>> 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)

>> Announcer: From around the globe, it's theCUBE with digital coverage of MIT Chief Data Officer and Information Quality symposium brought to you by SiliconANGLE Media. >> Hi everybody. This is Dave Vellante and welcome back to theCUBE's coverage of the MIT CDOIQ 2020 event. Of course, it's gone virtual. We wish we were all together in Cambridge. They were going to move into a new building this year for years they've done this event at the Tang Center, moving into a new facility, but unfortunately going to have to wait at least a year, we'll see, But we've got a great guest. Nonetheless, Doug Laney is here. He's a Business Value Strategist, the bestselling author, an analyst, consultant then a long time CUBE friend. Doug, great to see you again. Thanks so much for coming on. >> Dave, great to be with you again as well. So can I ask you? You have been an advocate for obviously measuring the value of data, the CDO role. I don't take this the wrong way, but I feel like the last 150 days have done more to accelerate people's attention on the importance of data and the value of data than all the great work that you've done. What do you think? (laughing) >> It's always great when organizations, actually take advantage of some of these concepts of data value. You may be speaking specifically about the situation with United Airlines and American Airlines, where they have basically collateralized their customer loyalty data, their customer loyalty programs to the tunes of several billion dollars each. And one of the things that's very interesting about that is that the third party valuations of their customer loyalty data, resulted in numbers that were larger than the companies themselves. So basically the value of their data, which is as we've discussed previously off balance sheet is more valuable than the market cap of those companies themselves, which is just incredibly fascinating. >> Well, and of course, all you have to do is look to the Trillionaire's Club. And now of course, Apple pushing two trillion to really see the value that the market places on data. But the other thing is of course, COVID, everybody talks about the COVID acceleration. How have you seen it impact the awareness of the importance of data, whether it applies to business resiliency or even new monetization models? If you're not digital, you can't do business. And digital is all about data. >> I think the major challenge that most organizations are seeing from a data and analytics perspective due to COVID is that their traditional trend based forecast models are broken. If you're a company that's only forecasting based on your own historical data and not taking into consideration, or even identifying what are the leading indicators of your business, then COVID and the economic shutdown have entirely broken those models. So it's raised the awareness of companies to say, "Hey, how can we predict our business now? We can't do it based on our own historical data. We need to look externally at what are those external, maybe global indicators or other kinds of markets that proceed our own forecasts or our own activity." And so the conversion from trend based forecast models to what we call driver based forecast models, isn't easy for a lot of organizations to do. And one of the more difficult parts is identifying what are those external data factors from suppliers, from customers, from partners, from competitors, from complimentary products and services that are leading indicators of your business. And then recasting those models and executing on them. >> And that's a great point. If you think about COVID and how it's changed things, everything's changed, right? The ideal customer profile has changed, your value proposition to those customers has completely changed. You got to rethink that. And of course, it's very hard to predict even when this thing eventually comes back, some kind of hybrid mode, you used to be selling to people in an office environment. That's obviously changed. There's a lot that's permanent there. And data is potentially at least the forward indicator, the canary in the coal mine. >> Right. It also is the product and service. So not only can it help you and improve your forecasting models, but it can become a product or service that you're offering. Look at us right now, we would generally be face to face and person to person, but we're using video technology to transfer this content. And then one of the things that I... It took me awhile to realize, but a couple of months after the COVID shutdown, it occurred to me that even as a consulting organization, Caserta focuses on North America. But the reality is that every consultancy is now a global consultancy because we're all doing business remotely. There are no particular or real strong localization issues for doing consulting today. >> So we talked a lot over the years about the role of the CDO, how it's evolved, how it's changed the course of the early... The pre-title days it was coming out of a data quality world. And it's still vital. Of course, as we heard today from the Keynote, it's much more public, much more exposed, different public data sources, but the role has certainly evolved initially into regulated industries like financial, healthcare and government, but now, many, many more organizations have a CDO. My understanding is that you're giving a talk in the business case for the CDO. Help us understand that. >> Yeah. So one of the things that we've been doing here for the last couple of years is a running an ongoing study of how organizations are impacted by the role of the CDO. And really it's more of a correlation and looking at what are some of the qualities of organizations that have a CDO or don't have a CDO. So some of the things we found is that organizations with a CDO nearly twice as often, mention the importance of data and analytics in their annual report organizations with a C level CDO, meaning a true executive are four times more often likely to be using data, to transform the business. And when we're talking about using data and advanced analytics, we found that organizations with a CIO, not a CDO responsible for their data assets are only half as likely to be doing advanced analytics in any way. So there are a number of interesting things that we found about companies that have a CDO and how they operate a bit differently. >> I want to ask you about that. You mentioned the CIO and we're increasingly seeing lines of reporting and peer reporting alter shift. The sands are shifting a little bit. In the early days the CDO and still predominantly I think is an independent organization. We've seen a few cases and increasingly number where they're reporting into the CIO, we've seen the same thing by the way with the chief Information Security Officer, which used to be considered the fox watching the hen house. So we're seeing those shifts. We've also seen the CDO become more aligned with a technical role and sometimes even emerging out of that technical role. >> Yeah. I think the... I don't know, what I've seen more is that the CDOs are emerging from the business, companies are realizing that data is a business asset. It's not an IT asset. There was a time when data was tightly coupled with applications of technologies, but today data is very easily decoupled from those applications and usable in a wider variety of contexts. And for that reason, as data gets recognized as a business, not an IT asset, you want somebody from the business responsible for overseeing that asset. Yes, a lot of CDOs still report to the CIO, but increasingly more CDOs you're seeing and I think you'll see some other surveys from other organizations this week where the CDOs are more frequently reporting up to the CEO level, meaning they're true executives. Along I advocated for the bifurcation of the IT organization into separate I and T organizations. Again, there's no reason other than for historical purposes to keep the data and technology sides of the organizations so intertwined. >> Well, it makes sense that the Chief Data Officer would have an affinity with the lines of business. And you're seeing a lot of organizations, really trying to streamline their data pipeline, their data life cycles, bringing that together, infuse intelligence into that, but also take a systems view and really have the business be intimately involved, if not even owned into the data. You see a lot of emphasis on self-serve, what are you seeing in terms of that data pipeline or the data life cycle, if you will, that used to be wonky, hard core techies, but now it really involving a lot more constituent. >> Yeah. Well, the data life cycle used to be somewhat short. The data life cycles, they're longer and they're more a data networks than a life cycle and or a supply chain. And the reason is that companies are finding alternative uses for their data, not just using it for a single operational purpose or perhaps reporting purpose, but finding that there are new value streams that can be generated from data. There are value streams that can be generated internally. There are a variety of value streams that can be generated externally. So we work with companies to identify what are those variety of value streams? And then test their feasibility, are they ethically feasible? Are they legally feasible? Are they economically feasible? Can they scale? Do you have the technology capabilities? And so we'll run through a process of assessing the ideas that are generated. But the bottom line is that companies are realizing that data is an asset. It needs to be not just measured as one and managed as one, but also monetized as an asset. And as we've talked about previously, data has these unique qualities that it can be used over and over again, and it generate more data when you use it. And it can be used simultaneously for multiple purposes. So companies like, you mentioned, Apple and others have built business models, based on these unique qualities of data. But I think it's really incumbent upon any organization today to do so as well. >> But when you observed those companies that we talk about all the time, data is at the center of their organization. They maybe put people around that data. That's got to be one of the challenge for many of the incumbents is if we talked about the data silos, the different standards, different data quality, that's got to be fairly major blocker for people becoming a "Data-driven organization." >> It is because some organizations were developed as people driven product, driven brand driven, or other things to try to convert. To becoming data-driven, takes a high degree of data literacy or fluency. And I think there'll be a lot of talk about that this week. I'll certainly mention it as well. And so getting the organization to become data fluent and appreciate data as an asset and understand its possibilities and the art of the possible with data, it's a long road. So the culture change that goes along with it is really difficult. And so we're working with 150 year old consumer brand right now that wants to become more data-driven and they're very product driven. And we hear the CIO say, "We want people to understand that we're a data company that just happens to produce this product. We're not a product company that generates data." And once we realized that and started behaving in that fashion, then we'll be able to really win and thrive in our marketplace. >> So one of the key roles of a Chief Data Officers to understand how data affects the monetization of an organization. Obviously there are four profit companies of your healthcare organization saving lives, obviously being profitable as well, or at least staying within the budget, depending upon the structure of the organization. But a lot of people I think oftentimes misunderstand that it's like, "Okay, do I have to become a data broker? Am I selling data directly?" But I think, you pointed out many times and you just did that unlike oil, that's why we don't like that data as a new oil analogy, because it's so much more valuable and can be use, it doesn't fall because of its scarcity. But what are you finding just in terms of people's application of that notion of monetization? Cutting costs, increasing revenue, what are you seeing in the field? What's that spectrum look like? >> So one of the things I've done over the years is compile a library of hundreds and hundreds of examples of how organizations are using data and analytics in innovative ways. And I have a book in process that hopefully will be out this fall. I'm sharing a number of those inspirational examples. So that's the thing that organizations need to understand is that there are a variety of great examples out there, and they shouldn't just necessarily look to their own industry. There are inspirational examples from other industries as well, many clients come to me and they ask, "What are others in my industry doing?" And my flippant response to that is, "Why do you want to be in second place or third place? Why not take an idea from another industry, perhaps a digital product company and apply that to your own business." But like you mentioned, there are a variety of ways to monetize data. It doesn't involve necessarily selling it. You can deliver analytics, you can report on it, you can use it internally to generate improved business process performance. And as long as you're measuring how data's being applied and what its impact is, then you're in a position to claim that you're monetizing it. But if you're not measuring the impact of data on business processes or on customer relationships or partner supplier relationships or anything else, then it's difficult to claim that you're monetizing it. But one of the more interesting ways that we've been working with organizations to monetize their data, certainly in light of GDPR and the California consumer privacy act where I can't sell you my data anymore, but we've identified ways to monetize your customer data in a couple of ways. One is to synthesize the data, create synthetic data sets that retain the original statistical anomalies in the data or features of the data, but don't share actually any PII. But another interesting way that we've been working with organizations to monetize their data is what I call, Inverted data monetization, where again, I can't share my customer data with you, but I can share information about your products and services with my customers. And take a referral fee or a commission, based on that. So let's say I'm a hospital and I can't sell you my patient data, of course, due to variety of regulations, but I know who my diabetes patients are, and I can introduce them to your healthy meal plans, to your gym memberships, to your at home glucose monitoring kits. And again, take a referral fee or a cut of that action. So we're working with customers and the financial services firm industry and in the healthcare industry on just those kinds of examples. So we've identified hundreds of millions of dollars of incremental value for organizations that from their data that we're just sitting on. >> Interesting. Doug because you're a business value strategist at the top, where in the S curve do you see you're able to have the biggest impact. I doubt that you enter organizations where you say, "Oh, they've got it all figured out. They can't use my advice." But as well, sometimes in the early stages, you may not be able to have as big of an impact because there's not top down support or whatever, there's too much technical data, et cetera, where are you finding you can have the biggest impact, Doug? >> Generally we don't come in and run those kinds of data monetization or information innovation exercises, unless there's some degree of executive support. I've never done that at a lower level, but certainly there are lower level more immediate and vocational opportunities for data to deliver value through, to simply analytics. One of the simple examples I give is, I sold a home recently and when you put your house on the market, everybody comes out of the woodwork, the fly by night, mortgage companies, the moving companies, the box companies, the painters, the landscapers, all know you're moving because your data is in the U.S. and the MLS directory. And it was interesting. The only company that didn't reach out to me was my own bank, and so they lost the opportunity to introduce me to a Mortgage they'd retain me as a client, introduce me to my new branch, print me new checks, move the stuff in my safe deposit box, all of that. They missed a simple opportunity. And I'm thinking, this doesn't require rocket science to figure out which of your customers are moving, the MLS database or you can harvest it from Zillow or other sites is basically public domain data. And I was just thinking, how stupid simple would it have been for them to hire a high school programmer, give him a can of red bull and say, "Listen match our customer database to the MLS database to let us know who's moving on a daily or weekly basis." Some of these solutions are pretty simple. >> So is that part of what you do, come in with just hardcore tactical ideas like that? Are you also doing strategy? Tell me more about how you're spending your time. >> I trying to think more of a broader approach where we look at the data itself and again, people have said, "If you tortured enough, what would you tell us? We're just take that angle." We look at examples of how other organizations have monetized data and think about how to apply those and adapt those ideas to the company's own business. We look at key business drivers, internally and externally. We look at edge cases for their customers' businesses. We run through hypothesis generating activities. There are a variety of different kinds of activities that we do to generate ideas. And most of the time when we run these workshops, which last a week or two, we'll end up generating anywhere from 35 to 50 pretty solid ideas for generating new value streams from data. So when we talk about monetizing data, that's what we mean, generating new value streams. But like I said, then the next step is to go through that feasibility assessment and determining which of these ideas you actually want to pursue. >> So you're of course the longtime industry watcher as well, as a former Gartner Analyst, you have to be. My question is, if I think back... I've been around a while. If I think back at the peak of Microsoft's prominence in the PC era, it was like windows 95 and you felt like, "Wow, Microsoft is just so strong." And then of course the Linux comes along and a lot of open source changes and low and behold, a whole new set of leaders emerges. And you see the same thing today with the Trillionaire's Club and you feel like, "Wow, even COVID has been a tailwind for them." But you think about, "Okay, where could the disruption come to these large players that own huge clouds, they have all the data." Is data potentially a disruptor for what appear to be insurmountable odds against the newbies" >> There's always people coming up with new ways to leverage data or new sources of data to capture. So yeah, there's certainly not going to be around for forever, but it's been really fascinating to see the transformation of some companies I think nobody really exemplifies it more than IBM where they emerged from originally selling meat slicers. The Dayton Meat Slicer was their original product. And then they evolved into Manual Business Machines and then Electronic Business Machines. And then they dominated that. Then they dominated the mainframe software industry. Then they dominated the PC industry. Then they dominated the services industry to some degree. And so they're starting to get into data. And I think following that trajectory is something that really any organization should be looking at. When do you actually become a data company? Not just a product company or a service company or top. >> We have Inderpal Bhandari is one of our huge guests here. He's a Chief-- >> Sure. >> Data Officer of IBM, you know him well. And he talks about the journey that he's undertaken to transform the company into a data company. I think a lot of people don't really realize what's actually going on behind the scenes, whether it's financially oriented or revenue opportunities. But one of the things he stressed to me in our interview was that they're on average, they're reducing the end to end cycle time from raw data to insights by 70%, that's on average. And that's just an enormous, for a company that size, it's just enormous cost savings or revenue generating opportunity. >> There's no doubt that the technology behind data pipelines is improving and the process from moving data from those pipelines directly into predictive or diagnostic or prescriptive output is a lot more accelerated than the early days of data warehousing. >> Is the skills barrier is acute? It seems like it's lessened somewhat, the early Hadoop days you needed... Even data scientist... Is it still just a massive skill shortage, or we're starting to attack that. >> Well, I think companies are figuring out a way around the skill shortage by doing things like self service analytics and focusing on more easy to use mainstream type AI or advanced analytics technologies. But there's still very much a need for data scientists and organizations and the difficulty in finding people that are true data scientists. There's no real certification. And so really anybody can call themselves a data scientist but I think companies are getting good at interviewing and determining whether somebody's got the goods or not. But there are other types of skills that we don't really focus on, like the data engineering skills, there's still a huge need for data engineering. Data doesn't self-organize. There are some augmented analytics technologies that will automatically generate analytic output, but there really aren't technologies that automatically self-organize data. And so there's a huge need for data engineers. And then as we talked about, there's a large interest in external data and harvesting that and then ingesting it and even identifying what external data is out there. So one of the emerging roles that we're seeing, if not the sexiest role of the 21st century is the role of the Data Curator, somebody who acts as a librarian, identifying external data assets that are potentially valuable, testing them, evaluating them, negotiating and then figuring out how to ingest that data. So I think that's a really important role for an organization to have. Most companies have an entire department that procures office supplies, but they don't have anybody who's procuring data supplies. And when you think about which is more valuable to an organization? How do you not have somebody who's dedicated to identifying the world of external data assets that are out there? There are 10 million data sets published by government, organizations and NGOs. There are thousands and thousands of data brokers aggregating and sharing data. There's a web content that can be harvested, there's data from your partners and suppliers, there's data from social media. So to not have somebody who's on top of all that it demonstrates gross negligence by the organization. >> That is such an enlightening point, Doug. My last question is, I wonder how... If you can share with us how the pandemic has effected your business personally. As a consultant, you're on the road a lot, obviously not on the road so much, you're doing a lot of chalk talks, et cetera. How have you managed through this and how have you been able to maintain your efficacy with your clients? >> Most of our clients, given that they're in the digital world a bit already, made the switch pretty quick. Some of them took a month or two, some things went on hold but we're still seeing the same level of enthusiasm for data and doing things with data. In fact some companies have taken our (mumbles) that data to be their best defense in a crisis like this. It's affected our business and it's enabled us to do much more international work more easily than we used to. And I probably spend a lot less time on planes. So it gives me more time for writing and speaking and actually doing consulting. So that's been nice as well. >> Yeah, there's that bonus. Obviously theCUBE yes, we're not doing physical events anymore, but hey, we've got two studios operating. And Doug Laney, really appreciate you coming on. (Dough mumbles) Always a great guest and sharing your insights and have a great MIT CDOIQ. >> Thanks, you too, Dave, take care. (mumbles) >> Thanks Doug. All right. And thank you everybody for watching. This is Dave Vellante for theCUBE, our continuous coverage of the MIT Chief Data Officer conference, MIT CDOIQ, will be right back, right after this short break. (bright music)

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Welcome back to the Cube. Here live in Las Vegas for aws reinvent I'm John for a devil on the ads, always extracting the signal from the noise. We're here for 1/7 reinvent of the eight years that they've had at what a wave. One of the biggest waves is the modernization of procurement, the modernization of business, commercial business and the rapid acceleration of public sector. We're here with the chief of public sector for AWS. Teresa Carlson, vice president publics that globally great to have you >>so great to have the Q begin this year. We appreciate you being here, >>so we're just seeing so much acceleration of modernization. Even in the commercial side, 80 talks about transformation. It's just a hard core on the public sector side. You have so many different areas transforming faster because they haven't transformed before. That's correct. This is a lot of change. What's changed the most for you in your business? >>Well, again, I'll be here 10 years this mad that A B s and my eighth reinvent, and what really changed, which was very exciting this year, is on Monday. We had 550 international government executives here from 40 countries who were talking about their modernization efforts at every level. Now again, think about that. 40 different governments, 550 executives. We had a fantastic day for them planned. It was really phenomenal because the way that these international governments or think about their budget, how much are they going to use that for maintaining? And they want to get that lesson last. Beckett for Modernization The Thin John It's a Beckett for innovation so that they continue not only modernized, but they're really looking at innovation cycles. So that's a big one. And then you heard from somewhere customers at the breakfast this morning morning from from a T. F. As part of the Department of Justice. What they're doing out. I'll call to back on firearms. They completely made you the cloud. They got rid of 20 years of technical debt thio the Veterans Administration on what they're digging for V A benefits to educational institutions like our mighty >>nose, and he had on stages Kino, Cerner, which the health care companies and what struck me about that? I think it relates to your because I want to get your reaction is that the health care is such an acute example that everyone can relate to rising costs. So cloud helping reduce costs increase the efficiencies and patient care is a triple win. The same thing happens in public sector. There's no place to hide anymore. You have a bona fide efficiencies that could come right out of the gate with cloud plus innovation. And it's happening in all the sectors within the public sector. >>So true. Well, Cerner is a great example because they won the award at V a Veteran's administration to do the whole entire medical records modernization. So you have a company on stage that's commercial as I met, commercial as they are public sector that are going into these large modernization efforts. And as you sit on these air, not easy. This takes focus and leadership and a real culture change to make these things happen. >>You know, the international expansion is impressive. We saw each other in London. We did the health care drill down at your office is, of course, a national health. And then you guys were in Bahrain, and what I deserve is it's not like these organizations. They're way behind. I mean, especially the ones that it moved to. The clouds are moving really fast. So well, >>they don't have as much technical debt internationally. It's what we see here in the U. S. So, like I was just in Africa and you know what we talked about digitizing paper. Well, there's no technology on that >>end >>there. It's kind of exciting because they can literally start from square one and get going. And there's a really hunger and the need to make that happen. So it's different for every country in terms of where they are in their cloud journey. >>So I want to ask you about some of the big deals. I'll see Jet eyes in the news, and you can't talk about it because it's in protest and little legal issues. But you have a lot of big deals that you've done. You share some color commentary on from the big deals and what it really means. >>Yeah, well, first of all, let me just say with Department of Defense, Jet are no jet. I We have a very significant business, you know, doing work at every part of different defense. Army, Navy, Air Force in the intelligence community who has a mission for d o d terminus a t o N g eight in a row on And we are not slowing down in D. O d. We had, like, 250 people at a breakfast. Are Lantian yesterday giving ideas on what they're doing and sharing best practices around the fence. So we're not slowing down in D. O d. We're really excited. We have amazing partners. They're doing mission work with us. But in terms of some really kind of fend, things have happened. We did a press announcement today with Finn Rat, the financial regulatory authority here in the U. S. That regulates markets at this is the largest financial transactions you'll ever see being processed and run on the cloud. And the program is called Cat Consolidated Audit Trail. And if you remember the flash crash and the markets kind of going crazy from 2000 day in 2008 when it started, Finneran's started on a journey to try to understand why these market events were happening, and now they have once have been called CAT, which will do more than 100 billion market points a day that will be processed on the cloud. And this is what we know of right now, and they'll be looking for indicators of nefarious behavior within the markets. And we'll look for indicators on a continuous basis. Now what? We've talked about it. We don't even know what we don't know yet because we're getting so much data, we're going to start processing and crunching coming out of all kinds of groups that they're working with, that this is an important point even for Finn rep. They're gonna be retiring technical debt that they have. So they roll out Cat. They'll be retiring other systems, like oats and other programs that they >>just say so that flash crash is really important. Consolidated, honest, because the flash crash, we'll chalk it up to a glitch in the system. Translation. We don't really know what happened. Soto have a consolidated auto trail and having the data and the capabilities, I understand it is really, really important for transparency and confidence in the >>huge and by the way, thinner has been working with us since 2014. They're one of our best partners and are prolific users of the cloud. And I will tell you it's important that we have industries like thin red regulatory authorities, that air going in and saying, Look, we couldn't possibly do what we're doing without cloud computing. >>Tell me about the technical debt because I like this conversation is that we talk about in the commercial side and developer kind of thinking. Most businesses start ups, Whatever. What is technical debt meet in public sector? Can you be specific? >>Well, it's years and years of legacy applications that never had any modernization associated with them in public sector. You know now, because you've talked about these procurement, your very best of your very savvy now public sector >>like 1995 >>not for the faint of heart, for sure that when you do procurement over the years when they would do something they wouldn't build in at new innovations or modernizations. So if you think about if you build a data center today a traditional data center, it's outdated. Tomorrow, the same thing with the procurement. By the time that they delivered on those requirements. They were outdated. So technical debt then has been built up years of on years of not modernizing, just kind of maintaining a status quo with no new insides or analytics. You couldn't add any new tooling. So that is where you see agencies like a T F. That has said, Wow, if I'm gonna if I'm gonna have a modern agency that tracks things like forensics understands the machine learning of what's happening in justice and public safety, I need to have the most modern tools. And I can't do that on an outdated system. So that's what we kind of call technical death that just maintains that system without having anything new that you're adding to >>their capabilities lag. Everything's products bad. Okay, great. Thanks for definite. I gotta ask you about something that's near and dear to our heart collaboration. If you look at the big successes in the world and within Amazon Quantum Caltex partnering on the quantum side, you've done a lot of collaboration with Cal Cal Poly for ground station Amazon Educate. You've been very collaborative in your business, and that's a continuing to be a best practice you have now new things like the cloud innovation centers. Talk about that dynamic and how collaboration has become an important part of your business model. >>What we use their own principles from Amazon. We got building things in our plan. Innovation centers. We start out piloting those two to see, Could they work? And it's really a public private partnership between eight MPs and universities, but its universities that really want to do something. And Cal Poly's a great example. Arizona State University A great example. The number one most innovative university in the US for like, four years in a row. And what we do is we go in and we do these public sector challenges. So the collaboration happens. John, between the public sector Entity, university with students and us, and what we bring to the table is technical talent, air technology and our mechanisms and processes, like they're working backwards processes, and they were like, We want you to bring your best and brightest students. Let's bring public sector in the bowl. They bring challenges there, riel that we can take on, and then they can go back and absorb, and they're pretty exciting. I today I talked about we have over 44 today that we've documented were working at Cal Poly. The one in Arizona State University is about smart cities. And then you heard We're announcing new ones. We've got two in France, one in Germany now, one that we're doing on cybersecurity with our mighty in Australia to be sitting bata rain. So you're going to see us Add a lot more of these and we're getting the results out of them. So you know we won't do if we don't like him. But right now we really like these partnerships. >>Results are looking good. What's going on with >>you? All right. And I'll tell you why. That why they're different, where we are taking on riel public sector issues and challenges that are happening, they're not kind of pie in the sky. We might get there because those are good things to do. But what we want to do is let's tackle things that are really homelessness, opioid crisis, human sex trafficking, that we're seeing things that are really in these communities and those air kind of grand. But then we're taking on areas like farming where we talked about Can we get strawberries rotting on the vine out of the field into the market before you lose billions of dollars in California. So it's things like that that were so its challenges that are quick and riel. And the thing about Cloud is you can create an application and solution and test it out very rapidly without high cost of doing that. No technical Dan, >>you mentioned Smart Cities. I just attended a session. Marty Walsh, the mayor of Boston's, got this 50 50 years smart city plan, and it's pretty impressive, but it's a heavy lift. So what do you see going on in smart cities? And you really can't do it without the cloud, which was kind of my big input cloud. Where's the data? What do you say, >>cloud? I O. T is a big part at these. All the centers that Andy talked about yesterday in his keynote and why the five G partnerships are so important. These centers, they're gonna be everywhere, and you don't even know they really exist because they could be everywhere. And if you have the five G capabilities to move those communications really fast and crypt them so you have all the security you need. This is game changing, but I'll give you an example. I'll go back to the kids for a minute at at Arizona State University, they put Io TI centers everywhere. They no traffic patterns. Have any parking slots? Airfield What Utilities of water, if they're trash bins are being filled at number of seats that are being taken up in stadiums. So it's things like that that they're really working to understand. What are the dynamics of their city and traffic flow around that smart city? And then they're adding things on for the students like Alexis skills. Where's all the activity? So you're adding all things like Alexa Abs, which go into a smart city kind of dynamic. We're not shop. Where's the best activities for about books, for about clothes? What's the pizza on sale tonight? So on and then two things like you saw today on Singapore, where they're taking data from all different elements of agencies and presenting that bad to citizen from their child as example Day one of a birth even before, where's all the service is what I do? How do I track these things? How do I navigate my city? to get all those service is the same. One can find this guy things they're not. They're really and they're actually happening. >>Seems like they're instrumented a lot of the components of the city learning from that and then deciding. Okay, where do we double down on where do we place? >>You're making it Every resilient government, a resilient town. I mean, these were the things that citizens can really help take intro Web and have a voice in doing >>threes. I want to say congratulations to your success. I know it's not for the faint of heart in the public sector of these days, a lot of blockers, a lot of politics, a lot of government lockers and the old procurement system technical debt. I mean, Windows 95 is probably still in a bunch of PCs and 50 45 fighters. 15 fighters. Oh, you've got a great job. You've been doing a great job and riding that wave. So congratulations. >>Well, I'll just say it's worth it. It is worth it. We are committed to public sector, and we really want to see everyone from our war fighters. Are citizens have the capabilities they need. So >>you know, you know that we're very passionate this year about going in the 2020 for the Cube and our audience to do a lot more tech for good programming. This'll is something that's near and dear to your heart as well. You have a chance to shape technology. >>Yes, well, today you saw we had a really amazing not for profit on stage with It's called Game Changer. And what we found with not for profits is that technology can be a game changer if they use it because it makes their mission dollars damage further. And they're an amazing father. And send a team that started game changer at. Taylor was in the hospital five years with terminal cancer, and he and his father, through these five years, kind of looked around. Look at all these Children what they need and they started. He is actually still here with us today, and now he's a young adult taking care of other young Children with cancer, using gaming technologies with their partner, twitch and eight MPs and helping analyze and understand what these young affected Children with cancer need, both that personally and academically and the tools he has He's helping really permit office and get back and it's really hard, Warren says. I was happy. My partner, Mike Level, who is my Gran's commercial sales in business, and I ran public Sector Day. We're honored to give them at a small token of our gift from A to B s to help support their efforts. >>Congratulates, We appreciate you coming on the Cube sharing the update on good luck into 2020. Great to see you 10 years at AWS day one. Still, >>it's day one. I feel like I started >>it like still, like 10 o'clock in the morning or like still a day it wasn't like >>I still wake up every day with the jump in my staff and excited about what I'm gonna do. And so I am. You know, I am really excited that we're doing and like Andy and I say we're just scratching the surface. >>You're a fighter. You are charging We love you, Great executive. You're the chief of public. Get a great job. Great, too. Follow you and ride the wave with Amazon and cover. You guys were documenting history. >>Yeah, exactly. We're in happy holidays to you all and help seeing our seventh and 20 >>so much. Okay, Cube coverage here live in Las Vegas. This is the cube coverage. Extracting the signals. Wanna shout out to eight of us? An intel for putting on the two sets without sponsorship, we wouldn't be able to support the mission of the Cube. I want to thank them. And thank you for watching with more after this short break.

>> Announcer: From San Mateo, California, it's theCUBE! Covering SnapLogic, Innovation Day, 2018. Brought to you by SnapLogic. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are in San Mateo, at what they call the crossroads, it's 92 and 101. If you're coming by and probably sitting in a traffic, look up and you'll see SnapLogic. It's their new offices. We're really excited to be here for Innovation Day. We're excited to have this CTO, James Markarian. James, great to see you and I guess, we we last talked was a couple years ago in New York City. >> Yeah that's right, and why was I there? It was like a big data show. >> That's right. >> And we we are two years later talking about big data. >> Big data, big data is fading a little bit, because now big data is really an engine, that's powering this new thing that's so exciting, which is all about analytics, and machine learning, and we're going to eventually stop saying artificial intelligence and say augmented intelligence, 'cause there's really nothing artificial about it. >> Yeah and we might stop saying big data and just talk about data because it's becoming so ubiquitous. >> Jeff: Right. >> I know that big data, it's not necessarily going away but it's sort of how we're thinking about handling it is, like kind of evolved over time, especially in the last couple of years. >> Right. >> That's what we're kind of seeing from our customers. >> 'Cause there's kind of an ingredient now, right? It's no longer this new shiny object now. It's just part of the infrastructure that helps you get everything else done. >> Yeah, and I think when you think about it, from like, an enterprise point of view, that that shift is going from experimentation to operationalizing. I think that the things you look for in experimentation, there's like, one set of things here looking for proving out the overall value, regardless maybe of cost and uptime and other things and as you operationalize you start thinking about other considerations that obviously Enterprise IT has to think about. >> Right, so if you think back to like, Hadoop Summit and Hadoop World who were first cracking their teeth, like in 2010 or around that time frame, one of the big discussions that always comes up and that was before kind of the rise of public cloud, you know which has really taken off over the last several years, there's this kind of ongoing debate between, do you move the data to the compute or do you move the compute to the data? There was always like, this monster data gravity issue which was almost insurmountable and many would say, oh, you're never going to get all your data into the cloud. It's just way too hard and way too expensive. But, now Amazon has Snowball and Snowball isn't big enough. They actually had a diesel truck that'll come and help you come move your data. Amazon rolled that thing across the stage a couple of years ago. The data gravity thing seems to be less and if you think of a world with infinite compute, infinite stored, infinite networking asyndetically approaching zero, not necessarily good news for some vendors out there but that's a world that we're eventually getting to that changes the way that you organize all this stuff. >> Yeah, I think so and so much has changed. I was fortunate to be one of the early speakers, like I used to do Worlds and everything, and I was adamantly proclaiming you know, the destiny of Hadoop as bright and shiny and there's this question about what really happened. I think that there's a kind of a few different variables that kind of shifted at the same time. One, is of course, this like glut of computing in the cloud happened and there are so many variables moving at once. It's like, How much time do you have Jeff? >> Ask them to get a couple more drinks for us. >> Seeing our lovely new headquarters here and one of the things is that there is no big data center. We have a little closet with some of the servers we keep around but mostly, everything we do is on Amazon. You're even looking at things like, commercial real estate is changing because I don't need all the cooling and the power and the space for my data center that I once had. >> Jeff: Right, right. >> I become a lot more space efficient than I used to be and so the cloud is really kind of changing everything. On the data side, you mention this like, interesting philosophical shift, going from I couldn't possibly do it in the cloud to why in the world would we not do things in the cloud. Maybe the one stall word in there being some fears about security. Obviously there's been a lot of breaches. I think that there's still a lot of introspection everyone needs to do about, are my on premise systems actually more secure than some of these cloud providers? It's really not clear that we know the answer to that. In fact, we suspect that some of the cloud providers are actually more secure because they are professionals about it and they have the best practice. >> And a whole lot of money. >> The other thing that happened that you didn't mention, that's approaching infinity and we're not quite there yet, is interconnect speeds. So it used to be the case that I have a bunch of mainframes and I have a tier rating system and I have a high speed interconnect that puts the two together. Now with fiber networks and just in general, you can run super high speed, like WAN. Especially if you don't care quite as much about latency. So if 500 millisecond latency is still okay with you. >> Great. >> You can do a heck of a lot and move a lot to the cloud. In fact, it's so good, that we went from worrying, could I do this in the cloud at all to well, why wouldn't I do somethings in Amazon and some things in Microsoft and some things in Google? Even if it meant replicating my data across all these environments. The backdrop for some of that is, we had a lot of customers and I was thinking that people would approach it this way, they would install on premise Hadoop, whether it's like Apache or Cloud Air or the other vendors and I would hire a bunch of folks that are the administrators and retire terra data and I'm going to put all my ETL jobs on there, etc. It turned out to be a great theory and the practice is real for some folks but it turned out to be moving a lot of things to kind of shifting sands because Hadoop was evolving at the time. A lot of customers were putting a lot of pressure on it, operational pressure. Again, moving from experimentation phase over to like, operational phase. >> Jeff: Right, right. >> When you don't have the uptime guarantee and I can't just hire somebody off the street to administer this, it has to be a very sharp, knowledgeable person that's very expensive, people start saying, what am I really getting from this and can I just dump it all in S3 and apply a bunch of technology there and let Amazon worry about keeping this thing up and running? People start to say, I used to reject that idea and now it's sounding like a very smart idea. >> It's so funny we talk about people processing tech all the time, right? But they call them tech shows, they don't call them people in process shows. >> Right. >> At least not the ones we go to but time and time again I remember talking to some people about the Hadoop situation and there's just like, no Hadoop people. Sometimes technology all day long. There just aren't enough people with the skills to actually implement it. It's probably changed now but I remember that was such a big problem. It's funny you talk about security and cloud security. You know, at AWS, on Tuesday night of Reinvent, they have a special, kind of a technical keynote speak and like, James Hamilton would go. In the amount of resources, and I just remember one talk he gave just on their cabling across the ocean, and the amount of resources that he can bring to bear, relative to any individual company, is so different; much less a mid-tier company or a small company. I mean, you can bring so much more resources, expertise and knowledge. >> Yeah, the economy is a scale, their just there. >> They're just crazy. >> That's right and that why you know, you sort of assume that the cloud sort of, eventually eats everything. >> Right, right. >> So there's no reason to believe this won't be one of those cases. >> So you guys are getting Extreme. So what is Snaplogic Extreme? >> Well, Snaplogic Extreme is kind of like a response to this trend of data moving from on premise to the cloud and there are some interesting dynamics of that movement. First of all, you need to get data into the cloud, first of all and we've been doing that for years. Connect to everything, dump it in S3, ADLS, etc. No problem. The thing we're seeing with cloud computing is like, there's another interesting shift. Not only is it kind of like mess for less, and let Amazon manage all this, and I probably refer to Amazon more than other vendors would appreciate. >> Right, right. They're the leaders so let's call a spade a spade. >> Yeah. >> Certainly Google and Microsoft are out there as well so those are the top three and we've acknowledged that. >> One of the interesting things about it is that you couldn't really adequately achieve on premises is the burstiness of your compute. I run at a steady state where I need, you know, 10 servers or a 100 servers, but every once in a while, I need like, 1,000 or 10,000 servers to apply to something. So what's the on premise model? Rack and stack, 10,000 machines, and it's like waiting for the great pumpkin, waiting for that workload to come that I've been waiting months and months for and maybe it never comes but I've been paying for it. I paid for a software license for the thing that I need to run there. I'm paying for the cabling and the racking and everything and the person administering. Make sure the disks are all operating in the case where it gets used. Now, all of a sudden, we are taking Amazon and they're saying, hey, pay us for what you're using. You can use reserved pricing and pay a lower rate for the things you might actually care about on a consistent basis but then I'm going to allow you to spike, and I'll just run the meter. So this has caused software vendors like us, to look at the way we charge and the way that we deploy our resources and say, hey, that's a very good model. We want to follow that and so we introduced Snaplogic Extreme, which has a few different components. Basically, it enables us to operate in these elastic environments, shift our thinking in pricing so that we don't think about like, node based or god forbid, core based pricing and say like, hey, basically pay us for what you do with your data and don't worry about how many servers it's running on. Let Snaplogic worry about spinning up and spinning down these machines because a lot of these workloads are data integration or application workloads that we know lots about. >> Right. >> So first of all, we manage these ephemeral, what we call ephemeral or elastic clusters. Second of all, the way that we distribute our workload is by generating Spark code currently. We use the same graphic environment that you use for everything but instead of running on our engines, we kind of spit out Spark code on the end that takes advantage of the massive scale out potential for these ephemeral environments. >> Right. >> We've also kind of built this in such a way that it's Spark today but it could be like, Native or some other engine like Flank or other things that come up. We really don't care like what back end engine actually is as long as it can run certain types of data oriented jobs. It's actually like lots of things in one. We combine out data acquisition and distribution capability with this like, massive elastic scale out capability. >> Yeah, it's unbelievable how you can spin that up and then of course, most people forget you need to spin it down after the event. >> James: Yeah, that's right. >> We talked to a great vendor who talked about, you know, my customer spends no money with me on the weekend, zero. >> James: Right. >> And I'm thrilled because they're not using me. When they do use me, then they're buying stuff. I think what's really interesting is how that changes. Also, your relationship with your customer. If you have a recurring revenue model, you have to continue to deliver a value. You have to stay close to your customer. You have to stay engaged because it's not a one time pop and then you send them the 15% or 20% maintenance bill. It's really this ongoing relationship and they're actually gaining value from your products each and every time you use that. It's a very different way. >> Yeah, that's right. I think it creates better relationships because you feel like, what we do is unproportionate to what they do and vise versa, so it has this fundamental fairness about it, if you will. >> Right, it's a good relationship but I want to go down another path before you turn the cameras on. Talk a little bit about the race always between the need for compute and the compute. It used to be personified best with Microsoft and Intel until we come out with a new chip and then Microsoft OS would eat up all the extra capacity and then they'd come up with a new chip and it was an ongoing thing. You made an interesting comment that, especially in the cloud world where the scale of these things is much, much bigger, that ran a world now where the compute and the storage have kind of, outpaced the applications, if you will, and there's an opportunity for the application to catch up. Oh by the way, we have this cool new thing called machine learning and augmented intelligence. I wonder if you could, is that what's going to fill or kind of rebalance the consumption pattern? >> Yeah, it seems that way and I always think about kind of like, compute and software spiraling around each other like a helix. >> Like at one point, one is leading the other and they sort of just, one eventually surpasses the other and then you need innovation on the other side. I think for a while, like if you turn the clock way back to like, when the Pentium was introduced and everyone was like, how are we ever going to use all of the compute power. >> Windows 95, whoo! >> You know, power of like the Pentium. Do I really need to run my spreadsheets 100% faster? There's no business value whatsoever in transacting faster, or like general user interface or like graphical user interfaces or rendering web pages. Then you start seeing this new glut, often led by like researchers first. Like, software applications coming up that use all of this power because in academia you can start saying, what if I did have infinite compute? What would I do differently? You see things, you know like VR and advanced gaming, come up on the consumer side. Then I think the real answer on the business side is AI and ML. The general trend I start thinking of is something I used to talk about, back in the old days, which is conversion of like, having machines work for us instead of us working for machines. The only way we're ever going to get there is by having higher and higher intelligence on the application side so that it kind of intuits more based on what it's seen before and what it knows about you, etc., in terms of the task that needs to get done. Then there's this whole new breed of person that you need in order to wield all that power because like Hadoop, it's not just natural. You don't just have people floating around like, hey, you know, I'm going to be an Uzi expert or a yarn expert. You don't run into people everyday that's like, oh, yeah, I know neural nets well. I'm a gradient descent expert or whatever you're model is. It's really going to drive like, lots of changes I think. >> Right, well hopefully it does and especially like we were talking about earlier, you know, within core curriculums at schools and stuff. We were with Grace Hopper and Brenda Wilkerson, the new head of the Anita Borg organization, was at this Chicago public school district and they're actually starting to make CS a requirement, along with biology and and physics and chemistry and some of these other things. >> Right. >> So we do have a huge, a huge dearth of that but I want to just close out on one last concept before I let you go and you guys are way on top of this. Greg talked about what you just talked about, which is making the computers work for us versus the other way around. That's where the democratization of the power that we heard a lot about the democratization of big data and the tools and now you guys you guys are talking about the democratization of the integration, especially when you have a bunch of cloud based applications that everybody has access to and maybe, needs to stitch together a different way. But when you look at this whole concept of democratization of that power, how do you see that kind of playing out over the next several years? >> Yeah, that's a very big- >> Sorry I didn't bring you a couple of beer before I brought that up. >> Oh no, I got you covered. So it's a very big, interesting question because I think that you know, first of all, it's one of these, god knows, we can't predict with a lot of accuracy how exactly that's going to look because we're sort of juxtaposing two things. One is, part of the initial move to the cloud was the failure to properly democratize data inside the enterprise, for whatever reason, and we didn't do it. Now we have the computer resources and the central, kind of web based access to everything. Great. Now we have Cambridge Analytica and like, Facebook and people really thinking about data privacy and the fact that we want ubiquitous safe access. I think we know how to make things ubiquitous. The question is, do we know how to make it safe and fair so that the right people are using the right data and the right way? It's a little bit like, you know, there's all these cautionary tales out there like, beware of AI and robotics and everything and nobody really thinks about the danger of the data that's there. It's a much more immediate problem and yet it's sort of like the silent killer until some scandal comes up. We start thinking about these different ways we can tackle it. Obviously there's great solutions for tokenization and encryption and everything at the data level but even if you have the access to it, the question is, how do you control that wildfire that could happen as soon as the horse leaves the barn. Maybe not in it's current form, but when you look at things like Blockchain, there's been a lot of predictions about how Blockchain can be used around like, data. I think that this privacy and this curation and tracking of who has the data, who has access to it and can we control it, I think you are looking at even more like, centralized and guarded access to this private data. >> Great, interesting times. >> Yeah, yeah Jeff, for sure. >> Alright James, well thanks for taking a couple of minutes with us. I really enjoyed the conversation. >> Yeah, it's always great. Thanks for having me Jeff. >> It's James on Jeff and you're watching theCUBE We're at the Snaplogic headquarters in San Mateo, California and thanks for watching. (electronic music)

>> Announcer: From San Mateo, California, it's theCUBE! Covering SnapLogic, Innovation Day, 2018. Brought to you by SnapLogic. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are in San Mateo, at what they call the crossroads, it's 92 and 101. If you're coming by and probably sitting in a traffic, look up and you'll see SnapLogic. It's their new offices. We're really excited to be here for Innovation Day. We're excited to have this CTO, James Markarian. James, great to see you and I guess, we we last talked was a couple years ago in New York City. >> Yeah that's right, and why was I there? It was like a big data show. >> That's right. >> And we we are two years later talking about big data. >> Big data, big data is fading a little bit, because now big data is really an engine, that's powering this new thing that's so exciting, which is all about analytics, and machine learning, and we're going to eventually stop saying artificial intelligence and say augmented intelligence, 'cause there's really nothing artificial about it. >> Yeah and we might stop saying big data and just talk about data because it's becoming so ubiquitous. >> Jeff: Right. >> I know that big data, it's not necessarily going away but it's sort of how we're thinking about handling it is, like kind of evolved over time, especially in the last couple of years. >> Right. >> That's what we're kind of seeing from our customers. >> 'Cause there's kind of an ingredient now, right? It's no longer this new shiny object now. It's just part of the infrastructure that helps you get everything else done. >> Yeah, and I think when you think about it, from like, an enterprise point of view, that that shift is going from experimentation to operationalizing. I think that the things you look for in experimentation, there's like, one set of things here looking for proving out the overall value, regardless maybe of cost and uptime and other things and as you operationalize you start thinking about other considerations that obviously Enterprise IT has to think about. >> Right, so if you think back to like, Hadoop Summit and Hadoop World who were first cracking their teeth, like in 2010 or around that time frame, one of the big discussions that always comes up and that was before kind of the rise of public cloud, you know which has really taken off over the last several years, there's this kind of ongoing debate between, do you move the data to the compute or do you move the compute to the data? There was always like, this monster data gravity issue which was almost insurmountable and many would say, oh, you're never going to get all your data into the cloud. It's just way too hard and way too expensive. But, now Amazon has Snowball and Snowball isn't big enough. They actually had a diesel truck that'll come and help you come move your data. Amazon rolled that thing across the stage a couple of years ago. The data gravity thing seems to be less and if you think of a world with infinite compute, infinite stored, infinite networking asyndetically approaching zero, not necessarily good news for some vendors out there but that's a world that we're eventually getting to that changes the way that you organize all this stuff. >> Yeah, I think so and so much has changed. I was fortunate to be one of the early speakers, like I used to do Worlds and everything, and I was adamantly proclaiming you know, the destiny of Hadoop as bright and shiny and there's this question about what really happened. I think that there's a kind of a few different variables that kind of shifted at the same time. One, is of course, this like glut of computing in the cloud happened and there are so many variables moving at once. It's like, How much time do you have Jeff? >> Ask them to get a couple more drinks for us. >> Seeing our lovely new headquarters here and one of the things is that there is no big data center. We have a little closet with some of the servers we keep around but mostly, everything we do is on Amazon. You're even looking at things like, commercial real estate is changing because I don't need all the cooling and the power and the space for my data center that I once had. >> Jeff: Right, right. >> I become a lot more space efficient than I used to be and so the cloud is really kind of changing everything. On the data side, you mention this like, interesting philosophical shift, going from I couldn't possibly do it in the cloud to why in the world would we not do things in the cloud. Maybe the one stall word in there being some fears about security. Obviously there's been a lot of breaches. I think that there's still a lot of introspection everyone needs to do about, are my on premise systems actually more secure than some of these cloud providers? It's really not clear that we know the answer to that. In fact, we suspect that some of the cloud providers are actually more secure because they are professionals about it and they have the best practice. >> And a whole lot of money. >> The other thing that happened that you didn't mention, that's approaching infinity and we're not quite there yet, is interconnect speeds. So it used to be the case that I have a bunch of mainframes and I have a tier rating system and I have a high speed interconnect that puts the two together. Now with fiber networks and just in general, you can run super high speed, like WAN. Especially if you don't care quite as much about latency. So if 500 millisecond latency is still okay with you. >> Great. >> You can do a heck of a lot and move a lot to the cloud. In fact, it's so good, that we went from worrying, could I do this in the cloud at all to well, why wouldn't I do somethings in Amazon and some things in Microsoft and some things in Google? Even if it meant replicating my data across all these environments. The backdrop for some of that is, we had a lot of customers and I was thinking that people would approach it this way, they would install on premise Hadoop, whether it's like Apache or Cloud Air or the other vendors and I would hire a bunch of folks that are the administrators and retire terra data and I'm going to put all my ETL jobs on there, etc. It turned out to be a great theory and the practice is real for some folks but it turned out to be moving a lot of things to kind of shifting sands because Hadoop was evolving at the time. A lot of customers were putting a lot of pressure on it, operational pressure. Again, moving from experimentation phase over to like, operational phase. >> Jeff: Right, right. >> When you don't have the uptime guarantee and I can't just hire somebody off the street to administer this, it has to be a very sharp, knowledgeable person that's very expensive, people start saying, what am I really getting from this and can I just dump it all in S3 and apply a bunch of technology there and let Amazon worry about keeping this thing up and running? People start to say, I used to reject that idea and now it's sounding like a very smart idea. >> It's so funny we talk about people processing tech all the time, right? But they call them tech shows, they don't call them people in process shows. >> Right. >> At least not the ones we go to but time and time again I remember talking to some people about the Hadoop situation and there's just like, no Hadoop people. Sometimes technology all day long. There just aren't enough people with the skills to actually implement it. It's probably changed now but I remember that was such a big problem. It's funny you talk about security and cloud security. You know, at AWS, on Tuesday night of Reinvent, they have a special, kind of a technical keynote speak and like, James Hamilton would go. In the amount of resources, and I just remember one talk he gave just on their cabling across the ocean, and the amount of resources that he can bring to bear, relative to any individual company, is so different; much less a mid-tier company or a small company. I mean, you can bring so much more resources, expertise and knowledge. >> Yeah, the economy is a scale, their just there. >> They're just crazy. >> That's right and that why you know, you sort of assume that the cloud sort of, eventually eats everything. >> Right, right. >> So there's no reason to believe this won't be one of those cases. >> So you guys are getting Extreme. So what is Snaplogic Extreme? >> Well, Snaplogic Extreme is kind of like a response to this trend of data moving from on premise to the cloud and there are some interesting dynamics of that movement. First of all, you need to get data into the cloud, first of all and we've been doing that for years. Connect to everything, dump it in S3, ADLS, etc. No problem. The thing we're seeing with cloud computing is like, there's another interesting shift. Not only is it kind of like mess for less, and let Amazon manage all this, and I probably refer to Amazon more than other vendors would appreciate. >> Right, right. They're the leaders so let's call a spade a spade. >> Yeah. >> Certainly Google and Microsoft are out there as well so those are the top three and we've acknowledged that. >> One of the interesting things about it is that you couldn't really adequately achieve on premises is the burstiness of your compute. I run at a steady state where I need, you know, 10 servers or a 100 servers, but every once in a while, I need like, 1,000 or 10,000 servers to apply to something. So what's the on premise model? Rack and stack, 10,000 machines, and it's like waiting for the great pumpkin, waiting for that workload to come that I've been waiting months and months for and maybe it never comes but I've been paying for it. I paid for a software license for the thing that I need to run there. I'm paying for the cabling and the racking and everything and the person administering. Make sure the disks are all operating in the case where it gets used. Now, all of a sudden, we are taking Amazon and they're saying, hey, pay us for what you're using. You can use reserved pricing and pay a lower rate for the things you might actually care about on a consistent basis but then I'm going to allow you to spike, and I'll just run the meter. So this has caused software vendors like us, to look at the way we charge and the way that we deploy our resources and say, hey, that's a very good model. We want to follow that and so we introduced Snaplogic Extreme, which has a few different components. Basically, it enables us to operate in these elastic environments, shift our thinking in pricing so that we don't think about like, node based or god forbid, core based pricing and say like, hey, basically pay us for what you do with your data and don't worry about how many servers it's running on. Let Snaplogic worry about spinning up and spinning down these machines because a lot of these workloads are data integration or application workloads that we know lots about. >> Right. >> So first of all, we manage these ephemeral, what we call ephemeral or elastic clusters. Second of all, the way that we distribute our workload is by generating Spark code currently. We use the same graphic environment that you use for everything but instead of running on our engines, we kind of spit out Spark code on the end that takes advantage of the massive scale out potential for these ephemeral environments. >> Right. >> We've also kind of built this in such a way that it's Spark today but it could be like, Native or some other engine like Flank or other things that come up. We really don't care like what back end engine actually is as long as it can run certain types of data oriented jobs. It's actually like lots of things in one. We combine out data acquisition and distribution capability with this like, massive elastic scale out capability. >> Yeah, it's unbelievable how you can spin that up and then of course, most people forget you need to spin it down after the event. >> James: Yeah, that's right. >> We talked to a great vendor who talked about, you know, my customer spends no money with me on the weekend, zero. >> James: Right. >> And I'm thrilled because they're not using me. When they do use me, then they're buying stuff. I think what's really interesting is how that changes. Also, your relationship with your customer. If you have a recurring revenue model, you have to continue to deliver a value. You have to stay close to your customer. You have to stay engaged because it's not a one time pop and then you send them the 15% or 20% maintenance bill. It's really this ongoing relationship and they're actually gaining value from your products each and every time you use that. It's a very different way. >> Yeah, that's right. I think it creates better relationships because you feel like, what we do is unproportionate to what they do and vise versa, so it has this fundamental fairness about it, if you will. >> Right, it's a good relationship but I want to go down another path before you turn the cameras on. Talk a little bit about the race always between the need for compute and the compute. It used to be personified best with Microsoft and Intel until we come out with a new chip and then Microsoft OS would eat up all the extra capacity and then they'd come up with a new chip and it was an ongoing thing. You made an interesting comment that, especially in the cloud world where the scale of these things is much, much bigger, that ran a world now where the compute and the storage have kind of, outpaced the applications, if you will, and there's an opportunity for the application to catch up. Oh by the way, we have this cool new thing called machine learning and augmented intelligence. I wonder if you could, is that what's going to fill or kind of rebalance the consumption pattern? >> Yeah, it seems that way and I always think about kind of like, compute and software spiraling around each other like a helix. >> Like at one point, one is leading the other and they sort of just, one eventually surpasses the other and then you need innovation on the other side. I think for a while, like if you turn the clock way back to like, when the Pentium was introduced and everyone was like, how are we ever going to use all of the compute power. >> Windows 95, whoo! >> You know, power of like the Pentium. Do I really need to run my spreadsheets 100% faster? There's no business value whatsoever in transacting faster, or like general user interface or like graphical user interfaces or rendering web pages. Then you start seeing this new glut, often led by like researchers first. Like, software applications coming up that use all of this power because in academia you can start saying, what if I did have infinite compute? What would I do differently? You see things, you know like VR and advanced gaming, come up on the consumer side. Then I think the real answer on the business side is AI and ML. The general trend I start thinking of is something I used to talk about, back in the old days, which is conversion of like, having machines work for us instead of us working for machines. The only way we're ever going to get there is by having higher and higher intelligence on the application side so that it kind of intuits more based on what it's seen before and what it knows about you, etc., in terms of the task that needs to get done. Then there's this whole new breed of person that you need in order to wield all that power because like Hadoop, it's not just natural. You don't just have people floating around like, hey, you know, I'm going to be an Uzi expert or a yarn expert. You don't run into people everyday that's like, oh, yeah, I know neural nets well. I'm a gradient descent expert or whatever you're model is. It's really going to drive like, lots of changes I think. >> Right, well hopefully it does and especially like we were talking about earlier, you know, within core curriculums at schools and stuff. We were with Grace Hopper and Brenda Wilkerson, the new head of the Anita Borg organization, was at this Chicago public school district and they're actually starting to make CS a requirement, along with biology and and physics and chemistry and some of these other things. >> Right. >> So we do have a huge, a huge dearth of that but I want to just close out on one last concept before I let you go and you guys are way on top of this. Greg talked about what you just talked about, which is making the computers work for us versus the other way around. That's where the democratization of the power that we heard a lot about the democratization of big data and the tools and now you guys you guys are talking about the democratization of the integration, especially when you have a bunch of cloud based applications that everybody has access to and maybe, needs to stitch together a different way. But when you look at this whole concept of democratization of that power, how do you see that kind of playing out over the next several years? >> Yeah, that's a very big- >> Sorry I didn't bring you a couple of beer before I brought that up. >> Oh no, I got you covered. So it's a very big, interesting question because I think that you know, first of all, it's one of these, god knows, we can't predict with a lot of accuracy how exactly that's going to look because we're sort of juxtaposing two things. One is, part of the initial move to the cloud was the failure to properly democratize data inside the enterprise, for whatever reason, and we didn't do it. Now we have the computer resources and the central, kind of web based access to everything. Great. Now we have Cambridge Analytica and like, Facebook and people really thinking about data privacy and the fact that we want ubiquitous safe access. I think we know how to make things ubiquitous. The question is, do we know how to make it safe and fair so that the right people are using the right data and the right way? It's a little bit like, you know, there's all these cautionary tales out there like, beware of AI and robotics and everything and nobody really thinks about the danger of the data that's there. It's a much more immediate problem and yet it's sort of like the silent killer until some scandal comes up. We start thinking about these different ways we can tackle it. Obviously there's great solutions for tokenization and encryption and everything at the data level but even if you have the access to it, the question is, how do you control that wildfire that could happen as soon as the horse leaves the barn. Maybe not in it's current form, but when you look at things like Blockchain, there's been a lot of predictions about how Blockchain can be used around like, data. I think that this privacy and this curation and tracking of who has the data, who has access to it and can we control it, I think you are looking at even more like, centralized and guarded access to this private data. >> Great, interesting times. >> Yeah, yeah Jeff, for sure. >> Alright James, well thanks for taking a couple of minutes with us. I really enjoyed the conversation. >> Yeah, it's always great. Thanks for having me Jeff. >> It's James on Jeff and you're watching theCUBE We're at the Snaplogic headquarters in San Mateo, California and thanks for watching. (electronic music)

>> Announcer: Live from Las Vegas it's theCube, covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back to The Cube's live coverage of ServiceNow Knowledge18, I'm your host Rebecca Knight along with my cohost, Dave Vellente. The biggest conference of ServiceNow, 18,000 people here at the Venetian. We're joined now by Marc Talluto, he is the DXC Fruition Global Practice Lead at DXC. Thanks so much for coming on the show. >> Thank you for having me, appreciate it. >> So let's start out by telling our viewers a little bit about what you do in your role within the organization. >> Sure, you know, just a brief history, so I was one of the co-founders and CEO of Fruition Partners. So we were acquired by CSC, now DXC, about almost three years ago and within DXC, you know, DXC made a very conscious decision to use ServiceNow as kind of a pivot point to digital transformations for the customers. So by acquiring Fruition and then further investments, so we've done acquisitions in Australia, mainland Europe, the Netherlands, we've really consolidated a lot of the best regional partners inside one DXC Fruition practice. So within this practice, that's where we do a lot of our transformation work with customers that are starting or continuing their ServiceNow journey. >> Marc you and I met in the early part of this decade when this show was a lot smaller and it was, you know, well under, maybe around 5,000, probably even a little bit smaller than that. And it was companies like Fruition that got in early. You didn't see the CSC/DXCs and the other big systems integrators and this thing has just exploded. What's your perspective on the last five, six years? >> Oh boy, well I will say a lot of this is driven, a lot of the growth, not just from ServiceNow but from the GSIs, the global system integrators, that really see ServiceNow, how it can really be applied to their customer base. And so in the last five years you went from people that were interested but really didn't understand what it could mean, 'cause you know, if it's perceived only as a ticketing tool it's like, oh, that's not important. But as it's now seen as a, really a service manager platform, that getting in and servicing IT is just a way to go help HR, to go help suck ups, all these other venues. So what we're seeing is really an explosion of the GSI community here trying to do acquisitions like we've done. So there's been about, in the last five years, 17 different acquisitions of all those regional players into those various global SIs. But then those global SIs themselves, as we've seen on some of the presentations here, I and DXC ourselves, we're now using ServiceNow internally as a way to automate a lot of our internal processes. Used to be what we called Customer Zero or the Lighthouse Account is now the GSI themselves. So I think they've really embraced the message we've been kind of saying all along, which is, yes it's good for IT, but it's really good for how you operate all your shared services' businesses. So that's been, and it's been just accelerating every year. >> Yeah, remind me, so when you started Fruition did you start with ServiceNow or did you have, had you had experience with other platforms before that? >> Yeah, so we actually started in 2003, so about five years before we ever met ServiceNow. >> Dave: There was no ServiceNow, really. >> No, yeah, so we were used to using the remedies of the world, I mean, the other kind of various tools that were out there. But we also weren't a system integrator when we started. We were an, it's funny 'cause you hear the messaging now, organizational change is more important, customer success is more important. Those are really the roots of our company. We were like, listen, the process needs to be better. You know, we're pouring in to governance and all these things, we could use Remedy, we could use other tools but we need to really figure out why people are choosing to engage to do service management or they just kind of go off and do their own thing. So for those five years that's all we did was talk to organizations about crawl, walk, run. How are you maturing from fragmented service offerings, fragmented support, to really kind of being able to centralize those operations and then extend outside of IT? And when we met ServiceNow it was like, it's like they were telling us what we've been telling customers for years so I was like, that's great. >> The lack of a tool, a platform, that really does what ServiceNow does, in a way it might've been a tailwind for your business 'cause complexity, but on the other hand you had to respond and you jumped on it early. I mean I would think a lot of SIs might've said, oh no, that takes complexity out, complexity is cash for us. You guys had a different philosophy, you said were going to get in early, talk about that journey, that position. >> True, well you know when we first met ServiceNow, like I said, 2008 when they were about 40 people total, you know, their entire company. And I think we were 10. So we were almost, you know, similar sizes. But you know what we were able to provide ServiceNow was explaining the customer journey. That the technology was very important, it was very lightweight and nimble but that customer journey, that customer needed to understand, what should I do first, what should I do next? What should my one year, two year, three year look like? And that's something that we've always kind of held, that we saw ServiceNow also as being this platform. We believed in the Glidefast story which was ServiceNow before ServiceNow, maybe we were one of the first ones to say, there's IT service managers, let's just talk about cloud service management, enterprise service management. So I feel like their story and our story, we've kind of been maturing together as we've seen customers really adopt the platform. And some of the great case studies that we've seen over the years, those have been our customers that we've helped encourage to say, what's the difference between an asset that's in IT and an asset that's in manufacturing, right? These are the same disciplines so let's help them go out there and do that. So it's been, it's obviously been a tidal wave of work. It's been very interesting expanding globally and you know, this is just a result of a lot of hard work on everybody's part. >> We're sort of, at this conference we're hearing that this is a real moment in time, when you were describing talking to companies, trying to understand those who were sort of happy to operate in this fragmented way versus those that were truly committed to a technological change and bringing things together. Is that true in your mind, that there really is a recognition on the part of companies and employers? This is, we need to get better at this. >> You know what we're hearing? We're hearing from very large enterprises, some of them and even Aerospace and Defense that are like, we have to recruit younger talent. They do have aging populations that'll be exiting their workforce. I see this from universities that recruit, obviously students, but it's then the workforce. The expectation is now so much higher that their experience with IT inside their employer is much closer to their experience as a consumer. We've been saying it for years but now it's really become a business imperative as customers, I should say as our customers, they are trying to make their workforce happier. Well not only just more productive, more engaged, but also, you know, retention. It's, I feel like it's the moment of the worker themselves. And look at other economic factors, unemployment's at a historic low. Finding people, you're competing for your own workforce to come work for you. They can't show up and you give them a Windows 95 machine or like an Office 2001 product suite, they're like, that's a reflection of how you as a company actually operate so all of those are kind of coming together in to this consumer like experience for the employees of our customers. >> And a lot of talk about new ways to work, the future of work. So what's your expectation going forward for how that affects business, affects your business, organizations? Sounds like they're closing the gap between consumer experiences and enterprise experiences, what's next? >> So you know, big word, friction, been frictionless. Right, like where's the efficiency, what is the friction in different departments working together? I think as people really do adopt this, call it the service manager platform, that system of engagement, once those silos start to come down, once they start to share that data, we see it in individual customers, they kind of go through this aha moment. They've cleaned up their data sources, they realize everything's on one platform, and then they're like, can't I build this, can't I build that, can't I build that? Yeah, you can, and it really starts to accelerate. So I think we'll see the barriers of these business units really fall, I think IT's role is going to shift to be almost a, we talk about a service management office not a project management office. So the service management office is, how well are all of my services, whether it's HR, whether it's finance, how are those services being consumed by my employees? So I think we'll see that pivot, it gets away from IT being more T, the technology, and more to the I. Like what information and services am I providing? I think really we are at that catalyst and as people start to adopt that it moves much more quickly from here. >> What's next, what is, going forward what do you see as the DXC ServiceNow strategy? >> Boy, so this is something that we've been working, so DXC's only been in existence for one year, right? But it came from HBES, it came from CSC, right, 26 billion dollar company, 180,000 people. DXC is putting all of their investment strategy around digital transformation, behind ServiceNow. So we have another team here that focuses completely on building ServiceNow offerings that are behind all the other DXC offerings. So what do I mean by that? The difference is whereas Fruition will go up to a customer and say, we'll help you do ServiceNow work, the platform DXC team says, we want to deliver cloud orchestration, we want to deliver desktop and mobility workforce call centers, but all of those are powered by ServiceNow at the back end, all of our analytics so we do a lot of other things as DXC, obviously billions of dollars worth but we're switching that all to be standardized on ServiceNow. So we're actually breaking down the silos in our own company of how our different departments work together. So if a customer buys a cloud orchestration platform and they're also a workplace and mobility customer and they also have maybe the HR BPO, that's all on ServiceNow. The DXC platform, DXC, built on ServiceNow. So that's everything DXC's throwing at it is to be that player. >> And do you see ServiceNow, is that the platform of platforms? >> Marc: Yes. >> And I mean, you guys really are a technology agnostic. But if it fits you'll use it. >> Well we're an independence offer provider. We don't create our own products like an IBM might or somebody else might and basically put those products in front of a customer when they're really not the right fit. >> So, I mean, you think we had John Donaho on early and he said, look, there's WorkDay and there's SalesForce and there's SAP, et cetera, et cetera, et cetera. We want to be the connective tissue to those platforms. Software companies are funny though, they all want to be the connective tissue. But if this is what ServiceNow does, so, do you feel like they are in a unique position to be that platform of platforms and-- >> I really do, and we've worked with a lot of other software companies that want to connect in to that ServiceNow ecosystem because what we find is other software products are like, listen, I might be really good at security, intrusion detection, but do I want to create a work flow? And I want to create the CMDB, that means that I have to go build an entire almost secondary product to my core competency. So if I'm really good at anti virus, if I'm really good at intrusion detection, even if I'm really good at reporting I still need people to act on the information I'm providing them. But I don't want to build that action engine, so that's what they're almost setting up their own boundary, saying let ServiceNow be the action engine for me and we'll just plug in to them. They're becoming the standard for how customers work between silos. >> Great, well Marc, thank you so much for coming on the show, this has been really fun talking to you. >> It's my pleasure, thank you, great to see you. >> I'm Rebecca Knight for Dave Vellante, we will have more from ServiceNow Knowledge18 just after this. (upbeat techno music)

>> Narrator: Live from Las Vegas, Nevada, it's the Cube, covering accelerate 2017, brought to you by Fortinet. Now here are your hosts, Lisa Martin and Peter Burris. >> Hi, welcome back to the cube, we are live in Las Vegas at Fortinet Accelerate 2017. I'm you host , Lisa Martin, joined by my cohost, Peter Burris, and we're really excited about or next guest. We are talking next with Derek Manky. Derek, you are-- first of all, welcome to the cube. >> Thank you very much, I'm excited to be here. >> You have a really important role in Fortinet, you are the Global Security Strategist. >> Correct, yes. >> You have a... Established yourself as a thought leader with over 15 year of cyber security expertise, and your goal is to make a positive impact towards the global war on cyber-crime, that's a big goal. >> That's a very, very big goal, but it's a big hairy goal, but it's... Critically important, I believe, I firmly believe this over my whole career, and I'm starting to see some good traction with the efforts that we're doing too. >> And it's becoming more, and more, critical every day as breaches, and hacks, are a daily occurrence, you're also the leader of FortiGuard Labs, you've got a team of over 200, tell our viewers that can't be here today, what is FortiGuard Labs, what are you doing to leverage threat intelligence to help Fortinet's customers. >> Sure, so we're trying to manage complexity, cause that's always the enemy of security, and we're trying to make it simple across the board, so we're managing security for all of our customers, 300 000 customers plus. That's a big deal, so we had to invest a lot into that in terms of how we can do that to make it simple to the end users. So what FortiGuard Labs is, is it's services we deliver to the end user, protection services across the spectrum, our whole product portfolio. So we have world-class expertise as a security vendor, 200 plus people on the team, experts in each domain. We have researchers, and experts, looking at things like industrial attacks, mobile problems, malicious websites, ripping apart, what we call reverse engineering, malware samples to find out digital fingerprints of who's creating these attacks, so we can work also in partnerships with that too. At the end of the day, we have the humans working on that, but we've also invested a ton into artificial intelligence, and machine learning, we have to comb through over 50 billion attacks in a day, and so the machines are also helping us to create a lot of this automated protection, that's all driven by our patents, by our world-class development teams, that gets down to the end user, so that they don't have to invest as much into their own security operations centers, cause that's a big OpEx, expansions to the expenditure, so we're helping to alleviate that issue, especially with this, as everybody knows, today, the big gap in cyber security, professionals, so that helps to alleviate that issue too. >> You said 50 billion attacks a day. >> That's correct sir, yes. Potential attacks. >> Oh, potential attacks. Clearly that means that increasing percentages of the total body of attacks are no longer coming from humans, they're coming from other things, >> Derek: Absolutely. >> And how's that playing out? >> It's a fascinating landscape right now. With every legitimate model, there's an illegitimate model to follow, especially with cyber crime, and what we see in the digital underground, dark web, all these sorts of things, you rewind back to the 90s, your opportunistic hacker was just trying to plot, plot, plot, a message bar on a Windows 95, or Windows 98 system at the time. Nowadays, of course, the attack surface has grown tremendously. You look back to DARPA, back in 1989, it had 60 000 system connected on the Internet, now we have IPv6, 20 plus billions connected devices, everything is a target now, especially with the Internet of Things. Smart televisions-- >> Peter: And a potential threat. >> Exactly, and a weapon. >> Exactly, and so to capitalize on that, what we're seeing now is cyber criminals developing automated systems of their own, to infect these systems, to report back to them, so they're doing a lot of that heavy work, to the heavy lifting, using their own machines to infect, and their own algorithms to infect these systems, and then from there, it'll escalate back up to them to further capitalize, and leverage those attacks. On any given minute, we're seeing between 500 000 to 700 000 hacking attempts across, and this is our own infrastructure, so we're leading in terms of firewalls in units shipped so we're able to get a good grasp on intelligence out there, what's happening, and in any given minute, well over 500 000 hacking attempts on systems worldwide. >> So every hour, 30 million. >> Derek: Yeah that's some quick math. >> Yeah, I'm amazing at multiplication. I almost got it wrong though, I have to say. 30 million hacks an hour. >> Yeah, and so our job is to identify that, we don't want to block things we shouldn't be, so there has to be a very big emphasis on quality of intelligence as well, we've done a lot with our machines to validate attacks, to be able to protect against those attacks, and not, especially when it comes to these attacks like intrusion prevention, that attack surface now, we got to be able to not just look at attacks on PCs now, so that's why that number keeps ticking up. >> Lisa: Right, proliferation of mobile, IoT. >> Derek: It's directly related, absolutely. >> So, this is clearly something that eyeballs are not going to solve. >> Not alone, so I'm very, very big advocate saying that we cannot win this war alone, just relying even on the brightest minds on the world, but we can also not just rely a hundred percent on machines to control, it's just like autonomous vehicles. You look at Tesla, and these other vehicles, and Google, what they're doing, it's a trust exercise again, you can never pass a hundred percent control to that automation. Rather you can get up to that 99 percent tile with automation, but you still need those bright minds looking at it. So to answer your questions, eyeballs alone, no, but the approach we've taken is to scale up, distribute, and use machines to identify it, to try to find that needle in a haystack, and then, escalate that to our bright minds, when we need to take a look at the big attacks that matter, and solve some more of the complex issues. >> Speaking of bright minds, you and your team, recently published an incredible blog on 2017 predictions. Wow, that's on the Fortinet blog? >> Derek: Yeah, that's correct >> We can find that? Really incredibly thorough, eye-opening, and there were six predictions, take us through maybe the top three. We talked about the proliferation of devices, the attack surface getting larger, more and more things becoming potential threats, what are the top three, maybe biggest threats that you were seeing, and is there any industry, in particular, that pops up as one of the prime targets? >> Absolutely. I'll get into some buckets on this, I think first, and foremost, what is primary now in what we're seeing is, what we're calling, autonomous malware, so this is the notion of, basically what we're just talking about to your question on what's driving this data, what's driving all these attack points. First of all, the Internet's been seeded with, what I call, ticking time bombs right now, we have 20 plus, whatever the number's going to be, all of these billions of devices that are connected, that are inherently, in my professional opinion, insecure. A lot of these devices are not following proper security development life cycles. >> Lisa: Is there accountability to begin with? >> No, not at this point. >> Right. >> Right. And that's something that DHS, and NIST, just released some guidelines on, at the end of last year, and I think we're going to see a lot of activity on accountability for that, but that has to be taken care of. Unfortunately right now, it's been seeded, this attack surfaces there, so we already have all these open avenues of attack, and that's why I call it a ticking time bomb, because it's been seeded, and now these are ripe for attack, and we're seeing attackers capitalize on this, so what we're seeing is the first indications of autonomous malware, malware that is capable of mapping out these vulnerable points. The machine's doing this, and the machine's attacking the other machines, so it's not just the eyeballs then, and the cyber criminals doing this. We saw last year, unprecedented DDoS attacks, this is directly related to Mirai BotNet. We had gone from a 600 gig to terabit plus DDoS attacks, that was unheard of before. They are leveraging all of these different IoT devices as a horsepower to attack these systems in a massive distributed denial-of-service attack. The interesting part about Mirai is that it's also using open-source intelligence as well, so this is something that humans, like a black hat attacker, would typically have to do, they would have to get reports back from one of their systems, and say, "okay, now I've found all these vulnerable systems, I'm going to attack all these systems.", but they're the glue, so they're now removing themselves as the glue, and making this completely automated, where a BotNet like Mirai is able to use Shodan, as an example, it's an open-source database, and say, "here are a whole bunch of vulnerable systems, I'm going to go attack it, and so that's to my point of view, that's the first indication of the smart-malware, because malware has always been guided by humans. But now, I think, we're starting to see a lot of, more of that intelligent attack, the offense, the intelligent offense being baked in to these pieces of malware. So I think it's going to open this whole new breed of attacks and malware, and obviously, we're in a whole new arms race when it comes to that. How can we get ahead of the bad guys, and so this is obviously what Fortinet instituting on the autonomous defense, our Security Fabric, and Fabric-ready approach, that's all about, beating them to the punch on that, having our machines, the defensive machines talk to each other, combine world-class intelligence like FortiGuard so that it can defend against those attacks, it's a though task, but I really firmly believe that this year is a year that we have the advantage, we can have the advantage as white hats to get one leg up on the black hat attackers. As I said, for 15 years at FortiGuard Labs, we have invested a ton into our AI machine, learning intelligence, so we're experts on the automation, I don't believe the black hat attackers are experts on automation. So I think for that reason, we have a really good opportunity this year, because you always hear about the black hats, another data breach, and all these things happening, they're always had the advantage, and I think, we can really turn the tables this year. >> You have some great experience working, not just in the private sector, but in the public sector as well, you've done work with NATO, with Interpol, with SERT, what is your perspective on public sector, and private sector, working together, is that essential to win this war on cyber crime? >> Absolutely, we need everybody at the table, we cannot win it, as one single vendor alone, a good example of that is, we're starting to do across the board, this is something, I firmly believe in, it's really near and dear to my heart, I've worked on it for the course of, well over six years now, and we have a lot of the existing partnerships, across organizations, so other security vendors, and experts, Cyber Threat Alliance is an excellent example, we're a founding member of that, and these are competitors, but security vendors getting together to level the playing field on intelligence, we can still really remain competitive on the solutions, and how we implement that intelligence, but at least-- it's like a Venn diagram, you look at that attack surface out there, you want to try to share all that information, so that you can deliver that to security controls, and protect against it. So, the Cyber Threat Alliance is a good example, but that's private sector. If you look at National Computer Emergency Response, law enforcement, we have made great inroads into that working with the likes of Computer Emergency Response, to give them intel. If we find bad stuff happening somewhere, we're not law enforcement, we can't go take the server down, and disrupt campaign, we can't arrest, or prosecute people, but they can, but they don't have all that expertise, and intelligence that we do, all the data points, so this is, you're starting to see a lot of this string up, and we're doing a lot of leadership in this area, and I think, it's absolutely essential. President Obama last year mentioned it, the Cyber Threat Alliance, and the public-private sector, needing to work together in one of his speeches at Stanford, and I believe it's the only way we can win this. You have to go up to the head of the snake too, if we just are always on the defense, and we're always just trying to disrupt cyber criminals, it's a slap on the wrist for them, they're going to go set up shop somewhere else. We need to be able to actually go and prosecute these guys, and we had a really good case last year, we took down, working with Interpol, and the EFCC, a 62 million dollar crime ring in the US. They went, and prosecuted the kingpin of this operation, out of Nigeria. It's an unprecedented random example, but we need to do more of that, but it's a good example of a healthy working public-private sector relationship >> What an incredible experience that you have, what you have achieved with FortiGuard Labs, what excites you most, going forward, we're just at the beginning of 2017, with what's been announced here, the partnerships that you guys have formed, what excites you most about this year, and maybe... Some of the key steps you want to take against cyber crime as Fortinet. >> Sure, so I think we want to, so Cyber Threat Alliance is a very big machine, there's a lot of exciting things happening, so that's going to be a really good initiative, that's going to carry forward momentum this year. What excites me most? Well, it's not always a good thing I guess, but if you look at all the bad news that's out there, like I said, I think it's just going to be, there's so much fuel, that's being thrown on the fire when it comes to attacks right now. Like I said, these time bombs that have been planted out there. We're going to see the year of IoT attacks for sure, a new version of Marai has already come out, they're starting to sell this, commercialize this, and it's even more advanced in terms of intelligence than the previous one, so that sort of stuff. It depends on your definition of the word, excites, of course, but these are the things that we have opportunity, and again I think going back to my first point, the white hats having, for the first time in my point of view, a leg up on the black hats, that opportunity, that really excites me. When we look at what's happening, moving forward in 2017, healthcare, I think, is going to be a very big thing in terms of attack targets, so we're going to be focused on that, in terms of attacks on, not just healthcare records, which are more valuable than financial records as an example, but medical devices, again the IoT play in healthcare, that's a big deal, we're starting to already see attacks on that. Smart cities as well, you look forward to the next three years, building management systems, a lot of people talk about SCADA industrial control, this is definitely a big attack target to a certain... Attack surface, obviously, power plants, electrical grids, but building management systems, and these automated systems that are being put in, even smart vehicles, and smart homes is another big target that's unfolding over the next year. >> Hard to air gap a home, and certainly not a city. >> Absolutely, yeah, and again it goes back to the point that a lot of these devices being installed in those homes are inherently, insecure. So that's a big focus for us, and that's a big thing FortiGuard is doing, is looking at what those attacks are, so we can defend against that at the network layer, that we can work with all of our business partners that are here at Accelerate this year, to deliver those solutions, and protect against it. >> Wow, it sounds like, and I think Peter would agree, your passion for what you do is very evident, as those bad actors are out there, and as the technologies on the baton are getting more advanced, and intelligent, as you say, it's great to hear what you, and your team are doing to help defend against that on the enterprise side, and one day on the consumer side as well. So Derek Manky, Global Security Strategist for Fortinet, thank you so much cube and sharing your expertise with us. >> It's my pleasure, any time, thank you very much. >> Well, on behalf of my cohost, Peter Burris, I'm Lisa Martin, you've been watching the Cube, and stick around, we'll be right back. 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