it's really not an uncommon story right you had a traditional data warehouse system you had you know some experts that you had to go through to get the data the business kind of felt like it didn't own the data you know it felt like it was imposing every time it made a request or maybe it was frustrated because it took so long and then by the time they got the data perhaps you know the market had shifted so it create a lot of frustration and then to your point but but it became very useful as a reporting tool and that was kind of this the sweet spot so so how did you overcome that and you know get to where you are today and you know kind of where are you today I was gonna say I think we're still overcoming that we'll see it'll see how this all goes right I think there's there's a couple of things that you know we've started to enable first off is just having that a concept of scale and enablement mentality and everything that we do so when we bring systems on we bring on everything we're starting to have those those components and pieces in place and we're starting to build more framework base reusable processes and procedures so that every ask is not brand new it's not this reinvent the wheel and resolve for for all that work so I think that's helped if expedite our time to market and really get some of the buy-in and support from around the organization and it's really just finding the right use cases and finding the different business partners to work with and partner with so that you help them through their journey as well is there I'm there on a similar roadmap and journey for for their own life cycles as well in their product element or whatever business line there so from a process standpoint that you kind of have to jettison the you mentioned waterfall before and move to a more being an agile approach did it require different different skill sets talk about the process and the people side of yeah it's been a it's been a shift we've tried to shift more towards I wouldn't call us more formal agile I would say we're a little bit more lean from a an iterative backlog type of approach right so what are you putting that work together in queues and having the queue of B reprioritized working with the business owners to help through those things has been a key success criteria for us and how we start to manage that work as opposed to opening formal project requests and and having all that work have to funnel through some of the old channels that like you mentioned earlier kind of distracted a little bit from from the way things had been done in the past and added some layers that people felt potentially wouldn't be necessary if they thought it was a small ask in their eyes you know I think it also led to a lot of some of the data silos and and components that we have in place today in the industry and I don't think our company is alone and having data silos and components of data in different locations but those are there for a reason though those were there because they're they're filling a need that has been missing or a gap in the solution so what we're trying to do is really take that to heart and evaluate what can we do to enable those mindsets and those mentalities and find out what was the gap and why did they have to go get a siloed solution or work around operations and technology and the channels that had been in place what would you say well your biggest challenges in getting from point A to point B point B being where you are today there were challenges on each of the components of the pillar right so people process technology people are hard to change right men behavioral type changes has been difficult that there's components of that that definitely has been in place same with the process side right so so changing it into that backlog style mentality and working with the users and having more that be sort of that maintenance type support work is is a different call culture for our organization and traditional project management and then the tool sets right the the tools and capabilities we had to look in and evaluate what tools do we need to Mabel this behavior in this mentality how do we enable more self-service the exploration how do we get people the data that they need when they need it and empower them to use so maybe you could share with us some of the outcomes and I know it's yeah we're never done in this business but but thinking about you know the investments that you've made in intact people in reprocessing you know the time it takes to get leadership involved what has been so far anyway the business outcome and you share any any metrics or it is sort of subjective a guidance I yeah I think from a subjective perspective the some of the biggest things for us has just been our ability to to truly start to have that very 60 degree view of the customer which we're probably never going to get they're officially right there's there everyone's striving for that but the ability to have you know all of that data available kind of at our fingertips and have that all consolidated now into one one location one platform and start to be that hub that starts to redistribute that data to our applications and infusing that out has been a key component for us I think some of the other big kind of components are differentiators for us and value that we can show from an organizational perspective we're in an M&A mode right so we're always looking from a merger and acquisition perspective our the model that we've built out from a data strategy perspective has proven itself useful over and over now in that M&A mentality of how do you rapidly ingest new data sets it had understood get it distributed to the right consumers it's fit our model exactly and and it hasn't been an exception it's been just part of our overall framework for how we get that data and it wasn't anything new that we had to do different because it was M&A just timelines were probably a little bit more expedited the other thing that's been interesting in some of the world that were in now right from a a Kovach perspective and having a pivot and start to change some of the way we do business and some of the PPP loans and and our business models sort of had to change overnight and our ability to work with our different lines of business and get them the data they need to help drive those decisions was another scenario where had we not had the foundational components there in the platform there to do some of this if we would have spun a little bit longer so your data ops approach I'm gonna use that term helped you in this in this kovat situation I mean you had the PPE you had you know of slew of businesses looking to get access to that money you had uncertainty with regard to kind of what the rules of the game were what you was the bank you had a Judah cape but you it was really kind of opaque in terms of what you had to do the volume of loans had to go through the roof in the time frame it was like within days or weeks that you had to provide these so I wonder if we could talk about that a little bit and how you're sort of approach the data helped you be prepared for that yeah no it was a race I mean the bottom line was it felt like a race right from from industry perspective as far as how how could we get this out there soon enough fast enough provide the most value to our customers our applications teams did a phenomenal job on enabling the applications to help streamline some of the application process for the loans themselves but from a data and reporting perspective behind the scenes we were there and we had some tools and capabilities and readiness to say we have the data now in our in our lake we can start to do some business driven decisions around all all of the different components of what's being processed on a daily basis from an application perspective versus what's been funded and how do those start to funnel all the way through doing some data quality checks and operational reporting checks to make sure that that data move properly and got booked in in the proper ways because of the rapid nature of how that was was all being done other covent type use cases as well we had some some different scenarios around different feed reporting and and other capabilities that the business wasn't necessarily prepared for we wouldn't have planned to have some of these types of things and reporting in place that we were able to give it because we had access to all the data because of these frameworks that we had put into place that we could pretty rapidly start to turn around some of those data some of those data points and analytics for us to make some some better decisions so given the propensity in the pace of M&A there has to be a challenge fundamentally in just in terms of data quality consistency governance give us the before and after you know before kind of before being the before the data ops mindset and after being kind of where you are today I think that's still a journey we're always trying to get better on that as well but the data ops mindset for us really has has shifted us to start to think about automation right pipelines that enablement a constant improvement and and how do we deploy faster deploy more consistently and and have the right capabilities in place when we need it so you know where some of that has come into place from an M&A perspective is it's really been around the building scale into everything that we do dezq real-time nature this scalability the rapid deployment models that we have in place is really where that starts to join forces and really become become powerful having having the ability to rapidly ingesting new data sources whether we know about it or not and then exposing that and having the tools and platforms be able to expose that to our users and enable our business lines whether it's covent whether it's M&A the use cases keep coming up right they we keep running into the same same concept which is how rapidly get people the data they need when they need it but still provide the rails and controls and make sure that it's governed and controllable on the way as well [Music] about the tech though wonder if we could spend some time on that I mean can you paint a picture of us so I thought what what what we're looking at here you've got you know some traditional IDI w's involved I'm sure you've got lots of data sources you you may be one of the zookeepers from the the Hadoop days with a lot of you know experimentation there may be some machine intelligence and they are painting a pic before us but sure no so we're kind of evolving some of the tool sets and capabilities as well we have some some generic kind of custom in-house build ingestion frameworks that we've started to build out for how to rapidly ingest and kind of script out the nature of of how we bring those data sources into play what we're what we've now started as well as is a journey down IBM compact product which is really gonna it's providing us that ability to govern and control all of our data sources and then start to enable some of that real-time ad hoc analytics and data preparation data shaping so some of the components that we're doing in there is just around that data discovery pointing that data sources rapidly running data profiles exposing that data to our users obviously very handy in the emanating space and and anytime you get new data sources in but then the concept of publishing that and leveraging some of the AI capabilities of assigning business terms in the data glossary and those components is another key component for us on the on the consumption side of the house for for data we have a couple of tools in place where Cognos shop we do a tableau from a data visualization perspective as well that what that were we're leveraging but that's where cloud pack is now starting to come into play as well from a data refinement perspective and giving the ability for users to actually go start to shape and prep their data sets all within that governed concept and then we've actually now started down the enablement path from an AI perspective with Python and R and we're using compact to be our orchestration tool to keep all that governed and controlled as well enable some some new AI models and some new technologies in that space we're actually starting to convert all of our custom-built frameworks into python now as well so we start to have some of that embedded within cloud pack and we can start to use some of the rails of those frameworks with it within them okay so you've got the ingest and ingestion side you've done a lot of automation it sounds like called the data profiling that's maybe what classification and automating that piece and then you've got the data quality piece the governance you got visualization with with tableau and and this kind of all fits together in a in an open quote unquote open framework is that right yeah I exactly I mean the the framework itself from our perspective where we're trying to keep the tools as as consistent as we can we really want to enable our users to have the tools that they need in the toolbox and and keep all that open what we're trying to focus on is making sure that they get the same data the same experience through whatever tool and mechanism that they're consuming from so that's where that platform mentality comes into place having compact in the middle to help govern all that and and reprovision some of those data sources out for us has it has been a key component for us well see if it sounds like you're you know making a lot of progress or you know so the days of the data temple or the high priest of data or the sort of keepers of that data really to more of a data culture where the businesses kind of feel ownership for their own data you believe self-service I think you've got confidence much more confident than the in the compliance and governance piece but bring us home just in terms of that notion of data culture and where you are and where you're headed no definitely I think that's that's been a key for us too as as part of our strategy is really helping we put in a strategy that helps define and dictate some of those structures and ownership and make that more clear some of the of the failures of the past if you will from an overall my monster data warehouse was around nobody ever owned it there was there wasn't you always ran that that risk of either the loudest consumer actually owned it or no one actually owned it what we've started to do with this is that Lake mentality and and having all that data ingested into our our frameworks the data owners are clear-cut it's who sends that data in what is the book record system for that source data we don't want a ability we don't touch it we don't transform it as we load it it sits there and available you own it we're doing the same mentality on the consumer side so we have we have a series of structures from a consumption perspective that all of our users are consuming our data if it's represented exactly how they want to consume it so again that ownership we're trying to take out a lot of that gray area and I'm enabling them to say yeah I own this I understand what I'm what I'm going after and and I can put the the ownership and the rule and rules and the stewardship around that as opposed to having that gray model in the middle that that that we never we never get but I guess to kind of close it out really the the concept for us is enabling people and end-users right giving them the data that they need when they need it and it's it's really about providing the framework and then the rails around around doing that and it's not about building out a formal bill warehouse model or a formal lessor like you mentioned before some of the you know the ivory tower type concepts right it's really about purpose-built data sets getting the giving our users empowered with the data they need when they need it all the way through and fusing that into our applications so that the applications and provide the best user experiences and and use the data to our advantage all about enabling the business I got a shove all I have you how's that IBM doing you know as a as a partner what do you like what could they be doing better to make your life easier sure I think I think they've been a great partner for us as far as that that enablement mentality the cloud pack platform has been a key for us we wouldn't be where we are without that tool said I our journey originally when we started looking at tools and modernization of our staff was around data quality data governance type components and tools we now because of the platform have released our first Python I models into the environment we have our studio capabilities natively because of the way that that's all container is now within cloud back so we've been able to enable new use cases and really advance us where we would have a time or a lot a lot more technologies and capabilities and then integrate those ourselves so the ability to have that all done has or and be able to leverage that platform has been a key to helping us get some of these roles out of this as quickly as we have as far as a partnership perspective they've been great as far as listening to what what the next steps are for us where we're headed what can we what do we need more of what can they do to help us get there so it's it's really been an encouraging encouraging environment I think they as far as what can they do better I think it's just keep keep delivering write it delivery is ping so keep keep releasing the new functionality and features and keeping the quality of the product intact well see it was great having you on the cube we always love to get the practitioner angle sounds like you've made a lot of progress and as I said when we're never finished in this industry so best of luck to you stay safe then and thanks so much for for sharing appreciate it thank you all right and thank you for watching everybody this is Dave Volante for the cube data ops in action we got the crowd chat a little bit later get right there but right back right of this short break [Music] [Music]

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hi, everybody. This is Dave Volante with Cuban. Welcome to the special digital presentation. We're really digging into how IBM is operational izing and automating the AI and data pipeline not only for its clients, but also for itself. And with me is Julie Lockner, who looks after offering management and IBM Data and AI portfolio really great to see you again. >>Great, great to be here. Thank you. Talk a >>little bit about the role you have here at IBM. >>Sure, so my responsibility in offering >>management and the data and AI organization is >>really twofold. One is I lead a team that implements all of the back end processes, really the operations behind any time we deliver a product from the Data and AI team to the market. So think about all of the release cycle management are seeing product management discipline, etcetera. The other role that I play is really making sure that I'm We are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology. It's something that I needed internally >>from my own line of business execution. But it's now something that our customers are looking for to implement in their shops as well. >>Well, good. I really want to get into that. So let's let's start with data ops. I mean, I think you know, a lot of people are familiar with Dev Ops. Not maybe not everybody's familiar with data ops. What do we need to know about data? >>Well, I mean, you bring up the point that everyone knows Dev ops. And in fact, I think you know what data ops really >>does is bring a lot of the benefits that Dev Ops did for application >>development to the data management organizations. So when we look at what is data ops, it's a data management. Uh, it is a data management set of principles that helps organizations bring business ready data to their consumers. Quickly. It takes it borrows from Dev ops. Similarly, where you have a data pipeline that associates a business value requirement. I have this business initiative. It's >>going to drive this much revenue or this must cost >>savings. This is the data that I need to be able to deliver it. How do I develop that pipeline and map to the data sources Know what data it is? Know that I can trust it. So ensuring >>that it has the right quality that I'm actually using, the data that it was meant >>for and then put it to use. So in in history, most data management practices deployed a waterfall like methodology. Our implementation methodology and what that meant is all the data pipeline >>projects were implemented serially, and it was done based on potentially a first in first out program management office >>with a Dev Ops mental model and the idea of being able to slice through all of the different silos that's required to collect the data, to organize it, to integrate it, the validate its quality to create those data integration >>pipelines and then present it to the dashboard like if it's a Cognos dashboard >>or a operational process or even a data science team, that whole end to end process >>gets streamlined through what we're pulling data ops methodology. >>So I mean, as you well know, we've been following this market since the early days of Hadoop people struggle with their data pipelines. It's complicated for them, there's a a raft of tools and and and they spend most of their time wrangling data preparing data moving data quality, different roles within the organization. So it sounds like, you know, to borrow from from Dev Ops Data offices is all about streamlining that data pipeline, helping people really understand and communicate across. End the end, as you're saying, But but what's the ultimate business outcome that you're trying to drive? >>So when you think about projects that require data to again cut costs Teoh Artemia >>business process or drive new revenue initiatives, >>how long does it take to get from having access to the data to making it available? That duration for every time delay that is spent wasted trying to connect to data sources, trying to find subject matter experts that understand what the data means and can verify? It's quality, like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fat, though the business value of data ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day, we don't have this data to make a decision where either making money or losing money, that's the value proposition of data ops. So it's about taking things that people are already doing today and figuring out the quickest way to do it through automation or work flows and just cutting through all the political barriers >>that often happens when these data's cross different organizational boundaries. >>Yes, sir, speed, Time to insights is critical. But in, you know, with Dev Ops, you really bringing together of the skill sets into, sort of, you know, one Super Dev or one Super ops. It sounds with data ops. It's really more about everybody understanding their role and having communication and line of sight across the entire organization. It's not trying to make everybody else, Ah, superhuman data person. It's the whole It's the group. It's the team effort, Really. It's really a team game here, isn't it? >>Well, that's a big part of it. So just like any type of practice, there's people, aspects, process, aspects and technology, right? So people process technology, and while you're you're describing it, like having that super team that knows everything about the data. The only way that's possible is if you have a common foundation of metadata. So we've seen a surgeons in the data catalog market in the last, you know, 67 years. And what what the what? That the innovation in the data catalog market has actually enabled us to be able >>to drive more data ops pipelines. >>Meaning as you identify data assets you captured the metadata capture its meaning. You capture information that can be shared, whether they're stakeholders, it really then becomes more of a essential repository for people don't really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority, like privacy rules included. Put it to use >>for models, um, dashboards, operational processes. >>Okay. And we're gonna talk about some examples. And one of them, of course, is IBM's own internal example. But help us understand where you advise clients to start. I want to get into it. Where do I get started? >>Yeah, I mean, so traditionally, what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow. And what we've said is, Look, there's an operator. There's an opportunity here to quickly define a small project, align into high value business initiative, target something that you can quickly gain access to the data, map out these pipelines and create a squad of skills. So it includes a person with Dev ops type programming skills to automate an instrument. A lot of the technology. A subject matter expert who understands the data sources in it's meeting the line of business executive who translate bringing that information to the business project and associating with business value. So when we say How do you get started? We've developed A I would call it a pretty basic maturity model to help organizations figure out. Where are they in terms of the technology, where are they in terms of organizationally knowing who the right people should be involved in these projects? And then, from a process perspective, we've developed some pretty prescriptive project plans. They help you nail down. What are the data elements that are critical for this business business initiative? And then we have for each role what their jobs are to consolidate the data sets map them together and present them to the consumer. We find that six week projects, typically three sprints, are perfect times to be able to a timeline to create one of these very short, quick win projects. Take that as an opportunity to figure out where your bottlenecks are in your own organization, where your skill shortages are, and then use the outcome of that six week sprint to then focus on billing and gaps. Kick off the next project and iterating celebrate the success and promote the success because >>it's typically tied to a business value to help them create momentum for the next one. >>That's awesome. I want to get into some examples, I mean, or we're both Massachusetts based. Normally you'd be in our studio and we'd be sitting here for face to face of obviously with Kobe. 19. In this crisis world sheltering in place, you're up somewhere in New England. I happened to be in my studio, but I'm the only one here, so relate this to cove it. How would data ops, or maybe you have a, ah, a concrete example in terms of how it's helped, inform or actually anticipate and keep up to date with what's happening with both. >>Yeah, well, I mean, we're all experiencing it. I don't think there's a person >>on the planet who hasn't been impacted by what's been going on with this Cupid pandemic prices. >>So we started. We started down this data obscurity a year ago. I mean, this isn't something that we just decided to implement a few weeks ago. We've been working on developing the methodology, getting our own organization in place so that we could respond the next time we needed to be able todo act upon a data driven decision. So part of the step one of our journey has really been working with our global chief data officer, Interpol, who I believe you have had an opportunity to meet with an interview. So part of this year Journey has been working with with our corporate organization. I'm in a line of business organization where we've established the roles and responsibilities we've established the technology >>stack based on our cloud pack for data and Watson knowledge padlock. >>So I use that as the context. For now, we're faced with a pandemic prices, and I'm being asked in my business unit to respond very quickly. How can we prioritize the offerings that are going to help those in critical need so that we can get those products out to market? We can offer a 90 day free use for governments and hospital agencies. So in order for me to do that as a operations lead or our team, I needed to be able to have access to our financial data. I needed to have access to our product portfolio information. I needed to understand our cloud capacity. So in order for me to be able to respond with the offers that we recently announced and you'll you can take a look at some of the examples with our Watson Citizen Assistant program, where I was able to provide the financial information required for >>us to make those products available from governments, hospitals, state agencies, etcetera, >>that's a That's a perfect example. Now, to set the stage back to the corporate global, uh, the chief data office organization, they implemented some technology that allowed us to, in just data, automatically classify it, automatically assign metadata, automatically associate data quality so that when my team started using that data, we knew what the status of that information >>was when we started to build our own predictive models. >>And so that's a great example of how we've been partnered with a corporate central organization and took advantage of the automated, uh, set of capabilities without having to invest in any additional resources or head count and be able to release >>products within a matter of a couple of weeks. >>And in that automation is a function of machine intelligence. Is that right? And obviously, some experience. But you couldn't you and I when we were consultants doing this by hand, we couldn't have done this. We could have done it at scale anyway. It is it is it Machine intelligence and AI that allows us to do this. >>That's exactly right. And you know, our organization is data and AI, so we happen to have the research and innovation teams that are building a lot of this technology, so we have somewhat of an advantage there, but you're right. The alternative to what I've described is manual spreadsheets. It's querying databases. It's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation. You have to wait for them to come back, and all of this was a manual process. And in the last five years, we've seen this data catalog market really become this augmented data catalog, and the augmentation means it's automation through AI. So with years of experience and natural language understanding, we can home through a lot of the metadata that's available electronically. We can calm for unstructured data, but we can categorize it. And if you have a set of business terms that have industry standard definitions through machine learning, we can automate what you and I did as a consultant manually in a matter of seconds. That's the impact that AI is have in our organization, and now we're bringing this to the market, and >>it's a It's a big >>part of where I'm investing. My time, both internally and externally, is bringing these types >>of concepts and ideas to the market. >>So I'm hearing. First of all, one of the things that strikes me is you've got multiple data, sources and data that lives everywhere. You might have your supply chain data in your er p. Maybe that sits on Prem. You might have some sales data that's sitting in a sas in a cloud somewhere. Um, you might have, you know, weather data that you want to bring in in theory. Anyway, the more data that you have, the better insights that you could gather assuming you've got the right data quality. But so let me start with, like, where the data is, right? So So it's it's anywhere you don't know where it's going to be, but you know you need it. So that's part of this right? Is being able >>to get >>to the data quickly. >>Yeah, it's funny. You bring it up that way. I actually look a little differently. It's when you start these projects. The data was in one place, and then by the time you get through the end of a project, you >>find out that it's moved to the cloud, >>so the data location actually changes. While we're in the middle of projects, we have many or even during this this pandemic crisis. We have many organizations that are using this is an opportunity to move to SAS. So what was on Prem is now cloud. But that shouldn't change the definition of the data. It shouldn't change. It's meaning it might change how you connect to it. It might also change your security policies or privacy laws. Now, all of a sudden, you have to worry about where is that data physically located? And am I allowed to share it across national boundaries right before we knew physically where it waas. So when you think about data ops, data ops is a process that sits on top of where the data physically resides. And because we're mapping metadata and we're looking at these data pipelines and automated work flows, part of the design principles are to set it up so that it's independent of where it resides. However, you have to have placeholders in your metadata and in your tool chain, where we're automating these work flows so that you can accommodate when the data decides to move. Because the corporate policy change >>from on prem to cloud. >>And that's a big part of what Data ops offers is the same thing. By the way, for Dev ops, they've had to accommodate building in, you know, platforms as a service versus on from the development environments. It's the same for data ops, >>and you know, the other part that strikes me and listening to you is scale, and it's not just about, you know, scale with the cloud operating model. It's also about what you were talking about is you know, the auto classification, the automated metadata. You can't do that manually. You've got to be able to do that. Um, in order to scale with automation, That's another key part of data office, is it not? >>It's a well, it's a big part of >>the value proposition and a lot of the part of the business case. >>Right then you and I started in this business, you know, and big data became the thing. People just move all sorts of data sets to these Hadoop clusters without capturing the metadata. And so as a result, you know, in the last 10 years, this information is out there. But nobody knows what it means anymore. So you can't go back with the army of people and have them were these data sets because a lot of the contact was lost. But you can use automated technology. You can use automated machine learning with natural, understand natural language, understanding to do a lot of the heavy lifting for you and a big part of data ops, work flows and building these pipelines is to do what we call management by exception. So if your algorithms say 80% confident that this is a phone number and your organization has a low risk tolerance, that probably will go to an exception. But if you have a you know, a match algorithm that comes back and says it's 99% sure this is an email address, right, and you have a threshold that's 98%. It will automate much of the work that we used to have to do manually. So that's an example of how you can automate, eliminate manual work and have some human interaction based on your risk threshold. >>That's awesome. I mean, you're right, the no schema on write said. I throw it into a data lake. Data Lake becomes a data swamp. We all know that joke. Okay, I want to understand a little bit, and maybe you have some other examples of some of the use cases here, but there's some of the maturity of where customers are. It seems like you've got to start by just understanding what data you have, cataloging it. You're getting your metadata act in order. But then you've got you've got a data quality component before you can actually implement and get yet to insight. So, you know, where are customers on the maturity model? Do you have any other examples that you can share? >>Yeah. So when we look at our data ops maturity model, we tried to simplify, and I mentioned this earlier that we try to simplify it so that really anybody can get started. They don't have to have a full governance framework implemented to to take advantage of the benefits data ops delivers. So what we did is we said if you can categorize your data ops programs into really three things one is how well do you know your data? Do you even know what data you have? The 2nd 1 is, and you trust it like, can you trust it's quality? Can you trust it's meeting? And the 3rd 1 is Can you put it to use? So if you really think about it when you begin with what data do you know, write? The first step is you know, how are you determining what data? You know? The first step is if you are using spreadsheets. Replace it with a data catalog. If you have a department line of business catalog and you need to start sharing information with the department's, then start expanding to an enterprise level data catalog. Now you mentioned data quality. So the first step is do you even have a data quality program, right. Have you even established what your criteria are for high quality data? Have you considered what your data quality score is comprised of? Have you mapped out what your critical data elements are to run your business? Most companies have done that for there. They're governed processes. But for these new initiatives And when you identify, I'm in my example with the covert prices, what products are we gonna help bring to market quickly? I need to be able to >>find out what the critical data elements are. And can I trust it? >>Have I even done a quality scan and have teams commented on it's trustworthiness to be used in this case, If you haven't done anything like that in your organization, that might be the first place to start. Pick the critical data elements for this initiative, assess its quality, and then start to implement the work flows to re mediate. And then when you get to putting it to use, there's several methods for making data available. One is simply making a gate, um, are available to a small set of users. That's what most people do Well, first, they make us spreadsheet of the data available, But then, if they need to have multiple people access it, that's when, like a Data Mart might make sense. Technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase, and that's a great way to get started. It doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case. Eventually, you'll get to the need to use a high performance CTL tool for data integration. But Nirvana is when you really get to that self service data prep, where users can weary a catalog and say these are the data sets I need. It presents you a list of data assets that are available. I can point and click at these columns I want as part of my data pipeline and I hit go and automatically generates that output or data science use cases for it. Bad news, Dashboard. Right? That's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something, you can do it. Push button. And that's where data obscurity should should bring organizations too. >>Well, Julie, I think there's no question that this covert crisis is accentuated the importance of digital. You know, we talk about digital transformation a lot, and it's it's certainly riel, although I would say a lot of people that we talk to we'll say, Well, you know, not on my watch. Er, I'll be retired before that all happens. Well, this crisis is accelerating. That transformation and data is at the heart of it. You know, digital means data. And if you don't have data, you know, story together and your act together, then you're gonna you're not gonna be able to compete. And data ops really is a key aspect of that. So give us a parting word. >>Yeah, I think This is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions. And if there hasn't been a more pressing time to do it, it's when our entire engagement becomes virtual like. This interview is virtual right. Everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes. You know, let's use the data that's available and use data ops to make sure that we can generate access. That data? No, it trust it, Put it to use so that we can respond to >>those in need when they need it. >>Julie Lockner, your incredible practitioner. Really? Hands on really appreciate you coming on the Cube and sharing your knowledge with us. Thank you. >>Thank you very much. It was a pleasure to be here. >>Alright? And thank you for watching everybody. This is Dave Volante for the Cube. And we will see you next time. >>Yeah, yeah, yeah, yeah, yeah

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi everybody this is David on tape with the cube and welcome to the special digital presentation we're really digging into how IBM is operationalizing and automating the AI and data pipeline not only for its clients but also for itself and with me is Julie Lochner who looks after offering management and IBM's data and AI portfolio Julie great to see you again okay great to be here thank you talk a little bit about the role you have here at IBM sure so my responsibility in offering management in the data and AI organization is really twofold one is I lead a team that implements all of the back-end processes really the operations behind anytime we deliver a product from the data AI team to the market so think about all of the release cycle management pricing product management discipline etc the other roles that I play is really making sure that um we are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology it's something that I needed internally from my own line of business execution but it's now something that our customers are looking for to implement in their shops as well well good I really want to get into that and so let's let's start with data ops I mean I think you know a lot of people are familiar with DevOps not maybe not everybody's familiar with the data Ops what do we need to know about data well I mean you bring up the point that everyone knows DevOps and and then in fact I think you know what data Ops really does is bring a lot of the benefits that DevOps did for application development to the data management organizations so when we look at what is data ops it's a data management it's a it's a data management set of principles that helps organizations bring business ready data to their consumers quickly it takes it borrows from DevOps similarly where you have a data pipeline that associates a business value requirement I have this business initiative it's gonna drive this much revenue or this much cost savings this is the data that I need to be able to deliver it how do I develop that pipeline and map to the data sources know what data it is know that I can trust it so ensuring that it has the right quality that I'm actually using the data that it was meant for and then put it to use so in in history most dated management practices deployed a waterfall like methodology or implementation methodology and what that meant is all the data pipeline projects were implemented serially and it was dawn based on potentially a first-in first-out program management office with a DevOps mental model and the idea of being able to slice through all of the different silos that's required to collect the data to organize it to integrate it to validate its quality to create those data integration pipelines and then present it to the dashboard like if it's a Cognos dashboard for a operational process or even a data science team that whole end-to-end process gets streamlined through what we're calling data ops methodology so I mean as you well know we've been following this market since the early days of a dupe and people struggle with their data pipelines it's complicated for them there's a raft of tools and and and they spend most of their time wrangling data preparing data improving data quality different roles within the organization so it sounds like you know to borrow from from DevOps data OPS's is all about REME lining that data pipeline helping people really understand and communicate across end to end as you're saying but but what's the ultimate business outcome that you're trying to drive so when you think about projects that require data to again cut cost to automate a business process or drive new revenue initiatives how long does it take to get from having access to the data to making it available that duration for every time delay that is spent wasted trying to connect to data sources trying to find subject matter experts that understand what the data means and can verify its quality like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fast so the business value of data Ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day we don't have this data to make a decision we're either making money or losing money that's the value proposition of data ops so it's about taking things that people are already doing today and figuring out the quickest way to do it through automation through workflows and just cutting through all of the political barriers that often happens when these data's cross different organizational boundaries yeah so speed time to insights is critical but to in and then you know with DevOps you're really bringing together the skill sets into sort of you know one super dev or one super ops it sounds with data ops it's really more about everybody understanding their role and having communication and line-of-sight across the entire organization it's not trying to make everybody a superhuman data person it's the whole it's the group it's the team effort really it's really a team game here isn't it well that's a big part of it so just like any type of practice there's people aspects process aspects and technology right so people process technology and while you're you're describing it like having that super team that knows everything about the data the only way that's possible is if you have a common foundation of metadata so we've seen a surgeons in the data catalog market and last you know six seven years and what what the what that the innovation in the data catalog market has actually enabled us to be able to drive more data ops pipelines meaning as you identify data assets you've captured the metadata you capture its meaning you capture information that can be shared whether they're stakeholders it really then becomes more of a essential repository for people to really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority like privacy rules included put it to use for models you know dashboards operational processes okay and and we're gonna talk about some examples and one of them of course is ibm's own internal example but but help us understand where you advise clients to start I want to get into it where do I get started yeah I mean so traditionally what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow and what we've said is look there's an opera there's an opportunity here to quickly define a small project align it to a high-value business initiative target something that you can quickly gain access to the data map out these pipelines and create a squad of skills so it includes a person with DevOps type programming skills to automate an instrument a lot of the technology a subject matter expert who understands the data sources and its meaning a line of business executive who can translate bringing that information to the business project and associating with business value so when we say how do you get started we've developed a I would call it a pretty basic maturity model to help organizations figure out where are they in terms of the technology where are they in terms of organizationally knowing who the right people should be involved in these projects and then from a process perspective we've developed some pretty prescriptive project plans that help you nail down what are the data elements that are critical for this business business initiative and then we have for each role what their jobs are to consolidate the datasets map them together and present them to the consumer we find that six-week projects typically three sprints are perfect times to be able to in a timeline to create one of these very short quick win projects take that as an opportunity to figure out where your bottlenecks are in your own organization where your skill shortages are and then use the outcome of that six-week sprint to then focus on filling in gaps kick off the next project and iterate celebrate the success and promote the success because it's typically tied to a business value to help them create momentum for the next one all right that's awesome I want to now get into some examples I mean or you're we're both massachusetts-based normally you'd be in our studio and we'd be sitting here face-to-face obviously with kovat 19 in this crisis we're all sheltering in place you're up in somewhere in New England I happen to be in my studio believe it but I'm the only one here so relate this to kovat how would data ops or maybe you have a concrete example in in terms of how it's helped inform or actually anticipate and keep up-to-date with what's happening with building yeah well I mean we're all experiencing it I don't think there's a person on the planet who hasn't been impacted by what's been going on with this coded pandemic crisis so we started we started down this data obscurity a year ago I mean this isn't something that we just decided to implement a few weeks ago we've been working on developing the methodology getting our own organization in place so that we could respond the next time we needed to be able to you know act upon a data-driven decision so part of step one of our journey has really been working with our global chief data officer Interpol who I believe you have had an opportunity to meet with an interview so part of this year journey has been working with with our corporate organization I'm in the line of business organization where we've established the roles and responsibilities we've established the technology stack based on our cloud pack for data and Watson knowledge catalog so I use that as the context for now we're faced with a pandemic crisis and I'm being asked in my business unit to respond very quickly how can we prioritize the offerings that are gonna help those in critical need so that we can get those products out to market we can offer a you know 90-day free use for governments and Hospital agencies so in order for me to do that as a operations lead for our team I needed to be able to have access to our financial data I needed to have access to our product portfolio information I needed to understand our cloud capacity so in order for me to be able to respond with the offers that we recently announced you know you can take a look at some of the examples with our Watson citizen assistant program where I was able to provide the financial information required for us to make those products available for governments hospitals state agencies etc that's a that's a perfect example now to to set the stage back to the corporate global chief data office organization they implemented some technology that allowed us to ingest data automatically classify it automatically assign metadata automatically associate data quality so that when my team started using that data we knew what the status of that information was when we started to build our own predictive models and so that's a great example of how we've partnered with a corporate central organization and took advantage of the automated set of capabilities without having to invest in any additional resources or headcount and be able to release products within a matter of a couple of weeks and in that automation is a function of machine intelligence is that right and obviously some experience but but you couldn't you and I when we were consultants doing this by hand we couldn't have done this we could have done it at scale anyways it is it machine intelligence an AI that allows us to do this that's exactly right and as you know our organization is data and AI so we happen to have the a research and innovation teams that are building a lot of this technology so we have somewhat of an advantage there but you're right the alternative to what I've described is manual spreadsheets it's querying databases it's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation you have to wait for them to come back and all of this was a manual process and in the last five years we've seen this data catalog market really become this augmented data catalog and that augmentation means it's automation through AI so with years of experience and natural language understanding we can comb through a lot of the metadata that's available electronically we can comb through unstructured data we can categorize it and if you have a set of business terms that have industry standard definitions through machine learning we can automate what you and I did as a consultant manually in a matter of seconds that's the impact the AI is had in our organization and now we're bringing this to the market and it's a it's a big part of where I'm investing my time both internally and externally is bringing these types of concepts and ideas to the market so I'm hearing first of all one of the things that strikes me is you've got multiple data sources and data lives everywhere you might have your supply chain data and your ERP maybe that sits on Prem you might have some sales data that's sitting in the SAS store in a cloud somewhere you might have you know a weather data that you want to bring in in theory anyway the more data that you have the better insights that you can gather assuming you've got the right data quality but so let me start with like where the data is right so so it sits anywhere you don't know where it's gonna be but you know you need it so that that's part of this right is being able to read it quickly yeah it's funny you bring it up that way I actually look a little differently it's when you start these projects the data was in one place and then by the time you get through the end of a project you find out that it's a cloud so the data location actually changes while we're in the middle of projects we have many or coming even during this this pandemic crisis we have many organizations that are using this as an opportunity to move to SAS so what was on Prem is now cloud but that shouldn't change the definition of the data it shouldn't change its meaning it might change how you connect to it um it might also change your security policies or privacy laws now all of a sudden you have to worry about where is that data physically located and am I allowed to share it across national boundaries right before we knew physically where it was so when you think about data ops data ops is a process that sits on top of where the data physically resides and because we're mapping metadata and we're looking at these data pipelines and automated workflows part of the design principles are to set it up so that it's independent of where it resides however you have to have placeholders in your metadata and in your tool chain where we oughta mating these workflows so that you can accommodate when the data decides to move because of corporate policy change from on-prem to cloud then that's a big part of what data Ops offers it's the same thing by the way for DevOps they've had to accommodate you know building in you know platforms as a service versus on from the development environments it's the same for data ops and you know the other part that strikes me and listening to you is scale and it's not just about you know scale with the cloud operating model it's also about what you're talking about is you know the auto classification the automated metadata you can't do that manually you've got to be able to do that in order to scale with automation that's another key part of data Ops is it not it's well it's a big part of the value proposition and a lot of a part of the business base right then you and I started in this business you know and Big Data became the thing people just move all sorts of data sets to these Hadoop clusters without capturing the metadata and so as a result you know in the last 10 years this information is out there but nobody knows what it means anymore so you can't go back with the army of people and have them query these data sets because a lot of the contact was lost but you can use automated technology you can use automated machine learning with natural under Snatcher Alang guaa Jing to do a lot of the heavy lifting for you and a big part of data ops workflows and building these pipelines is to do what we call management-by-exception so if your algorithms say you know 80% confident that this is a phone number and your organization has a you know low risk tolerance that probably will go to an exception but if you have a you know a match algorithm that comes back and says it's 99 percent sure this is an email address right and you I have a threshold that's 98% it will automate much of the work that we used to have to do manually so that's an example of how you can automate eliminate manual work and have some human interaction based on your risk threshold now that's awesome I mean you're right the no schema on right said I throw it into a data leg the data link becomes the data swap we all know that joke okay I want to understand a little bit and maybe you have some other examples of some of the use cases here but there's some of the maturity of where customers are I mean it seems like you got to start by just understanding what data you have cataloging it you're getting your metadata act in order but then you've got a you've got a data quality component before you can actually implement and get yet to insight so you know where our customers on the on the maturity model do you have any other examples that you can share yeah so when we look at our data ops maturity model we tried to simplify it I mentioned this earlier that we try to simplify it so that really anybody can get started they don't have to have a full governance framework implemented to take advantage of the benefits data ops delivers so what we did we said if you can categorize your data ops programs into really three things one is how well do you know your data do you even know what data you have the second one is and you trust it like can you trust its quality can you trust its meeting and the third one is can you put it to use so if you really think about it when you begin with what data do you know right the first step is you know how are you determining what data you know the first step is if you are using spreadsheets replace it with a data catalog if you have a department line of business catalog and you need to start sharing information with the departments then start expanding to an enterprise level data catalog now you mentioned data quality so the first step is do you even have a data quality program right have you even established what your criteria are for high quality data have you considered what your data quality score is comprised of have you mapped out what your critical data elements are to run your business most companies have done that for they're they're governed processes but for these new initiatives and when you identify I'm in my example with the Kovach crisis what products are we gonna help bring to market quickly I need to be able to find out what the critical data elements are and can I trust it have I even done a quality scan and have teams commented on its trustworthiness to be used in this case if you haven't done anything like that in your organization that might be the first place to start pick the critical data elements for this initiative assess its quality and then start to implement the workflows to remediate and then when you get to putting it to use there's several methods for making data available you know one is simply making a data Mart available to a small set of users that's what most people do well first they make a spreadsheet of the data available but then if they need to have multiple people access it that's when like a data Mart might make sense technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase and that's a great way to get started it doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case eventually you'll get to the need to use a high performance ETL tool for data integration but Nirvana is when you really get to that self-service data prep where users can query a catalog and say these are the data sets I need it presents you a list of data assets that are available I can point and click at these columns I want as part of my you know data pipeline and I hit go and it automatically generates that output for data science use cases for a Cognos dashboard right that's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something you can do it push button and that's where data observation to bring organizations to well Julie I think there's no question that this kovat crisis is accentuated the importance of digital you know we talk about digital transformation a lot and it's it's certainly real although I would say a lot of people that we talk to will say well you know not on my watch or I'll be retired before that all happens will this crisis is accelerating that transformation and data is at the heart of it you know digital means data and if you don't have your data you know story together and your act together then you're gonna you're not going to be able to compete and data ops really is a key aspect of that so you know give us a parting word all right I think this is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions and if there hasn't been a more pressing time to do it it's when our entire engagement becomes virtual like this interview is virtual write everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes you know let's use the data that's available and use data ops to make sure that we can iterate access that data know it trust it put it to use so that we can respond to those in need when they need it Julie Locker your incredible practitioner really hands-on really appreciate you coming on the Kuban and sharing your knowledge with us thank you okay thank you very much it was a pleasure to be here all right and thank you for watching everybody this is Dave Volante for the cube and we will see you next time [Music]

>> Paige: Hello everybody and thank you for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled Keep Data Private Prepare and Analyze Without Unencrypting With Voltage SecureData for Vertica. I'm Paige Roberts, Open Source Relations Manager at Vertica, and I'll be your host for this session. Joining me is Rich Gaston, Global Solutions Architect, Security, Risk, and Government at Voltage. And before we begin, I encourage you to submit your questions or comments during the virtual session, you don't have to wait till the end. Just type your question as it occurs to you, or comment, in the question box below the slide and then click Submit. There'll be a Q&A session at the end of the presentation where we'll try to answer as many of your questions as we're able to get to during the time. Any questions that we don't address we'll do our best to answer offline. Now, if you want, you can visit the Vertica Forum to post your questions there after the session. Now, that's going to take the place of the Developer Lounge, and our engineering team is planning to join the Forum, to keep the conversation going. So as a reminder, you can also maximize your screen by clicking the double arrow button, in the lower-right corner of the slides. That'll allow you to see the slides better. And before you ask, yes, this virtual session is being recorded and it will be available to view on-demand this week. We'll send you a notification as soon as it's ready. All right, let's get started. Over to you, Rich. >> Rich: Hey, thank you very much, Paige, and appreciate the opportunity to discuss this topic with the audience. My name is Rich Gaston and I'm a Global Solutions Architect, within the Micro Focus team, and I work on global Data privacy and protection efforts, for many different organizations, looking to take that journey toward breach defense and regulatory compliance, from platforms ranging from mobile to mainframe, everything in between, cloud, you name it, we're there in terms of our solution sets. Vertica is one of our major partners in this space, and I'm very excited to talk with you today about our solutions on the Vertica platform. First, let's talk a little bit about what you're not going to learn today, and that is, on screen you'll see, just part of the mathematics that goes into, the format-preserving encryption algorithm. We are the originators and authors and patent holders on that algorithm. Came out of research from Stanford University, back in the '90s, and we are very proud, to take that out into the market through the NIST standard process, and license that to others. So we are the originators and maintainers, of both standards and athureader in the industry. We try to make this easy and you don't have to learn any of this tough math. Behind this there are also many other layers of technology. They are part of the security, the platform, such as stateless key management. That's a really complex area, and we make it very simple for you. We have very mature and powerful products in that space, that really make your job quite easy, when you want to implement our technology within Vertica. So today, our goal is to make Data protection easy for you, to be able to understand the basics of Voltage Secure Data, you're going to be learning how the Vertica UDx, can help you get started quickly, and we're going to see some examples of how Vertica plus Voltage Secure Data, are going to be working together, in our customer cases out in the field. First, let's take you through a quick introduction to Voltage Secure Data. The business drivers and what's this all about. First of all, we started off with Breach Defense. We see that despite continued investments, in personal perimeter and platform security, Data breaches continue to occur. Voltage Secure Data plus Vertica, provides defense in depth for sensitive Data, and that's a key concept that we're going to be referring to. in the security field defense in depth, is a standard approach to be able to provide, more layers of protection around sensitive assets, such as your Data, and that's exactly what Secure Data is designed to do. Now that we've come through many of these breach examples, and big ticket items, getting the news around breaches and their impact, the business regulators have stepped up, and regulatory compliance, is now a hot topic in Data privacy. Regulations such as GDPR came online in 2018 for the EU. CCPA came online just this year, a couple months ago for California, and is the de-facto standard for the United States now, as organizations are trying to look at, the best practices for providing, regulatory compliance around Data privacy and protection. These gives massive new rights to consumers, but also obligations to organizations, to protect that personal Data. Secure Data Plus Vertica provides, fine grained authorization around sensitive Data, And we're going to show you exactly how that works, within the Vertica platform. At the bottom, you'll see some of the snippets there, of the news articles that just keep racking up, and our goal is to keep you off the news, to keep your company safe, so that you can have the assurance, that even if there is an unintentional, or intentional breach of Data out of the corporation, if it is protected by voltage Secure Data, it will be of no value to those hackers, and then you have no impact, in terms of risk to the organization. What do we mean by defense in depth? Let's take a look first at the encryption types, and the benefits that they provide, and we see our customers implementing, all kinds of different protection mechanisms, within the organization. You could be looking at disk level protection, file system protection, protection on the files themselves. You could protect the entire Database, you could protect our transmissions, as they go from the client to the server via TLS, or other protected tunnels. And then we look at Field-level Encryption, and that's what we're talking about today. That's all the above protections, at the perimeter level at the platform level. Plus, we're giving you granular access control, to your sensitive Data. Our main message is, keep the Data protected for at the earliest possible point, and only access it, when you have a valid business need to do so. That's a really critical aspect as we see Vertica customers, loading terabytes, petabytes of Data, into clusters of Vertica console, Vertica Database being able to give access to that Data, out to a wide variety of end users. We started off with organizations having, four people in an office doing Data science, or analytics, or Data warehousing, or whatever it's called within an organization, and that's now ballooned out, to a new customer coming in and telling us, we're going to have 1000 people accessing it, plus service accounts accessing Vertica, we need to be able to provide fine level access control, and be able to understand what are folks doing with that sensitive Data? And how can we Secure it, the best practices possible. In very simple state, voltage protect Data at rest and in motion. The encryption of Data facilitates compliance, and it reduces your risk of breach. So if you take a look at what we mean by feel level, we could take a name, that name might not just be in US ASCII. Here we have a sort of Latin one extended, example of Harold Potter, and we could take a look at the example protected Data. Notice that we're taking a character set approach, to protecting it, meaning, I've got an alphanumeric option here for the format, that I'm applying to that name. That gives me a mix of alpha and numeric, and plus, I've got some of that Latin one extended alphabet in there as well, and that's really controllable by the end customer. They can have this be just US ASCII, they can have it be numbers for numbers, you can have a wide variety, of different protection mechanisms, including ignoring some characters in the alphabet, in case you want to maintain formatting. We've got all the bells and whistles, that you would ever want, to put on top of format preserving encryption, and we continue to add more to that platform, as we go forward. Taking a look at tax ID, there's an example of numbers for numbers, pretty basic, but it gives us the sort of idea, that we can very quickly and easily keep the Data protected, while maintaining the format. No schema changes are going to be required, when you want to protect that Data. If you look at credit card number, really popular example, and the same concept can be applied to tax ID, often the last four digits will be used in a tax ID, to verify someone's identity. That could be on an automated telephone system, it could be a customer service representative, just trying to validate the security of the customer, and we can keep that Data in the clear for that purpose, while protecting the entire string from breach. Dates are another critical area of concern, for a lot of medical use cases. But we're seeing Date of Birth, being included in a lot of Data privacy conversations, and we can protect dates with dates, they're going to be a valid date, and we have some really nifty tools, to maintain offsets between dates. So again, we've got the real depth of capability, within our encryption, that's not just saying, here's a one size fits all approach, GPS location, customer ID, IP address, all of those kinds of Data strings, can be protected by voltage Secure Data within Vertica. Let's take a look at the UDx basics. So what are we doing, when we add Voltage to Vertica? Vertica stays as is in the center. In fact, if you get the Vertical distribution, you're getting the Secure Data UDx onboard, you just need to enable it, and have Secure Data virtual appliance, that's the box there on the middle right. That's what we come in and add to the mix, as we start to be able to add those capabilities to Vertica. On the left hand side, you'll see that your users, your service accounts, your analytics, are still typically doing Select, Update, Insert, Delete, type of functionality within Vertica. And they're going to come into Vertica's access control layer, they're going to also access those services via SQL, and we simply extend SQL for Vertica. So when you add the UDx, you get additional syntax that we can provide, and we're going to show you examples of that. You can also integrate that with concepts, like Views within Vertica. So that we can say, let's give a view of Data, that gives the Data in the clear, using the UDx to decrypt that Data, and let's give everybody else, access to the raw Data which is protected. Third parties could be brought in, folks like contractors or folks that aren't vetted, as closely as a security team might do, for internal sensitive Data access, could be given access to the Vertical cluster, without risk of them breaching and going into some area, they're not supposed to take a look at. Vertica has excellent control for access, down even to the column level, which is phenomenal, and really provides you with world class security, around the Vertical solution itself. Secure Data adds another layer of protection, like we're mentioning, so that we can have Data protected in use, Data protected at rest, and then we can have the ability, to share that protected Data throughout the organization. And that's really where Secure Data shines, is the ability to protect that Data on mainframe, on mobile, and open systems, in the cloud, everywhere you want to have that Data move to and from Vertica, then you can have Secure Data, integrated with those endpoints as well. That's an additional solution on top, the Secure Data Plus Vertica solution, that is bundled together today for a sales purpose. But we can also have that conversation with you, about those wider Secure Data use cases, we'd be happy to talk to you about that. Security to the virtual appliance, is a lightweight appliance, sits on something like eight cores, 16 gigs of RAM, 100 gig of disk or 200 gig of disk, really a lightweight appliance, you can have one or many. Most customers have four in production, just for redundancy, they don't need them for scale. But we have some customers with 16 or more in production, because they're running such high volumes of transaction load. They're running a lot of web service transactions, and they're running Vertica as well. So we're going to have those virtual appliances, as co-located around the globe, hooked up to all kinds of systems, like Syslog, LDAP, load balancers, we've got a lot of capability within the appliance, to fit into your enterprise IP landscape. So let me get you directly into the neat, of what does the UDx do. If you're technical and you know SQL, this is probably going to be pretty straightforward to you, you'll see the copy command, used widely in Vertica to get Data into Vertica. So let's try to protect that Data when we're ingesting it. Let's grab it from maybe a CSV file, and put it straight into Vertica, but protected on the way and that's what the UDx does. We have Voltage Secure protectors, an added syntax, like I mentioned, to the Vertica SQL. And that allows us to say, we're going to protect the customer first name, using the parameters of hyper alphanumeric. That's our internal lingo of a format, within Secure Data, this part of our API, the API is require very few inputs. The format is the one, that you as a developer will be supplying, and you'll have different ones for maybe SSN, you'll have different formats for street address, but you can reuse a lot of your formats, across a lot of your PII, PHI Data types. Protecting after ingest is also common. So I've got some Data, that's already been put into a staging area, perhaps I've got a landing zone, a sandbox of some sort, now I want to be able to move that, into a different zone in Vertica, different area of the schema, and I want to have that Data protected. We can do that with the update command, and simply again, you'll notice Voltage Secure protect, nothing too wild there, basically the same syntax. We're going to query unprotected Data. How do we search once I've encrypted all my Data? Well, actually, there's a pretty nifty trick to do so. If you want to be able to query unprotected Data, and we have the search string, like a phone number there in this example, simply call Voltage Secure protect on that, now you'll have the cipher text, and you'll be able to search the stored cipher text. Again, we're just format preserving encrypting the Data, and it's just a string, and we can always compare those strings, using standard syntax and SQL. Using views to decrypt Data, again a powerful concept, in terms of how to make this work, within the Vertica Landscape, when you have a lot of different groups of users. Views are very powerful, to be able to point a BI tool, for instance, business intelligence tools, Cognos, Tableau, etc, might be accessing Data from Vertica with simple queries. Well, let's point them to a view that does the hard work, and uses the Vertical nodes, and its horsepower of CPU and RAM, to actually run that Udx, and do the decryption of the Data in use, temporarily in memory, and then throw that away, so that it can't be breached. That's a nice way to keep your users active and working and going forward, with their Data access and Data analytics, while also keeping the Data Secure in the process. And then we might want to export some Data, and push it out to someone in a clear text manner. We've got a third party, needs to take the tax ID along with some Data, to do some processing, all we need to do is call Voltage Secure Access, again, very similar to the protect call, and you're writing the parameter again, and boom, we have decrypted the Data and used again, the Vertical resources of RAM and CPU and horsepower, to do the work. All we're doing with Voltage Secure Data Appliance, is a real simple little key fetch, across a protected tunnel, that's a tiny atomic transaction, gets done very quick, and you're good to go. This is it in terms of the UDx, you have a couple of calls, and one parameter to pass, everything else is config driven, and really, you're up and running very quickly. We can even do demos and samples of this Vertical Udx, using hosted appliances, that we put up for pre sales purposes. So folks want to get up and get a demo going. We could take that Udx, configure it to point to our, appliance sitting on the internet, and within a couple of minutes, we're up and running with some simple use cases. Of course, for on-prem deployment, or deployment in the cloud, you'll want your own appliance in your own crypto district, you have your own security, but it just shows, that we can easily connect to any appliance, and get this working in a matter of minutes. Let's take a look deeper at the voltage plus Vertica solution, and we'll describe some of the use cases and path to success. First of all your steps to, implementing Data-centric security and Vertica. Want to note there on the left hand side, identify sensitive Data. How do we do this? I have one customer, where they look at me and say, Rich, we know exactly what our sensitive Data is, we develop the schema, it's our own App, we have a customer table, we don't need any help in this. We've got other customers that say, Rich, we have a very complex Database environment, with multiple Databases, multiple schemas, thousands of tables, hundreds of thousands of columns, it's really, really complex help, and we don't know what people have been doing exactly, with some of that Data, We've got various teams that share this resource. There, we do have additional tools, I wanted to give a shout out to another microfocus product, which is called Structured Data Manager. It's a great tool that helps you identify sensitive Data, with some really amazing technology under the hood, that can go into a Vertica repository, scan those tables, take a sample of rows or a full table scan, and give you back some really good reports on, we think this is sensitive, let's go confirm it, and move forward with Data protection. So if you need help on that, we've got the tools to do it. Once you identify that sensitive Data, you're going to want to understand, your Data flows and your use cases. Take a look at what analytics you're doing today. What analytics do you want to do, on sensitive Data in the future? Let's start designing our analytics, to work with sensitive Data, and there's some tips and tricks that we can provide, to help you mitigate, any kind of concerns around performance, or any kind of concerns around rewriting your SQL. As you've noted, you can just simply insert our SQL additions, into your code and you're off and running. You want to install and configure the Udx, and secure Data software plants. Well, the UDx is pretty darn simple. The documentation on Vertica is publicly available, you could see how that works, and what you need to configure it, one file here, and you're ready to go. So that's pretty straightforward to process, either grant some access to the Udx, and that's really up to the customer, because there are many different ways, to handle access control in Vertica, we're going to be flexible to fit within your model, of access control and adding the UDx to your mix. Each customer is a little different there, so you might want to talk with us a little bit about, the best practices for your use cases. But in general, that's going to be up and running in just a minute. The security software plants, hardened Linux appliance today, sits on-prem or in the cloud. And you can deploy that. I've seen it done in 15 minutes, but that's what the real tech you had, access to being able to generate a search, and do all this so that, your being able to set the firewall and all the DNS entries, the basically blocking and tackling of a software appliance, you get that done, corporations can take care of that, in just a couple of weeks, they get it all done, because they have wait waiting on other teams, but the software plants are really fast to get stood up, and they're very simple to administer, with our web based GUI. Then finally, you're going to implement your UDx use cases. Once the software appliance is up and running, we can set authentication methods, we could set up the format that you're going to use in Vertica, and then those two start talking together. And it should be going in dev and test in about half a day, and then you're running toward production, in just a matter of days, in most cases. We've got other customers that say, Hey, this is going to be a bigger migration project for us. We might want to split this up into chunks. Let's do the real sensitive and scary Data, like tax ID first, as our sort of toe in the water approach, and then we'll come back and protect other Data elements. That's one way to slice and dice, and implement your solution in a planned manner. Another way is schema based. Let's take a look at this section of the schema, and implement protection on these Data elements. Now let's take a look at the different schema, and we'll repeat the process, so you can iteratively move forward with your deployment. So what's the added value? When you add full Vertica plus voltage? I want to highlight this distinction because, Vertica contains world class security controls, around their Database. I'm an old time DBA from a different product, competing against Vertica in the past, and I'm really aware of the granular access controls, that are provided within various platforms. Vertica would rank at the very top of the list, in terms of being able to give me very tight control, and a lot of different AWS methods, being able to protect the Data, in a lot of different use cases. So Vertica can handle a lot of your Data protection needs, right out of the box. Voltage Secure Data, as we keep mentioning, adds that defense in-Depth, and it's going to enable those, enterprise wide use cases as well. So first off, I mentioned this, the standard of FF1, that is format preserving encryption, we're the authors of it, we continue to maintain that, and we want to emphasize that customers, really ought to be very, very careful, in terms of choosing a NIST standard, when implementing any kind of encryption, within the organization. So 8 ES was one of the first, and Hallmark, benchmark encryption algorithms, and in 2016, we were added to that mix, as FF1 with CS online. If you search NIST, and Voltage Security, you'll see us right there as the author of the standard, and all the processes that went along with that approval. We have centralized policy for key management, authentication, audit and compliance. We can now see that Vertica selected or fetch the key, to be able to protect some Data at this date and time. We can track that and be able to give you audit, and compliance reporting against that Data. You can move protected Data into and out of Vertica. So if we ingest via Kafka, and just via NiFi and Kafka, ingest on stream sets. There are a variety of different ingestion methods, and streaming methods, that can get Data into Vertica. We can integrate secure Data with all of those components. We're very well suited to integrate, with any Hadoop technology or any big Data technology, as we have API's in a variety of languages, bitness and platforms. So we've got that all out of the box, ready to go for you, if you need it. When you're moving Data out of Vertica, you might move it into an open systems platform, you might move it to the cloud, we can also operate and do the decryption there, you're going to get the same plaintext back, and if you protect Data over in the cloud, and move it into Vertica, you're going to be able to decrypt it in Vertica. That's our cross platform promise. We've been delivering on that for many, many years, and we now have many, many endpoints that do that, in production for the world's largest organization. We're going to preserve your Data format, and referential integrity. So if I protect my social security number today, I can protect another batch of Data tomorrow, and that same ciphertext will be generated, when I put that into Vertica, I can have absolute referential integrity on that Data, to be able to allow for analytics to occur, without even decrypting Data in many cases. And we have decrypt access for authorized users only, with the ability to add LDAP authentication authorization, for UDx users. So you can really have a number of different approaches, and flavors of how you implement voltage within Vertica, but what you're getting is the additional ability, to have that confidence, that we've got the Data protected at rest, even if I have a DBA that's not vetted or someone new, or I don't know where this person is from a third party, and being provided access as a DBA level privilege. They could select star from all day long, and they're going to get ciphertext, they're going to have nothing of any value, and if they want to use the UDF to decrypt it, they're going to be tracked and traced, as to their utilization of that. So it allows us to have that control, and additional layer of security on your sensitive Data. This may be required by regulatory agencies, and it's seeming that we're seeing compliance audits, get more and more strict every year. GDPR was kind of funny, because they said in 2016, hey, this is coming, they said in 2018, it's here, and now they're saying in 2020, hey, we're serious about this, and the fines are mounting. And let's give you some examples to kind of, help you understand, that these regulations are real, the fines are real, and your reputational damage can be significant, if you were to be in breach, of a regulatory compliance requirements. We're finding so many different use cases now, popping up around regional protection of Data. I need to protect this Data so that it cannot go offshore. I need to protect this Data, so that people from another region cannot see it. That's all the kind of capability that we have, within secure Data that we can add to Vertica. We have that broad platform support, and I mentioned NiFi and Kafka, those would be on the left hand side, as we start to ingest Data from applications into Vertica. We can have landing zone approaches, where we provide some automated scripting at an OS level, to be able to protect ETL batch transactions coming in. We could protect within the Vertica UDx, as I mentioned, with the copy command, directly using Vertica. Everything inside that dot dash line, is the Vertical Plus Voltage Secure Data combo, that's sold together as a single package. Additionally, we'd love to talk with you, about the stuff that's outside the dash box, because we have dozens and dozens of endpoints, that could protect and access Data, on many different platforms. And this is where you really start to leverage, some of the extensive power of secure Data, to go across platform to handle your web based apps, to handle apps in the cloud, and to handle all of this at scale, with hundreds of thousands of transactions per second, of format preserving encryption. That may not sound like much, but when you take a look at the algorithm, what we're doing on the mathematics side, when you look at everything that goes into that transaction, to me, that's an amazing accomplishment, that we're trying to reach those kinds of levels of scale, and with Vertica, it scales horizontally. So the more nodes you add, the more power you get, the more throughput you're going to get, from voltage secure Data. I want to highlight the next steps, on how we can continue to move forward. Our secure Data team is available to you, to talk about the landscape, your use cases, your Data. We really love the concept that, we've got so many different organizations out there, using secure Data in so many different and unique ways. We have vehicle manufacturers, who are protecting not just the VIN, not just their customer Data, but in fact they're protecting sensor Data from the vehicles, which is sent over the network, down to the home base every 15 minutes, for every vehicle that's on the road, and every vehicle of this customer of ours, since 2017, has included that capability. So now we're talking about, an additional millions and millions of units coming online, as those cars are sold and distributed, and used by customers. That sensor Data is critical to the customer, and they cannot let that be ex-filled in the clear. So they protect that Data with secure Data, and we have a great track record of being able to meet, a variety of different unique requirements, whether it's IoT, whether it's web based Apps, E-commerce, healthcare, all kinds of different industries, we would love to help move the conversations forward, and we do find that it's really a three party discussion, the customer, secure Data experts in some cases, and the Vertica team. We have great enablement within Vertica team, to be able to explain and present, our secure Data solution to you. But we also have that other ability to add other experts in, to keep that conversation going into a broader perspective, of how can I protect my Data across all my platforms, not just in Vertica. I want to give a shout out to our friends at Vertica Academy. They're building out a great demo and training facilities, to be able to help you learn more about these UDx's, and how they're implemented. The Academy, is a terrific reference and resource for your teams, to be able to learn more, about the solution in a self guided way, and then we'd love to have your feedback on that. How can we help you more? What are the topics you'd like to learn more about? How can we look to the future, in protecting unstructured Data? How can we look to the future, of being able to protect Data at scale? What are the requirements that we need to be meeting? Help us through the learning processes, and through feedback to the team, get better, and then we'll help you deliver more solutions, out to those endpoints and protect that Data, so that we're not having Data breach, we're not having regulatory compliance concerns. And then lastly, learn more about the Udx. I mentioned, that all of our content there, is online and available to the public. So vertica.com/secureData , you're going to be able to walk through the basics of the UDX. You're going to see how simple it is to set up, what the UDx syntax looks like, how to grant access to it, and then you'll start to be able to figure out, hey, how can I start to put this, into a PLC in my own environment? Like I mentioned before, we have publicly available hosted appliance, for demo purposes, that we can make available to you, if you want to PLC this. Reach out to us. Let's get a conversation going, and we'll get you the address and get you some instructions, we can have a quick enablement session. We really want to make this accessible to you, and help demystify the concept of encryption, because when you see it as a developer, and you start to get your hands on it and put it to use, you can very quickly see, huh, I could use this in a variety of different cases, and I could use this to protect my Data, without impacting my analytics. Those are some of the really big concerns that folks have, and once we start to get through that learning process, and playing around with it in a PLC way, that we can start to really put it to practice into production, to say, with confidence, we're going to move forward toward Data encryption, and have a very good result, at the end of the day. This is one of the things I find with customers, that's really interesting. Their biggest stress, is not around the timeframe or the resource, it's really around, this is my Data, I have been working on collecting this Data, and making it available in a very high quality way, for many years. This is my job and I'm responsible for this Data, and now you're telling me, you're going to encrypt that Data? It makes me nervous, and that's common, everybody feels that. So we want to have that conversation, and that sort of trial and error process to say, hey, let's get your feet wet with it, and see how you like it in a sandbox environment. Let's now take that into analytics, and take a look at how we can make this, go for a quick 1.0 release, and let's then take a look at, future expansions to that, where we start adding Kafka on the ingest side. We start sending Data off, into other machine learning and analytics platforms, that we might want to utilize outside of Vertica, for certain purposes, in certain industries. Let's take a look at those use cases together, and through that journey, we can really chart a path toward the future, where we can really help you protect that Data, at rest, in use, and keep you safe, from both the hackers and the regulators, and that I think at the end of the day, is really what it's all about, in terms of protecting our Data within Vertica. We're going to have a little couple minutes for Q&A, and we would encourage you to have any questions here, and we'd love to follow up with you more, about any questions you might have, about Vertica Plus Voltage Secure Data. They you very much for your time today.

>>live from Miami, Florida It's the Q covering IBM is data in a I forum brought to you by IBM. >>We're back in Miami and you're watching the cubes coverage of the IBM data and a I forum. Tony hi. Amiss here is a distinguished engineer for Ditch the Digital and Cloud Business Analytics at IBM. Tony, first of all, congratulations on being a distinguished engineer. That doesn't happen often. Thank you for coming on the Cube. Thank you. So your area focus is on the B I and the Enterprise performance management space. >>Um, and >>if I understand it correctly, a big mission of yours is to try to modernize those make himself service, making cloud ready. How's that going? >>It's going really well. I mean, you know, we use things like B. I and enterprise performance management. When you really boil it down, there's that's analysis of data on what do we do with the data this useful that makes a difference in the world, and then this planning and forecasting and budgeting, which everyone has to do whether you are, you know, a single household or whether you're an Amazon or Boeing, which are also some of our clients. So it's interesting that we're going from really enterprise use cases, democratizing it all the way down to single user on the cloud credit card swipe 70 bucks a month >>so that was used to be used to work for Lotus. But Cognos is one of IBM's largest acquisitions in the software space ever. Steve Mills on his team architected complete transformation of IBM is business and really got heavily into it. I think I think it was a $5 billion acquisition. Don't hold me to that, but massive one of the time and it's really paid dividends now when all this sort of 2000 ten's came in and said, Oh, how Duke's gonna kill all the traditional b I traditional btw that didn't happen, that these traditional platforms were a fundamental component of people's data strategies, so that created the imperative to modernize and made sure that there could be things like self service and cloud ready, didn't it? >>Yeah, that's absolutely true. I mean, the work clothes that we run a really sticky were close right when you're doing your reporting, your consolidation or you're planning of your yearly cycle, your budget cycle on these technologies, you don't rip them out so easily. So yes, of course, there's competitive disruption in the space. And of course, cloud creates on opportunity for work loads to be wrong, Cheaper without your own I t people. And, of course, the era of digital software. I find it myself. I tried myself by it without ever talking to a sales person creates a democratization process for these really powerful tools that's never been invented before in that space. >>Now, when I started in the business a long, long time ago, it was called GSS decision support systems, and they at the time they promised a 360 degree view with business That never really happened. You saw a whole new raft of players come in, and then the whole B I and Enterprise Data Warehouse was gonna deliver on that promise. That kind of didn't happen, either. Sarbanes Oxley brought a big wave of of imperative around these systems because compliance became huge. So that was a real tailwind for it. Then her duke was gonna solve all these problems that really didn't happen. And now you've got a I, and it feels like the combination of those systems of record those data warehouse systems, the traditional business intelligence systems and all this new emerging tech together are actually going to be a game changer. I wonder if you could comment on >>well so they can be a game changer, but you're touching on a couple of subjects here that are connected. Right? Number one is obviously the mass of data, right? Cause data has accelerated at a phenomenal pace on then you're talking about how do I then visualize or use that data in a useful manner? And that really drives the use case for a I right? Because A I in and of itself, for augmented intelligence as we as we talk about, is only useful almost when it's invisible to the user cause the user needs to feel like it's doing something for them that super intuitive, a bit like the sort of transition between the electric car on the normal car. That only really happens when the electric car can do what the normal car can do. So with things like Imagine, you bring a you know, how do cluster into a B. I solution and you're looking at that data Well. If I can correlate, for example, time profit cost. Then I can create KP eyes automatically. I can create visualizations. I know which ones you like to see from that. Or I could give you related ones that I can even automatically create dashboards. I've got the intelligence about the data and the knowledge to know what? How you might what? Visualize adversity. You have to manually construct everything >>and a I is also going to when you when you spring. These disparage data sets together, isn't a I also going to give you an indication of the confidence level in those various data set. So, for example, you know, you're you're B I data set might be part of the General ledger. You know of the income statement and and be corporate fact very high confidence level. More sometimes you mention to do some of the unstructured data. Maybe not as high a confidence level. How our customers dealing with that and applying that first of all, is that a sort of accurate premise? And how is that manifesting itself in terms of business? Oh, >>yeah. So it is an accurate premise because in the world in the world of data. There's the known knowns on the unknown knowns, right? No, no's are what you know about your data. What's interesting about really good B I solutions and planning solutions, especially when they're brought together, right, Because planning and analysis naturally go hand in hand from, you know, one user 70 bucks a month to the Enterprise client. So it's things like, What are your key drivers? So this is gonna be the drivers that you know what drives your profit. But when you've got massive amounts of data and you got a I around that, especially if it's a I that's gone ontology around your particular industry, it can start telling you about drivers that you don't know about. And that's really the next step is tell me what are the drivers around things that I don't know. So when I'm exploring the data, I'd like to see a key driver that I never even knew existed. >>So when I talk to customers, I'm doing this for a while. One of the concerns they had a criticisms they had of the traditional systems was just the process is too hard. I got to go toe like a few guys I could go to I gotta line up, you know, submit a request. By the time I get it back, I'm on to something else. I want self serve beyond just reporting. Um, how is a I and IBM changing that dynamic? Can you put thes tools in the hands of users? >>Right. So this is about democratizing the cleverness, right? So if you're a big, broad organization, you can afford to hire a bunch of people to do that stuff. But if you're a startup or an SNB, and that's where the big market opportunity is for us, you know, abilities like and this it would be we're building this into the software already today is I'll bring a spreadsheet. Long spreadsheets. By definition, they're not rows and columns, right? Anyone could take a Roan Collin spreadsheet and turn into a set of data because it looks like a database. But when you've got different tabs on different sets of data that may or may not be obviously relatable to each other, that ai ai ability to be on introspect a spreadsheet and turn into from a planning point of view, cubes, dimensions and rules which turn your spreadsheet now to a three dimensional in memory cube or a planning application. You know, the our ability to go way, way further than you could ever do with that planning process over thousands of people is all possible now because we don't have taken all the hard work, all the lifting workout, >>so that three dimensional in memory Cuba like the sound of that. So there's a performance implication. Absolutely. On end is what else? Accessibility Maw wraps more users. Is that >>well, it's the ability to be out of process water. What if things on huge amounts of data? Imagine you're bowing, right? Howdy, pastors. Boeing How? I don't know. Three trillion. I'm just guessing, right? If you've got three trillion and you need to figure out based on the lady's hurricane report how many parts you need to go ship toe? Where that hurricane reports report is you need to do a water scenario on massive amounts of data in a second or two. So you know that capability requires an old lap solution. However, the rest of the planet other than old people bless him who are very special. People don't know what a laugh is from a pop tart, so democratizing it right to the person who says, I've got a set of data on as I still need to do what if analysis on things and probably at large data cause even if you're a small company with massive amounts of data coming through, people click. String me through your website just for example. You know what if I What if analysis on putting a 5% discount on this product based on previous sales have that going to affect me from a future sales again? I think it's the democratizing as the well is the ability to hit scale. >>You talk about Cloud and analytics, how they've they've come together, what specifically IBM has done to modernize that platform. And I'm interested in what customers are saying. What's the adoption like? >>So So I manage the Global Cloud team. We have night on 1000 clients that are using cloud the cloud implementations of our software growing actually so actually Maur on two and 1/2 1000. If you include the multi tenant version, there's two steps in this process, right when you've got an enterprise software solution, your clients have a certain expectation that your software runs on cloud just the way as it does on premise, which means in practical terms, you have to build a single tenant will manage cloud instance. And that's just the first step, right? Because getting clients to see the value of running the workload on cloud where they don't need people to install it, configure it, update it, troubleshoot it on all that other sort of I t. Stuff that subtracts you from doing running your business value. We duel that for you. But the future really is in multi tenant on how we can get vast, vast scale and also greatly lower costs. But the adoptions been great. Clients love >>it. Can you share any kind of indication? Or is that all confidential or what kind of metrics do you look at it? >>So obviously we look, we look a growth. We look a user adoption, and we look at how busy the service. I mean, let me give you the best way I can give you is a is a number of servers, volume numbers, right. So we have 8000 virtual machines running on soft layer or IBM cloud for our clients business Analytics is actually the largest client for IBM Cloud running those workloads for our clients. So it's, you know, that the adoption has been really super hard on the growth continues. Interestingly enough, I'll give you another factoid. So we just launched last October. Cognos Alex. Multi tenant. So it is truly multi infrastructure. You try, you buy, you give you credit card and away you go. And you would think, because we don't have software sellers out there selling it per se that it might not adopt as much as people are out there selling software. Okay, well, in one year, it's growing 10% month on month cigarette Ally's 10% month on month, and we're nearly 1400 users now without huge amounts of effort on our part. So clearly this market interest in running those softwares and then they're not want Tuesdays easer. Six people pretending some of people have 150 people pretending on a multi tenant software. So I believe that the future is dedicated is the first step to grow confidence that my own premise investments will lift and shift the cloud, but multi tenant will take us a lot >>for him. So that's a proof point of existing customer saying okay, I want to modernize. I'm buying in. Take 1/2 step of the man dedicated. And then obviously multi tenant for scale. And just way more cost efficient. Yes, very much. All right. Um, last question. Show us a little leg. What? What can you tell us about the road map? What gets you excited about the future? >>So I think the future historically, Planning Analytics and Carlos analytics have been separate products, right? And when they came together under the B I logo in about about a year ago, we've been spending a lot of our time bringing them together because, you know, you can fight in the B I space and you can fight in the planning space. And there's a lot of competitors here, not so many here. But when you bring the two things together, the connected value chain is where we really gonna win. But it's not only just doing is the connected value chain it and it could be being being vice because I'm the the former Lotus guy who believes in democratization of technology. Right? But the market showing us when we create a piece of software that starts at 15 bucks for a single user. For the same power mind you write little less less of the capabilities and 70 bucks for a single user. For all of it, people buy it. So I'm in. >>Tony, thanks so much for coming on. The kid was great to have you. Brilliant. Thank you. Keep it right there, everybody. We'll be back with our next guest. You watching the Cube live from the IBM data and a I form in Miami. We'll be right back.

>>live from Miami, Florida It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, everybody. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. (dramatic music) >> Hi, everyone, welcome to this week's Breaking Analysis. It's Friday, October 18th, and this is theCUBE Insights, powered by ETR. Today, ETR had its conference call, its webcast. It was in a quiet period, and it dropped this tome. I have spent the last several hours going through this dataset. It's just unbelievable. It's the fresh data from the October survey, and I'm going to share just some highlights with you. I wish I had a couple hours to go through all this stuff, but I'm going to just pull out some of the key points. Spending is flattening. We've talked about this in previous discussions with you. But, things are still healthy. We're just reverting back to pre 2018 levels and, obviously, keeping a very close eye on the spending data and the sectors. There is some uncertainty heading into Q four. It's not only tariffs, you know. 2020's an election year, so that causes some uncertainty and some concerns for people. But, the big theme from ETR is there's less experimentation going on. The last several years have been ones where we're pushing out digital initiatives, and there was a lot of experimentation, a lot of redundancy. So, I'm going to talk more about that. I'm going to focus on a couple of sectors. I'm going to share with you there's the overall sector analysis. Then, I'm going to focus in on Microsoft and AWS and talk a little bit about the cloud. Then, I'm going to give some other highlights and, particularly, around enterprise software. The other thing I'll say is that the folks from ETR are going to be in the Bay Area on October 28th through the 30th, and I would encourage you to spend some time with them. If you want to meet them, just, you know, contact me @dvellante on Twitter or David.Vellante@siliconangle.com. I have no dog in this fight. I get no money from these guys. We're just partners and friends, but I love their data. And, they've given me access to it, and it's great because I can share it with you, our community. So, let's get right into it. Alex, if you just bring up the first slide, what I want to show is the ETR pulse check survey demographics, so every quarter, ETR does these surveys. They've got a dataset comprising 4500 members, panelists if you will, that they survey each quarter. In this survey, 1336 responded, representing 457 billion in spending power, and you can see from this slide, you know, it's got a nice mix of large companies. Very heavily weighted toward North America, but you're talking about, you know, 12% AMIA out of 1300. Certainly substantial and statistically significant to get some trends overseas. You can see across all industries. And then, job titles, a lot of C level executives, VPs, architects, people who know what the spending climate looks like, so I really like the mix of data. Let me make some overall comments, and, Alex, the next slide sort of gives some snapshot here. The big theme is that there's a compression in tech spending, as they say. It's very tough to compare to compare to 2018, which was just a phenomenal year. I mentioned the tariffs. It was an election year. Election years bring uncertainty. Uncertainty brings conservatism, so that's something, obviously, that's weighing, I think, on buyers' minds. And, I'll give you some anecdotal comments in a moment that will underscore that. There's less redundancy in spending. This has been a theme of ETR's for quite some time now. The last few years have been a try everything type of mode. Digital initiatives were launched, let's say, starting in 2016. ETR called this, I love this, Tom DelVecchio, the CEO of ETR, called it a giant IT bake off where you were looking at, okay, cloud versus on prem or SaaS versus conventional models, new databases versus legacy databases, legacy storage versus sort of modern storage stacks. So, you had this big bake off going on. And, what's happening now is you're seeing less experimentation so less adoption of new technologies, and replacements are on the rise. So, people are making their bets. They're saying, "Okay, these technologies "are the ones we're going to bet on, "these emerging disruptive technologies." So, they're narrowing their scope of emerging technologies, and they're saying, "Okay, now, "we're going to replace the legacy stuff." So, you're seeing these new stacks emerging. I mentioned some others before, but things like cloud native versus legacy waterfall approaches. And, these new stacks are hitting both legacy and disruptive companies for the reasons that I mentioned before because we're replacing legacy, but at the same time, we're narrowing the scope of the new stuff. This is not necessarily good for the disruptors. Downturns, sometimes, are good for legacy because they're perceived as a safer bet. So, what I want to do, right now, is share with you some of the anecdotals from the survey, and I'll just, you know, call out some things. By the way, the first thing I would note is, you know, ETR did sort of an analysis of frequency of terms. Cloud, cost, replacing, change, moving, consolidation, migration, and contract were the big ones that stood out. But, let me just call a couple of the anecdotals. When they do these surveys, they'll ask open ended questions, and so these kind of give you a good idea as to how people are thinking. "We're projecting a hold based on impacts from tariffs. "Situation could change if tariff relief is reached. "We're really concerned about EU." Another one, "Shift to SaaS is accelerating "and driving TCO down. "Investing in 2019, we're implementing "and retiring old technologies in 2020. "There's an active effort to consolidate "the number of security vendor solutions. "We're doing more Microsoft." Let's see, "We have moved "to a completely outsourced infrastructure model, "so no longer purchasing storage," interesting. "In general, we're trying to reduce spending "based on current market conditions." So, people, again, are concerned. Storage, as a category, is way down. "We're moving from Teradata to AWS and a data lake." I'll make some comments, as well, later on about EDW and Snowflake in particular, who, you know, remains very healthy. "We're moving our data to G Suite and AWS. "We're migrating our SaaS offering to elastic. "We're sunsetting Cognos," which, of course, is owned by IBM. "Talend, we decided to drop after evaluating. "Tableau, we've decided to not integrate anymore," even though Tableau is, actually, looking very strong subsequent to the sales force acquisition. So, there's some comments there that people, again, are replacing and they're narrowing some of their focus on spending. All right, Alex, bring up the next slide. I want to share with you the sector momentum. So, we've talked about this methodology of net score. Every time ETR does one of these pulse surveys, they ask, "Are you spending more or are you spending less? "Or, are you spending the same?" And then, essentially, they subtract the spending less from the spending more, and the spending more included new adoptions. The spending less includes replacements. And, that comes out with a net score, and that net score is an indicator of momentum. And, what you can see here is, the momentum I've highlighted in red, is container orchestration, the container platforms, machine learning, AI, automation, big theme. We were just at the UiPath conference, huge theme on automation. And, of course, robotic process automation, RPA. Cloud computing remains very strong. This dotted red line that I put in there, that's at the, you know, 30%, 35% level. You kind of want to be above that line to really show momentum. Anything below that line is either holding serve, holding steady, but well below that line, when you start getting into the low 20s and the teens, is a red zone. That's a danger zone. You could see data warehouse software is kind of on that cusp. and I'm not, you know, a huge fan of the sector in general, but I love Snowflake and what they're doing and the share gains that are going on there. So, when you're below that red line, it's a game of share gain. Storage, same thing we've talked about. The overall storage sector is down. It's being pressured by cloud, as that anectdotal suggested. It's also being pressured by the fact that so much flash has been injected into the data center over the last couple of years. That given headroom for buyers. They don't need as much storage, so overall, the sector is soft. But then, you see companies, like Pure, continuing to gain share, so they're actually quite strong in this quarter survey. So, you could see some various sectors here. IT consulting and outsourced IT not looking strong, data center consolidation. By the way, you saw, in IBM's recent earnings, Jim Kavanaugh pointed to their outsourcing business as a real drag, you know. Some of these other sectors, you could see, actually, PC laptop, this is obviously a big impact for Dell and HP, you know, kind of holding steady. Actually, better than storage, so, you know, for that large of a segment, not necessarily such a bad thing. Okay, now, what I want to do, I want to shift focus and make some comments on Microsoft, specifically, and AWS. So, here's just some high level points on this slide on Microsoft. The N out of that total was 1200, so very large proportion of the survey is weighted toward Microsoft. So, a good observation space for Microsoft. Extremely positive spending outlook for this company. There's a lot of ways to get to Microsoft. You want cloud, there's Azure, you know. Visualization, you got Power BI. Collaboration, there's Teams. Of course, email and calendaring is Office 365. You need hiring data? Well, we just bought LinkedIn. CRM, ERP, there's Microsoft Dynamics. So, Microsoft is a lot of roads, to spend with Microsoft. Windows is not the future of Microsoft. Satya Nadella and company have done a great job of sort of getting out of that dogma and really expanding their TAM. You're seeing acceleration from Microsoft across all key sectors, cloud, apps, containers, MI, or machine intelligence, AI and ML, analytics, infrastructure software, data warehousing, servers, GitHub is strong, collaboration, as I mentioned. So, really, across the board, this portfolio of offerings powered by the scale of Azure is very strong. Microsoft has great velocity in the cloud, and it's a key bellwether. Now, the next slide, what it does is compares the cloud computing big three in the US, Azure, AWS, and GCP, Google Cloud Platform. This is, again, net score. This is infrastructure as a service, and so you can see here the yellow is Microsoft, that darker line is AWS, and GCP is that blue line down below. All three are actually showing great strength in the spending data. Azure has more momentum than AWS, so it's growing faster. We've seen this for a while, but I want to make a point here that didn't come up on the ETR call. But, AWS is probably two and a half to three times larger in infrastructure as a service than is Microsoft Azure, so remember, AWS has a $35 billion at least run rate business in infrastructure as a service. And, as I say, it's two and a half to three times, at least, larger than Microsoft, which is probably a run rate of, let's call it, 10 to 12 billion, okay. So, it's quite amazing that AWS is holding at that 66 to now dropping to 63% net score given that it's so large. And, of course, way behind is GCP, much smaller share. In fact, I think, probably, Alibaba has surpassed GCP in terms of overall market share. So, at any rate, you could see all three, strong momentum. The cloud continues its march. I'll make some comments on that a little bit later. But, Azure has really strong momentum. Let's talk, next slide if you will, Alex, about AWS. Smaller sample size, 731 out of the total, which is not surprising, right. Microsoft's been around a lot longer and plays in a lot more sectors. ETR has a positive to neutral outlook on AWS. Now, you have to be careful here because, remember, what ETR is doing is they're looking at the spending momentum and comparing that to consensus estimates, okay. So, ETR's business is helping, largely, Wall Street, you know, buy side analysts make bets, and so it's not only about how much money they make or what kind of momentum they have in aggregate. It's about how they're doing relative to expectation, something that I explained on the last Breaking Analysis. Spending on AWS continues to be very robust. They've got that flywheel effect. Make no mistake that this positive to neutral outlook is relative to expectations. Relative to overall market, AWS is, you know, kicking butt. Cloud, analytics, big data, data warehousing, containers, machine intelligence, even virtualization. AWS is growing and gaining share. My view, AWS will continue to outperform the marketplace for quite some time now, and it's gaining share from legacy players. Who's it hurting? You're seeing the companies within AWS's sort of sphere that are getting impacted by AWS. Oracle, IBM, SAP, you know, cloud Arrow, which we mentioned last time is at all time lows, Teradata. These accounts, inside of AWS respondents, are losing share. Now, who's gaining share? Snowflake is on a tear. Mongo is very strong. Microsoft, interestingly, remains strong in AWS. In fact, AWS runs a lot of Microsoft workloads. That's, you know, fairly well known. But, again, Snowflake, very strong inside of AWS accounts. There's no indication that, despite AWS's emphasis on database and, of course, data warehouse, that Snowflake's being impacted by that. The reverse, Snowflake is taking advantage of cloud momentum. The only real negative you can say about AWS is that Microsoft is accelerating faster than AWS, so that might upset Andy Jassy. But, he'll point out, I guess, what I pointed out before, that they're much larger. Take a look at AWS on this next slide. The net score across all AWS sectors, the ones I mentioned. And, this is the growth in Fortune 500, so you can see, very steady in the large accounts. That's that blue line, you know, dipped in the October 18 survey, but look at how strong it is, holding 67% in Fortune 500 accounts. And then, you can see, the yellow line is the market share. AWS continues to gain share in those large accounts when you weight that out in terms of spending. That's why I say AWS is going to continue to do very well in this overall market. So, just some, you know, comments on cloud. As I said, it continues to march, it continues to really be the watchword, the fundamental operating model. Microsoft, very strong, expanding its TAM everywhere, I mean, affecting, potentially, Slack, Box, Dropbox, New Relic, Splunk, IBM, and Security, Elastic. So, Microsoft, very strong here. AWS continues to grow, not as strong as '18, but much stronger than its peers, very well positioned in database and artificial intelligence. And so, not a lot of softness in AWS. I mentioned on one of the previous Breaking Analysis, Kubernetes', actually, container's a little soft, so we always keep an eye on that one. And, Google, again, struggling to make gains in cloud. One of the comments I made before is that the long term surveys for Google looked positive, but that's not showing up yet in the near term market shares. All right, Alex, if you want to bring up the next slide, I want to make some quick comments before I close, on enterprise software. There was a big workday scare this week. They kind of guided that their core HR business was not going to be as robust as it had been previously, so this pulled back all the SaaS vendors. And, you know, the stock got crushed, Salesforce got hit, ServiceNow got hit, Splunk got hit. But, I tell you, you look at the data in this massive dataset, ServiceNow remains strong, Salesforce looks, very slight deceleration, but very sound, especially in the Fortune 100 in that GPP, the giant public and private companies that I talked about on an earlier call. That's one of the best indicators of strength. Tableau, actually, very strong, especially in large accounts, so Salesforce seems to be doing a good job of integrating there. Splunk, (mumbles) coming up shortly, I think this month. Securities, the category is very strong, lifting all ships. Splunk looks really good. Despite some of the possible competition from Microsoft, there's no indication that Splunk is slowing. There's some anecdotal issues about pricing that I talked about before, but I think Splunk is really dealing with those. UiPath's another company. We were just out there this past week at the UiPath Forward conference. UiPath, in this dataset, when you take out some of the smaller respondents, smaller number of respondents, UiPath has one of the highest net scores in the entire sample. UiPath is on a tear. I talked to dozens of customers this week. Very strong momentum, and then moving into, got new areas, and I'll be focusing on the RPA sector a little later on. But, automation, in general, really has some tailwinds in the marketplace. And, you know, the other comment I'll make about RPA is a downturn actually could help RPA vendors, who, by the way, all the RPA vendors look strong. Automation Anywhere, UiPath, I mentioned, Blue Prism, you know, even some of the legacy companies like Pega look, actually, very strong. A downturn in the economy could help some of the RPA vendors because would be looking to do more with less, and automation, you know, could be something that they're looking toward. Snowflake I mentioned, again, they continue their tear. A very strong share in expansion. Slightly lower than previous quarters in terms of the spending momentum, but the previous quarters were off the charts. So, also very strong in large companies. All right, so let me wrap. So, buyers are planning for a slowdown. I mean, there's no doubt about that. It's something that we have to pay very close attention to, and I think the marker expects that. And, I think, you know, it's okay. There's less spaghetti against the wall, we're going to try everything, and that's having a moderating effect on spending, as is the less redundancy. People were running systems in parallel. As they say, they're placing bets, now, on both disruptive tech and on legacy tech, so they're replacing both in some cases. Or, they're not investing in some of the disruptive stuff because they're narrowing their investments in disruptive technologies, and they're also replacing some legacy. We're clearly seeing new adoptions down, according to ETR, and replacements up, and that's going to affect both legacy and disruptive vendors. So, caution is the watchword, but, overall, the market remains healthy. Okay, so thanks for watching. This is Dave Vellante for CUBE Insights, powered by ETR. Thanks for watching this Breaking Analysis. We'll see you next time. (dramatic music)

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE! (upbeat electronic music) Now, here's your host, Dave Vellante. >> Hi, everybody, this is Dave Vellante, and I want to share with you some recent survey data that talks to the IBM acquisition of Red Hat, which closed today. It's always really valuable to go out, talk to practitioners, see what they're doing, and it's a hard thing to do. It's very expensive to get this type of survey data. A lot of times, it's very much out of date. You might remember. Some of you might remember a company called the InfoPro. Its founder and CEO was Ken Male, and he raised some money from Gideon Gartner, and he had this awesome survey panel. Well, somehow it failed. Well, friends of mine at ETR, Enterprise Technology Research, have basically created a modern version of the InfoPro. It's the InfoPro on steroids with a modern interface and data science behind it. They've now been at this for 10 years. They built a panel of 4,500 users, practitioners that they can go to, a lot of C level folks, a lot of VP level and then some doers down at the engineering level, and they go out and periodically survey these folks, and one of the surveys they did back in October was what do you think of the IBM-Red Hat acquisition? And then they've periodically gone out and talked to customers of both Red Hat and IBM or both to get a sense of the sentiment. So given that the acquisition closed today, we wanted to share some of that data with you, and our friends at ETR shared with us some of their drill down data with us, and we're going to share it with you. So first of all, I want to summarize something that they said. Back in October, they said, "We view this acquisition as less of an attempt "by IBM to climb into the cloud game, cloud relevance, "but rather a strategic opportunity "to reboot IBM's early 1990s IT services business strategy." I couldn't agree with that more. I've said all along this is a services play connecting OpenShift from Red Hat into the what Ginni Rometty talks about as the 80% of the install base that is still on prem with the workloads at the backend of mission critical systems that need to be modernized. That's IBM's opportunity. That's why this is a front end loaded cashflow deal 'cause IBM can immediately start doing business through it services organization and generate cash. They went on to say, ETR said, "Here, IBM could position itself "as the de facto IT services partner "for Fortune 100 to Global 2000 organizations "and their digital transformations. "Therefore, in theory, this could reinvigorate "the global services business for IBM "and their overlapping customer bases "could alow IBM to recapture and accelerate a great deal "of service revenues that they have lost "over the past few years." Again, I couldn't agree more. It's less about a cloud play. It is definitely about a multi-cloud play, which is how IBM's positioning this, but services de-risks this entire acquisition in my opinion even though it's very large, 34 billion. Okay, I'm show you some data. So pull up this slide. So what ETR does is they'll go out. So this is a survey of right after the acquisition of about 132 Global 2000 practitioners across a bunch of different industries, energy, utilities, financial services, government, healthcare, IT, telco, retail consumers, so a nice cross section of industries and largely in North America but a healthy cross section of AMIA and APAC. And again, these are large enterprises. So what this slide shows is conditioned responses, which I love conditioned responses. It sort of forces people to answer which of the following best describes. But this says, "Given IBM's intent to acquire Red Hat, "do you believe your organization will be more likely "to use this new combination "or less likely in your digital transformation?" You can see here on the left hand side, the green, 23% positive, on the right hand side, 13% negative. So, the data doesn't necessarily support ETR's original conclusions and my belief that this all about services momentum because most IT people are going to wait and see. So you can see the fat middle there is 64%. Basically you're saying, "Yeah, we're going to wait and see. "This really doesn't change anything." But nonetheless, you see a meaningfully more positive sentiment than negative sentiment. The bottom half of this slide shows, the question is, "Do you believe that this acquisition "makes or will make IBM a legitimate competitor "in the cloud wars between AWS and Microsoft Azure?" You can see on the left hand side, it says 45% positive. Very few say, all the way on the left hand side, a very legitimate player in the cloud on par with AWS and Azure. I don't believe that's the case. But a majority said, "IBM is surely better off "with Red Hat than without Red Hat in the context of cloud." Again, I would agree with that. While I think this is largely a services play, it's also, as Stu Miniman pointed out in an earlier video with me, a cloud play. And you can see it's still 38% is negative on the right hand side. 15% absolutely not, IBM is far behind AWS and Azure in cloud. I would tend to agree with that, but IBM is different. They're trying to bring together its entire software portfolio so it has a competitive approach. It's not trying to take Azure and AWS head on. So you see 38% negative, 45% positive. Now, what the survey didn't do is really didn't talk to multi-cloud. This, to me, puts IBM at the forefront of multi-cloud, right in there with VMware. You got IBM-Red Hat, Google with Anthos, Cisco coming at it from a network perspective and, of course, Microsoft leveraging its large estate of software. So, maybe next time we can poke at the multi-cloud. Now, that survey was done of about over 150, about 157 in the Global 2000. Sorry, I apologize. That was was 137. The next chart that I'm going to show you is a sentiment chart that took a pulse periodically, which was 157 IT practitioners, C level executives, VPs and IT practitioners. And what this chart shows essentially is the spending intentions for Red Hat over time. Now, the green bars are really about the adoption rates, and you can see they fluctuate, and it's kind of the percentage on left hand side and time is on the horizontal axis. The red is the replacement. We're going to replace. We're not going to buy. We're going to replace. In the middle is that fat middle, we're going to stay flat. So the yellow line is essentially what ETR calls market share. It's really an indication of mind share in my opinion. And then the blue line is spending intentions net score. So what does that mean? What that means is they basically take the gray, which is staying the same, they subtract out the red, which is we're doing less, and they add in the we're going to do more. So what does this data show? Let's focus on the blue line. So you can see, you know, slightly declining, and then pretty significantly declining last summer, maybe that's 'cause people spend less in the summer, and then really dropping coming into the announcement of the acquisition in October of 2018, IBM announced the $34 billion acquisition of Red Hat. Look at the spike post announcement. The sentiment went way up. You have a meaningful jump. Now, you see a little dip in the April survey, and again, that might've been just an attenuation of the enthusiasm. Now, July is going on right now, so that's why it's phased out, but we'll come back and check that data later. So, and then you can see this sort of similar trend with what they call market share, which, to me, is, again, really mind share and kind of sentiment. You can see the significant uptick in momentum coming out of the announcement. So people are generally pretty enthusiastic. Again, remember, these are customers of IBM, customers of Red Hat and customer of both. Now, let's see what the practitioners said. Let's go to some of the open endeds. What I love about ETR is they actually don't just do the hardcore data, they actually ask people open ended questions. So let's put this slide up and share with you some of the drill down statements that I thought were quite relevant. The first one is right on. "Assuming IBM does not try to increase subscription costs "for RHEL," Red Hat Enterprise Linux, "then its organizational issues over sales "and support should go away. "This should fix an issue where enterprises "were moving away from RHEL to lower cost alternatives "with significant movement to other vendors. "This plus IBM's purchase of SoftLayer and deployment "of CloudFoundry will make it harder "for Fortune 1000 companies to move away from IBM." So a lot implied things in there. The first thing I want to mention is IBM has a nasty habit when it buys companies, particularly software companies, to raise prices. You certainly saw this with SPSS. You saw this with other smaller acquisitions like Ustream. Cognos customers complained about that. IBM buys software companies with large install bases. It's got a lock in spec. It'll raise prices. It works because financially it's clearly worked for IBM, but it sometimes ticks off customers. So IBM has said it's going to keep Red Hat separate. Let's see what it does from a pricing standpoint. The next comment here is kind of interesting. "IBM has been trying hard to "transition to cloud-service model. "However, its transition has not been successful "even in the private-cloud domain." So basically these guys are saying something that I've just said is that IBM's cloud strategy essentially failed to meet its expectations. That's why it has to go out and spend $34 billion with Red Hat. While it's certainly transformed IBM in some respects, IBM's still largely a services company, not as competitive as cloud as it would've liked. So this guys says, "let alone in this fiercely competitive "public cloud domain." They're not number one. "One of the reasons, probably the most important one, "is IBM itself does not have a cloudOS product. "So, acquiring Red Hat will give IBM "some competitive advantage going forward." Interesting comments. Let's take a look at some of the other ones here. I think this is right on, too. "I don't think IBM's goal is to challenge AWS "or Azure directly." 100% agree. That's why they got rid of the low end intel business because it's not trying to be in the commodity businesses. They cannot compete with AWS and Azure in terms of the cost structure of cloud infrastructure. No way. "It's more to go after hybrid multi-cloud." Ginni Rometty said today at the announcement, "We're the only hybrid multi-cloud, opensource vendor out there. Now, the third piece of that opensource I think is less important than competing in hybrid and multi-cloud. Clearly Red hat gives IMB a better position to do this with CoreOS, CentOS. And so is it worth 34 billion? This individual thinks it is. So it's a vice president of a financial insurance organization, again, IBM's strong house. So you can here some of the other comments here. "For customers doing significant business "with IBM Global Services teams." Again, outsourcing, it's a 10-plus billion dollar opportunity for IBM to monetize over the next five years, in my opinion. "This acquisition could help IBM "drive some of those customers "toward a multi-cloud strategy "that also includes IBM's cloud." Yes, it's a very much of a play that will integrate services, Red Hat, Linux, OpenShift, and of course, IBM's cloud, sprinkle in a little Watson, throw in some hardware that IBM has a captive channel so the storage guys and the server guys can sell their hardware in there if the customer doesn't care. So it's a big integrated services play. "Positioning Red Hat, and empowering them "across legacy IBM silos, will determine if this works." Again, couldn't agree more. These are very insightful comments. This is a largely a services and an integration play. Hybrid cloud, multi-cloud is complex. IBM loves complexity. IBM's services organization is number one in the industry. Red Hat gives it an ingredient that it didn't have before other than as a partner. IBM now owns that intellectual property and can really go hard and lean in to that services opportunity. Okay, so thanks to our friends at Enterprise Technology Research for sharing that data, and thank you for watching theCUBE. This is Dave Vellante signing off for now. Talk to you soon. (upbeat electronic music)

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody Dave Volante here with Stu minumum we have some breaking analysis we're gonna break down the acquisition of IBM Red Hat by IBM was announced today that it closed Stu was originally announced in October a 34 billion dollar acquisition so not a surprise surprise that it closed a little bit earlier than people thought people would thinkin you know well into the second half closed in July they got through all the all the issues in Europe what does this mean in your view to the industry yeah so Dave we did a lot of analysis when the deal was announced absolutely the the cloud and the ripples of change that are happening because of cloud are the impetus for this and you know the the question we've been having for years Dave is you know how many companies can stay kind of independent in you know their swimlane to what they're doing or are we going to see more massive consolidations we're not that far off of the 67 billion dollar acquisition of Dell buying EMC to go heavily into the enterprise market and of course there are cloud implications what happened there and you know we're watching the growth of cloud what's happening in the developer world you know we've watched Red Hat for a long time and you know Red Hat has a nice position in the world and it carved themselves out a nice role into what has been emerging as hybrid and multi cloud and in my opinion that's you know the number one reason why arvind and the IBM team you know when to take that 20-year partnership and turn it into you know now just part of the IBM portfolio Arvind Krishna executive at IBM a longtime player there so the the the deal is so you talked about Dells acquisition we've talked a lot about the VMware model keeping the company separate and of course Red Hat is not going to be a separately traded public company it is going to be a distinct unit inside of IBM's cloud and cognitive software group as I understand it is that right so the question is will it be reported separately or is it going to be oh we're gonna throw everything into our cloud number yeah so Dave this is where all of us that have watched and known IBM you know for our entire careers because they've been around over a hundred years on ask what's going to happen so from a reporting structure Jim Whitehurst reports to Ginny from a Wall Street standpoint it sounds like it's gonna be just thrown into the cloud piece you know Dave isn't it that the the the standard practice today that you throw lots of stuff in there so we can't figure out what your cloud business really is I mean let's look at Oracle or even Microsoft and what they had you know Amazon's probably the only one that clearly differentiates you know this is revenue that we all understand is cloud and can you know touch and feel it so sure I IBM you know you've got all of the the piece that used to be soft layer it's now the IBM cloud piece there are lots of software pieces in that mix the cloud and cognitive is a big umbrella and you know Red Hat adds a few billion dollars worth of revenue into that stream so IBM's assumptions here juni talks a lot about chapter two chapter one was a lot of front-end systems that sort of the growth was everybody thought everything was going into the cloud that's really not the way it is 80% of the workloads are still on Prem and in Chapter two was all about you know connecting those to any cloud multi-cloud heard her words the IBM cloud or the Amazon Google or Microsoft cloud etc etc she made the statement that that we are the only hybrid multi-cloud open source company okay I guess that's true does it matter that they're the only hybrid multi-cloud open source company and are they yeah so I mean Dave anytime a vendor tries to paint themselves as the number one or you know leader in the space it's you know that's how they're defining it that's not how customers think of it customers you know don't think is much about whether it's multi cloud or hybrid cloud they're doing cloud and they're working with you know more than one supplier it is very rare that you find somebody I'm all-in and then you dig in oh yeah wait I'm using office 365 and Salesforce and oh wait there was that cool new thing that Google announced that somebody off on the sides doing so we understand that today it's a multi cloud world tomorrow to be a multi cloud we're absolutely open source is growing you know at great leaps and bounds Red Hat is you know the you know best example we've had of that that trend something I've been watching for the last 20 years and you know it is impressive to see it but you know even when you talk to customers of you know most customers are not you know flag-waving I must do everything open-source you know that they have a little bit more nuanced view of it sure lots of companies are participating in contributing to open source but you know I've yet to talk to too many companies that were like well when I'm making this decision you know this is absolutely what it is am i concerned about my overall costs and I'm concerned about transparency am i concerned about you know security and how fast I can get things resolved and by the way open-source can help with a lot of those things that's what they need to think about but look IBM you know had a longtime partnership with Red Hat Red Hat has a strong position in the marketplace but they're not the only ones there you know you mentioned VMware Dave VMware cross has a strong play across multi cloud environments you know we see Red Hat at all of the cloud shows you see yeah IBM at many of the cloud shows but you've got Cisco out there with their play it is still you know this this chapter - if you agree with Ginny's terminology we are relatively early in that but you know IBM I believe is strengthened in their positioning I don't think it radically changes the landscape just because you know Red Hat is still going to stay you know working with the Amazons and Microsoft and Google's and and and other players out there so it doesn't dramatically change the landscape it just consolidates two players that already worked closely let me ask a question so I mean was clearly positioning this as a cloud play you know generally and you know in a multi cloud specifically is this a cloud play okay um so I'll say yes but Dave so absolutely the future and where the growth for Red Hat and where IBM and for this thirty four billion dollars to be successful the tip of the spear is open shift and therefore you know how does that new cloud native multi cloud environment you know where do they play but at its core you know red heads still Linux Red Hat Enterprise Linux you know is it stills you know that is the primary driver of revenue and Linux isn't going away as a matter of fact Linux is growing Microsoft you know just revealed that there are more Linux workloads sitting in Azure than there are windows we already knew that there were you know strong Linux out there and Microsoft is embrace Linux we saw Satya Nadella at Red Hat summit and you know we've seen that proliferation of linux out there so linux is still you know growing in it where it's being used out there and in the cloud you know linux is what most people are using so the reason why I think this acquisition is interesting Jim Whitehurst today said publicly that it was a great deal that IBM was getting but then he couched he said of course it's a great deal for our shareholders too so and Ginni chimed in and said yes it was a fair deal okay fine 34 billion you know we'll see the reason why I think IBM likes this deal and IBM you know generally has been been good over in history with acquisitions you know clearly some mega acquisitions like PwC which was transformative me we have time to talk about that Cognos and some of the other software acquisitions done quite well not a hundred percent but the reason why I think IBM likes this deal is because it's a good cash flow deal so I think in many ways and they don't talk about this because it's not sexy marketing but iBM is a services company over 60% of the company's revenue comes from professional services IBM loves complexity because they can bring in services throw the big blue blanket around you and do a lot of integration work and the reason is that I think this is an interesting acquisition from from a financial standpoint and Ginny says this all the time this is not about cost synergies this is about revenue opportunities when you try to put everything in the cloud you always run into the back-end systems and her point is that those back-end systems need to be modernized how do you modernize those back-end systems openshift it's not trivial to do that you need services and so iBM has a large install base probably by my estimate you know certainly tens of billions of dollars of opportunity there to modernize back-end systems using Red Hat technology and that means that it's a front-loaded deal from a cash flow standpoint that they will find automatically revenue Cyn to plug in to IBM's captive install base what are your thoughts yeah Dave III think that your analysis is spot-on so RedHat has been one of these most consistent you know revenue companies out there you steadily when they went from a billion dollars to now they're right around three billion dollars they had the March to five billion dollars they had a couple of minor blips in their quarterly earnings but if you plug in that IBM services organization you really have the opportunity to supercharge this is not the opportunity is to have that that huge IBM services organization really helped you know grow those engagements do more openshift you know get more Linux help ansible you know really become the standard for you know automation in the modern workplace the challenge is that too many IBM people get involved because the the thing that everybody's a little worried about is IBM's done well with a lot of those acquisitions but they don't leave them stand alone even you know VMware for many years was a standalone company today VMware in Dell they're one company they're in lockstep from a management standpoint and they're working closely together what differentiates RedHat is you know iBM has groups that are much larger than RedHat that do some of the same things but RedHat with their open-source mission and and where they're driving things and the innovation they drive they move a little bit faster than IBM traditionally does so can will the Red Hat brand the Red Hat people and Red Hat still stay independent enough so that they can till you know hop on that next wave you know they they jumped early into kubernetes and that was the wave that really helped them drive for what they're doing the open shift you know even Dave you know Red Hat ahead bought core OS which was a smaller company moving even faster than Red Hat and while they've done a really good job of integrating those people absolutely from what I've heard it is slowed things down a little bit just because Red Hat compared to core OS was a much bigger company and of course IBM is a be a myth compared to Red Hat so will they throw these groups together and you know who will be making the decisions and can they you know maintain that that culture and that growth mindset well the point is structure we bring up VMware a lot as the model and of course when EMC bought VMware for paltry six hundred million six thirty five million dollars it folded it in and then spun it back out which was the right move certainly allowed the ecosystem to blossom I don't think IBM is gonna take that same approach blue wash is the term they'll probably blue wash that now cuz no Dave they said iBM has said they will not blue eyes there's no purple red stay separate absolutely there's concerns you know so to get those revenue synergies there's there's you're gonna have to plug into IBM systems and that requires some some work and IBM generally good at that so we'll see we'll keep our eyes on that it's but but I would predict that IBM is not going to do a VMware like well it's going to be some kind of hybrid Dave one of the other things is you talked about so Jim Whitehurst you know executive respective had him on the cube a lot he's reporting to Ginny you know the question is is this Ginny's last big move and who replaces her yeah let's talk about succession planning so a lot of a lot of rumors that Whitehurst is is next he's 52 years old I've said I don't I don't think they would do that but but let's talk about it first of all just you know Jim white her side sort of interviewed him the number of times but but you know I'm quite well you think even watch the job so you know I talked with Jim a little bit at red hat summit you know he kind of makes light of it he said you know knowing IBM the way we all know IBM IBM has always taken somebody from inside to do that he feels that he has a strong mission still to drive Red Hat he is super passionate about Red Hat he wrote a book book about the open source culture and is still driving that so I think from everything I see from him that's still the job that he loves and wants to do and you know it's a very different challenge to run IBM I'm not saying he would turn it down if that was the direction that it went if it went down to it but I did not see him angling and positioning like that would be where he wants to go well and of course you know Jim is from North Carolina he's got that kind of southern folksy demeanor you know comes across as the so the nicest guy in the room he's also the smartest guy in the room but oh we'll see we'll see what happens there I've said that I think Martin Schroder is going to be the next CEO of IBM Martin Schroder did three years of combat duty as the CFO in in what was a tough time for IBM to be a CFO they were going through those big transitions talking about you know they had to had to do the the SoftLayer acquisition they had to put together those strategic initiatives and so he's has he has CFO chops so he understands finance deeply he ran you know when IBM's big services business he's now responsible for IBM's revenue generation he's a spokesperson you know in many ways for the company he's like the prototypical choice he would not be surprising at all to see IBM plug him right in a little bit of history as you know still him a bit of a history historian of the industry have been around for a while John Akers back in the early 1990s when IBM's mainframe business was was tanking and the whole company was was tanking and it was at the risk of actually believe it or not running out of money they were gonna split up the company because the industry was breaking apart Intel and microprocessors Microsoft and software C gated disk drives you know Oracle and databases and to be more competitive from a product standpoint they were gonna split the company up into pieces Gerstner came in and said no way Gerson it was you know CEO of American Express said no that's not how customers want to buy he bought PwC for a song compared to what Carly Fiorina at HP a Carly Fiorina at HP wanted to pay I think 15 billion for it I want to say IBM paid five billion or maybe even less for PwC it completely transformed the company it transformed IBM into a services company and that's where what IBM is today they don't like when you say that but that's where the revenue was coming from what that did now and they also started to buy software companies IBM was restricted from getting into applications for years and years and years because of the DOJ because they owned the mainframe they had a monopoly while Microsoft and Intel changed all that IBM started to buy software companies and bought lots of them so they became a services company with a collection of software assets and the main mainframe and you know the power they have a storage business and you know Finance I'd be a global finance business etc etc so my my point is I'm not sure Jim Whitehurst would want to run that you know it's it's kind of messy now what you need run that is somebody who really understands finance knows how to turn the knobs and that's why I think you know Martin Schroeder is actually an excellent pick for that to keep the cash flow going to keep the dividend going to keep the stock buybacks going it's still in my view not a growth play I think there's certainly near-term growth that can be had by modernizing applications but I don't look at IBM as a growth company I look at IBM as a portfolio company that throws off a lot of cash and if and when the market stops rewarding growth and profit list growth a company like IBM will become more favorable to investors yeah and the question at the end of the day is after spending thirty four billion dollars for red hat does IBM help weather the storm of what is happening with the phenomenal growth of AWS the changes happening in Microsoft build more of a relationship than they've already had with Google and help position themselves for this next wave of IT there's IBM helped create a lot of the waves that you know happen in IT well the pure play cloud players are in it for the long game you know you know Amazon's philosophy is give tools to builders and allow them to disrupt the you know traditional old guard whether it's old guard technology companies or old guard industry players and you've seen the stat of how many Fortune 1000 companies or you know have gone out of business in the last 20 or 30 years or whatever it is that's going to continue and Amazon and and certainly Google and Microsoft want to support that disruption by providing cloud tooling and put the data in the hands of people that allows them to create new business models now that doesn't mean everybody's gonna throw up there mainframes it's it's not gonna happen it's certainly not gonna happen overnight and probably will never happen but I just don't see how IBM becomes a growth company in that scenario the growth is going to be continue to be with the cloud well but Dave we had seen IBM I'd say struggle a little bit when it comes to the the developers these days and the Red Hat acquisition is definitely going to be a boon to them in this space because Red Hat all about the developers that that's what you know that their customers are so you know that that's such a huge community that they've already tapped into so Ginny has said this hybrid multi-cloud is a chapter two with a trillion dollar opportunity so who else is going after that trillion dollar opportunity let's let's lay it out there who are the multi cloud players VMware obviously IBM Red Hat with open shift is in there Google with anthos Cisco is coming at it from a network perspective so they have coming at it from their position of strength even though you know you know they're relatively new entrants well ever everybody wants to be the new management layer in this multi cloud environment what VMware had done is had you know vCenter became you know the console for everyone as they were consolidating all of their silos and when I go to a multi cloud environment right where do I live you know Microsoft has a strong play there that's the other you know VMware IBM Red Hat anthos Google Mentos Cisco and Microsoft yeah and of course the one that while they won't say that they are multi cloud you can't talk about multi cloud without talking about Amazon because Amazon is a piece of everyone's cloud environment we were seeing what they're doing with outpost there so they are the kind of Spectre looming over this entire multi-cloud discuss yeah right on I think you got to put Amazon into that mix they will be an entrance into this multi cloud play and it's not gonna be a winner-take-all deal I could say cisco is coming at it from a position of networking strength Microsoft has its software estate and it's gonna do very well there IBM Red Hat coming at it from a standpoint of modernizing applications and there's a services could play and services component there and VMware of course coming at it from the the infrastructure operating system I don't see Oracle as interested in that market there may be some smaller players like turbo anomic you know who probably get gobbled up by one of these guys that we just mentioned but that really is the landscape and this is you know five six companies a trillion dollars there's plenty to go around all right Stu final thoughts on on the the Red Hat news the IBM news that they've finalized the Red Hat acquisition yes so you know what you want to look for is you know first of all you know what's happening organizationally you know if open shift is the primary you know the the tip of the sphere what we're talking about here for this you know cloud native multi-cloud world you know what does you know the IBM Cloud messaging looked like they're gonna have an analyst event here in a couple of weeks that you know that they've invited all the analysts to going into what does that cloud portfolio looks like how do they sort through all of the kubernetes options that they've had today do they try to elevate IBM cloud to be a stronger player or will they let Red Hat continue to play across all of the cloud environments that they have so you know organization and product positioning of the two things that I'm looking at the most Tom Siebel said publicly yesterday that IBM is a great company national international treasure but they miss cloud and they missed a I I wouldn't agree totally they didn't miss cloud they were late to cloud they had to buy software they're in cloud just like Oracle's in cloud not as competitive as the AWS cloud but they're they've got a cloud yeah HP doesn't have a cloud Dell doesn't have a cloud these these two companies that I just mentioned do AI yeah they're not sound of generalized AI like what Google and Amazon and Facebook and Microsoft are doing IBM's trying to solve you know big chewy problems iBM is a services company as they said so you know Watson you see a lot of negative stories about Watson but Watson requires a lot of services to make it work and it's as they say solving different problems so they're a player in AI multi cloud is new and this move the acquisition of red hat yes thirty four billion dollars expensive it's not gonna be pretty on the balance sheet but they get good cash flow so they'll deal with that over time it puts them right in the mix as a leader in multi cloud so thanks to for breaking down the the acquisition and thank you for watching this is Dave Volante what's do min and then we'll see you next time

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Welcome back to Boston. Everybody watching the Cube, the leader and on the ground tech coverage. My name is David Locke. They still minimus here. John Barrier is also in the house. We're covering the active FIO data driven 19 event. Second year for this conference. It's all about data. It's all about being data driven. Charlie Quanis here. He's the director of data and a I offering management and IBM. Charlie, thanks for coming on The Cube. >> Happy to be here. Thank you. >> So active Theo has had a long history with IBM. Effect with company got started at a time the marketplace took a virtual ization product and allowed them to be be first really and then get heavily into the data virtualization. They since evolved that you guys are doing a lot of partnerships together. We're going to get into that, But talk about your role with an IBM and you know, what is this data and a I offering management thing? >> He absolutely eso data and a I is our business unit within IBN Overall Corporation, our focus and our mission is really about helping our customers drive better business outcomes through data. Leveraging data in the contacts and the pursuit of analytics and artificial intelligence are augmented intelligence. >> So >> a portion of the business that I'm part of his unified governance and integration and you think about data and I as a whole, you could think about it in the context of the latter day. I often times when we talk about data and I we talk about the foundational principles and capabilities that are required to help companies and our customers progress on their journey. They II and it really is about the information architecture that we help them build. That information architectures essentially a foundational prerequisite around that journey to a i. R. Analytics and those layers of the latter day I r. Collecting the data and making sure you haven't easily accessible to the individual's need it organizing the data. That's where the unified governance in Immigration folio comes into play. Building trusted business ready data, high quality with governance around that making shorts available to be used later, thie analyzed layer in terms of leveraging the data for analytics and die and then infuse across the organization, leveraging those models across the organization. So within that context of data and I, we partnered with Active Theo at the end of 2018. >> So before we get into that, I have started dropped. You know, probably Rob Thomas is, and I want a double click on what you just said. Rob Thomas is is famous for saying There is no way I without a training, no, no artificial intelligence without information architecture so sounds good. You talk about governance. That's obviously part of it. But what does that mean? No A without a. >> So it is really about the fundamental prerequisites to be able to have the underlying infrastructure around the data assets that you have. A fundamental tenet is that data is one of your tremendous assets. Any enterprise may have a lot of time, and effort has been spent investing and man hours invested into collecting the data, making sure it's available. But at the same time, it hasn't been freed up to be. A ploy used for downstream purpose is whether it's operational use cases or analytical cases, and the information architecture is really about How do you frame your data strategy so that you have that data available to use and to drive business outcomes later. And those business outcomes, maybe results of insights that are driven out of the way the data but they got could also be part of the data pipeline that goes into feeding things like application development or test data management. And that's one of the areas that were working with that feeling. >> So the information architecture's a framework that you guys essentially publish and communicate to your clients. It doesn't require that you have IBM products plugged in, but of course, you can certainly plug in. IBM products are. If you're smart enough to develop information architect here presumably, and you got to show where your products fit. You're gonna sell more stuff, but it's not a prerequisite. I confuse other tooling if I wanted to go there. The framework is a good >> prerequisite, the products and self of course, now right. But the framework is a good foundational. Construct around how you can think about it so that you can progress along that journey, >> right? You started talking about active fio. You're relationship there. See that created the Info sphere Virtual data pipeline, right? Why did you developed that product or we'll get into it? >> Sure, it's all part of our overall unified covers and integration portfolio. Like I said, that's that organized layer of the latter day I that I was referring to. And it's all about making sure you have clear visibility and knowing what they had assets that you have. So we always talk about in terms of no trust in use. No, the data assets you have. Make sure you understand the data quality in the classification around that data that you have trust the data, understand the lineage, understand how it's been Touch Haussmann, transformed building catalog around that data and then use and make sure it's usable to downstream applications of down street individuals. And the virtual data pipeline offering really helps us on that last category around using and making use of the data, the assets that you have putting it into directly into the hands of the users of that data. So whether they be data scientist and data engineers or application developers and testers. So the virtual data pipeline and the capabilities based on activity sky virtual appliance really help build a snapshot data provide the self service user interface to be able to get into the hands of application developers and testers or data engineers and data scientist. >> And why is that important? Is it because they're actually using the same O. R. O R. Substantially similar data sets across their their their their work stream. Maybe you could explain that it's important >> because the speed at which the applications are being built insights are being driven is requiring that there is a lot more agility and ability to self service into the data that you need. Traditional challenges that we see is you think about preparing to build an application or preparing to build an aye aye model, building it, deploy it and managing it the majority of the time. 80% of the time. Todd spilled front, preparing the data talking, trying to figure out what data you need asking for and waiting for two weeks to two months to try to get access to that data getting. And they're realizing, Oh, I got the wrong data. I need to supplement that. I need to do another iteration of the model going back to try to get more data on. That's you have the area that application developers and data scientists don't necessarily want to be spending their >> time on. >> And so >> we're trying to shrink >> that timeframe. And how do we shrink? That is by providing business users our line of business users, data scientist application developers with the individuals that are actually using the data to provide their own access to it, right To be able to get that snapshot that point in time, access to that point of production data to be able to then infuse it into their development process. They're testing process or the analytic development process >> is we're we're do traditional tooling were just traditional tooling fit in this sort of new world because you remember what the Duke came out. It was like, Oh, that enterprise data warehouses dead. And then you ask customers like What's one of the most important things you're doing in your big data? Play blind and they'd say, Oh, yeah, we need R w. So I could now collect more data for lower costs keep her longer low stuff. But the traditional btw was still critical, but well, you were just describing, you know, building a cube. You guys own Cognos Obviously, that's one of the biggest acquisitions that I'm being made here is a critical component. Um, you talk about data quality, integration, those things. It's all the puzzle fits together in this larger mosaic and help us understand that. Sure >> and well, One of the fundamental things to understand is you have to know what you have right, and the data catalogue is a critical component of that data strategy. Understanding where your enterprise assets sit, they could be structured information that may be a instruction information city and file repositories or e mails, for example. But understanding what you have, understanding how it's been touched, how it's been used, understanding the requirements and limitations around that data understanding. Who are the owners of that data? So building that catalog view of your overall enterprise assets fundamental starting point from a governess standpoint. And then from there, you can allow access to individuals that are interested in understanding and leveraging that date assets that you may have in one pool here challenges data exists across enterprise everywhere. Right silos that may have rose in one particular department that then gets murdered in with another department, and then you have two organization that may not even know what the other individual has. So the challenge is to try to break down those silos, get clarity of the visibility around what assets so that individuals condemned leverage that data for whatever uses they may have, whether it be development or testing or analytics. >> So if I could generalize the problem, Yeah, too much data, not enough value. And I'll talk about value in terms of things that you guys do that I'm inferring. Risk reduction. Correct uh, speed to insights. Andan. Ultimately, lowering costs are increasing revenue. That's kind of what it's all >> the way to talk about business outcomes in terms of increase revenue, decrease costs or reduce risk, right in terms of governance, those air the three things that you want to unlock for your customers and you don't think about governance and creating new revenue streams. We generally don't think about in terms of reducing costs, but you do think about it oftentimes in terms of reducing your risk profile and compliance. But the ability to actually know your data built trust and then use that data really does open up different opportunities to actually build new application new systems of engagement uses a record new applications around analytics and a I that will unlock those different ways that we can market to customers. Cell two customers engage our own employees. >> Yes. So the initial entry into the organism the budget, if you will, is around that risk reduction. Right? Can you stand that? I got all this data and I need to make sure that I'm managing a corner on the edicts of my organization. But you actually seeing we play skeptic, you're really seeing value beyond that risk reduction. I mean, it's been nirvana in the compliance and governance world, not just compliance and governance and, you know, avoiding fees and right getting slapped on the wrist or even something worse? Sure, but we can actually, through the state Equality Initiative and integration, etcetera, etcetera Dr. Other value. You actually seeing that? >> Yes. We are actually, particularly last year with the whole onslaught of GDP are in the European Union, and the implications of GDP are here in the U. S. Or other parts of the world. Really was a pervasive topic on a lot of what we were talking about was specifically that compliance make sure you stay on the right side of the regulation, but the same time investing in that data architecture, information, architecture, investing in the governance programme actually allowed our customers to understand the different components that are touching the individual. Because it's all about individual rights and individual privacy. It's understanding what they're buying, understanding what information we're collecting on them, understanding what permissions and consent that we have, the leverage their information really allowed. Our customers actually delivered that information and for a different purpose. Outside of the whole compliance mindset is compliance is a difficult nut to crack. There's requirements around it, but at the same time, they're our best effort requirements around that as well. So the driver for us is not necessarily just about compliance, But it's about what more can you do with that govern data that you already have? Because you have to meet those compliance department anyway, to be able to flip the script and talk about business value, business impact revenue, and that's everything. >> Now you So you're only about what, six months in correct this part of the partnership? All right, so it's early days, but how's it going and what can we expect going forward? >> Don't. Great. We have a terrific partner partnership with Octavio, Like tippy a virtual Or the IBM virtual data pipeline offering is part of our broader portfolio within unified governance and fits nicely to build out some of the test data management capability that we've already had. Optimal portfolio is part of our capability. Said it's really been focused around test data management building synthetic data, orchestrating test data management as well. And the virtual data pipeline offering actually is a nice compliment to that to build out our the robust portfolio now. >> All right, Charlie. Well, hey, thanks very much for coming in the house. The event >> has been terrific. It's been terrific. It's It's amazing to be surrounded by so many people that are excited about data. We don't get that everywhere. >> They were always excited about, Right, Charlie? Thanks so much. Thank you. Thank you. All right. Keep it right there, buddy. We're back with our next guest. A Valon Day, John. Furry and student Amanda in the house. You're watching the cube Active eo active Fio data driven. 2019. Right back

>> Narrator: From Chicago, it's The Cube. Covering Veritas Vision Solution Day 2018. Brought to you by Veritas. >> Welcome back to the Windy City everybody. You're watching The Cube, the leader in live tech coverage. We're goin' out to the events, we extract a signal from the noise. We're here at the Veritas Vision Solution Days in Chicago. We were just a few weeks ago we were at the iconic Tavern on the Green in New York City. We're here at the Palmer House Hotel, beautiful hotel right in downtown Chicago near the lake. It's just an awesome venue, it's great to be here. Arista Thurman III is here, he's the principle computer engineer at the Argonne National Labs. Great to see you, thanks for coming on The Cube. >> Yah, good to be here, thanks. >> So tell the audience about Argonne National Labs. What do you guys all about? >> About science, so we're all about the advancement of science. We do a lot of different experiments from technology for batteries and chemistry. The project we're working on is the advanced photon source, which is a light source that's used to collect data in experiments with a photon source. >> OK, so you're an IT practitioner, >> Arista Thurman: That is correct. >> Serving scientists. >> Arista Thurman: Yes. >> What's that like? Is that like an IT guy serving doctors? Are they kind of particular? >> Arista Thurman: A little bit. >> There's some challenges there, but yah it's great. So basically you have a unique customer base, and they have additional requirements. So, it's not like a normal customer base. They're very smart people. They have a lot of demands and needs, and we do our best to provide all the services they require. >> Yah, so given that they're technical people, they may not be IT people but they have an affinity to technology. First of all, it must be hard to BS them, right? (laughter) >> Arista Thurman: No doubt, no doubt. >> They'd cut through that, so you got to be straight with them. And they're probably pretty demanding, right? I mean, they have limited resources and limited time and limited budgets, and they're probably pounding you pretty hard. Is that the case, or are they more forgiving? >> They're great people to work with, but there can be some challenges. I mean, it's unique in the idea that they work on multiple platforms. So it's from Unix to Linux to Mac. Multiple computers in their offices, multiple data requirements. And a lot of things happen without a lot of process and planning. Some things are ad hoc. So, it puts a little bit of strain sometimes on you to try to make everything happen in the amount of time they have. And everything is There's some challenges with regard to how to get things done in a timely fashion when you don't know what's going to happen with some of these experiments. >> I mean I imagine, right? They can probably deal with a lot of uncertain processes because that's kind of their lives, right? You must have to cobble things together for them to get them a solution sometimes, is that the case? >> We do sometimes. I think it's all about getting enough funding and enough resources to take care of all the different experiments. >> Dave Vellante: A balancing act. >> Yah. >> Dave Vellante: Ya so you look after, compute and storage. >> Arista Thurman: Yes. >> Right, so talk about what's happening generally there and then specifically data protection. >> So in general, my primary focus is Linux. Linus administration, Red Hat Linux. And we've seen a lot of data growth over the last five years and we've got projection for more growth as we are planning for an upgrade. So we're going to change our bmine and make it more efficient. Have a better light source and that's all planned in the next two to three years. And so, there's a lot of extra projects on top of our normal workload. We have a lot of equipment that probably needs to be refreshed. There's resources and with IT and any kind of data management things change. So whatever we're doing today, in the next three years we'll be doing something different because things change with regard to CPU speeds, performance of IO networking, storage requirements. All those things are continually growing exponentially. And when scientists want to do more experiments and they get new resources in, it's going to require more resources for us to maintain and keep them operational at the speeds and performance they want. >> Yah, we do hundreds of events with The Cube. We do about 130 events this year, and a lot of them are so-called "big data" orientation. And when you go to those data oriented events, you hear a lot of, sort of the roots of that. Or at least similarities to the scientific technical computing areas and it's sort of evolved into big data. A lot of the disciplines are similar. So, you're talking about a lot of data here. Sometimes it's really fast data, and there's a lot of variety, presumably, in that data. So how much data are we talking about? Is it huge volumes? Maybe you could describe your data environment. >> Primarily we have things broken up into different areas. So we have some block storage, and that provides a lot of our virtual the back-end for our virtualization environments which is either Microsoft or Red Hat RHV. I would estimate that's somewhere in a petabyte range. And then we also have our NAS file systems which spread across multiple environments providing NFS version three and four and also to Windows clients CIFS and some of the Mac clients also utilize that. And that's at about a little less than a petabyte. We also have high performance computing and that's a couple petabytes, at least. And all those numbers are just estimates because we're constantly growing. >> Any given time it's changing. But you're talking about multiple petabytes. So how do you back up, how do you protect multiple petabytes? >> Well I think it has to, it's all about a balancing act 'cause it's hard to back up everything in that same time window. So we have multiple backup environments providing resources for individual platforms. Like for Windows we'd do something a little different than we'd do for Linux. And we have different retention policies. Some environments need to be retained, retention is three years and some is six months, some three months, and so you have to have a system of migrating your storage to faster discs and then layer off the tape for long term retention. It's a challenge that we're constantly fighting with. >> How do you use Veritas? You're a customer obviously? >> Yah, we've been a Veritas customer for many years and we utilize Veritas in our virtualization environments. They kind of help us out with central platform. We've actually explored other things but the most cost effective thing to us at this point has been Veritas. We utilize them to back up primarily our NAS and our black files, our black file systems that provide most of the virtualization. >> Why Veritas? What is it about them that you have an affinity for? There's a zillion other backup software vendors out there, why Veritas? >> I think we have invested a lot in Veritas over the years. Predating my time at Argonne we've been using Veritas. In my previous career, in Sun Microsystems we also had some kind of relationship with Veritas. So it's easy and I think, like I mentioned earlier, we explored other things but it wasn't cost effective to make that kind of change. And it's been a reliable product. It does require work but it has been a reliable product. >> So, you'd mentioned your Linux, Red Hat Linux. >> Arista Thurman: Yes. >> So you saw this IBM announced it's going to buy Red Hat for 34 billion dollars. What were your thoughts when you heard that news? >> I was like, "Wow, what is going to happen now?" I was like, "How is that going to impact us?" Is it going to change our licensing model? Or is it going to be a good thing, or a bad thing? Right now we just don't really know. We're just kind of waiting and seeing. But it's like, OK, I mean that's a big deal. It is a biggest deal certainly from IBM. Their biggest previous deal was I think Cognos at five billion, so this dwarfs that. The deal of course doesn't close probably till the second half of 2019. So it's going to take a while. But look, IBM is known when it buys software companies, saw this with SPSS, you've seen it with other companies that it buys, it often times will change the pricing model. How do you license Red Hat? Do you have an enterprise license agreement? Do you know offhand? >> We do have an agreement with them. >> Dave Vellante: Lock that in. Lock that long term in now before the deal goes down. >> One of my counterparts is in charge of that part of it. So I'm sure we'll be having that conversation shortly. >> Yah, interesting. Well listen, Arista thanks very much for coming on The Cube, really appreciate your insight. >> Thank you. >> It's great to meet you, all right, you're welcome. Thanks for watching everybody, it's a wrap from Chicago. This has been The Cube, Veritas Vision Days. Check out SiliconAngle.com for all the news. TheCube.net is where you'll find these videos and a lot of others. You'll see where The Cube is next. Wikibon.com for all the research. Thanks for the team here, appreciate your help on the ground. We're out from Chicago, this is Dave Vellante. We'll see ya next time.

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now here are your hosts, Dave Vellante and Stu Miniman. >> Hi everybody, Dave Vellante here with Stu Miniman. We're here to unpack the recent acquisition that IBM announced of Red Hat. $34 billon acquisition financed with cash and debt. And Stu, let me get us started. Why would IBM spend $34 billion on Red Hat? Its largest acquisition to date of a software company had been Cognos at $5 billion. This is a massive move. IBM's Ginni Rometty called this a game changer. And essentially, my take is that they're pivoting. Their public cloud strategy was not living up to expectations. They're pivoting to hybrid cloud. Their hybrid cloud strategy was limited because they didn't really have strong developer mojo, their Bluemix PaaS layer had really failed. And so they really needed to make a big move here, and this is a big move. And so IBM's intent, and Ginni Rometty laid out the strategy, is to become number one in hybrid cloud, the undisputed leader. And so we'll talk about that. But Stu, from Red Hat's perspective, it's a company you're very close to and you've observed for a number of years, Red Hat was on a path touting a $5 billion revenue plan, what happened? Why would they capitulate? >> Yeah Dave, on the face of it, Red Hat says that IBM will help it further its mission. We just listened to Arvin Krishna from IBM talking with Paul Cormier at Red Hat, and they talked about how they were gonna keep the Red Hat brand alive. IBM has a long history with open source. As you mentioned, I've been working with Red Hat, gosh, almost 20 years now, and we all think back to two decades ago, when IBM put a billion dollars into Linux and really pushed on open source. So these are not strangers, they know each other really well. Part of me looks at these from a cynicism standpoint. Somebody on Twitter said that Red Hat is hitting it at the peak of Kubernetes hype. And therefore, they're gonna get maximum valuation for where the stock is. Red Hat has positioned itself rather well in the hybrid cloud world, really the multicloud world, when you go to AWS, when you go to the Microsoft Azure environment, you talk to Google. Open source fits into that environment and Red Hat products specifically tie into those environments. Remember last year, in Boston, there's a video of Andy Jassy talking about a partnership with Red Hat. This year, up on stage, Microsoft with Azure partnering deeply with Red Hat. So Red Hat has done a nice job of moving beyond Linux. But Linux is still at its core. There definitely is concern that the operating system is less important today than it was in the past. It was actually Red Hat's acquisition of CoreOS for about $250 million earlier this year that really put a fine point on it. CoreOS was launched to be just enough Linux to live in this kind of container and Kubernetes world. And Red Hat, of course, like we've seen often, the company that is saying, "We're going to kill you", well you go and you buy them. So Red Hat wasn't looking to kill IBM, but definitely we've seen this trend of softwares eating the world, and open sources eating software. So IBM, hopefully, is a embracing that open source ethos. I have to say, Dave, for myself, a little sad to see the news. Red Hat being the paragon of open source. The one that we always go to for winning in this space. So we hope that they will be able to keep their culture. We've had a chance, many times, to interview Jim Whitehurst, really respected CEO. One that we think should stay involved in IBM deeply for this. But if they can keep and grow the culture, then it's a win for Red Hat. But still sorting through everything, and it feels like a little bit of a capitulation that Red Hat decides to sell off rather than keep its mission of getting to five billion and beyond, and be the leading company in the space. >> Well I think it is a bit of a capitulation. Because look, Red Hat is roughly a $3 billion company, growing at 20% a year, had that vision of five billion Its stock, in June, had hit $175. So while IBM's paying a 60% premium off of its current price, it's really only about 8 or 9% higher than where Red Hat was just a few months ago. And so I think, there's an old saying on Wall Street, the first disappointment is never the last. And so I think that Red Hat was looking at a long slog. They reduced expectations, they guided lower, and they were looking at the 90-day shot clock. And this probably wasn't going to be a good 'nother couple of years for Red Hat. And they're selling at the peak of the market, or roughly the peak of the market. They probably figured, hey, the window is closing, potentially, to do this deal. Maybe not such a bad time to get out, as opposed to trying to slog it out. Your thoughts. >> Yeah, Dave, I think you're absolutely right. When you look at where Red Hat is winning, they've done great in OpenStack but there's not a lot of excitement around OpenStack. Kubernetes was talked about lots in the announcement, in the briefings, and everything like that. I was actually surprised you didn't hear as much about just the core business. You would think you would be hearing about all the companies using Red Hat Enterprise Linux around the world. That ratable model that Red Hat really has a nice base of their environment. It was talking more about the future and where Kubernetes, and cloud-native, and all of that development will go. IBM has done middling okay with developers. They have a strong history in middleware, which is where a lot of the Red Hat development activity has been heading. It was interesting to hear, on the call, it's like, oh well, what about the customers that are using IBM too say, "Oh well, if customers want that, we'll still do it." What about IBM with Cloud Foundry? Well absolutely, if customers wanna still be doing it, they'll do that. So you don't hear the typical, "Oh well, we're going to take Red Hat technology "and push it through all of IBM's channel." This is in the IBM cloud group, and that's really their focus, as it is. I feel like they're almost limiting the potential for growth for Red Hat. >> Well so IBM's gonna pay for this, as I said, it's an all cash deal. IBM's got about 14 and a half billion dollars on the balance sheet. And so they gotta take out some debt. S&P downgraded IBM's rating from an A+ to an A. And so the ratings agency is going to be watching IBM's growth. IBM said this will add 200 basis points of revenue growth over the five year CAGR. But that means we're really not gonna see that for six, seven years. And Ginni Rometty stressed this is not a backend loaded thing. We're gonna find revenue opportunities through cross-selling and go-to-market. But we have a lot of questions on this deal, Stu. And I wanna sorta get into that. So first of all, again, I think it's the right move for IBM. It's a big move for IBM. Rumors were that Cisco might have been interested. I'm not sure if Microsoft was in the mix. So IBM went for it and, as I said, didn't pay a huge premium over where their stock was back in June. Now of course, back in June, the market was kind of inflated. But nonetheless, the strategy now is to go multi-cloud. The number one in the multi-cloud world. What is that multi-cloud leadership? How are we gonna measure multi-cloud? Is IBM, now, the steward of open source for the industry? To your point earlier, you're sad, Stu, I know. >> You bring up a great point. So I think back to three years ago, with the Wikibon we put together, our true private cloud forecast. And when we built that, we said, "Okay, here's the hardware, and software, "and services in private cloud." And we said, "Well let's try to measure hybrid cloud." And we spent like, six months looking at this. And it's like, well what is hybrid cloud? I've got my public cloud pieces, and I've got my private cloud pieces. Well there's some management layers and things that go in between. Do I count things like PaaS? So do you save people like Pivotal and Red Hat's OpenShift? Are those hybrid cloud? Well but they live either here or there. They're not usually necessarily helping with the migration and moving around. I can live in multiple environments. So Linux and containers live in the public, they live in the private, they don't just fly around in the ether. So measuring hybrid cloud, I think is really tough. Does IBM plus Red Hat make them a top leader in this hybrid multi-cloud world? Absolutely, they should be mentioned a lot more. When I go to the cloud shows, the public cloud shows, IBM isn't one of the first peak companies you think about. Red Hat absolutely is in the conversation. It actually should raise the profile of Red Hat because, while Red Hat plays in a lot of the conversations, they're also not the first company that comes to mind when you talk about them. Microsoft, middle of hybrid cloud. Oracle, positioning their applications in this multi-cloud world. Of course you can't talk about cloud, any cloud, without talking about Amazon's position in the marketplace. And SAS is the real place that it plays. So IBM, one of their biggest strengths is that they have applications. Dave, you know the space really well. What does this mean vis-à-vis Oracle? >> Well let's see, so Oracle, I think, is looking at this, saying, alright. I would say IBM is Oracle's number one competitor in the enterprise. You got SAP, and Amazon obviously in cloud, et cetera, et cetera. But let me put it this way, I think Oracle is IBM's number one competitor. Whether Oracle sees it that way or not. But they're clearly similar companies, in terms of their vertical integration. I think Oracle's looking at this, saying, hey. There's no way Oracle was gonna spend $34 billion on Red Hat. And I don't think they were interested in really spending any money on the alternatives. But does this put Canonical and SUSE in play? I think Oracle's gonna look at this and sort of message to its customers, "We're already number one in our world in hybrid cloud." But I wanna come back to the deal. I'm actually optimistic on the deal, from the standpoint of, I think IBM had to make a big move like this. Because it was largely just bumping along. But I'm not buying the narrative from Jim Whitehurst that, "Well we had to do this to scale." Why couldn't they scale with partners? I just don't understand that. They're open. This is largely, to me, a services deal. This is a big boon for IBM Services business. In fact, Jim Whitehurst, and Ginni even said that today on the financial analyst call, Jim said, "Our big constraint was "services scale and the industry expertise there." So what was that constraint? Why couldn't they partner with Accenture, and Ernie Young, and PwC, and the likes of Deloitte, to scale and preserve greater independence? And I think that the reason is, IBM sees an opportunity and they're going hard after it. So how will, or will, IBM change its posture relative to some of those big services plays? >> Yeah, Dave, I think you're absolutely right there. Because Red Hat should've been able to scale there. I wonder if it's just that all of those big service system integrators, they're working really closely with the public cloud providers. And while Red Hat was a piece of it, it wasn't the big piece of it. And therefore, I'm worried on the application migration. I'm worried about the adoption of infrastructure as a service. And Red Hat might be a piece in the puzzle, but it wasn't the driver for that change, and the move, and the modernization activities that were going on. That being said, OpenShift was a great opportunity. It plays in a lot of these environments. It'll be really interesting to see. And a huge opportunity for IBM to take and accelerate that business. From a services standpoint, do you think it'll change their position with regard to the SIs? >> I don't. I think IBM's gonna try to present, preserve Red Hat as an independent company. I would love to see IBM do what EMC did years ago with VMware, and float some portion of the company, and truly have it at least be quasi-independent. With an independent operating structure, and reporting structure from the standpoint of a public company. That would really signal to the partners that IBM's serious about maintaining independence. >> Yeah now, look Dave, IBM has said they will keep the brand, they will keep the products. Of all the companies that would buy Red Hat, I'm not super worried about kinda polluting open source. It was kinda nice that Jim Whitehurst would say, if it's a Red Hat thing, it is 100% open source. And IBM plays in a lot of these environments. A friend of mine on Twitter was like, "Oh hey, IBM's coming back to OpenDaylight or things like that." Because they'd been part of Cloud Foundry, they'd been part of OpenDaylight. There's certain ones that they are part of it and then they step back. So IBM, credibly open source space, if they can let Red Hat people still do their thing. But the concern is that lots of other companies are gonna be calling up project leads, and contributors in the open source community that might've felt that Red Hat was ideal place to live, and now they might go get their paycheck somewhere else. >> There's rumors that Jim Whitehurst eventually will take over IBM. I don't see it, I just don't think Jim Whitehurst wants to run Z mainframes and Services. That doesn't make any sense to me. Ginni's getting to the age where IBM CEOs typically retire, within the next couple of years. And so I think that it's more likely they'll bring in somebody from internally. Whether it's Arvin or, more likely, Jim Kavanaugh 'cause he's got the relationship with Wall Street. Let's talk about winners and losers. It's just, again, a huge strategic move for IBM. Frankly, I see the big winners is IBM and Red Hat. Because as we described before, IBM was struggling with its execution, and Red Hat was just basically, finally hitting a wall after 60-plus quarters of growth. And so the question is, will its customers win? The big concern I have for the customers is, IBM has this nasty habit of raising prices when it does acquisitions. We've seen it a number of times. And so you keep an eye on it, if I were a Red Hat customer, I'd be locking in some attractive pricing, longterm. And I would also be calling Mark Shuttleworth, and get his take, and get that Amdahl coffee cup on my desk, as it were. Other winners and losers, your thoughts on some of the partners, and the ecosystem. >> Yeah, when I look at this and say, compare it to Microsoft buying GitHub. We're all wondering, is this a real game changer for IBM? And if they embrace the direction. It's not like Red Hat culture is going to just take over IBM. In the Q&A with IBM, they said, "Will there be influence? Absolutely. "Is this a marriage of equals? No. "We're buying Red Hat and we will be "communicating and working together on this" But you can see how this can help IBM, as to the direction. Open source and the multi-cloud world is a huge, important piece. Cisco, I think, could've made a move like this. I would've been a little bit more worried about maintaining open source purity, if it was somebody like Cisco. There's other acquisitions, you mentioned Canonical and SUSE are out there. If somebody wanted to do this, the role of the operating system is much less important than it is today. You wouldn't have seen Microsoft up on stage at Red Hat Summit this year if Windows was the driver for Microsoft going forward. The cloud companies out there, to be honest, it really cements their presence out there. I don't think AWS is sitting there saying, "Oh jeez, we need to worry." They're saying, "Well IBM's capitulated." Realizing that, "Sure they have their own cloud, "and their environment, but they're going to be "successful only when they live in, "and around, and amongst our platform of Amazon." And Azure's gonna feel the same way, and same about Google. So there's that dynamic there. >> What about VMware? >> So I think VMware absolutely is a loser here. When I went back to say one of the biggest strengths of IBM is that they have applications. When you talk about Red Hat, they're really working, not only at the infrastructure layer, but working with developers, and working in that environment. The biggest weakness of VMware, is they don't own the applications. I'm paying licenses to VMware. And in a multi-cloud world, why do I need VMware? As opposed to Red Hat and IBM, or Amazon, or Microsoft, have a much more natural affinity for the applications and the data in the future. >> And what about the arms dealers? HPE and Dell, in particular, and of course, Lenovo. Wouldn't they prefer Red Hat being independent? >> Absolutely, they would prefer that they're gonna stay independent. As long as it doesn't seem to customers that IBM is trying to twist everybody's arms, and get you on to Z, or Power, or something like that. And continues to allow partnerships with the HPEs, Dells, Lenovos of the world. I think they'll be okay. So I'd say middling to impact. But absolutely, Red Hat, as an independent, was really the Switzerland of the marketplace. >> Ginni Rometty had sited three growth areas. One was Red Hat scale and go-to-market. I think there's no question about that. IBM could help with Red Hat's go-to-market. The other growth vector was IBM's products and software on the Red Hat stack. I'm less optimistic there, because I think that it's the strength of IBM's products, in and of themselves, that are largely gonna determine that success. And then the third was Services. I think IBM Services is a huge winner here. Having the bat phone into Red Hat is a big win for IBM Services. They can now differentiate. And this is where I think it's gonna be really interesting to see the posture of Accenture and those other big guys. I think IBM can now somewhat differentiate from those guys, saying, "Well wait, "we have exclusive, or not exclusive, "but inside baseball access to Red Hat." So that's gonna be an interesting dynamic to watch. Your final thoughts here. >> Yeah, yeah, Dave, absolutely. On the product integration piece, the question would be, you're gonna have OpenAPIs. This is all gonna work with the entire ecosystem. Couldn't IBM have done more of this without having to pay $34 billion and put things together? Services, absolutely, will be the measurement as to whether this is successful or not. That's probably gonna be the line out of them in financials, that we're gonna have to look at. Because, Dave, going back to, what is hybrid, and how do we measure it? What is success for this whole acquisition down the line? Any final pieces to what we should watch and how we measure that? >> So I think that, first of all, IBM's really good with acquisitions, so keep an eye on that. I'm not so concerned about the debt. IBM's got strong free cash flow. Red Hat throws off a billion dollars a year in free cash flow. This should be an accretive acquisition. In terms of operating profits, it might take a couple of years. But certainly from a standpoint of free cash flow and revenue growth, I think it's gonna help near-term. If it doesn't, that's something that's really important to watch. And then the last thing is culture. You know a lot of people at these companies. I know a lot of people at these companies. Look, the Red Hat culture drinks the Kool-Aid of open. You know this. Do they see IBM as the steward of open, and are they gonna face a brain drain? That's why it's no coincidence that Whitehurst and Rometty were down in North Carolina today. And Arvin and Paul Cormier were in Boston today. This is where a lot of employees are for Red Hat. And they're messaging. And so that's very, very important. IBM's not foolish. So that, to me, Stu, is a huge thing, is the culture. Dave, IBM is no longer the navy suit with the red tie, and everybody buttoned down. People are concerned about like, oh, IBM's gonna give the Red Hat people a dress code. Sure, the typical IBMer is not in a graphic tee and a hoodie. But, Dave, you've seen such a transformation in IBM over the last couple of decades. >> Yeah, definitely. And I think this really does, in my view, cement, now, the legacy of Ginny Rometty, which was kinda hanging on Watson, and Cognitive, and this sort of bespoke set of capabilities, and the SoftLayer acquisition. It, now, all comes together. This is a major pivot by IBM. I think, strategically, it's the right move for IBM. And I think, if in fact, IBM can maintain Red Hat's independence and that posture, and maintain its culture and employee base, I think it does change the game for IBM. So I would say, smart move, good move. Expensive but probably worth it. >> Yeah, where else would they have put their money, Dave? >> Yeah, right. Alright, Stu, thank you very much for unpacking this announcement. And thank you for watching. We'll see you next time. (mellow electronic music)

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