(upbeat music) >> Hello, everyone, welcome to theCUBE. I'm John Furrier, the host of theCUBE in Palo Alto, California. Also it's SiliconANGLE News. Got two great guests here to talk about AI, the impact of the future of the internet, the applications, the people. Amr Awadallah, the founder and CEO, Ed Alban is the CEO of Vectara, a new startup that emerged out of the original Cloudera, I would say, 'cause Amr's known, famous for the Cloudera founding, which was really the beginning of the big data movement. And now as AI goes mainstream, there's so much to talk about, so much to go on. And plus the new company is one of the, now what I call the wave, this next big wave, I call it the fifth wave in the industry. You know, you had PCs, you had the internet, you had mobile. This generative AI thing is real. And you're starting to see startups come out in droves. Amr obviously was founder of Cloudera, Big Data, and now Vectara. And Ed Albanese, you guys have a new company. Welcome to the show. >> Thank you. It's great to be here. >> So great to see you. Now the story is theCUBE started in the Cloudera office. Thanks to you, and your friendly entrepreneurship views that you have. We got to know each other over the years. But Cloudera had Hadoop, which was the beginning of what I call the big data wave, which then became what we now call data lakes, data oceans, and data infrastructure that's developed from that. It's almost interesting to look back 12 plus years, and see that what AI is doing now, right now, is opening up the eyes to the mainstream, and the application's almost mind blowing. You know, Sati Natel called it the Mosaic Moment, didn't say Netscape, he built Netscape (laughing) but called it the Mosaic Moment. You're seeing companies in startups, kind of the alpha geeks running here, because this is the new frontier, and there's real meat on the bone, in terms of like things to do. Why? Why is this happening now? What's is the confluence of the forces happening, that are making this happen? >> Yeah, I mean if you go back to the Cloudera days, with big data, and so on, that was more about data processing. Like how can we process data, so we can extract numbers from it, and do reporting, and maybe take some actions, like this is a fraud transaction, or this is not. And in the meanwhile, many of the researchers working in the neural network, and deep neural network space, were trying to focus on data understanding, like how can I understand the data, and learn from it, so I can take actual actions, based on the data directly, just like a human does. And we were only good at doing that at the level of somebody who was five years old, or seven years old, all the way until about 2013. And starting in 2013, which is only 10 years ago, a number of key innovations started taking place, and each one added on. It was no major innovation that just took place. It was a couple of really incremental ones, but they added on top of each other, in a very exponentially additive way, that led to, by the end of 2019, we now have models, deep neural network models, that can read and understand human text just like we do. Right? And they can reason about it, and argue with you, and explain it to you. And I think that's what is unlocking this whole new wave of innovation that we're seeing right now. So data understanding would be the essence of it. >> So it's not a Big Bang kind of theory, it's been evolving over time, and I think that the tipping point has been the advancements and other things. I mean look at cloud computing, and look how fast it just crept up on AWS. I mean AWS you back three, five years ago, I was talking to Swami yesterday, and their big news about AI, expanding the Hugging Face's relationship with AWS. And just three, five years ago, there wasn't a model training models out there. But as compute comes out, and you got more horsepower,, these large language models, these foundational models, they're flexible, they're not monolithic silos, they're interacting. There's a whole new, almost fusion of data happening. Do you see that? I mean is that part of this? >> Of course, of course. I mean this wave is building on all the previous waves. We wouldn't be at this point if we did not have hardware that can scale, in a very efficient way. We wouldn't be at this point, if we don't have data that we're collecting about everything we do, that we're able to process in this way. So this, this movement, this motion, this phase we're in, absolutely builds on the shoulders of all the previous phases. For some of the observers from the outside, when they see chatGPT for the first time, for them was like, "Oh my god, this just happened overnight." Like it didn't happen overnight. (laughing) GPT itself, like GPT3, which is what chatGPT is based on, was released a year ahead of chatGPT, and many of us were seeing the power it can provide, and what it can do. I don't know if Ed agrees with that. >> Yeah, Ed? >> I do. Although I would acknowledge that the possibilities now, because of what we've hit from a maturity standpoint, have just opened up in an incredible way, that just wasn't tenable even three years ago. And that's what makes it, it's true that it developed incrementally, in the same way that, you know, the possibilities of a mobile handheld device, you know, in 2006 were there, but when the iPhone came out, the possibilities just exploded. And that's the moment we're in. >> Well, I've had many conversations over the past couple months around this area with chatGPT. John Markoff told me the other day, that he calls it, "The five dollar toy," because it's not that big of a deal, in context to what AI's doing behind the scenes, and all the work that's done on ethics, that's happened over the years, but it has woken up the mainstream, so everyone immediately jumps to ethics. "Does it work? "It's not factual," And everyone who's inside the industry is like, "This is amazing." 'Cause you have two schools of thought there. One's like, people that think this is now the beginning of next gen, this is now we're here, this ain't your grandfather's chatbot, okay?" With NLP, it's got reasoning, it's got other things. >> I'm in that camp for sure. >> Yeah. Well I mean, everyone who knows what's going on is in that camp. And as the naysayers start to get through this, and they go, "Wow, it's not just plagiarizing homework, "it's helping me be better. "Like it could rewrite my memo, "bring the lead to the top." It's so the format of the user interface is interesting, but it's still a data-driven app. >> Absolutely. >> So where does it go from here? 'Cause I'm not even calling this the first ending. This is like pregame, in my opinion. What do you guys see this going, in terms of scratching the surface to what happens next? >> I mean, I'll start with, I just don't see how an application is going to look the same in the next three years. Who's going to want to input data manually, in a form field? Who is going to want, or expect, to have to put in some text in a search box, and then read through 15 different possibilities, and try to figure out which one of them actually most closely resembles the question they asked? You know, I don't see that happening. Who's going to start with an absolute blank sheet of paper, and expect no help? That is not how an application will work in the next three years, and it's going to fundamentally change how people interact and spend time with opening any element on their mobile phone, or on their computer, to get something done. >> Yes. I agree with that. Like every single application, over the next five years, will be rewritten, to fit within this model. So imagine an HR application, I don't want to name companies, but imagine an HR application, and you go into application and you clicking on buttons, because you want to take two weeks of vacation, and menus, and clicking here and there, reasons and managers, versus just telling the system, "I'm taking two weeks of vacation, going to Las Vegas," book it, done. >> Yeah. >> And the system just does it for you. If you weren't completing in your input, in your description, for what you want, then the system asks you back, "Did you mean this? "Did you mean that? "Were you trying to also do this as well?" >> Yeah. >> "What was the reason?" And that will fit it for you, and just do it for you. So I think the user interface that we have with apps, is going to change to be very similar to the user interface that we have with each other. And that's why all these apps will need to evolve. >> I know we don't have a lot of time, 'cause you guys are very busy, but I want to definitely have multiple segments with you guys, on this topic, because there's so much to talk about. There's a lot of parallels going on here. I was talking again with Swami who runs all the AI database at AWS, and I asked him, I go, "This feels a lot like the original AWS. "You don't have to provision a data center." A lot of this heavy lifting on the back end, is these large language models, with these foundational models. So the bottleneck in the past, was the energy, and cost to actually do it. Now you're seeing it being stood up faster. So there's definitely going to be a tsunami of apps. I would see that clearly. What is it? We don't know yet. But also people who are going to leverage the fact that I can get started building value. So I see a startup boom coming, and I see an application tsunami of refactoring things. >> Yes. >> So the replatforming is already kind of happening. >> Yes, >> OpenAI, chatGPT, whatever. So that's going to be a developer environment. I mean if Amazon turns this into an API, or a Microsoft, what you guys are doing. >> We're turning it into API as well. That's part of what we're doing as well, yes. >> This is why this is exciting. Amr, you've lived the big data dream, and and we used to talk, if you didn't have a big data problem, if you weren't full of data, you weren't really getting it. Now people have all the data, and they got to stand this up. >> Yeah. >> So the analogy is again, the mobile, I like the mobile movement, and using mobile as an analogy, most companies were not building for a mobile environment, right? They were just building for the web, and legacy way of doing apps. And as soon as the user expectations shifted, that my expectation now, I need to be able to do my job on this small screen, on the mobile device with a touchscreen. Everybody had to invest in re-architecting, and re-implementing every single app, to fit within that model, and that model of interaction. And we are seeing the exact same thing happen now. And one of the core things we're focused on at Vectara, is how to simplify that for organizations, because a lot of them are overwhelmed by large language models, and ML. >> They don't have the staff. >> Yeah, yeah, yeah. They're understaffed, they don't have the skills. >> But they got developers, they've got DevOps, right? >> Yes. >> So they have the DevSecOps going on. >> Exactly, yes. >> So our goal is to simplify it enough for them that they can start leveraging this technology effectively, within their applications. >> Ed, you're the COO of the company, obviously a startup. You guys are growing. You got great backup, and good team. You've also done a lot of business development, and technical business development in this area. If you look at the landscape right now, and I agree the apps are coming, every company I talk to, that has that jet chatGPT of, you know, epiphany, "Oh my God, look how cool this is. "Like magic." Like okay, it's code, settle down. >> Mm hmm. >> But everyone I talk to is using it in a very horizontal way. I talk to a very senior person, very tech alpha geek, very senior person in the industry, technically. they're using it for log data, they're using it for configuration of routers. And in other areas, they're using it for, every vertical has a use case. So this is horizontally scalable from a use case standpoint. When you hear horizontally scalable, first thing I chose in my mind is cloud, right? >> Mm hmm. >> So cloud, and scalability that way. And the data is very specialized. So now you have this vertical specialization, horizontally scalable, everyone will be refactoring. What do you see, and what are you seeing from customers, that you talk to, and prospects? >> Yeah, I mean put yourself in the shoes of an application developer, who is actually trying to make their application a bit more like magic. And to have that soon-to-be, honestly, expected experience. They've got to think about things like performance, and how efficiently that they can actually execute a query, or a question. They've got to think about cost. Generative isn't cheap, like the inference of it. And so you've got to be thoughtful about how and when you take advantage of it, you can't use it as a, you know, everything looks like a nail, and I've got a hammer, and I'm going to hit everything with it, because that will be wasteful. Developers also need to think about how they're going to take advantage of, but not lose their own data. So there has to be some controls around what they feed into the large language model, if anything. Like, should they fine tune a large language model with their own data? Can they keep it logically separated, but still take advantage of the powers of a large language model? And they've also got to take advantage, and be aware of the fact that when data is generated, that it is a different class of data. It might not fully be their own. >> Yeah. >> And it may not even be fully verified. And so when the logical cycle starts, of someone making a request, the relationship between that request, and the output, those things have to be stored safely, logically, and identified as such. >> Yeah. >> And taken advantage of in an ongoing fashion. So these are mega problems, each one of them independently, that, you know, you can think of it as middleware companies need to take advantage of, and think about, to help the next wave of application development be logical, sensible, and effective. It's not just calling some raw API on the cloud, like openAI, and then just, you know, you get your answer and you're done, because that is a very brute force approach. >> Well also I will point, first of all, I agree with your statement about the apps experience, that's going to be expected, form filling. Great point. The interesting about chatGPT. >> Sorry, it's not just form filling, it's any action you would like to take. >> Yeah. >> Instead of clicking, and dragging, and dropping, and doing it on a menu, or on a touch screen, you just say it, and it's and it happens perfectly. >> Yeah. It's a different interface. And that's why I love that UIUX experiences, that's the people falling out of their chair moment with chatGPT, right? But a lot of the things with chatGPT, if you feed it right, it works great. If you feed it wrong and it goes off the rails, it goes off the rails big. >> Yes, yes. >> So the the Bing catastrophes. >> Yeah. >> And that's an example of garbage in, garbage out, classic old school kind of comp-side phrase that we all use. >> Yep. >> Yes. >> This is about data in injection, right? It reminds me the old SQL days, if you had to, if you can sling some SQL, you were a magician, you know, to get the right answer, it's pretty much there. So you got to feed the AI. >> You do, Some people call this, the early word to describe this as prompt engineering. You know, old school, you know, search, or, you know, engagement with data would be, I'm going to, I have a question or I have a query. New school is, I have, I have to issue it a prompt, because I'm trying to get, you know, an action or a reaction, from the system. And the active engineering, there are a lot of different ways you could do it, all the way from, you know, raw, just I'm going to send you whatever I'm thinking. >> Yeah. >> And you get the unintended outcomes, to more constrained, where I'm going to just use my own data, and I'm going to constrain the initial inputs, the data I already know that's first party, and I trust, to, you know, hyper constrain, where the application is actually, it's looking for certain elements to respond to. >> It's interesting Amr, this is why I love this, because one we are in the media, we're recording this video now, we'll stream it. But we got all your linguistics, we're talking. >> Yes. >> This is data. >> Yep. >> So the data quality becomes now the new intellectual property, because, if you have that prompt source data, it makes data or content, in our case, the original content, intellectual property. >> Absolutely. >> Because that's the value. And that's where you see chatGPT fall down, is because they're trying to scroll the web, and people think it's search. It's not necessarily search, it's giving you something that you wanted. It is a lot of that, I remember in Cloudera, you said, "Ask the right questions." Remember that phrase you guys had, that slogan? >> Mm hmm. And that's prompt engineering. So that's exactly, that's the reinvention of "Ask the right question," is prompt engineering is, if you don't give these models the question in the right way, and very few people know how to frame it in the right way with the right context, then you will get garbage out. Right? That is the garbage in, garbage out. But if you specify the question correctly, and you provide with it the metadata that constrain what that question is going to be acted upon or answered upon, then you'll get much better answers. And that's exactly what we solved Vectara. >> Okay. So before we get into the last couple minutes we have left, I want to make sure we get a plug in for the opportunity, and the profile of Vectara, your new company. Can you guys both share with me what you think the current situation is? So for the folks who are now having those moments of, "Ah, AI's bullshit," or, "It's not real, it's a lot of stuff," from, "Oh my god, this is magic," to, "Okay, this is the future." >> Yes. >> What would you say to that person, if you're at a cocktail party, or in the elevator say, "Calm down, this is the first inning." How do you explain the dynamics going on right now, to someone who's either in the industry, but not in the ropes? How would you explain like, what this wave's about? How would you describe it, and how would you prepare them for how to change their life around this? >> Yeah, so I'll go first and then I'll let Ed go. Efficiency, efficiency is the description. So we figured that a way to be a lot more efficient, a way where you can write a lot more emails, create way more content, create way more presentations. Developers can develop 10 times faster than they normally would. And that is very similar to what happened during the Industrial Revolution. I always like to look at examples from the past, to read what will happen now, and what will happen in the future. So during the Industrial Revolution, it was about efficiency with our hands, right? So I had to make a piece of cloth, like this piece of cloth for this shirt I'm wearing. Our ancestors, they had to spend month taking the cotton, making it into threads, taking the threads, making them into pieces of cloth, and then cutting it. And now a machine makes it just like that, right? And the ancestors now turned from the people that do the thing, to manage the machines that do the thing. And I think the same thing is going to happen now, is our efficiency will be multiplied extremely, as human beings, and we'll be able to do a lot more. And many of us will be able to do things they couldn't do before. So another great example I always like to use is the example of Google Maps, and GPS. Very few of us knew how to drive a car from one location to another, and read a map, and get there correctly. But once that efficiency of an AI, by the way, behind these things is very, very complex AI, that figures out how to do that for us. All of us now became amazing navigators that can go from any point to any point. So that's kind of how I look at the future. >> And that's a great real example of impact. Ed, your take on how you would talk to a friend, or colleague, or anyone who asks like, "How do I make sense of the current situation? "Is it real? "What's in it for me, and what do I do?" I mean every company's rethinking their business right now, around this. What would you say to them? >> You know, I usually like to show, rather than describe. And so, you know, the other day I just got access, I've been using an application for a long time, called Notion, and it's super popular. There's like 30 or 40 million users. And the new version of Notion came out, which has AI embedded within it. And it's AI that allows you primarily to create. So if you could break down the world of AI into find and create, for a minute, just kind of logically separate those two things, find is certainly going to be massively impacted in our experiences as consumers on, you know, Google and Bing, and I can't believe I just said the word Bing in the same sentence as Google, but that's what's happening now (all laughing), because it's a good example of change. >> Yes. >> But also inside the business. But on the crate side, you know, Notion is a wiki product, where you try to, you know, note down things that you are thinking about, or you want to share and memorialize. But sometimes you do need help to get it down fast. And just in the first day of using this new product, like my experience has really fundamentally changed. And I think that anybody who would, you know, anybody say for example, that is using an existing app, I would show them, open up the app. Now imagine the possibility of getting a starting point right off the bat, in five seconds of, instead of having to whole cloth draft this thing, imagine getting a starting point then you can modify and edit, or just dispose of and retry again. And that's the potential for me. I can't imagine a scenario where, in a few years from now, I'm going to be satisfied if I don't have a little bit of help, in the same way that I don't manually spell check every email that I send. I automatically spell check it. I love when I'm getting type ahead support inside of Google, or anything. Doesn't mean I always take it, or when texting. >> That's efficiency too. I mean the cloud was about developers getting stuff up quick. >> Exactly. >> All that heavy lifting is there for you, so you don't have to do it. >> Right? >> And you get to the value faster. >> Exactly. I mean, if history taught us one thing, it's, you have to always embrace efficiency, and if you don't fast enough, you will fall behind. Again, looking at the industrial revolution, the companies that embraced the industrial revolution, they became the leaders in the world, and the ones who did not, they all like. >> Well the AI thing that we got to watch out for, is watching how it goes off the rails. If it doesn't have the right prompt engineering, or data architecture, infrastructure. >> Yes. >> It's a big part. So this comes back down to your startup, real quick, I know we got a couple minutes left. Talk about the company, the motivation, and we'll do a deeper dive on on the company. But what's the motivation? What are you targeting for the market, business model? The tech, let's go. >> Actually, I would like Ed to go first. Go ahead. >> Sure, I mean, we're a developer-first, API-first platform. So the product is oriented around allowing developers who may not be superstars, in being able to either leverage, or choose, or select their own large language models for appropriate use cases. But they that want to be able to instantly add the power of large language models into their application set. We started with search, because we think it's going to be one of the first places that people try to take advantage of large language models, to help find information within an application context. And we've built our own large language models, focused on making it very efficient, and elegant, to find information more quickly. So what a developer can do is, within minutes, go up, register for an account, and get access to a set of APIs, that allow them to send data, to be converted into a format that's easy to understand for large language models, vectors. And then secondarily, they can issue queries, ask questions. And they can ask them very, the questions that can be asked, are very natural language questions. So we're talking about long form sentences, you know, drill down types of questions, and they can get answers that either come back in depending upon the form factor of the user interface, in list form, or summarized form, where summarized equals the opportunity to kind of see a condensed, singular answer. >> All right. I have a. >> Oh okay, go ahead, you go. >> I was just going to say, I'm going to be a customer for you, because I want, my dream was to have a hologram of theCUBE host, me and Dave, and have questions be generated in the metaverse. So you know. (all laughing) >> There'll be no longer any guests here. They'll all be talking to you guys. >> Give a couple bullets, I'll spit out 10 good questions. Publish a story. This brings the automation, I'm sorry to interrupt you. >> No, no. No, no, I was just going to follow on on the same. So another way to look at exactly what Ed described is, we want to offer you chatGPT for your own data, right? So imagine taking all of the recordings of all of the interviews you have done, and having all of the content of that being ingested by a system, where you can now have a conversation with your own data and say, "Oh, last time when I met Amr, "which video games did we talk about? "Which movie or book did we use as an analogy "for how we should be embracing data science, "and big data, which is moneyball," I know you use moneyball all the time. And you start having that conversation. So, now the data doesn't become a passive asset that you just have in your organization. No. It's an active participant that's sitting with you, on the table, helping you make decisions. >> One of my favorite things to do with customers, is to go to their site or application, and show them me using it. So for example, one of the customers I talked to was one of the biggest property management companies in the world, that lets people go and rent homes, and houses, and things like that. And you know, I went and I showed them me searching through reviews, looking for information, and trying different words, and trying to find out like, you know, is this place quiet? Is it comfortable? And then I put all the same data into our platform, and I showed them the world of difference you can have when you start asking that question wholeheartedly, and getting real information that doesn't have anything to do with the words you asked, but is really focused on the meaning. You know, when I asked like, "Is it quiet?" You know, answers would come back like, "The wind whispered through the trees peacefully," and you know, it's like nothing to do with quiet in the literal word sense, but in the meaning sense, everything to do with it. And that that was magical even for them, to see that. >> Well you guys are the front end of this big wave. Congratulations on the startup, Amr. I know you guys got great pedigree in big data, and you've got a great team, and congratulations. Vectara is the name of the company, check 'em out. Again, the startup boom is coming. This will be one of the major waves, generative AI is here. I think we'll look back, and it will be pointed out as a major inflection point in the industry. >> Absolutely. >> There's not a lot of hype behind that. People are are seeing it, experts are. So it's going to be fun, thanks for watching. >> Thanks John. (soft music)

(upbeat music) >> On February 19th of this year, Barron's dropped an article declaring Oracle, a cloud giant and the article explained why the stock was a buy. Investors took notice and the stock ran up 18% over the next nine trading days and it peaked on March 9th, the day before Oracle announced its latest earnings. The company beat consensus earnings on both top-line and EPS last quarter, but investors, they did not like Oracle's tepid guidance and the stock pulled back. But it's still, as you can see, well above its pre-Barron's article price. What does all this mean? Is Oracle a cloud giant? What are its growth prospects? Now many parts of Oracle's business are growing including Fusion ERP, Fusion HCM, NetSuite, we're talking deep into the double digits, 20 plus percent growth. It's OnPrem legacy licensed business however, continues to decline and that moderates, the overall company growth because that OnPrem business is so large. So the overall Oracle's growing in the low single digits. Now what stands out about Oracle is it's recurring revenue model. That figure, the company says now it represents 73% of its revenue and that's going to continue to grow. Now two other things stood out on the earnings call to us. First, Oracle plans on increasing its CapEX by 50% in the coming quarter, that's a lot. Now it's still far less than AWS Google or Microsoft Spend on capital but it's a meaningful data point. Second Oracle's consumption revenue for Autonomous Database and Cloud Infrastructure, OCI or Oracle Cloud Infrastructure grew at 64% and 139% respectively and these two factors combined with the CapEX Spend suggest that the company has real momentum. I mean look, it's possible that the CapEx announcements maybe just optics in they're front loading, some spend to show the street that it's a player in cloud but I don't think so. Oracle's Safra Catz's usually pretty disciplined when it comes to it's spending. Now today on March 17th, Oracle announced updates towards Autonomous Data Warehouse and with me is David Floyer who has extensively researched Oracle over the years and today we're going to unpack the Oracle Autonomous Data Warehouse, ADW announcement. What it means to customers but we also want to dig into Oracle's strategy. We want to compare it to some other prominent database vendors specifically, AWS and Snowflake. David Floyer, Welcome back to The Cube, thanks for making some time for me. >> Thank you Vellante, great pleasure to be here. >> All right, I want to get into the news but I want to start with this idea of the autonomous database which Oracle's announcement today is building on. Oracle uses the analogy of a self-driving car. It's obviously powerful metaphor as they call it the self-driving database and my takeaway is that, this means that the system automatically provisions, it upgrades, it does all the patching for you, it tunes itself. Oracle claims that all reduces labor costs or admin costs by 90%. So I ask you, is this the right interpretation of what Oracle means by autonomous database? And is it real? >> Is that the right interpretation? It's a nice analogy. It's a test to that analogy, isn't it? I would put it as the first stage of the Autonomous Data Warehouse was to do the things that you talked about, which was the tuning, the provisioning, all of that sort of thing. The second stage is actually, I think more interesting in that what they're focusing on is making it easy to use for the end user. Eliminating the requirement for IT, staff to be there to help in the actual using of it and that is a very big step for them but an absolutely vital step because all of the competition focusing on ease of use, ease of use, ease of use and cheapness of being able to manage and deploy. But, so I think that is the really important area that Oracle has focused on and it seemed to have done so very well. >> So in your view, is this, I mean you don't really hear a lot of other companies talking about this analogy of the self-driving database, is this unique? Is it differentiable for Oracle? If so, why, or maybe you could help us understand that a little bit better. >> Well, the whole strategy is unique in its breadth. It has really brought together a whole number of things together and made it of its type the best. So it has a single, whole number of data sources and database types. So it's got a very broad range of different ways that you can look at the data and the second thing that is also excellent is it's a platform. It is fully self provisioned and its functionality is very, very broad indeed. The quality of the original SQL and the query languages, etc, is very, very good indeed and it's a better agent to do joints for example, is excellent. So all of the building blocks are there and together with it's sharing of the same data with OLTP and inference and in memory data paces as well. All together the breadth of what they have is unique and very, very powerful. >> I want to come back to this but let's get into the news a little bit and the announcement. I mean, it seems like what's new in the autonomous data warehouse piece for Oracle's new tooling around four areas that so Andy Mendelsohn, the head of this group instead of the guy who releases his baby, he talked about four things. My takeaway, faster simpler loads, simplified transforms, autonomous machine learning models which are facilitating, What do you call it? Citizen data science and then faster time to insights. So tooling to make those four things happen. What's your take and takeaways on the news? >> I think those are all correct. I would add the ease of use in terms of being able to drag and drop, the user interface has been dramatically improved. Again, I think those, strategically are actually more important that the others are all useful and good components of it but strategically, I think is more important. There's ease of use, the use of apex for example, are more important. And, >> Why are they more important strategically? >> Because they focus on the end users capability. For example, one of other things that they've started to introduce is Python together with their spatial databases, for example. That is really important that you reach out to the developer as they are and what tools they want to use. So those type of ease of use things, those types of things are respecting what the end users use. For example, they haven't come out with anything like click or Tableau. They've left that there for that marketplace for the end user to use what they like best. >> Do you mean, they're not trying to compete with those two tools. They indeed had a laundry list of stuff that they supported, Talend, Tableau, Looker, click, Informatica, IBM, I had IBM there. So their claim was, hey, we're open. But so that's smart. That's just, hey, they realized that people use these tools. >> I'm trying to exclude other people, be a platform and be an ecosystem for the end users. >> Okay, so Mendelsohn who made the announcement said that Oracle's the smartphone of databases and I think, I actually think Alison kind of used that or maybe that was us planing to have, I thought he did like the iPhone of when he announced the exit data way back when the integrated hardware and software but is that how you see it, is Oracle, the smartphone of databases? >> It is, I mean, they are trying to own the complete stack, the hardware with the exit data all the way up to the databases at the data warehouses and the OLTP databases, the inference databases. They're trying to own the complete stack from top to bottom and that's what makes autonomy process possible. You can make it autonomous when you control all of that. Take away all of the requirements for IT in the business itself. So it's democratizing the use of data warehouses. It is pushing it out to the lines of business and it's simplifying it and making it possible to push out so that they can own their own data. They can manage their own data and they do not need an IT person from headquarters to help them. >> Let's stay in this a little bit more and then I want to go into some of the competitive stuff because Mendelsohn mentioned AWS several times. One of the things that struck me, he said, hey, we're basically one API 'cause we're doing analytics in the cloud, we're doing data in the cloud, we're doing integration in the cloud and that's sort of a big part of the value proposition. He made some comparisons to Redshift. Of course, I would say, if you can't find a workload where you beat your big competitor then you shouldn't be in this business. So I take those things with a grain of salt but one of the other things that caught me is that migrating from OnPrem to Oracle, Oracle Cloud was very simple and I think he might've made some comparisons to other platforms. And this to me is important because he also brought in that Gartner data. We looked at that Gardner data when they came out with it in the operational database class, Oracle smoked everybody. They were like way ahead and the reason why I think that's important is because let's face it, the Mission Critical Workloads, when you look at what's moving into AWS, the Mission Critical Workloads, the high performance, high criticality OLTP stuff. That's not moving in droves and you've made the point often that companies with their own cloud particularly, Oracle you've mentioned this about IBM for certain, DB2 for instance, customers are going to, there should be a lower risk environment moving from OnPrem to their cloud, because you could do, I don't think you could get Oracle RAC on AWS. For example, I don't think EXIF data is running in AWS data centers and so that like component is going to facilitate migration. What's your take on all that spiel? >> I think that's absolutely right. You all crown Jewels, the most expensive and the most valuable applications, the mission-critical applications. The ones that have got to take a beating, keep on taking. So those types of applications are where Oracle really shines. They own a very large high percentage of those Mission Critical Workloads and you have the choice if you're going to AWS, for example of either migrating to Oracle on AWS and that is frankly not a good fit at all. There're a lot of constraints to running large systems on AWS, large mission critical systems. So that's not an option and then the option, of course, that AWS will push is move to a Roller, change your way of writing applications, make them tiny little pieces and stitch them all together with microservices and that's okay if you're a small organization but that has got a lot of problems in its own, right? Because then you, the user have to stitch all those pieces together and you're responsible for testing it and you're responsible for looking after it. And that as you grow becomes a bigger and bigger overhead. So AWS, in my opinion needs to have a move towards a tier-one database of it's own and it's not in that position at the moment. >> Interesting, okay. So, let's talk about the competitive landscape and the choices that customers have. As I said, Mendelssohn mentioned AWS many times, Larry on the calls often take shy, it's a compliment to me. When Larry Ellison calls you out, that means you've made it, you're doing well. We've seen it over the years, whether it's IBM or Workday or Salesforce, even though Salesforce's big Oracle customer 'cause AWS, as we know are Oracle customer as well, even though AWS tells us they've off called when you peel the onion >> Five years should be great, some of the workers >> Well, as I said, I believe they're still using Oracle in certain workloads. Way, way, we digress. So AWS though, they take a different approach and I want to push on this a little bit with database. It's got more than a dozen, I think purpose-built databases. They take this kind of right tool for the right job approach was Oracle there converging all this function into a single database. SQL JSON graph databases, machine learning, blockchain. I'd love to talk about more about blockchain if we have time but seems to me that the right tool for the right job purpose-built, very granular down to the primitives and APIs. That seems to me to be a pretty viable approach versus kind of a Swiss Army approach. How do you compare the two? >> Yes, and it is to many initial programmers who are very interested for example, in graph databases or in time series databases. They are looking for a cheap database that will do the job for a particular project and that makes, for the program or for that individual piece of work is making a very sensible way of doing it and they pay for ads on it's clear cloud dynamics. The challenge as you have more and more data and as you're building up your data warehouse in your data lakes is that you do not want to have to move data from one place to another place. So for example, if you've got a Roller,, you have to move the database and it's a pretty complicated thing to do it, to move it to Redshift. It's a five or six steps to do that and each of those costs money and each of those take time. More importantly, they take time. The Oracle approach is a single database in terms of all the pieces that obviously you have multiple databases you have different OLTP databases and data warehouse databases but as a single architecture and a single design which means that all of the work in terms of moving stuff from one place to another place is within Oracle itself. It's Oracle that's doing that work for you and as you grow, that becomes very, very important. To me, very, very important, cost saving. The overhead of all those different ones and the databases themselves originate with all as open source and they've done very well with it and then there's a large revenue stream behind the, >> The AWS, you mean? >> Yes, the original database is in AWS and they've done a lot of work in terms of making it set with the panels, etc. But if a larger organization, especially very large ones and certainly if they want to combine, for example data warehouse with the OLTP and the inference which is in my opinion, a very good thing that they should be trying to do then that is incredibly difficult to do with AWS and in my opinion, AWS has to invest enormously in to make the whole ecosystem much better. >> Okay, so innovation required there maybe is part of the TAM expansion strategy but just to sort of digress for a second. So it seems like, and by the way, there are others that are doing, they're taking this converged approach. It seems like that is a trend. I mean, you certainly see it with single store. I mean, the name sort of implies that formerly MemSQL I think Monte Zweben of splice machine is probably headed in a similar direction, embedding AI in Microsoft's, kind of interesting. It seems like Microsoft is willing to build this abstraction layer that hides that complexity of the different tooling. AWS thus far has not taken that approach and then sort of looking at Snowflake, Snowflake's got a completely different, I think Snowflake's trying to do something completely different. I don't think they're necessarily trying to take Oracle head-on. I mean, they're certainly trying to just, I guess, let's talk about this. Snowflake simplified EDW, that's clear. Zero to snowflake in 90 minutes. It's got this data cloud vision. So you sign on to this Snowflake, speaking of layers they're abstracting the complexity of the underlying cloud. That's what the data cloud vision is all about. They, talk about this Global Mesh but they've not done a good job of explaining what the heck it is. We've been pushing them on that, but we got, >> Aspiration of moment >> Well, I guess, yeah, it seems that way. And so, but conceptually, it's I think very powerful but in reality, what snowflake is doing with data sharing, a lot of reading it's probably mostly read-only and I say, mostly read-only, oh, there you go. You'll get better but it's mostly read and so you're able to share the data, it's governed. I mean, it's exactly, quite genius how they've implemented this with its simplicity. It is a caching architecture. We've talked about that, we can geek out about that. There's good, there's bad, there's ugly but generally speaking, I guess my premise here I would love your thoughts. Is snowflakes trying to do something different? It's trying to be not just another data warehouse. It's not just trying to compete with data lakes. It's trying to create this data cloud to facilitate data sharing, put data in the hands of business owners in terms of a product build, data product builders. That's a different vision than anything I've seen thus far, your thoughts. >> I agree and even more going further, being a place where people can sell data. Put it up and make it available to whoever needs it and making it so simple that it can be shared across the country and across the world. I think it's a very powerful vision indeed. The challenge they have is that the pieces at the moment are very, very easy to use but the quality in terms of the, for example, joints, I mentioned, the joints were very powerful in Oracle. They don't try and do joints. They, they say >> They being Snowflake, snowflake. Yeah, they don't even write it. They would say use another Postgres >> Yeah. >> Database to do that. >> Yeah, so then they have a long way to go. >> Complex joints anyway, maybe simple joints, yeah. >> Complex joints, so they have a long way to go in terms of the functionality of their product and also in my opinion, they sure be going to have more types of databases inside it, including OLTP and they can do that. They have obviously got a great market gap and they can do that by acquisition as well as they can >> They've started. I think, I think they support JSON, right. >> Do they support JSON? And graph, I think there's a graph database that's either coming or it's there, I can't keep all that stuff in my head but there's no reason they can't go in that direction. I mean, in speaking to the founders in Snowflake they were like, look, we're kind of new. We would focus on simple. A lot of them came from Oracle so they know all database and they know how hard it is to do things like facilitate complex joints and do complex workload management and so they said, let's just simplify, we'll put it in the cloud and it will spin up a separate data warehouse. It's a virtual data warehouse every time you want one to. So that's how they handle those things. So different philosophy but again, coming back to some of the mission critical work and some of the larger Oracle customers, they said they have a thousand autonomous database customers. I think it was autonomous database, not ADW but anyway, a few stood out AON, lift, I think Deloitte stood out and as obviously, hundreds more. So we have people who misunderstand Oracle, I think. They got a big install base. They invest in R and D and they talk about lock-in sure but the CIO that I talked to and you talked to David, they're looking for business value. I would say that 75 to 80% of them will gravitate toward business value over the fear of lock-in and I think at the end of the day, they feel like, you know what? If our business is performing, it's a better business decision, it's a better business case. >> I fully agree, they've been very difficult to do business with in the past. Everybody's in dread of the >> The audit. >> The knock on the door from the auditor. >> Right. >> And that from a purchasing point of view has been really bad experience for many, many customers. The users of the database itself are very happy indeed. I mean, you talk to them and they understand why, what they're paying for. They understand the value and in terms of availability and all of the tools for complex multi-dimensional types of applications. It's pretty well, the only game in town. It's only DB2 and SQL that had any hope of doing >> Doing Microsoft, Microsoft SQL, right. >> Okay, SQL >> Which, okay, yeah, definitely competitive for sure. DB2, no IBM look, IBM lost its dominant position in database. They kind of seeded that. Oracle had to fight hard to win it. It wasn't obvious in the 80s who was going to be the database King and all had to fight. And to me, I always tell people the difference is that the chairman of Oracle is also the CTO. They spend money on R and D and they throw off a ton of cash. I want to say something about, >> I was just going to make one extra point. The simplicity and the capability of their cloud versions of all of this is incredibly good. They are better in terms of spending what you need or what you use much better than AWS, for example or anybody else. So they have really come full circle in terms of attractiveness in a cloud environment. >> You mean charging you for what you consume. Yeah, Mendelsohn talked about that. He made a big point about the granularity, you pay for only what you need. If you need 33 CPUs or the other databases you've got to shape, if you need 33, you've got to go to 64. I know that's true for everyone. I'm not sure if that's true too for snowflake. It may be, I got to dig into that a little bit, but maybe >> Yes, Snowflake has got a front end to hiding behind. >> Right, but I didn't want to push it that a little bit because I want to go look at their pricing strategies because I still think they make you buy, I may be wrong. I thought they make you still do a one-year or two-year or three-year term. I don't know if you can just turn it off at any time. They might allow, I should hold off. I'll do some more research on that but I wanted to make a point about the audits, you mentioned audits before. A big mistake that a lot of Oracle customers have made many times and we've written about this, negotiating with Oracle, you've got to bring your best and your brightest when you negotiate with Oracle. Some of the things that people didn't pay attention to and I think they've sort of caught onto this is that Oracle's SOW is adjudicate over the MSA, a lot of legal departments and procurement department. Oh, do we have an MSA? With all, Yes, you do, okay, great and because they think the MSA, they then can run. If they have an MSA, they can rubber stamp it but the SOW really dictateS and Oracle's gotcha there and they're really smart about that. So you got to bring your best and the brightest and you've got to really negotiate hard with Oracle, you get trouble. >> Sure. >> So it is what it is but coming back to Oracle, let's sort of wrap on this. Dominant position in mission critical, we saw that from the Gartner research, especially for operational, giant customer base, there's cloud-first notion, there's investing in R and D, open, we'll put a question Mark around that but hey, they're doing some cool stuff with Michael stuff. >> Ecosystem, I put that, ecosystem they're promoting their ecosystem. >> Yeah, and look, I mean, for a lot of their customers, we've talked to many, they say, look, there's actually, a tail at the tail way, this saves us money and we don't have to migrate. >> Yeah. So interesting, so I'll give you the last word. We started sort of focusing on the announcement. So what do you want to leave us with? >> My last word is that there are platforms with a certain key application or key parts of the infrastructure, which I think can differentiate themselves from the Azures or the AWS. and Oracle owns one of those, SAP might be another one but there are certain platforms which are big enough and important enough that they will, in my opinion will succeed in that cloud strategy for this. >> Great, David, thanks so much, appreciate your insights. >> Good to be here. Thank you for watching everybody, this is Dave Vellante for The Cube. We'll see you next time. (upbeat music)

(techno music) >> Announcer: From the Corinium Chief Analytics Officer Conference Spring San Francisco. It's theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Parc 55 Hotel at the Corinium Chief Analytics Officer Spring 2018 event, about 100 people, pretty intimate affair. A lot of practitioners here talking about the challenges of Big Data and the challenges of Analytics. We're really excited to have a very special Cube guest. I think he was the first guy to launch his company on theCUBE. It was Big Data New York City 2013. I remember it distinctly. It's Prakash Nanduri, the co-founder and CEO of Paxata. Great to see you. >> Great seeing you. Thank you for having me back. >> Absolutely. You know we got so much mileage out of that clip. We put it on all of our promotional materials. You going to launch your company? Launch your company on theCUBE. >> You know it seems just like yesterday but it's been a long ride and it's been a fantastic ride. >> So give us just a quick general update on the company, where you guys are now, how things are going. >> Things are going fantastic. We continue to grow. If you recall, when we launched, we launched the whole notion of democratization of information in the enterprise with self service data prep. We have gone onto now delivered real value to some of the largest brands in the world. We're very proud that 2017 was the year when massive amount of adoption of Paxata's adaptive information platform was taken across multiple industries, financial services, retail, CPG, high tech, in the OIT space. So, we just keep growing and it's the usual challenges of managing growth and managing, you know, the change in the company as you, as you grow from being a small start-up to know being a real company. >> Right, right. There's good problems and bad problems. Those are the good problems. >> Yes, yes. >> So, you know, we do so many shows and there's two big themes over and over and over like digital transformation which gets way over used and then innovation and how do you find a culture of innovation. In doing literally thousands of these interviews, to me it seems pretty simple. It is about democratization. If you give more people the data, more people the tools to work with the data, and more people the power to do something once they find something in the data, and open that up to a broader set of people, they're going to find innovations, simply the fact of doing it. But the reality is those three simple steps aren't necessarily very easy to execute. >> You're spot on, you're spot on. I like to say that when we talk about digital transformation the real focus should be on the deed . And it really centers around data and it centers around the whole notion of democratization, right? The challenge always in large enterprises is democratization without governance becomes chaos. And we always need to focus on democratization. We need to focus on data because as we all know data is the new oil, all of that, and governance becomes a critical piece too. But as you recall, when we launched Paxata, the entire vision from day one has been while the entire focus around digitization covers many things right? It covers people processes. It covers applications. It's a very large topic, the whole digital transformation of enterprise. But the core foundation to digital transformation, data democratization governance, but the key issue is the companies that are going to succeed are the companies that turn data into information that's relevant for every digital transformation effort. >> Right, right. >> Because if you do not turn raw data into information, you're just dealing with raw data which is not useful >> Jeff: Right >> And it will not be democratized. >> Jeff: Right >> Because the business will only consume the information that is contextual to their need, the information that's complete and the information that is clean. >> Right, right. >> So that's really what we're driving towards. >> And that's interesting 'cause the data, there's so many more sources of data, right? There's data that you control. There's structured data, unstructured data. You know, I used to joke, just the first question when you'd ask people "Where's your data?", half the time they couldn't even, they couldn't even get beyond that step. And that's before you start talking about cleaning it and making it ready and making it available. Before you even start to get into governance and rights and access so it's a really complicated puzzle to solve on the backend. >> I think it starts with first focusing on what are the business outcomes we are driving with digital transformation. When you double-click on digital transformation and then you start focusing on data and information, there's a few things that come to fore. First of all, how do I leverage information to improve productivity in my company? There's multiple areas, whether it is marketing or supply chain or whatever. The second notion is how do I ensure that I can actually transform the culture in my company and attract the brightest and the best by giving them the the environment where democratization of information is actually reality, where people feel like they're empowered to access data and turn it into information and then be able to do really interesting things. Because people are not interested on being subservient to somebody who gives them the data. They want to be saying "Give it to me. "I'm smart enough. "I know analytics. "I think analytically and I want to drive my career forward." So the second thing is the cultural aspect to it. And the last thing, which is really important is every company, regardless of whether you're making toothpicks or turbines, you are looking to monetize data. So it's about productivity. It's about cultural change and attracting of talent. And it's about monetization. And when it comes to monetization of data, you cannot be satisfied with only covering enterprise data which is sitting in my enterprise systems. You have to be able to focus on, oh, how can I leverage the IOT data that's being generated from my products or widgets. How can I generate social immobile? How can I consume that? How can I bring all of this together and get the most complete insight that I need for my decision-making process? >> Right. So, I'm just curious, how do you see it your customers? So this is the chief analytics officer, we go to chief data officer, I mean, there's all these chief something officers that want to get involved in data and marketing is much more involved with it. Forget about manufacturing. So when you see successful cultural change, what drives that? Who are the people that are successful and what is the secret to driving the cultural change that we are going to be data-driven, we are going to give you the tools, we are going to make the investment to turn data which historically was even arguably a liability 'cause it had to buy a bunch o' servers to stick it on, into that now being an asset that drives actionable outcomes? >> You know, recently I was having this exact discussion with the CEO of one of the largest financial institutions in the world. This gentleman is running a very large financial services firm, is dealing with all the potential disruption where they're seeing completely new type of PINTEC products coming in, the whole notion of blockchain et cetera coming in. Everything is changing. Everything looks very dramatic. And what we started talking about is the first thing as the CEO that we always focus on is do we have the right people? And do we have the people that are motivated and driven to basically go and disrupt and change? For those people, you need to be able to give them the right kind of tools, the right kind of environment to empower them. This doesn't start with lip service. It doesn't start about us saying "We're going to be on a digital transformation journey" but at the same time, your data is completely in silos. It's locked up. There is 15,000 checks and balances before I can even access a simple piece of data and third, even when I get access to it, it's too little, too late or it's garbage in, garbage out. And that's not the culture. So first, it needs to be CEO drive, top down. We are going to go through digital transformation which means we are going to go through a democratization effort which means we are going to look at data and information as an asset and that means we are not only going to be able to harness these assets, but we're also going to monetize these assets. How are we going to do it? It depends very much on the business you're in, the vertical industry you play in, and your strengths and weaknesses. So each company has to look at it from their perspective. There's no one size fits all for everyone. >> Jeff: Right. >> There are some companies that have fantastic cultures of empowerment and openness but they may not have the right innovation or the right kind of product innovation skills in place. So it's about looking at data across the board. First from your culture and your empowerment, second about democratization of information which is where a company like Paxata comes in, and third, along with democratization, you have to focus on governance because we are for-profit companies. We have a fiducial responsibility to our customers and our regulators and therefore we cannot have democratization without governance. >> Right, right >> And that's really what our biggest differentiation is. >> And then what about just in terms of the political play inside the company. You know, on one hand, used to be if you held the information, you had the power. And now that's changed really 'cause there's so much information. It's really, if you are the conduit of information to help people make better decisions, that's actually a better position to be. But I'm sure there's got to be some conflicts going through digital transformation where I, you know, I was the keeper of the kingdom and now you want to open that up. Conversely, it must just be transformational for the people on the front lines that finally get the data that they've been looking for to run the analysis that they want to rather than waiting for the weekly reports to come down from on high. >> You bet. You know what I like to say is that if you've been in a company for 10, 15 years and if you felt like a particular aspect, purely selfishly, you felt a particular aspect was job security, that is exactly what's going to likely make you lose your job today. What you thought 10 years ago was your job security, that's exactly what's going to make you lose your job today. So if you do not disrupt yourself, somebody else will. So it's either transform yourself or not. Now this whole notion of politics and you know, struggle within the company, it's been there for as long as, humans generally go towards entropy. So, if you have three humans, you have all sort of issues. >> Jeff: Right, right. >> The issue starts frankly with leadership. It starts with the CEO coming down and not only putting an edict down on how things will be done but actually walking the walk with talking the talk. If, as a CEO, you're not transparent, it you're not trusting your people, if you're not sharing information which could be confidential, but you mention that it's confidential but you have to keep this confidential. If you trust your people, you give them the ability to, I think it's a culture change thing. And the second thing is incentivisation. You have to be able to focus on giving people the ability to say "by sharing my data, "I actually become a hero." >> Right, right. >> By giving them the actual credit for actually delivering the data to achieve an outcome. And that takes a lot of work. But if you do not actually drive the cultural change, you will not drive the digital transformation and you will not drive the democratization of information. >> And have you seen people try to do it without making the commitment? Have you seen 'em pay the lip service, spend a few bucks, start a project but then ultimately they, they hamstring themselves 'cause they're not actually behind it? >> Look, I mean, there's many instances where companies start on digital transformation or they start jumping into cool terms like AI or machine-learning, and there's a small group of people who are kind of the elites that go in and do this. And they're given all the kind of attention et cetera. Two things happen. Because these people who are quote, unquote, the elite team, either they are smart but they're not able to scale across the organization or many times, they're so good, they leave. So that transformation doesn't really get democratized. So it is really important from day one to start a culture where you're not going to have a small group of exclusive data scientists. You can have those people but you need to have a broader democratization focus. So what I have seen is many of the siloed, small, tight, mini science projects end up failing. They fail because number one, either the business outcome is not clearly identified early on or two, it's not scalable across the enterprise. >> Jeff: Right. >> And a majority of these exercises fail because the whole information foundation that is taking raw data turning it into clean, complete, potential consumable information, to feed across the organization, not just for one siloed group, not just one data science team. But how do you do that across the company? That's what you need to think from day one. When you do these siloed things, these departmental things, a lot of times they can fail. Now, it's important to say "I will start with a couple of test cases" >> Jeff: Right, right. >> "But I'm going to expand it across "from the beginning to think through that." >> So I'm just curious, your perspective, is there some departments that are the ripest for being that leading edge of the digital transformation in terms of, they've got the data, they've got the right attitude, they're just a short step away. Where have you seen the great place to succeed when you're starting on kind of a smaller PLC, I don't know if you'd say PLC, project or department level? >> So, it's funny but you will hear this, it's not rocket science. Always they say, follow the money. So, in a business, there are three incentives, making more money, saving money, or staying out of jail. (laughs) >> Those are good. I don't know if I'd put them in that order but >> Exactly, and you know what? Depending on who are you are, you may have a different order but staying out of jail if pretty high on my list. >> Jeff: I'm with you on that one. >> So, what are the ambiants? Risk and compliance. Right? >> Jeff: Right, right. >> That's one of those things where you absolutely have to deliver. You absolutely have to do it. It's significantly high cost. It's very data and analytic centric and if you find a smart way to do it, you can dramatically reduce your cost. You can significantly increase your quality and you can significantly increase the volume of your insights and your reporting, thereby achieving all the risk and compliance requirements but doing it in a smarter way and a less expensive way. >> Right. >> That's where incentives have really been high. Second, in making money, it always comes down to sales and marketing and customer success. Those are the three things, sales, marketing, and customer success. So most of our customers who have been widely successful, are the ones who have basically been able to go and say "You know what? "It used to take us eight months "to be able to even figure out a customer list "for a particular region. "Now it takes us two days because of Paxata "and because of the data prep capabilities "and the governance aspects." That's the power that you can deliver today. And when you see one person who's a line of business person who says "Oh my God. "What used to take me eight months, "now it's done in half a day". Or "What use to take me 22 days to create a report, "is now done in 45 minutes." All of a sudden, you will not have a small kind of trickle down, you will have a tsunami of democratization with governance. That's what we've seen in our customers. >> Right, right. I love it. And this is just so classic too. I always like to joke, you know, back in the day, you would run your business based on reports from old data. Now we want to run your business with stuff you can actually take action on now. >> Exactly. I mean, this is public, Shameek Kundu, the chief data officer of Standard Chartered Bank and Michael Gorriz who's the global CIO of Standard Chartered Bank, they have embraced the notion that information democratization in the bank is a foundational element to the digital transformation of Standard Chartered. They are very forward thinking and they're looking at how do I democratize information for all our 87,500 employees while we maintain governance? And another major thing that they are looking at is they know that the data that they need to manipulate and turn into information is not sitting only on premise. >> Right, right. >> It's sitting across a multi-cloud world and that's why they've embraced the Paxata information platform to be their information fabric for a multi-cloud hybrid world. And this is where we see successes and we're seeing more and more of this, because it starts with the people. It starts with the line of business outcomes and then it starts with looking at it from scale. >> Alright, Prakash, well always great to catch up and enjoy really watching the success of the company grow since you launched it many moons ago in New York City >> yes Fantastic. Always a pleasure to come back here. Thank you so much. >> Alright. Thank you. He's Prakash, I'm Jeff Frick. You're watching theCUBE from downtown San Francisco. Thanks for watching. (techno music)

(upbeat music) >> Welcome to this week's edition of Wikibon Research Meeting on the Cube. This week we're going to talk about a rather important issue that raises a lot of questions about the future of the industry and that is, how are information technology organizations going to manage the wide array of new applications, new types of users, new types of business relationships that's going to engender significant complexity in the way applications are organized, architected and run. One of the possibilities is that we'll see an increased use of machine learning, ultimately inside information technology and operations management applications and while this has tremendous potential, it's not without risk and it's not going to be simple. These technologies sound great on paper but they typically engender an enormous amount of work and a lot of complexity themselves to run. Having said that, there are good reasons to suspect that this approach will in fact be crucial to ultimately helping IT achieve the productivity that it needs to support digital business needs. Now a big challenge here is that the technology, while it looks good, as I said, nonetheless is pretty immature and today's world, there's a breadth first and a depth first approach to thinking about this. Breadth first works on or worries about end to end visibility into how applications work across multiple clouds, on premise in the cloud, across applications, wherever they might be. You get an enormous amount of visibility and alerts but you also get a lot of false positives and that creates a challenge because these tools just don't have enormous visibility into how the individual components are working or how their relationships are set up, they just look at the broad spectrum of how work is being conducted. The second class is looking at depth first which is really based on the digital twin notion that's popular within the IOT world and that is vendors delivering out of the box models that are capable of doing a great job of creating a digital simulacrum of a particular resource so that it can be modeled and tracked and tested. Now again, a lot of potential, a lot of questions about how machine learning and iTom are going to come together. George, what is one of the key catalysts here? Somewhere in here there's a question about people. >> Okay there's a talent question, always with the introduction of new technology, it's people processed technology. The people end of the equation here is that we've been trying to upskill and create a new class of application developer as Jim has identified. This new class is a data scientist and they focus on data intensive applications and machine learning technology. The reason I bring up the technology is when we have this landscape that you described, that is getting so complex where we're building on business transaction applications, extending them with systems of engagement and then the operational infrastructure that supports both of them, we're getting many orders of magnitude more complexity in multiple dimensions and in data and so we need a major step function in the technology to simplify the management of that because just the way we choked on the deployment, mainstream deployment of big data technology in terms of lack of the specialized administrators, we are similarly choking on the deployment of very high value machine learning applications because it takes a while to train a new generation of data scientists. >> So George, we got a lot of challenges here in trying to train people but we're also expecting that we're going to be better trained technology with some of these new questions, so Jim let me throw it to you. When we think ultimately about this machine learning approach, what are some of the considerations that people have to worry about as they envision the challenges associated with training some of these new systems? >> Yeah I think one of the key challenges with training new systems for iTom is, do you have a reference data set? The predominant approach to machine learning is something called supervised learning where you're training it on rhythm against some data that represents what you're trying to detect or predict or classify. If for IT and operations management, you're looking for anomalies, for unprecedented events, black swan events and so forth. Clearly, if they're unprecedented, there's probably not going to be a reference data set that you can use to detect them or hopefully before they happen and neutralize them. That's an important consideration and supervised learning breaks down if you can't find a reference data example. Now there are approaches to machine learning, they're called cluster analysis or unsupervised learning, alert to something called cluster analysis algorithms which would be able to look for clusters in the data that might be indicative of correlations that might be useful to drill into, might be indicative of anomalous events and so forth. What I'm getting as it that when you're then considering ML, machine learning in the broader perspective of IT and operations management, do you go supervised learning, do you go with unsupervised learning for the anopolis, do you, if you want to remediate it, that you have a clear set of steps to follow from precedent, you might also want something called reinforcement learning. What I'm getting at is that all the aspects of training the models to acquire the knowledge necessary to manage the IT operations. >> Jim, let me interrupt, what we've got here is a lot of new complexity and we've got a need for more people and we've got a need for additional understanding of how we're going to train these systems but this is going to become an increasingly challenging problem. David Floyer, you've done some really interesting research on with the entire team that we call unigrid. Unigrid is looking at the likely future of systems as we're capable of putting more data proximate to other data and use that as a basis for dramatically improving our ability to, in a speedy, nearly real-time way, drive automation between many of these new application forms. It seems as though depth first, or what we're calling depth first, is going to be an essential element of how unigrid's going to deploy. Take us through that scenario and what do you think about how these are going to come together? >> Yes, I agree. The biggest, in our opinion, the biggest return on investment is going to come from being able to take the big data models, the complex models and make those simple enough that they can, in real time, help the acceleration, the automation of business processes. That seems to be the biggest return on this and unigrid is allowing a huge amount more data to be available in near real-time, 100 to 1000 times more data and that gives us an opportunity for business analytics which includes of course AI and machine learning and basic models, etc. to be used to take that data and apply it to the particular business problem, whether it be fraud control, whether it be any other business processing. The point I'm making here is that coding techniques are going to be very, very stretched. Coding techniques for an edge application in the enterprise itself and also of course coding techniques for pushing down stuff to the IOT and to the other agents. Those coding techniques are going to focus on performance first to begin with. At the same time, a lot of that coding will come from ISVs into existing applications and with it, the ISVs have the problem of ensuring that this type of system can be managed. >> So George, I'm going to throw it back to you at this point in time because based on what Dave has just said, that there's new technology on the horizon that has the potential to drive the business need for this type of technology, we'll get to that in a little bit more detail in a second, but is it possible that at least the depth first side of these ML and IT and iTom applications could become the first successful packaged apps that use machine learning in a featured way? >> That's my belief, and the reason is that even though there's going to be great business value in linking, say big data apps and systems of record and web mobile apps, say for fraud prevention or detection applications where you really want low latency integration, most of the big data applications today are more high latency integration where you're doing training and inferencing more in batch mode and connecting them with high latency with the systems of record or web and mobile apps. When you have that looser connection, high latency connection, it's possible to focus just on the domain, the depth first. Because it's depth first, the models have much more knowledge built in about the topology and operation of that single domain and that knowledge is what allows them to have very precise and very low latency remediation either recommendations or automated actions. >> But the challenge with just looking at it from a depth first standpoint is that as the infrastructure, as the relationships amongst technologies and toolings inside an infrastructure application portfolio is that information is not revealed and becomes more crucial overall to the operation of the system. Now we got to look a little bit at this notion of breadth first, the idea of tooling support end to end. That's a little bit more problematic, there's a lot of tools that are trying to do that today, a lot of services trying to do that today, but one of the things that's clearly missing is an overall good understanding of the dependency that these two tools have on machine learning. Jim, what can you tell us about how overall some of these breadth first products seem to be dependent or not on some of these technologies. >> Yeah, first of all breadth first products, what's neat is above, basically an overall layer is graph analysis, graph modeling to be able to follow a hundred interactions of transactions and business flows across your distributed IT infrastructure, to be able to build that entire narrative of what's causing a problem or might be causing a problem. That's critically important but as you're looking at depth first and you just go back and forth between depth first, like digital twin as a fundamental concept and a fundamentally important infrastructure for depth first, because the digital twin infrastructure maintains the data that can be used for training data for supervised machine learning looking into issues from individual entities. If you can combine overall graph modeling at the breadth first level for iTom with the supervised learning based on digital twin for depth first, that makes for a powerful combination. I'm talking in a speculative way, George has been doing the research, but I'm seeing a lot of uptake of graph modeling technology in the sphere, now maybe George could tell us otherwise, but I think that's what needs to happen. >> I think conceptually, the technology is capable of providing this George, I think that it's going to take some time however, to see it fully exploited. What do you got to say about that? >> I do want to address Jim, your comments about training which is the graph that you're referring to is precisely the word when I use topology figuring that more people will understand that and it's in the depth first product that the models have been pre-trained, supervised and trained by the vendor so they come baked in to know how to figure out the customer's topology and build what you call the graph. Technically, that's the more correct way of describing it and that those models, pre-trained and supervised have enough knowledge also to figure out the behavior which I call the operations of those applications, it's when you get into the breadth first that it's harder because you have no bounds to make assumptions about, it's harder to figure out that topology and operational behavior. >> But coming back to the question I asked, the fact that it's not available today, as depth first products accrete capabilities and demonstrate success, and let's presume that they are because there is evidence that they are, that will increase the likelihood that they are generating data that can then be used by breadth first products. But that raises an interesting question. It's a question that certainly I've thought about as well, is that is, Nick, ultimately where is the clearing house for ascertaining the claims these technologies will not and work together, have you seen examples in the past of standards, at this level of complexity coming together that can ensure that claims in fact, or that these technologies can in fact increasingly work together. Have we've seen other places where this has happened? >> Good question. My answer is that I don't know. >> Well but there have been standards bodies for example that did some extremely complex stuff in IO. Where we saw an explosion in the number of storage and printer and other devices and we saw separation of function between CPUs and channels where standards around SCUZI and what not, in fact were relatively successful, but I don't know that they're going to be as, but there is specific engineering tests at the electricity and physics level and it's going to be interesting to see whether those types of tests emerge here in the software world. All right, I want to segue from this directly into business impacts because ultimately there's a major question for every user that's listening to this and that is this is new technology, we know the business is going to demand it in a lot of ways. The machine learning in business activities, as David Floyer talked about, business processes, but the big question is how is this going to end up in the IT organization? In fact is it going to turn into a crucial research that makes IT more or less successful? Neil Raden, we've got examples of this happening again in the past, where significant technology discontinuities just hit both the business and IT at the same time. What happened? >> Well, in a lot of cases it was a disaster. In many more cases, it was a financial disaster. We had companies spending hundreds of billions of dollars implementing an ERP system and at the end, they still didn't have what they wanted. Look, people not just in IT, not just in business, not just in technology, consistently take complex problems and try to reduce them to something simple so they can understand them. Nowhere is that more common than in medical research where they point at a surrogate endpoint and they try to prove the surrogate endpoint but they end up proving nothing about the disease they're trying to cure. I think that this problem now, it's gone beyond an inventory of applications and organizations, far too complex for people to really grasp all at once. Rather than come up with a simplified solution, I think we can be looking to software vendors to be coming up with packages to do this. But it's not going to be a black box. It's going to require a great deal of configuration and tuning within each company because everyone's a little different. That's what I think is going to happen and the other thing is, I think we're going to have AI on AI. You're going to have a data scientist work bench where the work bench recommends which models to try, runs the replicates, crunches the numbers, generates the reports, keeps track of what's happening, goes back to see what's happened because five years ago, data scientists were basically doing everything in R and Java and Python and there's a mountain of terrible code out there that's unmaintainable because they're not professional programmers, so we have to fix that. >> George? >> Neil, I would agree with you for the breadth first products where the customer has to do a lot of the training on the job with their product. But in the depth first products, they actually build in such richly trained models that there really is, even in the case of some of the examples that we've researched, they don't even have facilities for customers to add say the complex event processing for analytics for new rules. In other words, they're trained to look at the configuration settings, the environment variables, the setup across services, the topology. In other words it's like Steve Jobs says, it just works on a predefined depth first domain like a big data stack. >> So we're likely to see this happen in the depth first and then ultimately see what happens in the breadth first but at the end of the day, it still has to continue to attract capital to make these technologies work, make them evolve and make the business cases possible. David, again you have spent a lot of time looking at this notion of business case and we can see that there's a key value to using machine learning in say fraud detection, but putting shoes on the cobbler's children of IT has been a problem for years. What do you think? Are we going to see IT get the resources it needs starting with depth first but so that it can build out a breadth oriented solution? >> My view is that for what it's worth, is we're going to focus or IT is going to focus on getting in applications which use these technologies and they will go into the places for that business where it makes most sense. If you're an insurance company, you can make hundreds of millions of dollars with fraud detection. If you are in other businesses, you want to focus on security or potential security. The applications that go in with huge amounts more data and more complexity within them, initially in my view will be managed as specific applications and the requirements of AI requirements to manage them will be focused on those particular applications, often by the ISVs themselves. Then from that, they'll be learning about how to do it and from that will come broader type of solutions. >> That's further evidence that we're going to see a fair amount of initial successes more in the depth first side, application specific management. But there's going to be a lot of efforts over the next few years for breadth first companies to grow because there's potentially significant increasing returns from being the first vendor out there that can build the ecosystem that ties all of these depth first products together. Neil, I want to leave you with a last thought here. You mentioned it earlier and you've done a lot of work on this over the years, you assert that at the end of the day, a lot of these new technologies, similar to what David just said, are going to come in through applications by application providers themselves. Just give us a quick sense of what that scenario's going to look like. >> I think that the technology sector runs on two different concepts. One is I have a great idea, maybe I could sell it. Did you hear that, I just got a message my connection was down there. Technology vendors will say that I have a, >> All right we're actually losing you, so Dave Alante, let me give you the last word. When you think about some of the organizational implications of doing this, what do we see as some of the biggest near term issues that IT's going to have to focus on to move from being purely reactive to actually getting out in front and perhaps even helping to lead the business to adopt these technologies. >> Well I think it's worth instructive to review the problem that's out there and the business impact that it'll have an what many of the vendors have proposed through software, but I think there are also some practical things that IT organizations can do before they start throwing technology at the problem. We all know that IT has been reactive generally to operations issues and it's affected a laundry list of things in the business, not only productivity, availability of critical systems, data quality, application performance and on and on. But the bottom line is it increases business risk and cost and so when the organizations that I talk to, they obviously want to be proactive. Vendors are promising that they have tools to allow them to be more proactive, but they really want to reduce the false positives. They don't want to chase down trivial events and of course cloud complicates all this. What the vendor community has done is it's promised end to end visibility on infrastructure platforms including clouds and the ability to discover and manage events and identify anomalies in a proactive manner. Maybe even automate remediation steps, all important things, I would suggest that these need to map to critical business processes and organizations need to have an understanding or they're not going to understand the business impact and it's got to extend to cloud. Now, is AI and ML the answer, maybe, but before going there, I would suggest that organizations look at three things that they can do. The first is, the fact is that most outages on infrastructure come from failed or poorly applied changes, so start with good change management and you'll attack probably 70% of the problem in our estimation. The second thing that we, I think would point to users, is that they should narrow down their promises and get their SLA's firmed up so they can meet them and exceed them and build up credibility with an organization before taking on wider responsibilities and increasing project skills and I think the third thing is start acting like a cloud provider. You got to be clear about the services that you offer, you want to communicate the SLA's, you know clearly they're associated with those services and charge for them appropriately so that you can fund your business. Do these three things before you start throwing technology at the problem. >> That's a great wrap. The one thing I'd add to that Dave, before we actually get to the wrap itself is that I find it intriguing that the processes of thinking through the skills we need and the training that we're going to have to do of people and increasing the training, whether it's supervised, unsupervised, reinforced, of some of these systems, will help us think through exactly the type of prescriptions that you just put forward. All right, let's wrap. This has been a great research meeting. This week, we talked about the emergence of machine learning technologies inside IT operations management solutions. The observation we make is that increasingly, businesses becoming dependent on multicloud including a lot of SAS technologies and application forms and using that as a basis for extending their regional markets and providing increasingly specialized services to customers. This is putting an enormous pressure on the relationship between brand, customer experience and technology management. As customers demand to be treated more uniquely, the technology has to respond, but as we increase the specificity of technology, it increases the complexity associated with actually managing that technology. We believe that there will be an opportunity for IT organizations to utilize machine learning and related AI type and big data technologies inside their iTom capabilities but that the journey to get there is not going to be simple. It's not going to be easy and it's going to require an enormous amount of change. The first thing we observe is that there is this idea of what we call breadth first technology or breadth first machine learning in iTom, which is really looking end to end. The problem is, without concrete deep models, we look at individual resources or resource pools, end up with a lot of false positives and you lose a lot of the opportunity to talk about how different component trees working together. Depth first, which is probably the first place that machine learning's going to show up in a lot of these iTom technologies, provides an out of the box digital twin from the vendor that typically involves or utilizes a lot of testing on whether or not that twin in fact is representative and is an accurate simulacrum of the resource that's under management. Our expectation is that we will see a greater utilization of depth first tooling inactivity, even as users continue to experiment with breadth first options. As we look on the technology horizon, there will be another forcing function here and that is the emergence of what we call unigrid. The idea that increasingly you can envision systems that bring storage, network and computing under a single management framework at enormous scale, putting data very close to other data so that we can run dramatically new forms of automation within a business, and that is absolutely going to require a combination of depth first as well as breadth first technology to evolve. A lot of need, lot of change on how the IT organization works, a lot of understanding of how this training's going to work. The last point we'll make here is that this is not something that's going to work if IT pursues this in isolation. This is not your old IT where we advocated for some new technology, bought it in, played for it, create a solution and look around for the problem to work with. In fact, the way that this is likely to happen and it further reinforces the depth first approach of being successful here is we'll likely see the business demand certain classes of applications that can in fact be made more functional, faster, more reliable, more integratable through some of these machine learning like technologies to provide a superior business outcome. That will require significant depth first capabilities in how we use machine learning to manage those applications. Speed them up, make them more complex, make them more integrated. We're going to need a lot of help to ensure that we're capable of improving the productivity of IT organizations and related partnerships that actually sustain a business's digital business capabilities. What's the bottom line? What's the action item? The action item here is user organizations need to start exploring these new technologies, but do so in a way that has proximate near term implications for how the organization works. For example, remember that most outages are in fact created not by technology but by human error. Button up how you think about utilizing some of these technologies to better capture and report and alert folks, alert the remainder of the organization to human error. The second thing to note very importantly, is that the promises of technology are not to be depended upon as we work with business to establish SLA's. Get your SLA's in place so the business can in fact have visibility to some of the changes that you're making through superior SLA's because that will help you with the overall business case. Now very importantly, cloud suppliers are succeeding as new business entities because they're doing a phenomenal job of introducing this and related technologies into their operations. The cloud business is not just a new procurement model. It's a new operating model and start to think about how your overall operating plans and practices and commitments are or are not ready to fully incorporate a lot of these new technologies. Be more of a cloud supplier yourselves. All right, that closes this week's Friday research meeting from Wikibon on the Cube. We're going to be here next week, talk to you soon. (upbeat music)

>> Transactions, totally on track with the original schedule, we're getting all the regulatory approvals, everything is kind of lined up. Financing 100%, fully committed. You know, we're going to only accelerate that. >> Announcer: Cube coverage of the EMC World 2016 continues in a moment. (techno beat sounds) Live from Silicon Valley, it's theCube, covering Mobile World Congress 2017. Brought to you by Intel. >> Hello and welcome to theCube here live in Palo Alto studios for a special two days of coverage of Mobile World Congress 2017. The hashtag is MWC17. Get on Twitter, tweet us at theCube. We'll be answering questions. I'm John Furrier, with Peter Burris, the next two days breaking down Mobile World Congress. We've got a great bunch of guests coming in. We'll be covering all the action here in Palo Alto. 8:00 a.m. through the whole day. As the day winds down in Barcelona, we'll be covering all the top news, all the analysis here on theCube, so stay with us, multiple days. Go to thecube365.net/mwc17. If you're watching this, that's where the live broadcast will be. Also we'll be on Twitter. Peter, good to see you, two days, getting geared up. Mobile World Congress is changing as a show from phone to IOT, AI, autonomous vehicles. Certainly a lot of action to talk about. Saturday and Sunday. The pre show releases is all phone, it's all the time. They're kind of getting the phone stuff out of the way earlier and now they're in the throws of the show and it should be exciting. >> Well yeah, because the usecases that the industry is following right now are, require or presume that significant amounts of processing can happen virtually anywhere. The Internet of things and people, which kind of brings together the idea of what can you do on your phone if you're a human being, and what can you do with a device or a machine somewhere with a bunch of censors demands that we have very high speed, secure low latency networks. And that's what 5G is promising. >> Well we're super excited. For the folks watching, we are now going to be having our new studio here in Palo Alto. We just moved in in January, 4500 square feet. Now we can cover events, we don't have to be there with theCube. We will not be there, there's not enough room in Barcelona, a it's a long flight, but we do have people on the ground, and we'll be covering it here in the studio, and we'll be calling folks on the ground this morning and tomorrow morning to get the lay of the land. They'll be coming back from their dinners, from their parties, and find out what the vibe. But certainly we have all the action at theCube365.net/mwc17, so check it out there. And again, the top news, again this is all sponsored by Intel, want to give a shot out to Intel. This would not be possible without Intel's sponsorship. They're certainly on the ground, as well as support from SAP Cloud with their news that they're being renamed HANA Cloud. So I want to give a shout out and thank Intel and thank SAP, check them out. They've got huge transformational demos. Intel really leading the charge out there, so I want to make sure that we give a thanks to Intel. Peter, the big story, I want to get your thoughts on this. Just jump right in. Saturday and Sunday, you saw a combination of the tone setting up leading into the weekend, and through the weekend. One was 5G, the 5G is the key enabler for wireless, bringing in gigabits of speed to the phone. Are the apps ready? That's the questions we're going to find out, and we're going to dig into. Is 5G ready for prime time? And certainly all the glam and sizzle was the new phones. LG had a good announcement. Samsung had a big announcement, although they're not going to be at the show, but surprisingly Nokia and Blackberry, two old guard phone guys, kind of rebooting. Blackberry trying to put out their keynote product, and also with Nokia, they rolled out the three, the six, three, five, and six products for new phones to try to get into the Apple game. And now the 3310, which is the old school phone. So you saw the phones. And then the other player that announced a phone and watch was Huawei, and they're also in the infrastructure game. So 5G wireless connectivity and phones, and then in the middle we have yet to hear some of the things, so as you look at the market and your research that you're covering, digital business, the business value of technology, what's your take on this? >> Well, John, the industry for the past probably 15, 20 years has been driven by what you do in the consumer markets. That's where you get the volumes that drive down or generate economies, that drive down costs, that make new volumes possible. And so 5G is going to be, the Mobile World Congress is a representation of that symbiotic relationship between the consumer and the enterprise world. So that on the one hand you have the consumer markets with the phones driving a lot of the volumes that are going to dictate the rate at which a lot of this stuff happens. On the other hand, you have enterprises which are aggressively considering those new use cases about IOT and as we say IOT and P. And other considerations that are in many respects really worth where some of those first adoptions are going to be, so it's an interesting dance between consumer and enterprise now where one fuels the growth in the other. Even if the actual applications are not linked. By that I mean we do say IOT and P, internet of things and people, which presumes that there's going be a lot of sensors on your phone. There's going to be a lot of sensors on your body that are tied to your phone, et cetera. But that's not necessarily the thing that's going to dictate the new application architectures that happen within the enterprise around some of these other things. That's going to be driven by what we call the edge. >> I love this IOT and P, p for people, but things are people, so Internet of things is the big trend. And for the mainstream people IOT is kind of a nuance, it's kind of industry discussion. But AI seems to encapsulate that people see the autonomous vehicles. They see things like smart cities. That kind of gives folks a touch point, or mental model for some of the real meat on the bone, the real change that's happening. Talk about the IOT piece in particular because when you talk about the people aspect of it, the edge of the network used to be an IT or technology concept, a device at the edge of the network. You talk to it, data gets sent to it, but now you've got watches, you have more of an Apple-esque like environment, mention the consumer. But there's still a lot of stuff in between, under the hood around IOT that's going to come out. It's called network transformation and industry parlance. Where's the action there, what's your take on that? You guys do a lot of research on this. >> Well the action is that data has real costs. And data is a real thing. Just very quickly, on the distinction between IOT and IOT and P, the only reason why we draw that distinction, and this is important, I think about what happens in that middle, is that building thing for people and building things for machines is two very, very different set of objectives. So the whole notion of operational technology and SCADA which is driven what's been happening a lot in IOT over the last 20 years. There's a legacy there that we have to accommodate. Has been very focused on building for machines. The building for people I think is going to be different, and that's what the middle is going to have to accommodate. That middle is going to have to accommodate both the industrial implications, or the industrial use cases, as well as the more consumer or employee or human use cases. And that's a nontrivial challenge because both of those can be very, very different. One you're focusing a little bit more on brutal efficiency. The other one more on experience and usability. I don't know the last time that anybody really worried about the experience that a machine had, you know the machine experience of an application. But we have to worry about that all the time with people. So when we think about the edge, John, there's a number of things that we've got to worry about. We have to worry about physical realities, it takes time to move something from point A to point B, even information. The speed of light is a reality. And that pushes things out more to the edge. You have to worry about bandwidth. One of the things that's interesting about IOT, or about 5G as it relates to IOT, while we may get higher bandwidth speeds sometimes, for the most part 5G is going to provide a greater density of devices and things, that's probably where the bandwidth is going to go. And so the idea is we can put a lot more sensors onto a machine or into a phone or into some use case and drive a lot more sources of data, that then have to get processed somewhere, and increasingly that's going to be processed at the edge. >> So Peter, I want to get your thoughts, and one of the things for the folks watching, is I spent a lot of time this week with you talking about the show and looking at the outcome of what we wanted to do and understand the analysis of what is happening at Mobile World Congress. Yes, it's a device show, it's always been about the phones, 4G, and there's been this you know inch by inch move the ball, first and ten, move the chains, and use the football analogy, but now it seems to be a whole new shift. You go back 10 years, iPhone was announced in 2007, we seem to be at a moment with we need to step up function to move the industry. So I want to get your thoughts for the folks that you're talking to, IT folks, or even CXOs or architects on the service provider side. There's a collision between IT, traditional business, and service providers who have been under the gun, the telecoms who have been trying to figure out a business model for competing against over the top and moving from the phone business model to a digital business model. So your business value of technology work that Wikibon has been doing, is very relevant. I want to get your thoughts on what does it take, is the market ready for this business value of technology because 5G gives that step up function. Are the apps ready for prime time? Are the people who are putting solutions in place for the consumers, whether it's for business or consumers themselves, service providers, telecoms or businesses with IT in the enterprise, is the market ready? Is this a paradigm shift? What's your thoughts and how do you tease that out for the folks that are trying to implement this stuff? >> Well is it a paradigm shift? Well yeah, as the word should be properly used, but the paradigm shift is, there is a lot of things that go into that. So what we like to say, John, when we talk to our users about what's happening, we like to say that the demarkation point, we're in the middle of right now. Now is a period of maximum turbulence, and before this it was I had known processes, accounting, HR, even supply chains, somewhat falls into that category, but the technology was unknown. So do I use a mainframe, do I use a mini computer? What kind of network do I use? What software base do I use? What stack do I use? All of these are questions, and it took 50 years for us to work out, and we've got a pretty good idea what that technology set's going to look like right now. There's always things at the margin, so we know it's going to be Cloud. We know it's going to be very fast networks like 5G. We know there's going to be a range of different devices that we're using, but the real question is before was known process, unknown technology, now it's unknown technology, or unknown process and known technology, because we do know what that base is going to look like. What those stacks are broadly going to look like. But the question is how are we going to apply this? What does it mean to follow a consumer? What does it mean from a privacy standpoint to collect individual's information? What does it mean to process something in a location and not be able to move data or the consequences of that processing somewhere else? These are huge questions that the industry is going to have to address. So when we think about the adoption of some of this stuff, it's going to be a real combination of what can the technology do, but also what can we do from a physical, legal, economic, and other standpoint. And this is not something that the computing industry has spent a lot of time worrying about. Computing has always focused not on what should do, but what can we do. And the question of what should we do with this stuff is going to become increasingly important. >> And the turbulence point is even compounded by the fact that even the devices themselves and the networks are becoming more powerful. If you look at what Cloud is doing with compute. If you look at some of the devices, even just the chip wars between Intel and say Qualcomm for instance. Intel had a big announcement about their new radio chip. Qualcomm has the Snapdragon, we know Qualcomm is in the Apple iPhone. Now Intel has an opportunity to get that kind of business. You got Huawei trying. >> I think they're both in the Apple iPhone right now, but I think your point is. >> Huawei is trying to be on Apple. In their announcements, they're going very Apple like, and they have network gear, so we know them from the infrastructure standpoint, but everyone wants to be, Apple seems to be the theme. But again the devices also have power, so you have process change, new value chains are developing and the device will be more popular. So again this is a big turbulent time, and I want to get your thoughts on the four areas that are popping out of Mobile World Congress. One, autonomous vehicles, two, entertainment and media. Smart cities and smart homes seem to be the four areas that have this notion of combining the technologies and the power that are going to generate these new expectations by consumers and users, and create new value opportunities for businesses and telcom's around the world, your thoughts? >> Those are four great use cases, John. But they all come back to a single notion, and the single notion, this is something that you know. We've been focused on it at Wikibon for quite some time. What is digital business? Digital business is the application of data to differentially sustain and create customers. So what you just described, those four use cases, are all how are we going to digitize, whether it be the city, the home, the car, or increasingly entertainment, and what will that mean from a business model, from a consumer standpoint, from a loyalty standpoint, et cetera? As well as a privacy and legal obligation standpoint. So, but all of them have different characteristics, right. So the car is going to have an enormous impact because it is a self contained unit that either does or does not work. It's pretty binary. Either you do have an autonomous car that works, or you don't, you don't want to see your 'yes it works' in a ditch somewhere. Entertainment is a little bit more subtle because entertainment is already so much digital content out there, and there's only going to be more, but what does that mean? Virtual reality, augmented reality, when we start talking about... >> Just by the way, a big theme of the Samsung announcement is all this teasing out the VR, virtual reality and augmented reality. >> Absolutely, and that's going to, look, because it's not just about getting data in, you also have to enact the results of the AI and the analysis. We call it systems of enactment. You have to then have technologies that allow you to, like a transducer, move from the digital world back into the analog world where human beings actually spend our time. We don't have digital transducers. >> Well that's a great point. The virtual reality use case that Samsung pointed out, and the hanging fruit is in hospitals. >> Peter: Yeah. >> Doctors can look at VR and say, hey I want to have, we've heard that football players like Tom Brady, used VR to look at defenses and offenses to get a scheming kind of thing. >> And there's no question we're going to see VR and AR, augmented reality, in entertainment as well, and media as well, but a lot of the more interesting use cases, at least from my perspective, are going to be how does that apply in the world of business. When we think about connected cities, now we're starting to talk about the relationship between all three. What does it mean, where is the edge in autonomous car? Is it in the car, or is in some metropolitan area? Or some cell like technology. And the connected city in part is going to be about how does a city provide a set of services to a citizenry, so that the citizen can do more autonomous things while still under control. >> It changes the relationship between the person, consumer, and the analog metaphor. So for instance, whether it's a car or the city, a town or city has to provide services to residents. And in an analog world, that's garbage, that's street cleaning, et cetera, having good roads. Now it's going to be, paths for autonomous vehicles, and autonomous vehicles is interesting, I just shared a post on the 365, theCube365.net/MWC17, where Autoblog ran a post that said, Silicon Valley is failing in the car business. But they looked at it too narrowly. They looked at it from the car manufacturing standpoint, not from the digital services that is impacting transportation, and this is the new normal. >> Look, you and I talked about this in theCube a year ago, was the car going to be a, was the car going to be a peripheral or is a car going to be a computer? And it's become pretty clear that the car is going to be a computer. And anybody who argues that Silicon Valley has lost that, has absolutely no idea what they're talking about. Let's be honest. >> John: Yeah, it's true. >> You're going to put more processing in a car, love Detroit, love what's going on in Japan, love elsewhere in the world, but the computers and the chips are going to come from a Silicon Valley company. >> Yeah, and I would agree with that. >> And software. >> Yeah, transportation doesn't change, but the device does. So final thought I want to get before we end the segment is as we say in theCube, and as Dave Vellante used to say, just squint through the noise or all the action at Mobile World Congress, how do you advise folks and how you looking through all this action, how would you advise doers out there, people who are trying to make sense of this, what should they be squinting through? What should they be looking for for reading the tea leaves of Mobile World Congress? >> I'd say the first and most important thing is there's so much turbulence that IT professionals have built their careers on trying to have the sober, be the ones who have the sober outlook on what technology can do. When we look at the amazing things that you can do with technology, it almost looks like magic. But it's not, these are still computers that fail if you give them the wrong instructions, and that's because you build the wrong software and et cetera. And I think the real important thing that we're telling our clients is focus on the sober reality of what it means to create value out of all this technology. You have to say what's the business want to do, what's the business use case? How am I going to architect it, how am I going to build it, what's the physical realities? What's the legal realities, et cetera? So it's try to get a little bit more sober and pragmatic about this stuff even as we get wowed by what all this technology can do and ultimately will mean. >> And the sober reality comes down to putting the value equation together, synthesizing what's ready, what's prime time, and again, it's an Apple world right now. I think this show is interestingly turning into an app show for business IT enterprise and telcom service providers, so we're going to bring all the action. We've got some great guests, we've got entrepreneurs with Ruth Cohen, who is a founder of Virtustream. We got SAP coming on, we got a call in to Lynn Comp who is at Intel, she's going to be on the phone with us giving us some commentary and what's going on at Mobile World Congress. From under the hood, in the network, all the action, we have more analysis with Peter. We have the global vice-president of the Cloud platform and SAP coming in, Tom Joyce, a technology executive. Willie Lou is the chairman of the 6G, talking about the impact of the wireless and that transformation. Ensargo Li, who is former HPE executive who built out their NFE function for the communications group, commentating on what's real and what's not. Stay tuned, more Cube coverage for two days from Mobile World Congress. Here in Palo Alto, bringing you all the action and analysis. Be right back with more after this short break. (techno beat sounds)