>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

>>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.

(upbeat music) >> Welcome back everyone to theCUBEs live coverage in San Francisco for VMware Explorer. I'm John Furrier with my host, Dave Vellante. Two sets, three days of wall to wall coverage. This is day two. We got a great guest, Scott Baker, CMO at IBM, VP of Infrastructure at IBM. Great to see you. Thanks for coming on. >> Hey, good to see you guys as well. It's always a pleasure. >> ()Good time last night at your event? >> Great time last night. >> It was really well-attended. IBM always has the best food so that was good and great props, magicians, and it was really a lot of fun, comedians. Good job. >> Yeah, I'm really glad you came on. One of the things we were chatting, before we came on camera was, how much changed. We've been covering IBM storage days, back on the Edge days, and they had the event. Storage is the center of all the conversations, cyber security- >> ()Right? >> ... But it's not just pure cyber. It's still important there. And just data and the role of multi-cloud and hybrid cloud and data and security are the two hottest areas, that I won't say unresolved, but are resolving themselves. And people are talking. It's the most highly discussed topics. >> Right. >> ()Those two areas. And it's just all on storage. >> Yeah, it sure does. And in fact, what I would even go so far as to say is, people are beginning to realize the importance that storage plays, as the data custodian for the organization. Right? Certainly you have humans that are involved in setting strategies, but ultimately whatever those policies are that get applied, have to be applied to a device that must act as a responsible custodian for the data it holds. >> So what's your role at IBM and the infrastructure team? Storage is one only one of the areas. >> ()Right. >> You're here at VMware Explore. What's going on here with IBM? Take us through what you're doing there at IBM, and then here at VMware. What's the conversations? >> Sure thing. I have the distinct pleasure to run both product marketing and strategy for our storage line. That's my primary focus, but I also have responsibility for the mainframe software, so the Z System line, as well as our Power server line, and our technical support organization, or at least the services side of our technical support organization. >> And one of the things that's going on here, lot of noise going on- >> Is that a bird flying around? >> Yeah >> We got fire trucks. What's changed? 'Cause right now with VMware, you're seeing what they're doing. They got the Platform, Under the Hood, Developer focus. It's still an OPS game. What's the relationship with VMware? What are you guys talking about here? What are some of the conversations you're having here in San Francisco? >> Right. Well, IBM has been a partner with VMware for at least the last 20 years. And VMware does, I think, a really good job about trying to create a working space for everyone to be an equal partner with them. It can be challenging too, if you want to sort of throw out your unique value to a customer. So one of the things that we've really been working on is, how do we partner much stronger? When we look at the customers that we support today, what they're looking for isn't just a solid product. They're looking for a solid ecosystem partnership. So we really lean in on that 20 years of partnership experience that we have with IBM. So one of the things that we announced was actually being one of the first VMware partners to bring both a technical innovation delivery mechanism, as well as technical services, alongside VMware technologies. I would say that was one of the first things that we really leaned in on, as we looked out at what customers are expecting from us. >> So I want to zoom out a little bit and talk about the industry. I've been following IBM since the early 1980s. It's trained in the mainframe market, and so we've seen, a lot of things you see come back to the mainframe, but we won't go there. But prior to Arvind coming on, it seemed like, okay, storage, infrastructure, yeah it's good business, and we'll let it throw off some margin. That's fine. But it's all about services and software. Okay, great. With Arvind, and obviously Red Hat, the whole focus shift to hybrid. We were talking, I think yesterday, about okay, where did we first hear hybrid? Obviously we heard that a lot from VMware. I heard it actually first, early on anyway, from IBM, talking hybrid. Some of the storage guys at the time. Okay, so now all of a sudden there's the realization that to make hybrid work, you need software and hardware working together. >> () Right. So it's now a much more fundamental part of the conversation. So when you look out, Scott, at the trends you're seeing in the market, when you talk to customers, what are you seeing and how is that informing your strategy, and how are you bringing together all the pieces? >> That's a really awesome question because it always depends on who, within the organization, you're speaking to. When you're inside the data center, when you're talking to the architects and the administrators, they understand the value in the necessity for a hybrid-cloud architecture. Something that's consistent. On The Edge, On-Prem, in the cloud. Something that allows them to expand the level of control that they have, without having to specialize on equipment and having to redo things as you move from one medium to the next. As you go upstack in that conversation, what I find really interesting is how leaders are beginning to realize that private cloud or on-prem, multi cloud, super cloud, whatever you call it, whatever's in the middle, those are just deployment mechanisms. What they're coming to understand is it's the applications and the data that's hybrid. And so what they're looking for IBM to deliver, and something that we've really invested in on the infrastructure side is, how do we create bidirectional application mobility? Making it easy for organizations, whether they're using containers, virtual machines, just bare metal, how do they move that data back and forth as they need to, and not just back and forth from on-prem to the cloud, but effectively, how do they go from cloud to cloud? >> Yeah. One of the things I noticed is your pin, says I love AI, with the I next to IBM and get all these (indistinct) in there. AI, remember the quote from IBM is, "You can't have AI without IA." Information architect. >> () Right. >> () Rob Thomas. >> Rob Thomas (indistinct) the sound bites. But that brings up the point about machine learning and some of these things that are coming down the like, how is your area devolving the smarts and the brains around leveraging the AI in the systems itself? We're hearing more and more softwares being coded into the hardware. You see Silicon advances. All this is kind of, not changing it, but bringing back the urgency of, hardware matters. >> That's right. >> () At the same time, it's still software too. >> That's right. So let's connect a couple of dots here. We talked a little bit about the importance of cyber resiliency, and let's talk about a little bit on how we use AI in that matter. So, if you look at the direct flash modules that are in the market today, or the SSDs that are in the market today, just standard-capacity drives. If you look at the flash core modules that IBM produces, we actually treat that as a computational storage offering, where you store the data, but it's got intelligence built into the processor, to offload some of the responsibilities of the controller head. The ability to do compression, single (indistinct), deduplication, you name it. But what if you can apply AI at the controller level, so that signals that are being derived by the flash core module itself, that look anomalous, can be handed up to an intelligence to say, "Hey, I'm all of a sudden getting encrypted rights from a host that I've never gotten encrypted rights for. Maybe this could be a problem." And then imagine if you connect that inferencing engine to the rest of the IBM portfolio, "Hey, Qradar. Hey IBM Guardian. What's going on on the network? Can we see some correlation here?" So what you're going to see IBM infrastructure continue to do is invest heavily into entropy and the ability to measure IO characteristics with respect to anomalous behavior and be able to report against that. And the trick here, because the array technically doesn't know if it's under attack or if the host just decided to turn on encryption, the trick here is using the IBM product relationships, and ecosystem relationships, to do correlation of data to determine what's actually happening, to reduce your false positives. >> And have that pattern of data too. It's all access to data too. Big time. >> That's right. >> And that innovation comes out of IBM R&D? Does it come out of the product group? Is it IBM research that then trickles its way in? Is it the storage innovation? Where's that come from? Where's that bubble up? That partnership? >> Well, I got to tell you, it doesn't take very long in this industry before your counterpart, your competitor, has a similar feature. Right? So we're always looking for, what's the next leg? What's the next advancement that we can make? We knew going into this process, that we had plenty of computational power that was untapped on the FPGA, the processor running on the flash core module. Right? So we thought, okay, well, what should we do next? And we thought, "Hey, why not just set this thing up to start watching IO patterns, do calculations, do trending, and report that back?" And what's great about what you brought up too, John, is that it doesn't stay on the box. We push that upstack through the AIOPS architecture. So if you're using Turbonomic, and you want to look applications stack down, to know if you've got threat potential, or your attack surface is open, you can make some changes there. If you want to look at it across your infrastructure landscape with a storage insight, you could do that. But our goal here is to begin to make the machine smarter and aware of impacts on the data, not just on the data they hold onto, but usage, to move it into the appropriate tier, different write activities or read activities or delete activities that could indicate malicious efforts that are underway, and then begin to start making more autonomous, how about managed autonomous responses? I don't want to turn this into a, oh, it's smart, just turn it on and walk away and it's good. I don't know that we'll ever get there just yet, but the important thing here is, what we're looking at is, how do we continually safeguard and protect that data? And how do we drive features in the box that remove more and more of the day to day responsibility from the administrative staff, who are technically hired really, to service and solve for bigger problems in the enterprise, not to be a specialist and have to manage one box at a time. >> Dave mentioned Arvind coming on, the new CEO of IBM, and the Red Hat acquisition and that change, I'd like to get your personal perspective, or industry perspective, so take your IBM-hat off for a second and put the Scott-experience-in-the-industry hat on, the transformation at the customer level right now is more robust, to use that word. I don't want to say chaotic, but it is chaotic. They say chaos in the cloud here at VM, a big part of their messaging, but it's changing the business model, how things are consumed. You're seeing new business models emerge. So IBM has this lot of storage old systems, you're transforming, the company's transforming. Customers are also transforming, so that's going to change how people market products. >> () Right. >> For example, we know that developers and DevOps love self-service. Why? Because they don't want to install it. Let me go faster. And they want to get rid of it, doesn't work. Storage is infrastructure and still software, so how do you see, in your mind's eye, with all your experience, the vision of how to market products that are super important, that are infrastructure products, that have to be put into play, for really new architectures that are going to transform businesses? It's not as easy as saying, "Oh, we're going to go to market and sell something." The old way. >> () Right. >> This shifting happening is, I don't think there's an answer yet, but I want to get your perspective on that. Customers want to hear the storage message, but it might not be speeds and fees. Maybe it is. Maybe it's not. Maybe it's solutions. Maybe it's security. There's multiple touch points now, that you're dealing with at IBM for the customer, without becoming just a storage thing or just- >> () Right. >> ... or just hardware. I mean, hardware does matter, but what's- >> Yeah, no, you're absolutely right, and I think what complicates that too is, if you look at the buying centers around a purchase decision, that's expanded as well, and so as you engage with a customer, you have to be sensitive to the message that you're telling, so that it touches the needs or the desires of the people that are all sitting around the table. Generally what we like to do when we step in and we engage, isn't so much to talk about the product. At some point, maybe later in the engagements, the importance of speeds, feeds, interconnectivity, et cetera, those do come up. Those are a part of the final decision, but early on it's really about outcomes. What outcomes are you delivering? This idea of being able to deliver, if you use the term zero trust or cyber-resilient storage capability as a part of a broader security architecture that you're putting into place, to help that organization, that certainly comes up. We also hear conversations with customers about, or requests from customers about, how do the parts of IBM themselves work together? Right? And I think a lot of that, again, continues to speak to what kind of outcome are you going to give to me? Here's a challenge that I have. How are you helping me overcome it? And that's a combination of IBM hardware, software, and the services side, where we really have an opportunity to stand out. But the thing that I would tell you, that's probably most important is, the engagement that we have up and down the stack in the market perspective, always starts with, what's the outcome that you're going to deliver for me? And then that drags with it the story that would be specific to the gear. >> Okay, so let's say I'm a customer, and I'm buying it to zero trust architecture, but it's going to be somewhat of a long term plan, but I have a tactical need. I'm really nervous about Ransomware, and I don't feel as though I'm prepared, and I want an outcome that protects me. What are you seeing? Are you seeing any patterns? I know it's going to vary, but are you seeing any patterns, in terms of best practice to protect me? >> Man, the first thing that we wanted to do at IBM is divorce ourselves from the company as we thought through this. And what I mean by that is, we wanted to do what's right, on day zero, for the customer. So we set back using the experience that we've been able to amass, going through various recovery operations, and helping customers get through a Ransomware attack. And we realized, "Hey. What we should offer is a free cyber resilience assessment." So we like to, from the storage side, we'd like to look at what we offer to the customer as following the NIST framework. And most vendors will really lean in hard on the response and the recovery side of that, as you should. But that means that there's four other steps that need to be addressed, and that free cyber-resilience assessment, it's a consultative engagement that we offer. What we're really looking at doing is helping you assess how vulnerable you are, how big is that attack surface? And coming out of that, we're going to give you a Vendor Agnostic Report that says here's your situation, here's your grade or your level of risk and vulnerability, and then here's a prioritized roadmap of where we would recommend that you go off and start solving to close up whatever the gaps or the risks are. Now you could say, "Hey, thanks, IBM. I appreciate that. I'm good with my storage vendor today. I'm going to go off and use it." Now, we may not get some kind of commission check. We may not sell the box. But what I do know is that you're going to walk away knowing the risks that you're in, and we're going to give you the recommendations to get started on closing those up. And that helps me sleep at night. >> That's a nice freebie. >> Yeah. >> Yeah, it really is, 'cause you guys got deep expertise in that area. So take advantage of that. >> Scott, great to have you on. Thanks for spending time out of your busy day. Final question, put a plug in for your group. What are you communicating to customers? Share with the audience here. You're here at VMware Explorer, the new rebranded- >> () Right? >> ... multi-cloud, hybrid cloud, steady state. There are three levels of transformation, virtualization, hybrid cloud, DevOps, now- >> Right? >> ... multi-cloud, so they're in chapter three of their journey- >> That's right. >> Really innovative company, like IBM, so put the plugin. What's going on in your world? Take a minute to explain what you want. >> Right on. So here we are at VMware Explorer, really excited to be here. We're showcasing two aspects of the IBM portfolio, all of the releases and announcements that we're making around the IBM cloud. In fact, you should come check out the product demonstration for the IBM Cloud Satellite. And I don't think they've coined it this, but I like to call it the VMware edition, because it has all of the VMware services and tools built into it, to make it easier to move your workloads around. We certainly have the infrastructure side on the storage, talking about how we can help organizations, not only accelerate their deployments in, let's say Tanzu or Containers, but even how we help them transform the application stack that's running on top of their virtualized environment in the most consistent and secure way possible. >> Multiple years of relationships with VMware. IBM, VMware together. Congratulations. >> () That's right. >> () Thanks for coming on. >> Hey, thanks (indistinct). Thank you very much. >> A lot more live coverage here at Moscone west. This is theCUBE. I'm John Furrier with Dave Vellante. Thanks for watching. Two more days of wall-to-wall coverage continuing here. Stay tuned. (soothing music)

>> Voice Over: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Okay. Welcome back everyone. To theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We've got a great segment here on the power of hybrid cloud and AI. And I'm excited to have Rob Thomas, Senior Vice President of IBM's cloud and Data platform, CUBE alumni. Been on going back years and years talking about data. Rob, great to see you, a leader at IBM. Thanks for joining. >> John. Great to see you hope everybody is safe and well and great to be with you again. >> Yeah, love the progress, love the Hybrid Cloud distributed computing, meets operating systems, meets modern applications at the center of it is the new cloud equation. And of course data continues to be the value proposition as the platform. And as you quoted many times and I love your favorite quote. There's no AI without IA. So you got to have the architecture. So that still rings true today and it's just so evergreen and so relevant and cooler than ever with machine learning and AI operations. So let's just jump in. IBM's announced, host a new products and updates at Think. Tell us what you're most excited about and what should people pay attention to. >> Maybe I'll connect two thoughts here. There is no AI without IA, still true today. Meaning, customers that want to do AI need an information architecture. There was an IDC report just last year that said, "Despite all the progress on data, still 90% of data in organizations is either unused or underutilized." So what's amazing is after all the time we've been talking John, we're still really just getting started. Then that kind of connects to another thought, which is I still believe that AI is not going to replace managers, but managers that use AI will replace the managers that do not. And I'd say that's the backdrop for all the announcements that we're doing this week. It's things like auto SQL. How do you actually automate the creation of SQL queries in a large distributed data warehouse? It's never been done before, now we're doing it. It's things like Watson Orchestrate which is super powers in the hands of any business user, just to ask for something to get done. Just ask for a task to get completed. Watson Orchestrator will do that for you. It's maximo mobile. So anybody working in the field now has access to an AI system on their device for how they're managing their assets. So this is all about empowering people and users that use these products are going to have an advantage over the users that are not, that's what I'm really excited about. >> So one of the things that's coming out as Cloud Pak for Data, AI powered automation these are kind of two that you kind of touched upon the SQL thing their. Cloud Pak is there, you got it for Data and this automation trend. What is that about? Why is it important? Can you share with us the relevance of those two things? >> Let's talk broadly about automation. There's two huge markets here. There's the market for RPA business process, $30 billion market. There's the market for AIOps, which is growing 22%, that's on its way to $40 billion. These are enormous markets. Probably the biggest bet IBM has made in the last year is in automation. Explicitly in Watson AIOps. Last June in Think we announced Watson AIOps, then we did the acquisition of Instana, then we announced our intent to acquire Turbonomic. At this point, we're the only company that has all the pieces for automating how you run your IT systems. That's what I mean when I say AIOps. So really pleased with the progress that we've made there. But again, we're just getting started. >> Yeah. Congratulations on the Turbonomic. I was just commenting on that when that announced. IBM buying into the Cloud and the Hybrid cloud is interesting because the shift has happened. It's Public Cloud, it's on premises as Edge. Those two things as a system, it's more important ever than the modernization of the apps that you guys are talking about and having the under the cover capabilities. So as Cloud and Data merge, this kind of control plane concept, this architecture, as you'd said IA. You can't have AI without IA. What is that architecture look like? Can you break down the elements of what's involved? I know there's predictive analytics, there's automation and security. What are the pillars of this architecture? What are the four concepts? If you can explain that. >> Yeah, let's start with the basics. So Hybrid Cloud is about you build your software runs once and you run it anywhere you want, any public cloud,any private cloud. That assumes containers are important to the future of software. We are a hundred percent convinced that is true. OpenShift is the platform that we build on and that many software companies in the world are now building on because it gives you portability for your applications. So then you start to think about if you have that common fabric for Hybrid Cloud, how do you deliver value to customers in addition to the platform? To me, that's four big things. It's automation, we talked about that. It's security, it's predictions. How do you actually make predictions on your data? And then it's modernization. Meaning, how do you actually help customers modernize their applications and get to the Cloud? So those are the things we always talk about, automate, secure, modernize, predict. I think those are the four most important things for every company that's thinking about Cloud and AI. >> Yeah, it's interesting. I love the security side is one of the big conversations in AIOps and day two operations or whatever it's called is shifting left, getting security into the Cloud native kind of development pipeline. But speaking of secure, you have a customer that was talking about this Dow Chemical. About IB empowering Dow zero trust architecture. Could you explain that deal and how that's working? Because that's again, huge enterprise customer, very big scale at scale, zero trust is big, part of it. What is this? >> Let's start with the basics. So what is zero trust mean? It means to have a secure business, you have to start with the assumption that nothing can be trusted. That means you have to think about all aspects of your security practice. How do you align on a security strategy? How do you protect your data assets? How do you manage security threats? So we always talk about a line, protect, manage back to modernize, which is how do you bring all your systems forward to do this? That's exactly what we're doing with the Dow as you heard in that session, which is they've kind of done that whole journey from how they built a security strategy that was designed with zero trust in mind, they're protecting data assets, they're managing cyber threats in real time with a relatively low number of false positives which are the issue that most companies have. They're a tremendous example of a company that jumped on this and has had a really big impact. And they've done it without interfering with their business operations, meaning anybody can lock everything down but then you can't really run your business if you're doing that. They've done it, I think in a really intelligent way. >> That's awesome. We always talk about the big waves. You always give great color commentary on the trends. Right now though, the tsunami seems to be a confluence of many things coming together. What are some of the big trends in waves you're seeing now specifically on the tech side, on the technology side, as well as the business side right now? 'Cause coming out of post COVID, it's pretty clear cloud-native is powering a new growth strategy for customers. Dow was one of them, you just commented on it but there's a bigger wave happening here, both on the tech theater and in the business theater. Can you share your views on and your opinions and envision on these trends? >> I think there's three profound trends that are actually pretty simple to understand. One is, technology is going to decentralize again. We've always gone from centralized architectures to decentralized. Mainframe was centralized, internet mobile decentralized. The first version of public cloud was centralized, meaning bringing everything to one place. Technology is decentralized and again, with Hybrid Cloud, with Edge, pretty straight forward I think that's a trend that we can ride and lead for the next decade. Next is around automation that we talked about. There was a McKinsey report that said, "120 billion hours a year are going to be automated with things like Watson Orchestrator, Watson AIOps." What we're doing around Cloud Pak for automation, we think that time is now. We think you can start to automate in your business today and you may have seen the--example where we're doing customer care and they're now automating 70% of their inbound customer inquiries. It's really amazing. And then the third is around data. The classical problem, I mentioned 90% is still unused or underutilized. This trend on data is not about to slow down because the data being collected is still multiplying 10 X every year and companies have to find a way to organize that data as they collected. So that's going to be a trend that continues. >> You know, I just kind of pinched myself sometimes and hearing you talk with some of our earlier conversations in theCUBE, people who have been on this data mindset have really been successful because it's evolving and growing and it's changing and it's adding more input into the system and the technology is getting better. There's more cloud scales. You mentioned automation and scale are huge. And I think this really kind of wakes everyone up. And certainly the pandemic has woken everyone up to the fact that this is driving new experiences for users and businesses, right? So this is, and then those experiences become expectations. This is the classic UX paradigm that grows from new things. So I got to ask you, with the pandemic what is the been the most compelling ways you seen people operate, create new expectations? Because new things are coming, new big things, and new incremental things are happening. So evolution and revolutionary capabilities. Can you share some examples and your thoughts? >> We've collected a decent bit of data on this. And what's interesting is how much AI has accelerated since the pandemic started. And it's really in five areas, it's customer care that we talked about, virtual agents, customer service, how you do that. It's employee experience. So somewhere to customer care but how do you take care of your employees using AI? Third is around AIOps, we talked about that. Fourth is around regulatory compliance and fifth is around financial planning and budgeting. These are the five major use cases of AI that are getting into production in companies over the last year that's going to continue to accelerate. So I think it's actually fairly clarifying now that we really understand these are the five big things. I encourage anybody watching, pick one of these, get started, then pick the second, then pick the third. If you are not doing all five of these, 12, 18, 24 months from now, you are going to be behind. >> So give us an example of some things that have surprised you in the pandemic and things that blew you away. Like, wow, I didn't see that coming. Can you share on things that you've seen evolve? Cause you're a year ahead of the business units of Cloud and Data, big part of IBM and you see customer examples. Just quickly share some notable use cases or just anecdotal examples of just things that jumped out at you that said, "Wow, that's going to be a double-down moment or that's not going to be anymore." Exposes, the pandemic exposes the good, bad and the ugly. I mean, people got caught off guard, some got a tailwind, some had a headwind, some are retooling. What's your thoughts on what you can you share any examples? >> Like everybody, many things have surprised me in the last year. I am encouraged at how fast many companies were able to adjust and adapt for this world. So that's a credit to all the resiliency that they built into their processes, their systems and their people over time. Related to that, the thing that really sticks out to me again, is this idea of using AI to serve your customers and to serve your employees. We had a hundred customers that went live with one of those two use cases in the first 35 days of the pandemic. Just think about that acceleration. I think without the pandemic, for those hundred it might've taken three years and it happened in 35 days. It's proof that the technology today is so powerful. Sometimes it just takes the initiative to get started and to do something. And all those companies have really benefited from this. So it's great to see. >> Great. Rob, great to have you on. Great to have your commentary on theCUBE. Could you just quickly share in 30 seconds, what is the most important thing people should pay attention to and Think this year from your perspective? What's the big aha moment that you think they could walk away with? >> We have intentionally made this a very technology centric event. Just go look at the demos, play with the technology. I think you will be impressed and start to see, let's say a bit of a new IBM in terms of how we're making technology accessible and easy for anybody to use. >> All right. Rob Thomas, Senior Vice President of IBM cloud and Data platform. Great to have you on and looking forward to seeing more of you this year and hopefully in person. Thanks for coming on theCUBE virtual. >> Thanks, John. >> Okay. I'm John Furrier with theCUBE. Keep coverage of IBM Think 2021. Thank you for watching. (soft music)

(soft music) >> Voice Over: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Okay. Welcome back everyone. To theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We've got a great segment here on the power of hybrid cloud and AI. And I'm excited to have Rob Thomas, Senior Vice President of IBM's cloud and Data platform, CUBE alumni. Been on going back years and years talking about data. Rob, great to see you, a leader at IBM. Thanks for joining. >> John. Great to see you hope everybody is safe and well and great to be with you again. >> Yeah, love the progress, love the Hybrid Cloud distributed computing, meets operating systems, meets modern applications at the center of it is the new cloud equation. And of course data continues to be the value proposition as the platform. And as you quoted many times and I love your favorite quote. There's no AI without IA. So you got to have the architecture. So that still rings true today and it's just so evergreen and so relevant and cooler than ever with machine learning and AI operations. So let's just jump in. IBM's announced, host a new products and updates at Think. Tell us what you're most excited about and what should people pay attention to. >> Maybe I'll connect two thoughts here. There is no AI without IA, still true today. Meaning, customers that want to do AI need an information architecture. There was an IDC report just last year that said, "Despite all the progress on data, still 90% of data in organizations is either unused or underutilized." So what's amazing is after all the time we've been talking John, we're still really just getting started. Then that kind of connects to another thought, which is I still believe that AI is not going to replace managers, but managers that use AI will replace the managers that do not. And I'd say that's the backdrop for all the announcements that we're doing this week. It's things like auto SQL. How do you actually automate the creation of SQL queries in a large distributed data warehouse? It's never been done before, now we're doing it. It's things like Watson Orchestrate which is super powers in the hands of any business user, just to ask for something to get done. Just ask for a task to get completed. Watson Orchestrator will do that for you. It's Maximo Mbo. So anybody working in the field now has access to an AI system on their device for how they're managing their assets. So this is all about empowering people and users that use these products are going to have an advantage over the users that are not, that's what I'm really excited about. >> So one of the things that's coming out as Cloud Pak for Data, AI powered automation these are kind of two that you kind of touched upon the SQL thing their. Cloud Pak is there, you got it for Data and this automation trend. What is that about? Why is it important? Can you share with us the relevance of those two things? >> Let's talk broadly about automation. There's two huge markets here. There's the market for RPA business process, $30 billion market. There's the market for AIOps, which is growing 22%, that's on its way to $40 billion. These are enormous markets. Probably the biggest bet IBM has made in the last year is in automation. Explicitly in Watson AIOps. Last June in Think we announced Watson AIOps, then we did the acquisition of Instana, then we announced our intent to acquire Turbonomic. At this point, we're the only company that has all the pieces for automating how you run your IT systems. That's what I mean when I say AIOps. So really pleased with the progress that we've made there. But again, we're just getting started. >> Yeah. Congratulations on the Turbonomic. I was just commenting on that when that announced. IBM buying into the Cloud and the Hybrid cloud is interesting because the shift has happened. It's Public Cloud, it's on premises as Edge. Those two things as a system, it's more important ever than the modernization of the apps that you guys are talking about and having the under the cover capabilities. So as Cloud and Data merge, this kind of control plane concept, this architecture, as you'd said IA. You can't have AI without IA. What is that architecture look like? Can you break down the elements of what's involved? I know there's predictive analytics, there's automation and security. What are the pillars of this architecture? What are the four concepts? If you can explain that. >> Yeah, let's start with the basics. So Hybrid Cloud is about you build your software runs once and you run it anywhere you want, any public cloud,any private cloud. That assumes containers are important to the future of software. We are a hundred percent convinced that is true. OpenShift is the platform that we build on and that many software companies in the world are now building on because it gives you portability for your applications. So then you start to think about if you have that common fabric for Hybrid Cloud, how do you deliver value to customers in addition to the platform? To me, that's four big things. It's automation, we talked about that. It's security, it's predictions. How do you actually make predictions on your data? And then it's modernization. Meaning, how do you actually help customers modernize their applications and get to the Cloud? So those are the things we always talk about, automate, secure, modernize, predict. I think those are the four most important things for every company that's thinking about Cloud and AI. >> Yeah, it's interesting. I love the security side is one of the big conversations in AIOps and day two operations or whatever it's called is shifting left, getting security into the Cloud native kind of development pipeline. But speaking of secure, you have a customer that was talking about this Dow Chemical. About IB empowering Dow zero trust architecture. Could you explain that deal and how that's working? Because that's again, huge enterprise customer, very big scale at scale, zero trust is big, part of it. What is this? >> Let's start with the basics. So what is zero trust mean? It means to have a secure business, you have to start with the assumption that nothing can be trusted. That means you have to think about all aspects of your security practice. How do you align on a security strategy? How do you protect your data assets? How do you manage security threats? So we always talk about a line, protect, manage back to modernize, which is how do you bring all your systems forward to do this? That's exactly what we're doing with the Dow as you heard in that session, which is they've kind of done that whole journey from how they built a security strategy that was designed with zero trust in mind, they're protecting data assets, they're managing cyber threats in real time with a relatively low number of false positives which are the issue that most companies have. They're a tremendous example of a company that jumped on this and has had a really big impact. And they've done it without interfering with their business operations, meaning anybody can lock everything down but then you can't really run your business if you're doing that. They've done it, I think in a really intelligent way. >> That's awesome. We always talk about the big waves. You always give great color commentary on the trends. Right now though, the tsunami seems to be a confluence of many things coming together. What are some of the big trends in waves you're seeing now specifically on the tech side, on the technology side, as well as the business side right now? 'Cause coming out of post COVID, it's pretty clear cloud-native is powering a new growth strategy for customers. Dow was one of them, you just commented on it but there's a bigger wave happening here, both on the tech theater and in the business theater. Can you share your views on and your opinions and envision on these trends? >> I think there's three profound trends that are actually pretty simple to understand. One is, technology is going to decentralize again. We've always gone from centralized architectures to decentralized. Mainframe was centralized, internet mobile decentralized. The first version of public cloud was centralized, meaning bringing everything to one place. Technology is decentralized and again, with Hybrid Cloud, with Edge, pretty straight forward I think that's a trend that we can ride and lead for the next decade. Next is around automation that we talked about. There was a McKinsey report that said, "120 billion hours a year are going to be automated with things like Watson Orchestrator, Watson AIOps." What we're doing around Cloud Pak for automation, we think that time is now. We think you can start to automate in your business today and you may have seen the C QVS example where we're doing customer care and they're now automating 70% of their inbound customer inquiries. It's really amazing. And then the third is around data. The classical problem, I mentioned 90% is still unused or underutilized. This trend on data is not about the slow down because the data being collected is still multiplying 10 X every year and companies have to find a way to organize that data as they collected. So that's going to be a trend that continues. >> You know, I just kind of pinched myself sometimes and hearing you talk with some of our earlier conversations in theCUBE, people who have been on this data mindset have really been successful because it's evolving and growing and it's changing and it's adding more input into the system and the technology is getting better. There's more cloud scales. You mentioned automation and scale are huge. And I think this really kind of wakes everyone up. And certainly the pandemic has woken everyone up to the fact that this is driving new experiences for users and businesses, right? So this is, and then those experiences become expectations. This is the classic UX paradigm that grows from new things. So I got to ask you, with the pandemic what is the been the most compelling ways you seen people operate, create new expectations? Because new things are coming, new big things, and new incremental things are happening. So evolution and revolutionary capabilities. Can you share some examples and your thoughts? >> We've collected a decent bit of data on this. And what's interesting is how much AI has accelerated since the pandemic started. And it's really in five areas, it's customer care that we talked about, virtual agents, customer service, how you do that. It's employee experience. So somewhere to customer care but how do you take care of your employees using AI? Third is around AIOps, we talked about that. Fourth is around regulatory compliance and fifth is around financial planning and budgeting. These are the five major use cases of AI that are getting into production in companies over the last year that's going to continue to accelerate. So I think it's actually fairly clarifying now that we really understand these are the five big things. I encourage anybody watching, pick one of these, get started, then pick the second, then pick the third. If you are not doing all five of these, 12, 18, 24 months from now, you are going to be behind. >> So give us an example of some things that have surprised you in the pandemic and things that blew you away. Like, wow, I didn't see that coming. Can you share on things that you've seen evolve? Cause you're a year ahead of the business units of Cloud and Data, big part of IBM and you see customer examples. Just quickly share some notable use cases or just anecdotal examples of just things that jumped out at you that said, "Wow, that's going to be a double-down moment or that's not going to be anymore." Exposes, the pandemic exposes the good, bad and the ugly. I mean, people got caught off guard, some got a tailwind, some had a headwind, some are retooling. What's your thoughts on what you can you share any examples? >> Like everybody, many things have surprised me in the last year. I am encouraged at how fast many companies were able to adjust and adapt for this world. So that's a credit to all the resiliency that they built into their processes, their systems and their people over time. Related to that, the thing that really sticks out to me again, is this idea of using AI to serve your customers and to serve your employees. We had a hundred customers that went live with one of those two use cases in the first 35 days of the pandemic. Just think about that acceleration. I think without the pandemic, for those hundred it might've taken three years and it happened in 35 days. It's proof that the technology today is so powerful. Sometimes it just takes the initiative to get started and to do something. And all those companies have really benefited from this. So it's great to see. >> Great. Rob, great to have you on. Great to have your commentary on theCUBE. Could you just quickly share in 30 seconds, what is the most important thing people should pay attention to and Think this year from your perspective? What's the big aha moment that you think they could walk away with? >> We have intentionally made this a very technology centric event. Just go look at the demos, play with the technology. I think you will be impressed and start to see, let's say a bit of a new IBM in terms of how we're making technology accessible and easy for anybody to use. >> All right. Rob Thomas, Senior Vice President of IBM cloud and Data platform. Great to have you on and looking forward to seeing more of you this year and hopefully in person. Thanks for coming on theCUBE virtual. >> Thanks, John. >> Okay. I'm John Furrier with theCUBE. Keep coverage of IBM Think 2021. Thank you for watching. (soft music)

>> (vocalizing) >> Narrator: From theCube studios in Palo Alto and Boston, it's theCube. Covering IBM Think, brought to you by IBM. >> Hi everybody, this is Dave Vallante and this is our continuing coverage of Think 2020, the digital event experience. This is the post-thing, the sort of halo effect, the afterthoughts, and joining me is Rob Thomas, he's back. The Senior Vice president of Cloud and Data Platform. Rob, thanks for taking some time to debrief on Think. >> Absolutely Dave, great to be here, good to see you again. >> Yeah, so you have a great event, you guys put it together in record time. I want to talk about sort of your innovation agenda. I mean, you are at the heart of innovation. You're talking cloud, data, AI, really the pillars of innovation, I could probably add in edge to extend the cloud. But I wonder if you could talk about your vision for the innovation agenda and how you're bringing that to customers. I mean, we heard from PayPal, you talked about Royal Bank of Scotland, Credit Mutual, a number of customer examples. How are you bringing innovation forward with the customer? >> I wouldn't describe innovation, maybe I'd give it two different categories. One is, I think the classic term would be consumerization, and you're innovating by making interiorized technology really easy to use. That's why we built out a huge design capability, it's why we've been able to get products like Watson Assistant to get companies live in 24 hours. That's the consumerization aspect, just making enterprise products really easy to use. The second aspect is even harder, which is, how do you tap into an institution like IBM Research, where we're doing fundamental invention. So, one of our now strengths in the last couple of months was around taking technology out of IBM Debater, project Debater, the AI system that could debate humans and then putting that into enterprised products. And, you saw companies like PayPal that are using Watson Assistant and now they have access to that kind of language capability. There's only two aspects here, there's the consumerization and then there's about fundamental technology that really changes how businesses can operate. >> I mean, the point you made about speed and implementation in your key note was critical, I mean really, within 24 hours, very important during this pandemic. Talk about automation, you know, you would think by now right, everything's automation. But, now you're seeing a real boom in automation and it really is driven by AI, all this data, so there's seems to be a next wave, almost a renaissance, if you will, in automation. >> There is and I think automation, when people hear first of the term, it's sometimes a scary term. Because people are like hey, is this going to take my job? Gain a lot of momentum for automation is a difficult, repetitive tasks that nobody really wanted to do in the first place. Whether it's things like data matching, containerizing an application. All these are really hard things and the output's great, but nobody really wants to do that work, they just want the outcome. And, as we've started to demonstrate different use cases for automation that are in that realm, a lot of momentum has taken off, that we're seeing. >> I want to come back to this idea of consumerization and simplification. I mean, when you think about what's been happening over the last several years. And, you and I have talked about this a lot, AI for consumer versus AI for business and enterprise. And really, one of the challenges for the encumbrance, if you will, is to really become data driven, put data at the core and apply machine intelligence to that, just to that data. Now the good news is, they don't have to invent all this stuff, because guys like you are doing that and talk about how you're making that simple. I mean, cloud packs is an example of that, simplification, but talk about how customers are going to be able to tap into AI without having to be AI inventors. >> Well, the classic AI problem actually is a data problem, and the classic data problem is data slide over, which is a company has got a lot of data but it's spread across a hundred or a thousand or tens of thousands different repositories or locations. Our strategy when we say a hybrid cloud is about how do we unify those data storage. So, it's called PaaS, on red hat open shift. We do a lot of things like data virtualization, really high performance. So, we take what is thousands of different data sources and we have that packed like a single fluid item. So then, when you're training models, you can train your models in one place and connect to all your data. That is the big change that's happening and that's how you take something like hybrid cloud, and it actually starts to impact your data architecture. And once you're doing that, then AI becomes a lot easier, because the biggest AI challenge that I described is, where's the data? Is the data in a usable form? >> A lot of times in this industry, you know, we go whale hunting, there are a lot of big companies out there, a lot of times they take priority. You know, at the same time though, a lot of the innovations are coming from companies, you know, we've never even heard of that could be multi-billion dollar companies by the end of the decade. So, how can, you know, small companies and mid-sized companies tap into this trend? Is it just for the big whales or could the small guys participate? >> The thing that's pretty amazing about modern cloud and data technology, I'll call it, is it's accessible to companies of any size. When we talked about, you know, the hundred or so clients that have adopted Watson Assistant since COVID-19 started, many of those are very small institutions with no IT staff or very limited IT staff. Though, we're making this technology very accessible. when you look at something like data, now a small company may not have a hundred different repositories, which is fine, but what they do have is they do want to make better predictions, they do want to automate, they do want to optimize the business processes that they're running in their business. And, the way that we've transformed our model consumption base starting small, it's really making technology available to, you know, from anywhere from the local deli to the Fortune 50 Company. >> So, last question is, What are your big takeaways from Think? I would ask that question normally when we're in a live event. It's a little different with the digital event, but there are still takeaways. What was your reaction and what do to leave people with? >> Even as we get back to doing physical events, which I'm positive will happen at some point. What we learned is there is something great about an immersive digital experience. So, I think the future of events is probably higher than this. Meaning, a big digital experience, to complement the physical experience. That's one big takeaway because the reaction was so positive to the content and how people could access it. Second one is the, all the labs that we did. So, for developers, builders, those were at capacity, meaning we didn't even take any more. So, there's definitively a thirst in the market for developing new applications, developing new data products, developing new security products. That's clear just by the attendance that we saw, that's exciting. Now, I'd say third, that is that AI is now moving into the mainstream, that was clear from the customer examples, whether it was with Tansa or UPS or PayPal that I mentioned before, that was talking with me. AI is becoming accessible to every company, that's pretty exciting. >> Well, the world is hybrid, oh you know the lab, the point you're making about labs is really important. I've talked to a number of individuals saying, "Hey I'm using this time to update my skills. I'm working longer hours, maybe different times of the day, but I'm going to skill up." And you know, the point about AI, 37 years ago, when I started in this business AI was all the buzz and it didn't happen. It's real this time and I'm really excited Rob, that you're at the heart of all this innovation, so really, I appreciate you taking the time. And, best of luck, stay safe, and hopefully we'll see you face to face. >> Offscreen Man: Sure. >> Thanks Dave, same to you and the whole team at theCube, take care. >> Thank you Rob, and thank you for watching everybody, this is Dave Vellante for theCube and our coverage of IBM Think 2020, the digital event experience and the post-event. We'll see you next time. (music)

>>From the cube studios in Palo Alto in Boston. It's the cube covering the IBM thing brought to you by IBM. We're back and this is Dave Vellante and you're watching the cube and we're covering wall-to-wall the IBM 2020 I think digital experience. Rob Thomas is here. He's the senior vice president of clouds and data. Right. Warm rub. Always a pleasure to see you. I wish you were face to face, but Hey, we're doing the best we can. As you say, doing the best we can. Great to see you Dave. Hope family safe, healthy, happy as best you can be. Yeah. Ditto. You back out your Robin. Congratulations on on the new role, you and the cube. We've been riding this data wave for quite some time now. It's really been incredible. It really is. And last year I talked to you about how clients, we're slowly making progress on data strategy, starting to experiment with AI. >>We've gotten to the point now where I'd say it's game on for AI, which is exciting to see and that's a lot of what the theme of this year's think is about. Yeah, and I definitely want to dig into that, but I want to start by asking you sort of moves that you saw you're in there seeing your clients make with regard to the cobot night covert 19 crisis. Maybe how you guys are helping them in very interested in what you see as sort of longterm and even, you know, quasi permanent as a result of this. I would first say it this way. I don't, I'm not sure the crisis is going to change businesses as much as it's going to be accelerating. What would have happened anyway, regardless of the industry that you're in. We see clients aggressively looking at how do we get the digital faster? >>How do we automate more than we ever have before? There's the obvious things like business resiliency and business continuity, managing the distributed workforce. So to me, what we've seen is really about, and acceleration, not necessarily in a different direction, but an acceleration on. The thing is that that we're already kind of in the back of their minds or in the back of their plans now that as we'll come to the forefront and I'm encouraged because we see clients moving at a rate and pace that we'd never seen before that's ultimately going to be great for them, great for their businesses. And so I'm really happy to see that you guys have used Watson to really try to get, you know, some good high fidelity answers to the citizens. I wonder if you could explain that initiative. Well, we've had this application called Watson assistant for the last few years and we've been supporting banks, airlines, retailers, companies across all industries and helping them better interact with our customers and in some cases, employees. >>We took that same technology and as we saw the whole covert 19 situation coming, we said, Hey, we can evolve Watson assistant to serve citizens. And so it started by, we started training the models, which are intent based models in Watson assistant on all the publicly available data from the CDC as an example. And we've been able to build a really powerful virtual agent to serve really any citizen that has questions about and what they should be doing. And the response has been amazing. I mean, in the last two weeks we've gone live with 20 organizations, many of which are state and local governments. Okay. Also businesses, the city of Austin children's healthcare of Atlanta. Mmm. They local governments in Spain and Greece all over the world. And in some instances these clients have gotten live in less than 24 hours. Meaning they have a virtual agent that can answer any question. >>They can do that in less than 24 hours. It's actually been amazing to see. So proud of the team that built this over time. And it was kind of proof of the power of technology when we're dealing with any type of a challenge. You know, I had a conversation earlier with Jamie Thomas about quantum and was asking her sort of how your clients are using it. The examples that came up were financial institutions, pharmaceutical know battery manufacturers, um, airlines. And so it strikes me when you think about uh, machine intelligence and AI, the type of AI that you're yeah, at IBM is not consumer oriented AI. It's really designed for businesses. And I wonder if you could sort of add some color to that. Yeah, let's distinguish the difference there. Cause I think you've said it well consumer AI is smart speakers things in our home, you know, music recommendations, photo analysis and that's great and it enriches all of our personal lives. >>AI for business is very different. This is about how do you make better predictions, how do you optimize business processes, how do you automate things that maybe your employees don't want to do in the first time? Our focus in IBM as part of, we've been doing with Watson is really anchoring on three aspects of AI language. So understanding language because the whole business world is about communication of language, trust meaning trusted AI. You understand the models, you understand the data. And then third automation and the whole focus of what we're doing here in the virtual think experience. It's focused on AI for automation. Whether that's automating business processes or the new announcement this week, which is around automating AI opera it operations for a CIO. You, you've talked the years about this notion of an AI ladder. You actually, I actually wrote a book on it and uh, but, but it's been hard for customers to operationalize AI. >>Mmm. We talked about this last year. Thanks. What kind of progress, uh, have we made in the last 12 months? There's been a real recognition of this notion that your AI is only as good as your data. And we use the phrase, there's no AI without IAA, meaning information architecture, it's all the same concept, which is that your data, it has to be ready for AI if you want to too get successful outcomes with AI and the steps of those ladders around how you collect data, how you organize data, how you analyze data, how you infuse that into your business processes. seeing major leaps forward in the last nine months where organizations are understanding that connection and then they're using that to really drive initiatives around AI. So let's talk about that a little bit more. This notion of AI ops, I mean it's essentially the take the concept of dev ops and apply it to the data pipeline if you will. >>Everybody, you know, complains, you know, data scientists complained that all, they spent all their time wrangling data, improving data quality, they don't have line of sight across their organization with regard to other data specialists, whether it's data engineers or even developers. Maybe you could talk a little bit more about that announcement and sort of what you're doing in that area. Sure. So right. Let me put a number on it because the numbers are amazing. Every year organizations lose 2016 point $5 billion of revenue because of outages in it system. That is a staggering number when you think about it. And so then you say, okay, so how do you break down and attack that problem? Well, do you have to get better at fixing problems or you have to get better at avoiding problems altogether. And as you may expect, a little bit of both. You, you want to avoid problems obviously, but in an uncertain world, you're always going to deal with unforeseen challenges. >>So the also the question becomes how fast can you respond and there's no better use of AI. And then to do, I hope you like those tasks, which is understanding your environment, understanding what the systems are saying through their data and identifying issues become before they become outages. And once there is an outage, how do you quickly triage data across all your systems to figure out where is the problem and how you can quickly address it. So we are announcing Watson AI ops, which is the nervous system for a CIO, the manager, all of their systems. What we do is we just collect data, log data from every source system and we build a semantic layer on top that. So Watson understands the systems, understands the normal behavior, understands the acceptable ranges, and then anytime something's not going like it should, Watson raises his hand and says, Hey, you should probably look at this before it becomes a problem. >>We've partnered with companies like Slack, so the UI for Watson AI ops, it's actually in Slack so that companies can use and employees can use a common collaboration tool too. Troubleshoot or look at either systems. It's, it's really powerful. So that we're really proud of. Well I just kind of leads me to my next question, which I mean, IBM got the religion 20 years ago on openness. I mean I can trace it back to the investment you made and Lennox way back when. Um, and of course it's a huge investment last year in red hat, but you know, open source company. So you just mentioned Slack. Talk about open ecosystems and how that it fits into your AI and data strategy. Well, if you think about it, if we're going to take on a challenge this grand, which is AI for all of your it by definition you're going to be dealing with full ecosystem of different providers because every organization has a broad set of capabilities we identified early on. >>That means that our ability to provide open ecosystem interoperability was going to be critical. So we're launching this product with Slack. I mentioned with box, we've got integrations into things like PagerDuty service now really all of the tools of modern it architecture where we can understand the data and help clients better manage those environments. So this is all about an open ecosystem and that's how we've been approaching it. Let's start, it's really about data, applying machine intelligence or AI to that data and about cloud for scale. So I wonder what you're seeing just in terms of that sort of innovation engine. I mean obviously it's gotta be secure. It's, it seems like those are the pillars of innovation for the next 10 plus years. I think you're right. And I would say this whole situation that we're dealing with has emphasized the importance of hybrid deployment because companies have it capabilities on public clouds, on private clouds, really everywhere. >>And so being able to operate that as a single architecture, it's becoming very important. You can use AI to automate tasks across that whole infrastructure that makes a big difference. And to your point, I think we're going to see a massive acceleration hybrid cloud deployments using AI. And this will be a catalyst for that. And so that's something we're trying to help clients with all around the world. You know, you wrote in your book that O'Reilly published that AI is the new electricity and you talked about problems. Okay. Not enough data. If your data is you know, on prem and you're only in the cloud, well that's a problem or too much data. How you deal with all that data, data quality. So maybe we could close on some of the things that you know, you, you talked about in that book, you know, maybe how people can get ahold of it or any other, you know, so the actions you think people should take to get smart on this topic. >>Yeah, so look, really, really excited about this. Paul's the capitalists, a friend of mine and a colleague, we've published this book working with a Riley called the a ladder and it's all the concepts we talked about in terms of how companies can climb this ladder to AI. And we go through a lot of different use cases, scenarios, I think. Yeah. Anybody reading this is going to see their company in one of these examples, our whole ambition was to hopefully plant some seeds of ideas for how you can start to accelerate your journey to AI in any industry right now. Well, Rob, it's always great having you on the cube, uh, your insights over the years and you've been a good friend of ours, so really appreciate you coming on and, uh, and best of luck to you, your family or wider community. I really appreciate it. Thanks Dave. Great to be here and again, wish you and the whole cube team the best and to all of our clients out there around the world. We wish you the best as well. All right. You're watching the cubes coverage of IBM think 20, 20 digital, the vent. We'll be right back right after this short break. This is Dave Volante.

Narrator: From theCUBE studios in Palo Alto and Boston, it's theCUBE! Covering IBM Think, brought to you by IBM. >> Hello everybody, we're back. And this is theCUBE, and we're covering IBM Think 2020, the Digital Think, and we are covering wall-to-wall. We're here with Keith Dyer, who's the Vice President of Sales and Channels at Cisco, and Laura Guio, long-time friend to CUBE Alum, she's the General Manager of the Global Cisco Alliance, and California Senior State Exec. Folks, welcome back to theCUBE, good to see you again. >> Nice to see you, Dave. >> Good to see you too, Dave. >> Hey, I got to ask you, Laura, what's this California Senior State Executive into your title? Tell me about that. >> So, I'm responsible for all of the IBM population here in the state of California, and during this time of COVID-19, it's been very interesting, so I manage all the, as I call it, care and feeding of the employees up and down the state, and how we're responding to the shelter-in-place orders, and how IBM is responding from an employee perspective. >> Yeah, you know, I've interviewed a number of CXOs, some from both your companies, and that's the theme that we keep hearing, Keith, is: Number one is the health and wellbeing and safety of our employees, and then once that's confirmed, get to work. >> Yeah, it's a completely different environment that we're in, and I mean, Cisco and IBM both being big global companies, coming from being in offices and in environments of working closely with one another to sheltering in  home and working out of our home offices, I think the thing that both of our companies have the ability to do is to empower our folks to do that. And we're doing that, we're doing that both from an individual perspective, with our tools and our technologies, but we're also doing that together, with a lot of the things that this partnership and this alliance brings to this, which is really, you know, being able to provide IT services to remote workers and to be able to still keep this economy moving along. >> Yeah, along with our data partner, ETR, we were one of the first to report that sort of work-from-home offset, how budgets are shifting, in fact, 20% of the CIOs that we surveyed, 1200 CIOs, said their budgets were actually increasing. So, I wonder, Laura, if you could talk about the, you guys had a relationship with Cisco and IBM for a long time. Maybe, talk about some of the go-to-market highlights, and I want to double-click on that. >> Yeah, so we've had a long-standing relationship, over 20 years, that we've partnered together in the marketplace. And because of that long-standing relationship, it gives us an opportunity, not at just the very senior levels of this relationship, but all the way out to the field in the sellers, on what's needed out there from a client perspective. We're constantly coming out with new, integrated solutions, things that answer the questions and the problems that our customers are trying to solve. One in particular, right now, is called Private Cloud Infrastructures as a Service. This with Cisco Technology, and IBM Technology and Services gives the client an answer on how to get that private Cloud in their facility and not have to have the CAPEC question on getting that server portion of that in there. Cisco has a unique opportunity with IBM, to offer that customer. >> So Keith, one of the things I'd like to talk about with any go-to-market strategies is, you get together when you get a market partner and you try to identify the ideal customer, what's the right profile, What's the value proposition. And I'm wondering, just generally, what does that look like for you guys, and then specifically, how has that changed, or has that changed as a result of COVID-19? >> Well, I think a couple of things: One, one of the things where Cisco and IBM have long been partners together has been from a security perspective, and as we move into this new class of workers that are working remotely, and that are working in environments where security is paramount, and one of the work that we've done together around threat management and the way we both have put security measures and security products in place and solutions to help remote workers to be able to work with security into their networks. >> Yeah, so in our reporting, we've noted that it's not just video collaboration tools that are on the uptake, it is things like, whether it's VPNs, networking bandwidth, wide area networks, securing that remote infrastructure. So Laura, maybe, you could help us understand what IBM's bringing to the table, and maybe we can talk about what Cisco's bringing to the table here. >> Well, when you look at it from an IBM perspective, our huge client base out there from a services perspective. Generally, where we start, those customers are looking for end-to-end solutions. So when you take technologies like Cisco has, and combine it with the breadth of technology, around Cloud, Hybrid Cloud, Security, that gives the ability to a client to come to one place, get that end-to-end solution, and feel secure that it is an enterprise-quality solution, that they don't have to worry about all the other part pieces they have to plug in there. >> Yeah, one of the things we've been talking about is: I was just talking to Rob Thomas about this, he said, "You know, Dave, I don't know if anything's "going to really dramatically change with COVID-19, "maybe, it is, maybe it isn't, "but definitely some things are being accelerated. "And when you think about the acceleration to Cloud, talking about the industry angle, Laura, Edge, IOT, I wonder if you guys could talk a little bit about, maybe, start with Keith, do you see there are some learnings here in this period, during this pandemic, that maybe will accelerate, sort of some of those Edge discussions, or the things that we've learned that maybe, would have taken longer to put into practice? Let us start with Keith. >> Yeah, I think first and foremost, it's just getting at the data, and being able to have that data to a decision faster, and that's the whole reason we're really investing around Edge technologies, so that we can take that data in, we can hope it helps us make decisions faster, and get to outcomes for customers better, and a part of that becomes around having the right security postures, but also then being able to link up back to the data center, which is what we do with IBM around HyperCloud. >> Laura, anything you'd add to that from an industry perspective? >> Yeah, I think that the technology that Cisco brings to the table really it helps accelerate that solution, and get what the client's looking for. We had a recent example, well, at the end of last year; we met with a number of manufacturing customers in Europe. And we took them through a solution that we have with the Edge and Security that Cisco offers, the pieces that IBM brings to the table, but the manufacturers really looked at this and said, "Wow! This really gives me that Edge technology that I need, "it provides all the security that I'm looking for, "and allows this manufacturing to line autonomously, "run without having to have that intervention "that a number of other solutions would require." >> You know, it's kind of a sensitive topic when I talk to executives, and when we talk to the CIOs and CSOs with ETR in the roundtable, there was a sensitivity to, and sort of a negative sensitivity to so-called "the ambulance chasing." And so what they don't want is, "Hey, here's a free trial for, you know, "but you got to swipe your credit card, "you have to promise to sign something. "We just don't have time for that." I bring that up because Cisco and IBM came up in this roundtable as two companies, there were others, too, by the way, that were really responding well from the customer perspective. And these were industries that were hard-hit, you know, we're talking about airlines, we're talking about hospitality, really hard-hit types of industries, and they called out IBM, Cisco, and as I say, seven or eight other companies, so I think the industry, because you guys are large companies, established companies, they expect more of you. They expect kind of adult supervision, if you will, in the room. I wonder if you could talk about, maybe, some of the other things that, but first of all, react to that, and tell me the other things, Laura, that, maybe, you guys have done, either as individual companies or jointly. >> Yeah, I'll start and I'll let Keith answer here. So, I liked the comment, "the adults in the room". What we're finding as customers are coming to companies like Cisco and IBM and saying, "Look, I need a solid enterprise solution. "I'm looking for somebody who's tested it, tried-and-true, "that you've got recognition in the industry, "that you're going to bring a complete, "solid solution forward." And so we are being tapped into as two companies, to really bring us two to the clients, they don't have a whole lot of time right now to go figure it out, and they believe in us, and what we've been able to provide for the market. >> Yeah, and one of the things that I would add to that was that the investment that both of our companies are making, really just in our customers, and helping them get through this journey. You know, we both have fantastic CEOs, who are really visionaries, and who are really beginning to look at, and how they can help accelerate our customers, so that when we get on the other side we're stronger and we're able to deliver technology, and be able to deliver to our customers. You know, Laura and I, we're inundated, almost on a daily basis of requests and support. And we've actually had a grassroots effort that really kind of bore up through our sales teams are providing education and providing services in the education sector, using IBM technology, and using Cisco Webex Technology. We've been partnering with other partners, such as Samsung and Apple, to deliver those on devices, and you know, these aren't necessarily things that came out of the CEO offices, these were solutions and efforts that are grassrooted up through our organization, because of the strong partnership that we have in the industry. >> I love that, because, I mean, we've all been touched by education, kids' remote learning, healthcare's another one. I mean, everybody knows somebody, you know, a nurse, or now the first responders, "the today's heroes", that are having to really risk their lives, literally, every day when they go into work, and that is happening on the front lines, so Keith, I appreciate your comment, that it's a grassroots effort and Laura, you got a new CEO, you know, Arvind, stepped into this and I'm excited to talk to him about his first moves, but any other color you can add to that, or other initiatives that you've seen in the field? >> Yeah, so Keith touched on it just a moment ago there, you talk about the ICUs in the hospitals. Almost a month ago when this all started, I sat there watching the news, watching people dying in the hospital without a chance to really talk to their family members, and the burden that it was putting onto the health care professionals. We came up with, I said, there's a solution there, went to Keith, said, "You know, we've got Webex, "we've got other things in the portfolio," went to Samsung, they have devices that are military-grade, that'll work there. We were able to put a solution together pretty quickly. We've got a number of hospitals that are evaluating it right now, we're almost ready to roll this out, but that just goes to a mature company that has all this security and interactions with other companies that have the part pieces that you need, and then test it, make sure it's secure, that it's enterprise-grade, and get it out there. There's not many companies in the world that can do that. >> Well, I think that goes to what you were saying before, I called it "adult supervision," but I talked to Sri Srinivasan, who runs Cisco's Collaboration division, and as they say, the CIOs told us, "You know, we're really off-put "by people trying to sell us," but what Sri told me was that Cisco made a free-offering, no swipe of the credit card, "Hey, if you buy something down the road that's fine, "if you don't, you know, doesn't matter." And that's the kind of leadership that I think people expect from companies like IBM and Cisco, quite frankly. >> Yeah, and you know, Dave, what Sri and what Chuck did there, you know, that wasn't easy to do, I mean, we've essentially doubled and almost tripled our capacity of Webex as we've gone through this, and we were just absolutely, that organization that is working well overtime, overtime, overtime. Laura and I were able to take that, take some of that technology, be able to get out in the front, and truly it's not about creating revenue right now, it's about helping get our customers through this crisis together. We'll worry about, you know, commercial opportunities that come down the road. >> Yeah, and those will happen, those are going to be outcomes of your business practices, and talking to Rob Thomas, and again, and he'd been the data angle here, all the data, the data sources, the data quality, you're seeing it. You see even the maps, you see even the real-time updates, I mean, things change, literally, on a day-to-day basis, and that's kind of IBM's wheelhouse, really. >> Yeah, yeah. And we're addressing a lot of that with what we're doing here between our two companies, and providing that solution, getting to that data, get it securely where it needs to be. We've been on the forefront of providing from an IBM perspective, around the COVID information that's being used around the world through our weather company application that we have out there. We've offered up the mainframe technologies, and our supercomputers around, be able to help hospitals and those that are working on vaccines and all of that information, so you've got to have the networking piece of that, you've got to have the technology that it works on, and then you've got to have that data that you can access and manipulate quickly to get those answers out. >> Yeah, and Cisco, IBM, it's been a partnership that made a lot of sense, there's not a ton of overlap in your portfolios, which is quite amazing given the size of your companies. You know, there is some, but generally speaking, it's been a pretty productive partnership. Keith, Laura, thanks so much for coming on theCUBE, sharing a little bit of information, and thanks for what you're doing during this crisis. Stay safe. >> Thanks Dave. Thanks Dave. >> All right, you're welcome. And thank you for watching. Everybody, this is Dave Vellante, our wall-to-wall coverage of IBM's Digital Think 2020. You're watching theCUBE. (upbeat music)

>>Yeah, >>from the Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Welcome back, everybody. This is Dave Volante of the Cube. You're watching our continuous coverage of IBM stink 2020. The digital version of it. De Bug is here. She's the HR VP and Chief leadership learning and inclusion officer at IBM. Good to see you. >>Great to see you as well. Thanks for having me. >>You're very welcome. While we're in the same region of New England, you know which we're face to face at Mosconi. But, you know, we're doing the best we can, right? Absolutely. So I got to ask you So one of your roles is you're responsible for executive leadership succession. So I remember I was in ah, lobby hotel in Barcelona when I heard that Arvind Krishna was taking over, is the CEO of IBM and I have sat there and wrote a blogger tapped out of log on my mobile phone, but a little did you know. And, you know, at that time we had a glimpse of what was coming, but I don't think we really fully understood. Ah, and and So I'm wondering, how do you prepare for that type of succession? >>Well, you know, I think our leaders now are all encountering unexpected circumstances where we have big plans and big actions. We plan. But the front contact is asking us to rethink them in all kinds of ways. So, of course, IBM is the kind of company who had a very well thought through kind of world class succession process. But none of us thought that we would be integrating Arvind and launching him into his new role as the CEO working from home. So we had to do what every leader at IBM is doing right now, which is starting from a position of resilience, taking a deep breath, thinking through what's really happening to me, to my work, to my situation right now. Um, a lot of us are working from home. A lot of us are adjusting to physical distancing. There are many leaders here who are deeply worried about their families, their their lives, their situation. And so you're starting from a position of personal resilience, making sure we put our own oxygen mask ons. We can, I think clearly and make decisions was an important first step. Second, focus on empathy. Leaders across IBM right now are really focused on making sure they understand the situation people are in, that they understand the physical, emotional, mental health and needs and requirements of every IBM are, uh, so that they can make really good decisions about priorities. And then it's time to focus on what's mission critical. What's urgent to compartmentalize and relentlessly prioritize. So we can all be successful. All of those lessons of by two succession, like they do to every other work and let us Teoh reimagine and create really interesting digital intimacy opportunities to connect Arvin with every IBM around the world through new kinds of social channels. And overall, I think it's been a really incredible experience. >>Yeah, yoga breaths are a good thing that this is his time, aren't they? Oh, >>I want to ask. Depressed for sure, >>right? No doubt. Um, so and you know, you guys probably had a little bit of ah, canary in a coal mine leading sort of visibility on this cause you've obviously got a presence in China and throughout the world, and so you probably a little bit ahead of of other U S. Based Is that fair? >>Well, we certainly are a global company. And so you know the idea that everyone is going through this in the same way? Same time? It's just not accurate. We have people all over the world, and I think we did have our, you know, early lessons from our colleagues in China who are incredibly resilient, who showed us the way with great social distancing discipline and really working hard together to help each other be successful in challenging times. And we've learned that in every community around the world that's been impacted, and I think that's been one of the most surprising and amazing things about the school experience is the way we've been able to leverage digital technologies at scale, to connect with one another, learn from one another and support each other through a very, very challenging experience. >>So, Deb, you've got inclusion in your title. Um, and so that z relatively new thing. Um, I wonder if you could address sort of what that means you to IBM And why is it so important right now? >>Inclusion is, you know, sort of the core of what makes it possible for us to benefit from each other's incredible talents. I like to say, you know, diversity is important to make sure you have the right people the table. But inclusion is how you turn that talent that's at the table into magic. Inclusion is what allows every one of us bring our uniqueness to the tip. Want to contribute, And it couldn't be more important than right now. Inclusion is the most important ingredient to helping people thrive and difficult times. It allows team members to quickly orient to new ways of being together on an inclusive leader, is able to manage it in a digitally distributed environment and create a new context for people to connect with one another. Ask the right questions to allow team members to manage the competing priorities of homeschooling working, living all in the same environment. Eso inclusive leaders really create a context for each other's contribution and success. You'll hear again and again in the description of how IBM leaders are thriving in this time. How we're stepping up and stepping in Where are the embers, our communities and our clients on finding ways to include, learn, take the best insights and accelerate productivity and the right solutions in this challenging environment. Inclusion is one of IBM's biggest assets right now. >>Well, you mentioned that you kind of connecting Arvin digitally with, you know, the broader IBM community. So that's kind of interesting, right? I mean, leading digitally. He has no choice, you know, other than he is not the only leader at IBM, obviously is the top leader, but there are many, many leaders at IBM. So how is this sort of we're talking today through the Cube's digital? How is this digital revolution really affecting people's ability to lead? How are they stepping up to that challenge? >>Sure. Well, IBM, like all our clients, have been on a journey of digital transformation for the last several years that this is really putting it to test in it a very different way. You know, it's presenting new challenges and new opportunities. The opportunities are incredible. New tools like we're using, you know, WebEx and Trillo and slack and your role and your all based in the in the IBM Cloud, really enabling full digital collaboration at a whole different scale than ever before and leveraging new kinds of leadership insights and new kinds of leadership mindsets. To benefit from all that great ability to collaborate, a synchronously Teoh create digitally distributed creative conversations and then as leaders, knowing how to harness all that creativity and provide the right context for people to share, to move product quickly, be more agile in our production of outcomes and solutions. That's right at home. In my group, for example, we're creating new digital communities and coming up with new solutions with our, you know, includes inclusion communities, new solutions with our teams to help enable leadership and new learning solutions all over the company. It also working digitally presents a new challenge. Is trying to figure out, um, how to help people balance the challenges of being at home and things that we might have relied on face to face contact for, to create different levels of trust and interactivity. Learn new skills, etcetera. Some leaders have recognizing some of those challenges gotten together and, you know, taking a work from home pledge, helping each other figure out and co create with parents were working at home. How Teoh navigate this new digital, totally distributed remote work situation we're in or, um, you know, figuring out how to teach each other how to use new tools. So I think, uh, you know, if I were going to give advice to any leader now, I would say it's a good time to assess your digital presence in your digital savviness and then think about how you're showing up in these digital forums. Are you trying to do things in the same way you were doing them just doing them online? Or have you really rethought your digital present? And are you really using that environment to create the maximum context of creativity and inclusion? >>You have your theme. >>You know, Dave, I was having a conversation earlier with an IBM executive and a Cisco executive, and I kind of joke that you know what people need right now. They don't need people selling them Stuff II D practitioners. They're putting out fires and, you know, changes in some industries where you're just trying to keep the company's alive. And I joked, That's kind of what they need is some adult supervision. And what do you see as IBM is role in this sort of during this crisis and maybe even post this prices? How would you define that. >>Absolutely so look. IBM is a trusted partner to the companies of the world who are facing the same challenge we're facing and trying to digitally transform themselves and thrive as the world continues to grow and change. And certainly this current context. What's the whole thing in a different, different relief? But fundamentally, IBM is the most important technology company in the world because we have the technology that industry expertise and the position of trust with our clients they don't need. What they need from IBM is not selling them something. But they need our partnership to imagine themselves in the future, reinvent themselves toward that future, too, to thrive during this incredible challenge and maintain business continuity while they become who they're going to be in the next terrorist. So, you know, it's a challenge for all of us. We are a huge global company and 173 countries and, you know, 350,000 people uniquely positioned to help. We have, you know, incredible technology. We have, you know, the call for code with our developers all over the world helping to solve these issues, we have, you know, many ways in which IBM is positioned socially to make a difference in helping with skill, acquisition, super compute capacity in many, many ways that we can help as a business. But closer to home, we're also able to help companies imagine how they can emerge stronger by re inventing their digitally reinventing their business processes and their leadership and talent cultures for how they can thrive in that in the New America. >>Rob Thomas and I were talking about how you know things most coveted. Maybe maybe they change. Maybe they don't, but but that's certainly is gonna be an acceleration. Ah t some things you're mentioning, you know, digital transformation. Um, certainly people are more willing to look at the cloud. You know, this whole work from home infrastructure seems to be some thing that has legs. Do you think inclusion is going to be one of these things that gets accelerated as a result of this pandemic? >>Absolutely. Do I mean, I think we have lived in an era where this kind of concept was sort of nice, tohave or viewed as a Z important, but maybe not essential. I think that's really being transformed by the current environment, and people are expecting their companies to provide a context that is psychologically safe, inclusive and on helps them do their best work when it matters most. Those are the companies that are going to emerge from this challenge stronger. And so IBM is culturally. Last year we talked a lot at think about the compelling call to action. To be equal that comes from IBM is deep commitment to diversity and inclusion and in every era challenging ourselves to doom or to create a context of full inclusion and equality. Well, this year we're expanding that concept to include all forms of equality. We started with gender equality. Now we're looking at full inclusion for all, and in this circumstance it could not be more important. And so I do think, you know, you said it well, you know, there are all kinds of capabilities that will be transformed and scaled. As a result of this. Our technology environment will be different or commitment to infrastructure working from home. Lots of things will be different. I think one of them is a call to action for leaders to be more inclusive and to to create the context where everyone can be. >>Well, I think it's important that companies like IBM lead in this regard. Sometimes, you know, it's harder for smaller companies they may not have. The resource is they've been out of the network. Uh, and so, you know, setting the example as IBM is very important. But thank you so much for coming on the Cube and sharing your philosophy IBM philosophy in your best practice, etcetera with us on the Cube. Appreciate it. >>Thanks so much for having me be safe and be Well, >>yeah, back at you. You too. And thank you for watching everybody. This is Dave Volante for the Cube's continuous coverage of IBM. Think 2020 The digital thing. Keep right there. Right back. >>Yeah, yeah, yeah.

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat last year in 2019 IBM made the biggest M&A move of the year with a 34 billion dollar acquisition of red hat it positioned IBM for the next decade after what was a very tumultuous tenure by CEO Ginni Rometty who had to shrink in order to grow unfortunately she didn't have enough time to do the grille part that has now gone toward Arvind Krishna the new CEO of IBM this is Dave Volante and I'm here with Stu minimun and this is our Red Hat keynote analysis is our 7th year doing the Red Hat summit and we're very excited to be here this is our first year doing Stu the Red Hat summit post IVM acquisition we've also got IBM think next week so what we want to do for you today is review what's going on at the Red Hat summits do you've been wall-to-wall with the interviews we're gonna break down the announcements IBM had just announced its quarter so we get some glimpse as to what's happening in the business and then we're gonna talk about going forward what the prognosis is for both IBM and Red Hat well and Dave of course our audience understands there's a reason why we're sitting farther apart than normal in our studio and you know why we're not in San Francisco where the show is supposed to be this year last year it's in Boston Red Hat summit goes coast-to-coast every year it's our seventh year doing the show first year doing it all digital of course our community is always online but you know real focus you know we're gonna talk about Dave you know you listen to the keynote speeches it's not the as we sit in our preview it's not the hoopla we had a preview with pork or mayor ahead of the event where they're not making big announcements most of the product pieces we're all out front it's open source anyway we know when it's coming for the most part some big partnership news of course strong customer momentum but a different tenor and the customers that Red Hat's lined up for me their interview all talking you know essential services like medical your your energy services your communication services so you know real focus I think Dave both IBM and right making sure that they are setting the appropriate tone in these challenging times yeah I mean everybody who we talked to says look at the employees and safety comes first once we get them working from home and we know that they're safe and healthy we want to get productive and so you've seen as we've reported that that shift to the work from home infrastructure and investments in that and so now it's all about how do we get closer to clients how do we stay close to clients and be there for them and I actually have you know business going forward you know the good news for IBM is it's got strong cash flow it's got a strong balance sheet despite you know the acquisition I mean it's just you know raise some more you know low low cost debt which you know gives them some dry powder going forward so I think IBM is gonna be fine it's just there's a lot of uncertainty but let's go back to your takeaways from the Red Hat Summit you've done you know dozens of interviews you got a good take on the company what are you top three takeaways - yeah so first of all Dave you know the focus everybody has is you know what does Red Hat do for the cloud story for IBM OpenShift especially is absolutely a highlight over 2,000 customers now from some really large ones you know last year I interviewed you know Delta you've got you know forward and Verizon up on stage for the keynote strong partnership with Microsoft talking about what they're doing so OpenShift has really strong momentum if you talk about you know where is the leadership in this whole kubernetes space Red Hat absolutely needs to be in that discussion not only are they you know other than Google the top contributor really there but from a customer standpoint the experience what they've built there but what I really liked from Red Hat standpoint is it's not just an infrastructure discussion it's not OPM's and containers and there's things we want to talk about about VMs and containers and even server lists from Red Hat standpoint but Red Hat at its core what it is it they started out as an operating system company rel Red Hat Enterprise Linux what's the tie between the OS and the application oh my god they've got decades of experience how do you build applications everything from how they're modernizing Java with a project called Korkis through how their really helping customers through this digital transformation I hear a similar message from Red Hat and their customers that I hear from Satya Nadella at Microsoft is we're building lots of applications we need to modernize what they're doing in Red Hat well positioned across the stack to not only be the platform for it but to help all of the pieces to help me modernize my applications build new ones modernize some of the existing ones so OpenShift a big piece of it you know automation has been a critical thing for a while we did the cube last year at ansible fest for the first time from Red Hat took that acquisition has helped accelerate that community in growth and they're really Dave pulling all the pieces together so it's what you hear from Stephanie shirasu ironically enough came over from IBM to run that business inside a Red Hat well you know now she's running it inside Red Hat and there's places that this product proliferate into the IBM portfolio next week when we get where it I didn't think I'm sure we'll hear a lot about IBM cloud packs and look at what's underneath IBM cloud packs there's open shift there's rel all those pieces so you know I know one of the things we want to talk about Davis you know what does that dynamic of Red Hat and IBM mean so you know open shift automation the full integration both of the Red Hat portfolio and how it ties in with IBM would be my top three well red hat is now IBM I mean it's a clearly part of the company it's there's a company strategy going forward the CEO Arvind Krishna is the architect of the Red Hat acquisition and so you know that it's all in on Red Hat Dave I mean just the nuance there of course is the the thing you hear over and over from the Red Hatters is Red Hat remains Red Hat that cultural shift is something I'd love to discuss because you know Jim Whitehurst now he's no longer a Red Hat employee he's an IBM employee so you've got Red Hat employees IBM employees they are keeping that you know separation wall but obviously there's flowing in technology and come on so come on in tech you look at it's not even close to what VMware is VMware is a separate public company has separate reporting Red Hat doesn't I mean yes I hear you yo you got the Red Hat culture and that's good but it's a far cry from you know a separate entity with full transparency the financials and and so I I hear you but I'm not fully buying it but let's let's get into it let's take a look at at the quarter because that I think will give us an indication as to how much we actually can understand about RedHat and and again my belief is it's really about IBM and RedHat together I think that is their opportunity so Alex if you wouldn't mind pulling up the first slide these are highlights from IBM's q1 and you know we won't spend much time on the the the IBM side of the business although we wanted to bring some of that in but hit the key here as you see red hat at 20% revenue growth so still solid revenue growth you know maybe a little less robust than it was you know sequentially last quarter but still very very strong and that really is IBM's opportunity here 2,200 clients using red hat and an IBM container platforms the key here is when Ginni Rometty announced this acquisition along with Arvind Krishna and Jim Whitehurst she said this is going to be this is going to be cash flow free cash flow accretive in year one they've already achieved that they said it's gonna be EPS accretive by year two they are well on their way to achieving that why we talked about this do it's because iBM has a huge services organization that it can plug open shift right into and begin to modernize applications that are out there I think they cited on the call that they had a hundred ongoing projects and that is driving immediate revenue and allows IBM to from a financial standpoint to get an immediate return so the numbers are pretty solid yeah absolutely Dave and you know talking about that there is a little bit of the blurring a line between the companies one of the product pieces that came out at the show is IBM has had for a couple of years think you know MCM multi cloud management there was announced that there were actually some of the personnel and some of the products from IBM has cut have come into Retta of course Red Hat doing what they always do they're making it open source and they're it's advanced cluster management really from my viewpoint this is an answer to what we've seen in the kubernetes community for the last year there is not one kubernetes distribution to rule them all I'm going to use what my platforms have and therefore how do I manage across my various cloud environments so Red Hat for years is OpenShift lives everywhere it sits on top of VMware virtualization environments it's on top of AWS Azure in Google or it just lives in your Linux farms but ACM now is how do I manage my kubernetes environment of course you know super optimized to work with OpenShift and the roadmap as to how it can manage with Azure kubernetes and some of the other environments so you know you now have some former IBM RS that are there and as you said Dave some good acceleration in the growth from the Red Hat numbers we'd seen like right around the time that the acquisition happened Red Hat had a little bit of a down quarter so you know absolutely the services and the the scale that IBM can bring should help to bring new logos of course right now Dave with the current global situation it's a little bit tough to go and be going after new business yeah and we'll talk about that a little bit but but I want to come back to sort of when I was pressing you before on the trip the true independence of Red Hat by the way I don't think that's necessarily a wrong thing I'll give an example look at Dell right now why is Dell relevant and cloud well okay but if Dell goes to market says we're relevant in cloud because of VMware well then why am I talking to you why don't I talk to VMware and so so my point is that that in some regards you know having that integration is there is a real advantage no you know you were that you know EMC and the time when they were sort of flip-flopping back and forth between integrated and not and separate and not it's obviously worked out for them but it's not necessarily clear-cut and I would say in the case of IBM I think it's the right move why is that every Krista talked about three enduring platforms that IBM has developed one is mainframe that's you know gonna here to stay the second was middleware and the third is services and he's saying that hybrid cloud is now the fourth and during platform that they want to build well how do they gonna build that what are they gonna build that on they're gonna build that an open shift they they're there other challenges to kind of retool their entire middleware portfolio around OpenShift not unlike what Oracle did with with Fusion when it when it bought Sun part of the reason - pod Sun was for Java so these are these are key levers not necessarily in and of themselves you know huge revenue drivers but they lead to awesome revenue opportunities so that's why I actually think it's the right move that what IBM is doing keep the Red Hat to the brand and culture but integrate as fast as possible to get cash flow or creative we've achieved that and get EPS accretive that to me makes a lot of sense yeah Dave I've heard you talk often you know if you're not a leader in a position or you know here John Chambers from Cisco when he was running it you know if I'm not number one or number two why am I in it how many places did IBM have a leadership position Red Hat's a really interesting company because they have a leadership position in Linux obviously they have a leadership position now in kubernetes Red Hat culturally of course isn't one to jump up and down and talk about you know how they're number one in all of these spaces because it's about open source it's about community and you know that does require a little bit of a cultural shift as IBM works with them but interesting times and yeah Red Hat is quietly an important piece of the ecosystem let me let me bring in some meteor data Alex if you pull up that that's that second slide well and I've shown this before in braking analysis and what this slide shows in the vertical axis shows net score net score is a measure of spending momentum spending velocity the the horizontal axis is is is called market share it's really not market share it's it's really a measure of pervasiveness the the mentions in the data set we're talking about 899 responses here out of over 1200 in the April survey and this is a multi cloud landscape so what I did here Stu I pulled on containers container platforms of container management and cloud and we positioned the companies on this sort of XY axis and you can see here you obviously have in the upper right you've got Azure in AWS why do I include AWS and the multi cloud landscape you answered that question before but yesterday because Dave even though Amazon might not allow you to even use the word multi cloud you can't have a discussion of multi cloud without having Amazon in that discussion and they've shifted on hybrid expect them to adjust their position on multi-cloud in the future yeah now coming back to this this this data you see kubernetes is on the kubernetes I know is another company but ETR actually tracks kubernetes you can see how hot it is in terms of its net score and spending momentum yeah I mean Dave do you know the thing the the obvious thing to look at there is if you see how strong kubernetes is if IBM plus red hat can keep that leadership in kubernetes they should do much better in that space than they would have on with just their products alone and that's really the lead of this chart that really cuts to the chase do is you see you see red Red Hat openshift has really strong spending momentum although I will say if you back up back up to say April July October 18 19 it actually was a little higher so it's been pushed down remember this is the April survey that what's ran from mid-march to mid April so we're talking right in the middle of the pandemic okay so everybody's down but nonetheless you can see the opportunity is for IBM and Red Hat to kind of meet in the middle leverage IBM's massive install base in its in its services presence in its market presence its pervasiveness so AKA market share in this rubric and then use Red Hat's momentum and kind of meet in the middle and that's the kind of point that we have here with IBM's opportunity and that really is why IBM is a leader in at least a favorite in my view in multi cloud well Dave if you'd look two years ago and you said what was the competitive landscape Red Hat was an early leader in the kubernetes you know multi-cloud discussion today if you ask everybody well who's doing great and kubernetes you have to talk about all the different options that amazon has Amazon still has their own container management with ACS of course IKS is doing strong and well and Amazon whatever they do they we know they're going to be competitive Microsoft's there but it's not all about competition in this space Dave because you know we see Red Hat partnering across these environments they do have a partnership with AWS they do have you know partnership with you know Microsoft up on stage there so where it was really interesting Dave you know one of the things I was coming into this show looking is what is Red Hat's answer to what VMware is really starting to do in this space so vSphere 7 rolled out and that is the ga of project Pacific so taking virtualization in containers and putting them together Red Hat of course has had virtualization for a long time with KVM they have a different answer of how they're doing openshift virtualization and it rather than saying here's my virtual environment and i can also do kubernetes on it they're saying containers are the future and where you want to go and we can bring your VMs into containers really shift them the way you have really kind of a lift and shift but then modernize them Dave customers are good you know you want to meet customers where they are you want to help them move forward virtualization in general has been a you don't want to touch your applications you want to just you know let it ride forever but the real the real driver for companies today is I've got to build new apps I need to modernize on my environment and you know Red Hat is positioning and you know I like what I'm hearing from them I like what I'm hearing from my dad's customers on how they're helping take both the physical the virtual the containers in the cloud and bring them all into this modern era yeah and and you know IBM made an early bet on on kubernetes and obviously around Red Hat you could see actually on that earlier slide we showed you IBM we didn't really talk about it they said they had 23% growth in cloud which is that they're a twenty two billion dollar business for IBM you're smiling yeah look good for IBM they're gonna redefine cloud you know let AWS you know kick and scream they're gonna say hey here's how we define cloud we include our own pram we include Cano portions of our consulting business I mean I honestly have no idea what's in the 22 billion and how if they're growing 22 billion at 23% wow that's pretty awesome I'm not sure I think they're kind of mixing apples and oranges there but it makes for a good slide yeah you would say wait shouldn't that be four billion you added he only added two or three billion you know numbers can tell a story but you can also manipulate but the point is the point is I've always said this near term the to get you know return on this deal it's about plugging OpenShift into services and modernizing applications long term it's about maintaining IBM and red-hats relevance in the hybrid cloud world which is I don't know how big it is it's a probably a trillion-dollar opportunity that really is critical from a strategy standpoint do I want to ask you about the announcements what about any announcements that you saw coming from Red Hat are relevant what do we need to know there yeah so you know one of the bigger ones we already talked about that you know multi cloud manager what Red Hat has the advanced cluster management or ACM absolutely is an era an area we should look VMware Tong's ooh Azure Ark Google anthos and now ACM from Red Hat in partnership with IBM is an area still really early Dave I talked to some of the executives in the space and say you know are we going to learn from the mistakes of multi vendor management Dave you know you think about the CA and BMC you know exactly of the past will we have learned for those is this the right way to do it it is early but Red Hat obviously has a position here and they're doing it um did hear plenty about how Red Hat is plugging into all the IBM environments Dave Z power you know the cloud solutions and of course you know IBM solutions across the board my point of getting a little blue wash but hey it's got to happen I think that's a smart move right you know we talked about you know really modernizing the applications in the environments I talked a bit about the virtualization piece the other one if you say okay how do I pull the virtualization forward what about the future so openshift serverless is the other one it's really a tech preview at this point it's built off of the K native project which is part of the CNC F which is basically how do I still have you know containers and kubernetes underneath can that plug into server list order server let's get it rid of it everything so IBM Oracle Red Hat and others really been pushing hard on this Kay native solution it is matured a lot there's an ecosystem growing as how it can connect to Asher how it can connect to AWS so definitely something from that appdev piece to watch and Dave that's where I had some really good discussions with customers as well as the the Red Hat execs and their partners that boundary between the infrastructure team and the app dev team they're hoping to pull them together and some of the tooling actually helps ansible is a great example of that in the past but you know others in the portfolio and lastly if you want to talk a huge opportunity for Red Hat IBM and it's a jump ball for everyone is edge computing so Red Hat I've talked to them for years about what they were doing in the opened stack community with network function virtualization or NFV Verizon was up on stage I've got an interview for Red Hat summit with Vodafone idea which has 300 million subscribers in India and you know the Red Hat portfolio really helping a lot of the customers there so it's the telco edge is where we see a strong push there it's definitely something we've been watching from the you know the big cloud players and those partnerships Dave so you know last year Satya Nadella was up on the main stage with Red Hat this year Scott Guthrie you know there he's at every Microsoft show and he's not the red head show so it is still ironic for those of us that have watched this industry and you say okay where are some of the important partnerships for Red Hat its Microsoft I mean you know we all remember when you know open-source was the you know evil enemy for from Microsoft and of course Satya Nadella has changed things a lot it's interesting to watch I'm sure we'll talk more at think Dave you know Arvind Krishna the culture he will bring in with the support of Jim Whitehurst comes over from IBM compared to what Satya has successfully done at Microsoft well let's talk about that let's let's talk about let's bring it home with the sort of near-term midterm and really I want to talk about the long term strategic aspects of IBM and Red Hat's future so near-term IBM is suspended guidance like everybody okay they don't have great visibility some some some things to watch by the way a lot of people are saying no just you know kind of draw draw a red line through this quarter you just generally ignore it I disagree look at cash flow look balance sheets look at what companies are doing and how they're positioning that's very important right now and will give us some clues and so there's a couple of things that we're watching with IBM one is their software business crashed in March and software deals usually come in big deals come in at the end of the quarter people were too distracted they they stopped spending so that's a concern Jim Cavanaugh on the call talked about how they're really paying attention to those services contracts to see how they're going are they continuing what's the average price of those so that's something that you got to watch you know near-term okay fine again as I said I think IBM will get through this what really I want to talk about to do is the the prospects going forward I'm really excited about the choice that IBM made the board putting Arvind Krishna in charge and the move that he made in terms of promoting you know Jim Whitehurst to IBM so let's talk about that for a minute Arvind is a technical visionary and it's it's high time that I VM got back to it being a technology company first because that's what IBM is and and I mean Lou Gerstner you know arguably save the company they pivoted to services Sam Palmisano continue that when Ginny came in you know she had a services heritage she did the PWC deal and IBM really became a services company first in my view Arvind is saying explicitly we want to lead with technology and I think that's the right move of course iBM is going to deliver outcomes that's what high-beams heritage has been for the last 20 years but they are a technology company and having a technology visionary at the lead is very important why because IBM essentially is the leader prior to Red Hat and one thing mainframes IBM used to lead in database that used to lead in storage they used to lead in the semiconductors on and on and on servers now they lead in mainframes and and now switch to look at Red Hat Red Hat's a leader you know they got the best product out there so I want you to talk about how you see that shift to more of a sort of technical and and product focus preserving obviously but your thoughts on the move the culture you're putting Jim as the president I love it I think it was actually absolutely brilliant yeah did Dave absolutely I know we were excited because we you know personally we know both of those leaders they are strong leaders they are strong technically Dave when I think about all the companies we look at I challenge anybody to find a more consistent and reliable pair of companies than IBM and Red Hat you know for years it was you know red hat being an open-source company and you know the way their business model said it it's not the you know Evan flow of product releases we know what the product is going to be the roadmaps are all online and they're gonna consistently grow what we've seen Red Hat go from kind of traditional software models to the subscription model and there are some of the product things we didn't get into too much as to things that they have built into you know Red Hat Enterprise Linux and expanding really their cloud and SAS offerings to enhance those environments and that that's where IBM is pushing to so you know there's been some retooling for the modern era they are well positioned to help customers through that you know digital transformation and as you said Dave you and I we both read the open organization by Jim lighters you know he came in to Red Hat you know really gave some strong leadership the culture is strong they they have maintained you know really strong morale and I talked to people inside you know was their concern inside when IBM was making the acquisition of course there was we've all seen some acquisitions that have gone great when IBM has blue washed them they're trying to make really strong that Red Hat stays Red Hat to your point you know Dave we've already seen some IBM people go in and some of the leadership now is on the IBM side so you know can they improve the product include though improve those customer outcomes and can Red Hat's culture actually help move IBM forward you know company with over a hundred years and over 200,000 employees you'd normally look and say can a 12,000 person company change that well with a new CEO with his wing and you know being whitehurst driving that there's a possibility so it's an interesting one to watch you know absolutely current situations are challenging you know red hats growth is really about adding new logos and that will be challenged in the short term yeah Dave I I love you shouldn't let people off the hook for q2 maybe they need to go like our kids this semester is a pass/fail rather than a grid then and then a letter grade yeah yeah and I guess my point is that there's information and you got to squint through it and I think that look at to me you know this is like Arvin's timing couldn't be better not that he orchestrated it but I mean you know when Ginny took over I mean was over a hundred million a hundred billion I said many times that I beams got a shrink to grow she just ran out of time for the Gro part that's now on Arvind and I think that so he's got the cove in mulligan first of all you know the stocks been been pressured down so you know his tenure he's got a great opportunity to do with IBM in a way what such an adela did is doing at Microsoft you think about it they're both deep technologists you know Arvind hardcore you know computer scientist Indian Institute of Technology Indian Institute of Technology different school than Satya went to but still steeped in in a technical understanding a technical visionary who can really Drive you know product greatness you know in a I would with with Watson we've talked a lot about hybrid cloud quantum is something that IBM is really investing heavily in and that's a super exciting area things like blockchain some of these new areas that I think IBM can lead and it's all running on the cloud you know look IBM generally has been pretty good with acquisitions they yes they fumbled a few but I've always made the point they are in the cloud game IBM and Oracle yeah they're behind from a you know market share standpoint but they're in the game and they have their software estate in their pass a state to insulate them from the race to the bottom so I really like their prospects and I like the the organizational structure that they put in place in it by the way it's not just Arvind Jim you mentioned Paul Cormier you know Rob Thomas has been been elevated to senior VP really important in the data analytic space so a lot of good things going on there yeah and Dave one of the questions you've been asking and we've been all talking to leaders in the industry you know what changes permanently after the this current situation you know automation you know more adoption of cloud the importance of developers are there's even more of a spotlight on those environments and Red Hat has strong positioning in that space a lot of experience that they help their customers and being open source you know very transparent there I both IBM and Red Hat are doing a lot to try to help the community they've got contests going online to you know help get you know open source and hackers and people working on things and you know strong leadership to help lead through these stormy weathers so Stuart's gonna be really interesting decade and the cube will be here to cover it hopefully hopefully events will come back until they do will be socially responsible and and socially distant but Stu thanks for helping us break down the the red hat and sort of tipping our toe into IBM more coverage and IBM think and next week this is Dave alotta for Stu minimun you're watching the cube and our continuous coverage of the Red Hat summit keep it right there be back after this short break you [Music]

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host Stu minimun hi this is a cute conversation from our Boston area studio I'm Stu minimun and joining for this deep dive into partnership discussions is Brian Regan the CMO of activity Oh Brian great to see you and happy 2020 great to see you used to thanks all right so we had a conversation with yourself and a shuch talking about 10c some of the activities the general momentum of ectopy oh but really want to spend a little bit of time talking about partnerships so Activia being a software company always has add a number of partnerships so you know when we talk a little bit of just the philosophy of the company and you know how important that is for you know technology partnerships as well as the go-to-market absolutely and I think we you know in 2019 we really increased our focus our investments and really our entire company alignment towards five types of partners specifically one was relatively new partnership for us which is a software partnership with IBM and their data and AI division of IBM under Arvid Krishna and Rob Thomas that we really the OEM our product to go after the test data management market opportunity and really become a data platform for a lot of their initiatives that involve Watson and and analytics as well as test data management that was a huge new partnership for us in 2019 well of course a new area of partnership because IBM I understand is probably the longest and oldest partnership that activity oh is that absolutely so the software group was probably the last group that we have partnered with in inside of the IBM corporation but we saw incredible traction throughout the year great pipeline growth from literally the beginning of the the Inc signing the the paper and have a roster of incredible logos to show for it over the last 12 months yeah it's always interesting to look if you talk about software and how a ifit's to it that was 2019 one of the things we said just you know okay what is AI are along that spectrum but you know how do these things stitch together everything to a Maya feed for the training algorithms or there are other things I can do so that sounds like you found some areas where customers are going to be working at leveraging your solution absolutely and certainly with IBM's acquisition of Red Hat and their embrace of containers and kubernetes that application modernization intersection point where we can bring data into containers is going to be a big theme for us in 2020 as well okay exciting stuff so that's on the software piece so if you have software hardware still matters into C 20 it turns out we still need to run things on servers and storage so and and switches and the like so we're fortunate to have partnered with Dell EMC as one of our focus infrastructure partners we have reference architectures for converged infrastructure using the rail and their rack designs on the VX flex OS underneath and really going after the database cloning market opportunity so bringing a essentially a data center pod architecture with Activia software running inside to power these databases of service opportunities that exist in a large enterprises alright interesting that you know EMC was not one that I would have thought would have been the first one to partnership Dell EMC with a much broader portfolio it seems a natural fit absolutely and and we were excited actually to based on client demand to also introduce the support to write to data domain so we can actually support data domain essentially we treated almost like an object target to increase the useful life and actually increase the power of data domain within these broader infrastructures that the enterprise clients have you know I had a great conversation with the shuch talking about what one of the things about 10 C is we've known for a long time that object storage is so important for the storage industry and where we want to go but customers shouldn't have to think about it it's just how we enable that and that leads up to of course cloud is big piece absolutely NC there so so where the important partnership from a cloud standpoint so certainly all of the clouds for us in our multi cloud effort are important we we support seven of the hyper scalars and and certainly you know Alibaba cloud IBM cloud Oracle cloud VMware cloud in addition to the three that people think about most but from a go-to-market standpoint we were probably the most embedded with Google cloud over the last year to 18 months again we've aligned a lot of both go to market but also engineering efforts to make sure that we're supporting Google cloud in the best way possible bringing the most compelling and differentiated offerings particularly for database workloads for backup dr and ultimately database cloning well congratulations important partnership especially when you talk about that engineering standpoint Google is not one just to make oh you know we made a handshake and it's good it really they dig in from an engineering standpoint and we know that Google makes the smartest stuff out there they'll tell you that so if you you've gone through the wringer on that that that really speaks to the architectural absolutely piece of the environment and and credit to a shook in the entire engineering organization I mean that is to your point very much an engineering first and then go to market second type of relationship and we're delighted to be in the go-to-market side of that okay go to market then is probably another way piece of absolutely so the last two types of partners that were really focused on for 2020 and we certainly got very serious in 2019 one is global systems integrators and TCS has really emerged is a really key partner for us in that landscape when we think about the enterprise accounts that we target you know a billion and up in revenue they're in every single one of them and we have several wins that we can look back on 2019 and credit their influence they are certainly helping the application modernization initiatives within all of these enterprises and partnering with active Pheo to really bring a data management and test data management capability to bear really was an important step for us in nineteen that we hope to accelerate in 2020 and then the the last piece and last but not least from a go-to-market standpoint is the chat and you know important channel partners whether it's Trace 3 particularly on the west coast whether it's data trend you know from the Midwest and East Coast these types of channel partners have really helped us you know become embedded in some of the largest accounts in in North America as well as globally and really are the the trusted adviser inside of those accounts that we want to continue to enable with compelling differentiated offerings like Tennessee yeah there were a lot of transformations going on in the channel they were all trying to figure out how they live in that multi-cloud world seems a natural fit for those that are thriving and surviving absolutely in this era that those would be the ones that you'd be working with absolutely so as a software company you know the part of our power is the ecosystem power and but we believe that by continuing to foster these multifaceted relationships they all have actually really fascinating benefits across the board the IBM relationship for example has ecosystem benefits in their channel and their systems integrators the Dell EMC relationship has you know ripple effects into their channel and their distribution points of distribution so we believe it's a very complimentary ecosystem that we're building we're excited at the possibility of an even stronger 2020 because of it awesome the one that you mentioned actually in our earlier conversation talking about active intensity si P of course a big important partner also a huge it's an important partner from a standpoint of it's maybe the most critical workload in most enterprises that use sa P and being a part of their technology stack inside of the Hana enterprise cloud is a critical capability for us but it's also an important point of distribution as they go out to their enterprise customers and are looking to become more relevant in a broader sense of data management so we're certainly excited about the work that we're doing with them we're delighted about the influence that they've had in terms of our roadmap and pushing our platform to be even more capable particularly for Hana workloads all right a lot of different pieces Brian congratulations on all that happened in 2019 and looking forward to watching the momentum in 2020 Thanks looking forward to being back all right lots more coverage from us at the cube dotnet of course will be lots of shows feel free to reach out on Twitter I'm at Stu and thank you for watching the cube

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>We're back in Miami. Welcome everybody. You watching the cube, the leader in live tech coverage. We're here at the IBM data and AI forum. Wow. What a day. 1700 customers. A lot of hands on labs sessions. What used to be the IBM analytics university is sort of morphed into this event. Now you see the buzz is going on. At least the Galean is here. She's the vice president of global sales for IBM data and AI. Welcome to the cube. Thank you for coming on. So this event is buzzing the double from last year almost. And uh, congratulations. >>Well, thank you very much. We have con, uh, lots of countries represented here. We have customers from small to large, every industry represented. And a, it's a, I can see a marked difference in the conversations in just a year around our, how customers want to figure out how to embark on this journey to AI. >>So yeah. So why are they come here? What's the, what's the primary motivation? >>Well, I think one IBM is recognized as the leader in AI and we just came out in the IDC survey as the three time w you know, leader, a recognized leader in AI. And when they come here they know they're going to hear from other clients who have embarked on similar journeys. They know they're going to have access to experts, hands on labs, and we bring our entire IBM team that's focused on data and AI to this event. So it's intimate, it's high skilled, it's high energy and they are learning a ton while they're. >>Yeah, a lot of content and you're educating but you're also trying to inspire people. I mean a raise. I was the hub this morning, he wrote this book, but he's this extreme, extreme, extreme like ultra marathoner. Uh, which I thought was a great talk this morning. And then you did a, I thought a good job of sort of connecting, you know, his talk of anything's possible to now bringing AI into the equation. What are you hearing from customers in terms of what they want to make possible and, and what's that conversation like in the field? >>Well, it's interesting because there is a huge recognition that every client that I talked to you, and they all want to understand this, that they have to be transforming their businesses on this journey to AI. So they all recognize that they need to start. Now. What I find when I talk to clients is that they're all coming in at different entry points. There's a maturity curve. So some are figuring out, you know, how do I move away from just Excel spreadsheets? I'm still running my business on Excel, right? And these are no banks in major that are operating on Excel spreadsheets and they're looking at niche competitors, you know, digital banks that are entering the scene. And if they don't change the way they operate, they're not going to survive. So a lot of companies are coming in knowing that they're low on the maturity curve and they better do something to move up that curve pretty fast. >>Some are in almost the second turn of the crank where they've invested in a lot of the AI technologies, they've built data science platforms, and now they're figuring out how do they get that next rev of productivity improvement? How do they come up with that next business idea that's going to give them that competitive advantage? So what I find is every client is embarking on this journey, which is a big difference where I think we were even a year, 18 months ago, where they were sort of just, okay, this is interesting. Now there I better do something. >>Okay, so you're a resource, you know, as the head of global sales for this group. So when you talk to customers that are immature, if I hear you right, they're saying, help us get started because we're going to fall behind. Uh, we're inefficient right now. We're drowning in spreadsheets, data. Our data quality is not where it needs to be. Help. Where do we start? What do you tell them? >>Well, one, we have a formula that we've proven works with clients. Um, we bring them into our garages where we will do design thinking, architectural workshops, and we figure out a use case because what we try not to do with our clients is boil the ocean. We want them to sh to have something that they can prove success around very quickly, create that minimal viable product, bring it back to the business so that the business can see, Oh, I understand. And then evolve that use case. So we will bring technical specialists, we will bring folks that are our own data scientists to these garage environments and we will work with them on building out this first use case. >>Explain the garage a little bit more. Is that those, those are sort of centers of excellence around the world or how do I tap them as a customer? Is it, is it a freebie? Is it for pay? Isn't it like the data science elite team? How does it all work? >>Well, it is. There are a number of physical locations and it's open to all clients. We have created these with co-leadership from across the entire IBM company. So our services organization, our cloud cognitive organization, all play a role in these garages. So we have a formal structure where a team can engage through a request process into the garages. We will help them define the use case they want to bring into the garage. We will bring them in for a period of time and provide the resources and capabilities and skills and that's not charged to the client. So we're trying to get them started now that they'll take that back to their company and then they will look at follow on opportunities and those may, you know, work out to be different services opportunities as they move forward. But we're on that get started phase. >>Yeah. Yeah. I mean you're a fraud for profit company, so it's great to have a loss leader, but the line outside the door at the garage must be huge for people that want to get in. Hi. How are you managing the dominion? >>Yes, well we're increasing obviously our capacity around the garages. Um, and we're still making customers aware of the garages. So there's still, because it's a commitment on their side, like they just can't come in and kick the tires. We ask them to bring their line of business along with their technical teams into the garages because that's where you get the best product coming out of it. When you know you've got something that's going to solve a business problem, but you have to have buy in from both sides. >>I want to ask you about the AI ladder. You know, Rob Thomas has been using this construct for awhile. It didn't just come out of thin air. I'm sure there was a lot of customer input and a lot of debate about what should be on the ladder. We went, when I first heard of the day AI ladder, it was, there was data in IAA analytics, ML and AI, sort of the building, the technical or technology building blocks. It's now become verbs, which I love, which is collect, organize, analyze and infuse, which is all about operationalizing and scaling. How is that resonating with customers and how do they fit into that methodology or framework? >>Well, I'll tell you, I use that framework with every single client and I described that there is a set of steps and you know, obviously to the ladder that every customer has to embark upon. And it starts with some very basic principles and as soon as you start with the very basic principles, every client is like, of course like it seems so obvious that first and foremost you have to date as the foundation, right? AI is not created out of, you know, someone in a back room. The foundation to AI is, is information and data. Yet every customer, every customer struggles with that data is coming from multiple systems, multiple sources that they can't get to the data fast enough. They're shipping data around an organization. It's not managed. And yet that they know that in five years, the data they think they need today is going to be completely different. >>It could be 12 months, but certainly in the future. So how do you build out that architecture that allows them to build that now, but have the agility to grow as the requirements change? You start with that basic discussion and they're like, well of course. So that's collect and then you bring it up and you talk about how do you govern that data? How do you know where that data originated? Who is the owner? How do you know what that data means? What system did it come from? What's the, you know, who has access to it? How do you create that set of govern data? And we'll of course every client recognizes they have that set of issues. So I could continue working my way up the ladder and every client realizes that, okay, I re I'm, here's where I am today. What you just painted for me is absolutely what I need to focus on and address. Now help me get from a to B. >>So I'm really interested in this discussion because it sounds like you're a very disciplined sales leaders and you said you use the ladder with virtually every client and I presume your sales teams use the ladder. So you train your salespeople how to converse the ladder. And then the other observation I'd love your thoughts on this is every step of the ladder has these questions. So you're asking customers questions and I'm sure it catalyzes conversation, the, the answers to which you have solutions presumably from any of them. But I wonder if you could talk about that. >>Well, let me tell about the ladder and how we're using it with our Salesforce because it was a unifying approach, not just within our own team, our data and AI team, but outside of data and AI. Because not only did we explain it to clients this way, but to the rest of IBM, our business partners, our whole ecosystem. So unifying in that we started every single conversation with our sales team on enabling them on how do they talk to their clients, our materials, our use cases, our references, our marketing campaigns. We tied everything to this unified approach and it's made a huge difference in how we communicate our value to clients and explain this journey to AI in in comprehensive steps that everyone could understand and relate to. >>Love it. How is the portfolio evolving to map into that framework? And what can we expect going forward? What can you share with us at least? >>Well, the other amazing feat I'll call it that we produced around this is I'll talk to a client and I'll describe these capabilities and then I will say to a customer, you don't have to do every one of these things that I've just described, but you can implement what you need when you need it. Because we have built all of this into a unified platform called cloud pack for data and it's a modern data platform. It's built on an open infrastructure built on red hat OpenShift so that you can run it on your own premises as a private cloud or on public clouds, whether that be IBM or Amazon or Zohre. It allows you to have a framework, a platform built on this open modern infrastructure with access to all these capabilities I've just described as services and you decide completely open what services you need to deploy when you grow the platform as you need it. And, Oh, by the way, if you don't have the red hat OpenShift environment set up, we'll package that in a system and I will roll in the system to you and allow you to have access to the capabilities in ours. >>How's the red hat conversation going? I would imagine a lot of the traditional IBM customers are stoked. He just picked up red hat, you know, a very innovative company, open source mindset. Um, at the same time I would imagine a lot of red hat customers saying, is IBM really gonna? Let them keep their culture. How's that conversation going in the field? >>Well, I will tell you we've been a hundred percent consistent in terms of everything that you've heard Jenny and Arvin Krishna talk about in the fact that we are going to maintain their culture, keep them as that separate entity inside of IBM. It's absolutely perpetrated throughout the entire IBM company. Um, we have a lot to learn from, from them as I'm sure they have to learn from us, but it truly is operating and I see it in the clients that I'm working with as a real win-win. >>If you had to take one thing away from this event that you want customers to, to remember, what would it be? >>Start now. Um, because if you don't begin on this journey to AI, you will find yourselves, you know, fighting against new competitors, uh, increasing costs, you know, you have to improve productivity. Every client is embarking on this journey to AI start now. >>And when you were talking about, uh, the maturity model and, and one of those levels was folks that had started already and they wanted to get to the next level, when you go into those clients, do you discern a different sort of attitude? We've started, we're down the path. Did they have more of a spring in their step? Are they like chomping at the bit to really go faster and extend their lead relative to the competition competition? What's the dynamic like in those accounts? >>That's a great question because I was with a client this afternoon, um, a large manufacturer of, uh, of goods and they are at this turning point where they did kind of phase one, they implemented cloud pack for data and they did it to just join some of their disparate systems. Now, I mean, I, I barely got a word in because he was so excited cause he's, now what I'm going to do is I'm going to figure out where my factories should go based on where my products are selling. So he's now looking at how he can change his whole distribution process as a result of getting access to this data and analytics that he never had before. Um, and I was like, okay, well just tell me how I can help you. And he was like, no way ahead. >>So this was the big kickoff day. I know yesterday there was sort of deep learning hands on stuff, the big keynotes. Today we're only here for one day. What are we going to miss? What's, what's happening tomorrow? >>Well, it's a bit of a repeat of today. So we'll have another keynote tomorrow from Beth Smith who runs our Watson, uh, business for IBM. We'll have more hands on labs. We have a lot of customer presentations where they're sharing their best practices. Um, lots of fun. >>Where do you want to see this event go? And what kind of, what's next in an IBM event land? >>Well, the feedback from last year this year says we have to do this again next year. It's, it's, it will be bigger because I think this year approves that it's already doubled and we'll probably see a similar dynamic. Um, so I fully expect us to be here. Well, maybe not here. We're sort of outgrowing this hotel. Um, but doing this event again next year, >>AI machine learning automation, uh, I'll throw in cloud. These are the hottest topics going. Elise, thanks very much for coming to the cube was great to have you. >>It's great. It's great meeting with you. >>It. Thank you for watching everybody. That's a wrap from Miami. Go to siliconangle.com check out all the news of the cube.net is where you'll find all these videos and follow the, uh, the Twitter handles at the cube at the cube three 65. I'm Dave Volante. We're out. We'll see you next time.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome back to downtown Miami. Everybody. We're here at the Intercontinental hotel covering the IBM data AI form hashtag data AI forum. My name is Dave Volante and you're watching the cube, the leader in live tech coverage. Ritika gunner is here. She's the vice president of data and AI expert labs and learning at IBM. Ritika, great to have you on. Again, always a pleasure to be here. Dave. I love interviewing you because you're a woman executive that said a lot of different roles at IBM. Um, you know, you've, we've talked about the AI ladder. You're climbing the IBM ladder and so it's, it's, it's, it's awesome to see and I love this topic. It's a topic that's near and dear to the cubes heart, not only women in tech, but women in AI. So great to have you. Thank you. So what's going on with the women in AI program? We're going to, we're going to cover that, but let me start with women in tech. It's an age old problem that we've talked about depending on, you know, what statistic you look at. 15% 17% of, uh, of, of, of the industry comprises women. We do a lot of events. You can see it. Um, let's start there. >>Well, obviously the diversity is not yet there, right? So we talk about women in technology, um, and we just don't have the representation that we need to be able to have. Now when it comes to like artificial intelligence, I think the statistic is 10 to 15% of the workforce today in AI is female. When you think about things like bias and ethicacy, having the diversity in terms of having male and female representation be equal is absolutely essential so that you're creating fair AI, unbiased AI, you're creating trust and transparency, set of capabilities that really have the diversity in backgrounds. >>Well, you work for a company that is as chairman and CEO, that's, that's a, that's a woman. I mean IBM generally, you know, we could see this stuff on the cube because IBM puts women on a, we get a lot of women customers that, that come on >>and not just because we're female, because we're capable. >>Yeah. Well of course. Right. It's just because you're in roles where you're spokespeople and it's natural for spokespeople to come on a forum like this. But, but I have to ask you, with somebody inside of IBM, a company that I could say the test to relative to most, that's pretty well. Do you feel that way or do you feel like even a company like IBM has a long way to go? >>Oh, um, I personally don't feel that way and I've never felt that to be an issue. And if you look at my peers, um, my um, lead for artificial intelligence, Beth Smith, who, you know, a female, a lot of my peers under Rob Thomas, all female. So I have not felt that way in terms of the leadership team that I have. Um, but there is a gap that exists, not necessarily within IBM, but in the community as a whole. And I think it goes back to you want to, you know, when you think about data science and artificial intelligence, you want to be able to see yourself in the community. And while there's only 10 to 15% of females in AI today, that's why IBM has created programs such as women AI that we started in June because we want strong female leaders to be able to see that there are, is great representation of very technical capable females in artificial intelligence that are doing amazing things to be able to transform their organizations and their business model. >>So tell me more about this program. I understand why you started it started in June. What does it entail and what's the evolution of this? >>So we started it in June and the idea was to be able to get some strong female leaders and multiple different organizations that are using AI to be able to change their companies and their business models and really highlight not just the journey that they took, but the types of transformations that they're doing and their organizations. We're going to have one of those events tonight as well, where we have leaders from Harley Davidson in Miami Dade County coming to really talk about not only what was their journey, but what actually brought them to artificial intelligence and what they're doing. And I think Dave, the reason that's so important is you want to be able to understand that those journeys are absolutely approachable. They're doable by any females that are out there. >>Talk about inherent bias. The humans are biased and if you're developing models that are using AI, there's going to be inherent bias in those models. So talk about how to address that and why is it important for more diversity to be injected into those models? >>Well, I think a great example is if you took the data sets that existed even a decade ago, um, for the past 50 years and you created a model that was to be able to predict whether to give loans to certain candidates or not, all things being equal, what would you find more males get these loans than females? The inherent data that exists has bias in it. Even from the history based on what we've had yet, that's not the way we want to be able to do things today. You want to be able to identify that bias and say all things being equal, it is absolutely important that regardless of whether you are a male or a female, you want to be able to give that loan to that person if they have all the other qualities that are there. And that's why being able to not only detect these things but have the diversity and the kinds of backgrounds of people who are building AI who are deploying this AI is absolutely critical. >>So for the past decade, and certainly in the past few years, there's been a light shined on this topic. I think, you know, we were at the Grace Hopper conference when Satya Nadella stuck his foot in his mouth and it said, Hey, it's bad karma for you know, if you feel like you're underpaid to go complain. And the women in the audience like, dude, no way. And he, he did the right thing. He goes, you know what, you're right. You know, any, any backtrack on that? And that was sort of another inflection point. But you talk about the women in, in AI program. I was at a CDO event one time. It was I and I, an IBM or had started the data divas breakfast and I asked, can I go? They go, yeah, you can be the day to dude. Um, which was, so you're seeing a lot of initiatives like this. My question is, are they having the impact that you would expect and that you want to have? >>I think they absolutely are. Again, I mean, I'll go back to, um, I'll give you a little bit of a story. Um, you know, people want to be able to relate and see that they can see themselves in these females leaders. And so we've seen cases now through our events, like at IBM we have a program called grow, which is really about helping our female lead female. Um, technical leaders really understand that they can grow, they can be nurtured, and they have development programs to help them accelerate where they need to be on their technical programs. We've absolutely seen a huge impact from that from a technology perspective. In terms of more females staying in technology wanting to go in the, in those career paths as another story. I'll, I'll give you kind of another kind of point of view. Um, Dave and that is like when you look at where it starts, it starts a lot earlier. >>So I have a young daughter who a year, year and a half ago when I was doing a lot of stuff with Watson, she would ask me, you know, not only what Watson's doing, but she would say, what does that mean for me mom? Like what's my job going to be? And if you think about the changes in technology and cultural shifts, technology and artificial intelligence is going to impact every job, every industry, every role that there is out there. So much so that I believe her job hasn't been invented yet. And so when you think about what's absolutely critical, not only today's youth, but every person out there needs to have a foundational understanding, not only in the three RS that you and I know from when we grew up have reading, writing and arithmetic, we need to have a foundational understanding of what it means to code. And you know, having people feel confident, having young females feel confident that they can not only do that, that they can be technical, that they can understand how artificial intelligence is really gonna impact society. And the world is absolutely critical. And so these types of programs that shed light on that, that help bridge that confidence is game changing. >>Well, you got kids, I >>got kids, I have daughters, you have daughter. Are they receptive to that? So, um, you know, I think they are, but they need to be able to see themselves. So the first time I sent my daughter to a coding camp, she came back and said, not for me mom. I said, why? Because she's like, all the boys, they're coding in their Minecraft area. Not something I can relate to. You need to be able to relate and see something, develop that passion, and then mix yourself in that diverse background where you can see the diversity of backgrounds. When you don't have that diversity and when you can't really see how to progress yourself, it becomes a blocker. So as she started going to grow star programs, which was something in Austin where young girls coded together, it became something that she's really passionate about and now she's Python programming. So that's just an example of yes, you need to be able to have these types of skills. It needs to start early and you need to have types of programs that help enhance that journey. >>Yeah, and I think you're right. I think that that is having an impact. My girls who code obviously as a some does some amazing work. My daughters aren't into it. I try to send them to coder camp too and they don't do it. But here's my theory on that is that coding is changing and, and especially with artificial intelligence and cognitive, we're a software replacing human skills. Creativity is going to become much, much more important. My daughters are way more creative than my sons. I shouldn't say that, but >>I think you just admitted that >>they, but, but in a way they are. I mean they've got amazing creativity, certainly more than I am. And so I see that as a key component of how coding gets done in the future, taking different perspectives and then actually codifying them. Your, your thoughts on that. >>Well there is an element of understanding like the outcomes that you want to generate and the outcomes really is all about technology. How can you imagine the art of the possible with technology? Because technology alone, we all know not useful enough. So understanding what you do with it, just as important. And this is why a lot of people who are really good in artificial intelligence actually come from backgrounds that are philosophy, sociology, economy. Because if you have the culture of curiosity and the ability to be able to learn, you can take the technology aspects, you can take those other aspects and blend them together. So understanding the problem to be solved and really marrying that with the technological aspects of what AI can do. That's how you get outcomes. >>And so we've, we've obviously talking in detail about women in AI and women in tech, but it's, there's data that shows that diversity drives value in so many different ways. And it's not just women, it's people of color, it's people of different economic backgrounds, >>underrepresented minorities. Absolutely. And I think the biggest thing that you can do in an organization is have teams that have that diverse background, whether it be from where they see the underrepresented, where they come from, because those differences in thought are the things that create new ideas that really innovate, that drive, those business transformations that drive the changes in the way that we do things. And so having that difference of opinion, having healthy ways to bring change and to have conflict, absolutely essential for progress to happen. >>So how did you get into the tech business? What was your background? >>So my background was actually, um, a lot in math and science. And both of my parents were engineers. And I have always had this unwavering, um, need to be able to marry business and the technology side and really figure out how you can create the art of the possible. So for me it was actually the creativity piece of it where you could create something from nothing that really drove me to computer science. >>Okay. So, so you're your math, uh, engineer and you ended up in CS, is that right? >>Science. Yeah. >>Okay. So you were coded. Did you ever work as a programmer? >>Absolutely. My, my first years at IBM were all about coding. Um, and so I've always had a career where I've coded and then I've gone to the field and done field work. I've come back and done development and development management, gone back to the field and kind of seen how that was actually working. So personally for me, being able to create and work with clients to understand how they drive value and having that back and forth has been a really delightful part. And the thing that drives me, >>you know, that's actually not an uncommon path for IBM. Ours, predominantly male IBM, or is in the 50 sixties and seventies and even eighties. Who took that path? They started out programming. Um, I just think, trying to think of some examples. I know Omar para, who was the CIO of Aetna international, he started out coding at IBM. Joe Tucci was a programmer at IBM. He became CEO of EMC. It was a very common path for people and you took the same path. That's kind of interesting. Why do you think, um, so many women who maybe maybe start in computer science and coding don't continue on that path? And what was it that sort of allowed you to break through that barrier? >>No, I'm not sure why most women don't stay with it. But for me, I think, um, you know, I, I think that every organization today is going to have to be technical in nature. I mean, just think about it for a moment. Technology impacts every part of every type of organization and the kinds of transformation that happens. So being more technical as leaders and really understanding the technology that allows the kinds of innovations and business for informations is absolutely essential to be able to see progress in a lot of what we're doing. So I think that even general CXOs that you see today have to be more technically acute to be able to do their jobs really well and marry those business outcomes with what it fundamentally means to have the right technology backbone. >>Do you think a woman in the white house would make a difference for young people? I mean, part of me says, yeah, of course it would. Then I say, okay, well some examples you can think about Margaret Thatcher in the UK, Angela Merkel, and in Germany it's still largely male dominated cultures, but I dunno, what do you think? Maybe maybe that in the United States would be sort of the, >>I'm not a political expert, so I wouldn't claim to answer that, but I do think more women in technology, leadership role, CXO leadership roles is absolutely what we need. So, you know, politics aside more women in leadership roles. Absolutely. >>Well, it's not politics is gender. I mean, I'm independent, Republican, Democrat, conservative, liberal, right? Absolutely. Oh yeah. Well, companies, politics. I mean you certainly see women leaders in a, in Congress and, and the like. Um, okay. Uh, last question. So you've got a program going on here. You have a, you have a panel that you're running. Tell us more about. >>Well this afternoon we'll be continuing that from women leaders in AI and we're going to do a panel with a few of our clients that really have transformed their organizations using data and artificial intelligence and they'll talk about like their backgrounds in history. So what does it actually mean to come from? One of, one of the panelists actually from Miami Dade has always come from a technical background and the other panelists really etched in from a non technical background because she had a passion for data and she had a passion for the technology systems. So we're going to go through, um, how these females actually came through to the journey, where they are right now, what they're actually doing with artificial intelligence in their organizations and what the future holds for them. >>I lied. I said, last question. What is, what is success for you? Cause I, I would love to help you achieve that. That objective isn't, is it some metric? Is it awareness? How do you know it when you see it? >>Well, I think it's a journey. Success is not an endpoint. And so for me, I think the biggest thing I've been able to do at IBM is really help organizations help businesses and people progress what they do with technology. There's nothing more gratifying than like when you can see other organizations and then what they can do, not just with your technology, but what you can bring in terms of expertise to make them successful, what you can do to help shape their culture and really transform. To me, that's probably the most gratifying thing. And as long as I can continue to do that and be able to get more acknowledgement of what it means to have the right diversity ingredients to do that, that success >>well Retika congratulations on your success. I mean, you've been an inspiration to a number of people. I remember when I first saw you, you were working in group and you're up on stage and say, wow, this person really knows her stuff. And then you've had a variety of different roles and I'm sure that success is going to continue. So thanks very much for coming on the cube. You're welcome. All right, keep it right there, buddy. We'll be back with our next guest right after this short break, we're here covering the IBM data in a AI form from Miami 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?

>>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. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome everybody to the port of Miami. My name is Dave Vellante and you're watching the cube, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM data and AI form. The hashtag is data AI forum. This is IBM's. It's formerly known as the, uh, IBM analytics university. It's a combination of learning peer network and really the focus is on AI and data. And there are about 1700 people here up from, Oh, about half of that last year, uh, when it was the IBM, uh, analytics university, about 600 customers, a few hundred partners. There's press here, there's, there's analysts, and of course the cube is covering this event. We'll be here for one day, 128 hands-on sessions or ER or sessions, 35 hands on labs. As I say, a lot of learning, a lot of technical discussions, a lot of best practices. >>What's happening here. For decades, our industry has marched to the cadence of Moore's law. The idea that you could double the processor performance every 18 months, doubling the number of transistors, you know, within, uh, the footprint that's no longer what's driving innovation in the it and technology industry today. It's a combination of data with machine intelligence applied to that data and cloud. So data we've been collecting data, we've always talked about all this data that we've collected and over the past 10 years with the advent of lower costs, warehousing technologies in file stores like Hadoop, um, with activity going on at the edge with new databases and lower cost data stores that can handle unstructured data as well as structured data. We've amassed this huge amount of, of data that's growing at a, at a nonlinear rate. It's, you know, this, the curve is steepening is exponential. >>So there's all this data and then applying machine intelligence or artificial intelligence with machine learning to that data is the sort of blending of a new cocktail. And then the third piece of that third leg of that stool is the cloud. Why is the cloud important? Well, it's important for several reasons. One is that's where a lot of the data lives too. It's where agility lives. So cloud, cloud, native of dev ops, and being able to spin up infrastructure as code really started in the cloud and it's sort of seeping to to on prem, slowly and hybrid and multi-cloud, ACC architectures. But cloud gives you not only that data access, not only the agility, but also scale, global scale. So you can test things out very cheaply. You can experiment very cheaply with cloud and data and AI. And then once your POC is set and you know it's going to give you business value and the business outcomes you want, you can then scale it globally. >>And that's really what what cloud brings. So this forum here today where the big keynotes, uh, Rob Thomas kicked it off. He uh, uh, actually take that back. A gentleman named Ray Zahab, he's an adventure and ultra marathon or kicked it off. This Jude one time ran 4,500 miles in 111 days with two ultra marathon or colleagues. Um, they had no days off. They traveled through six countries, they traversed Africa, the continent, and he took two showers in a 111 days. And his whole mission is really talking about the power of human beings, uh, and, and the will of humans to really rise above any challenge would with no limits. So that was the sort of theme that, that was set for. This, the, the tone that was set for this conference that Rob Thomas came in and invoked the metaphor of superheroes and superpowers of course, AI and data being two of those three superpowers that I talked about in addition to cloud. >>So Rob talked about, uh, eliminating the good to find the great, he talked about some of the experiences with Disney's ward. Uh, ward Kimball and Stanley, uh, ward Kimball went to, uh, uh, Walt Disney with this amazing animation. And Walter said, I love it. It was so funny. It was so beautiful, was so amazing. Your work 283 days on this. I'm cutting it out. So Rob talked about cutting out the good to find, uh, the great, um, also talking about AI is penetrated only about four to 10% within organizations. Why is that? Why is it so low? He said there are three things that are blockers. They're there. One is data and he specifically is referring to data quality. The second is trust and the third is skillsets. So he then talked about, you know, of course dovetailed a bunch of IBM products and capabilities, uh, into, you know, those, those blockers, those challenges. >>He talked about two in particular, IBM cloud pack for data, which is this way to sort of virtualize data across different clouds and on prem and hybrid and and basically being able to pull different data stores in, virtualize it, combine join data and be able to act on it and apply a machine learning and AI to it. And then auto AI a way to basically machine intelligence for artificial intelligence. In other words, AI for AI. What's an example? How do I choose the right algorithm and that's the best fit for the use case that I'm using. Let machines do that. They've got experience and they can have models that are trained to actually get the best fit. So we talked about that, talked about a customer, a panel, a Miami Dade County, a Wunderman Thompson, and the standard bank of South Africa. These are incumbents that are using a machine intelligence and AI to actually try to super supercharge their business. We heard a use case with the Royal bank of Scotland, uh, basically applying AI and driving their net promoter score. So we'll talk some more about that. Um, and we're going to be here all day today, uh, interviewing executives, uh, from, uh, from IBM, talking about, you know, what customers are doing with a, uh, getting the feedback from the analysts. So this is what we do. Keep it right there, buddy. We're in Miami all day long. This is Dave Olanta. You're watching the cube. We'll be right back right after this short break..