(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)

>>president >>so called big data promised to usher in a new era of innovation where companies competed on the basis of insights and agile decision making. There's little question that social media giants, search leaders and e commerce companies benefited. They had the engineering shops and the execution capabilities to take troves of data and turned them into piles of money. But many organizations were not as successful. They invested heavily in data architecture is tooling and hyper specialized experts to build out their data pipelines. Yet they still struggle today to truly realize they're busy. Did data in their lakes is plentiful but actionable insights aren't so much chaos. Search is a cloud based startup that wants to change this dynamic with a new approach designed to simplify and accelerate time to insights and dramatically lower cost and with us to discuss his company and its vision for the future is cuba Lem Ed Walsh had great to see you. Thanks for coming back in the cube. >>I always love to be here. Thank you very much. It's always a warm welcome. Thank you. >>Alright, so give us the update. You guys have had some big funding rounds, You're making real progress on the tech, taking it to market what's new with chaos surgery. >>Sure. Actually even a lot of good exciting things happen. In fact just this month we need some, you know, obviously announced some pretty exciting things. So we unveiled what we consider the industry first multi model data late platform that we allow you to take your data in S three. In fact, if you want to show the image you can, but basically we allow you to put your data in S three and then what we do is we activate that data and what we do is a full index of the data and makes it available through open a P. I. S. And the key thing about that is it allows your end users to use the tools are using today. So simply put your data in your cloud option charge, think Amazon S three and glacier think of all the different data. Is that a natural act? And then we do the hard work. And the key thing is to get one unified delic but it's a multi mode model access so we expose api like the elastic search aPI So you can do things like search or using cabana do log analytics but you can also do things like sequel, use Tableau looker or bring relational concepts into cabana. Things like joins in the data back end. But it allows you also to machine learning which is early next year. But what you get is that with that because of a data lake philosophy, we're not making new transformations without all the data movement. People typically land data in S. Three and we're on the shoulders of giants with us three. Um There's not a better more cost effective platform. More resilient. There's not a better queuing system out there and it's gonna cost curve that you can't beat. But basically so people store a lot of data in S. Three. Um But what their um But basically what you have to do is you E. T. L. Out to other locations. What we do is allow you to literally keep it in place. We index in place. We write our hot index to rewrite index, allow you to go after that but published an open aPI S. But what we avoid is the GTL process. So what our index does is look at the data and does full scheme of discovery normalization, were able to give sample sets. And then the refinery allows you to advance transformations using code. Think about using sequel or using rejects to change that data pull the dead apartheid things but use role based access to give that to the end user. But it's in a format that their tools understand cabana will use the elasticsearch ap or using elasticsearch calls but also sequel and go directly after data by doing that. You get a data lake but you haven't had to take the three weeks to three months to transform your data. Everyone else makes you. And you talk about the failure. The idea that Alex was put your data there in a very scalable resilient environment. Don't do transformation. It was too hard to structure for databases and data. Where else is put it there? We'll show you how value out Largely un delivered. But we're that last mile. We do exactly that. Just put it in s. three and we activated and activate it with a piece that the tools of your analysts use today or what they want to use in the future. That is what's so powerful. So basically we're on the shoulders of giants with street, put it there and we light it up and that's really the last mile. But it's this multi model but it's also this lack of transformation. We can do all the transformation that's all to virtually and available immediately. You're not doing extended GTL projects with big teams moving around a lot of data in the enterprise. In fact, most time they land and that's three and they move it somewhere and they move it again. What we're saying is now just leave in place well index and make it available. >>So the reason that it was interesting, so the reason they want to move in the S three was the original object storage cloud. It was, it was a cheap bucket. Okay. But it's become much more than that when you talk to customers like, hey, I have all this data in this three. I want to do something with it. I want to apply machine intelligence. I want to search it. I want to do all these things, but you're right. I have to move it. Oftentimes to do that. So that's a huge value. Now can I, are you available in the AWS marketplace yet? >>You know, in fact that was the other announcement to talk about. So our solution is one person available AWS marketplace, which is great for clients because they've been burned down their credits with amazon. >>Yeah, that's that super great news there. Now let's talk a little bit more about data. Like you know, the old joke of the tongue in cheek was data lakes become data swamps. You sort of know, see no schema on, right. Oh great. I can put everything into the lake and then it's like, okay, what? Um, so maybe double click on that a little bit and provide a little bit more details to your, your vision there and your philosophy. >>So if you could put things that data can get after it with your own tools on elastic or search, of course you do that. If you don't have to go through that. But everyone thinks it's a status quo. Everyone is using, you know, everyone has to put it in some sort of schema in a database before they can get access to what everyone does. They move it some place to do it. Now. They're using 1970s and maybe 1980s technology. And they're saying, I'm gonna put it in this database, it works on the cloud and you can go after it. But you have to do all the same pain of transformation, which is what takes human. We use time, cost and complexity. It takes time to do that to do a transformation for an user. It takes a lot of time. But it also takes a teams time to do it with dBS and data scientists to do exactly that. And it's not one thing going on. So it takes three weeks to three months in enterprise. It's a cost complexity. But all these pipelines for every data request, you're trying to give them their own data set. It ends up being data puddles all over this. It might be in your data lake, but it's all separated. Hard to govern. Hard to manage. What we do is we stop that. What we do is we index in place. Your dad is already necessary. Typically retailing it out. You can continue doing that. We really are just one more use of the data. We do read only access. We do not change that data and you give us a place in. You're going to write our index. It's a full rewrite index. Once we did that that allows you with the refinery to make that we just we activate that data. It will immediately fully index was performant from cabana. So you no longer have to take your data and move it and do a pipeline into elasticsearch which becomes kind of brittle at scale. You have the scale of S. Three but use the exact same tools you do today. And what we find for like log analytics is it's a slightly different use case for large analytics or value prop than Be I or what we're doing with private companies but the logs were saving clients 50 to 80% on the hard dollars a day in the month. They're going from very limited data sets to unlimited data sets. Whatever they want to keep an S. Three and glacier. But also they're getting away from the brittle data layer which is the loosen environment which any of the data layers hold you back because it takes time to put it there. But more importantly It becomes brittle at scale where you don't have any of that scale issue when using S. three. Is your dad like. So what what >>are the big use cases Ed you mentioned log analytics? Maybe you can talk about that. And are there any others that are sort of forming in the marketplace? Any patterns that you see >>Because of the multi model we can do a lot of different use cases but we always work with clients on high R. O. I use cases why the Big Bang theory of Due dad like and put everything in it. It's just proven not to work right? So what we're focusing first use cases, log analytics, why as by way with everything had a tipping point, right? People were buying model, save money here, invested here. It went quickly to no, no we're going cloud native and we have to and then on top of it it was how do we efficiently innovate? So they got the tipping point happens, everyone's going cloud native. Once you go cloud native, the amount of machine generated data that you have that comes from the environment dramatically. It just explodes. You're not managing hundreds or thousands or maybe 10,000 endpoints, you're dealing with millions or billions and also you need this insight to get inside out. So logs become one of the things you can't keep up with it. I think I mentioned uh we went to a group of end users, it was only 60 enterprise clients but we asked him what's your capture rate on logs And they said what do you want it to be 80%, actually 78 said listen we want eight captured 80 200 of our logs. That would be the ideal not everything but we need most of it. And then the same group, what are you doing? Well 82 had less than 50%. They just can't keep up with it and every everything including elastic and Splunk. They work harder to the process to narrow and keep less and less data. Why? Because they can't handle the scale, we just say landed there don't transform will make it all available to you. So for log analytics, especially with cloud native, you need this type of technology and you need to stop, it's like uh it feels so good when you stop hitting your head against the wall. Right? This detail process that this type of scale just doesn't work. So that's exactly we're delivering the second use case uh and that's with using elastic KPI but also using sequel to go after the same data representation. And we come out with machine learning. You can also do anomaly detection on the same data representation. So for a log uh analytic use case series devops setups. It's a huge value problem now the same platform because it has sequel exposed. You can do just what we use the term is agile B. I people are using you think about look or tableau power bi I uh metabolic. I think of all these toolsets that people want to give and uh and use your business or coming back to the centralized team every single week asking for new datasets. And they have to be set up like a data set. They have to do an e tail process that give access to that data where because of the way just landed in the bucket. If you have access to that with role based access, I can literally get you access that with your tool set, let's say Tableau looker. You know um these different data sets literally in five minutes and now you're off and running and if you want a new dataset they give another virtual and you're off and running. But with full governance so we can use to be in B I either had self service or centralized. Self service is kind of out of control, but we can move fast and the centralized team is it takes me months but at least I'm in control. We allow you do both fully governed but self service. Right. I got to >>have lower. I gotta excel. All right. And it's like and that's the trade off on each of the pieces of the triangle. Right. >>And they make it easy, we'll just put in a data source and you're done. But the problem is you have to E T L the data source. And that's what takes the three weeks to three months in enterprise and we do it virtually in five minutes. So now the third is actually think about um it's kind of a combination of the two. Think about uh you love the beers and diaper stories. So you know, think about early days of terror data where they look at sales out data for business and they were able to look at all the sales out data, large relational environment, look at it, they crunch all these numbers and they figured out by different location of products and the start of they sell more sticker things and they came up with an analogy which everyone talked about beers and diapers. If you put it together, you sell more from why? Because afternoon for anyone that has kids, you picked up diapers and you might want to grab a beer of your home with the kids. But that analogy 30 years ago, it's now well we're what's the shelf space now for approximate company? You know it is the website, it's actually what's the data coming from there. It's actually the app logs and you're not capturing them because you can't in these environments or you're capturing the data. But everyone's telling, you know, you've got to do an E. T. L. Process to keep less data. You've got to select, you got to be very specific because it's going to kill your budget. You can't do that with elastic or Splunk, you gotta keep less data and you don't even know what the questions are gonna ask with us, Bring all the app logs just land in S. three or glacier which is the most it's really shoulders of giants right? There's not a better platform cost effectively security resilience or through but to think about what you can stream and the it's the best queuing platform I've ever seen in the industry just landed there. And it's also very cost effective. We also compress the data. So by doing that now you match that up with actually relatively small amount of relational data and now you have the vaccine being data. But instead it's like this users using that use case and our top users are always, they start with this one then they use that feature and that feature. Hey, we just did new pricing is affecting these clients and that clients by doing this. We get that. But you need that data and people aren't able to capture it with the current platforms. A data lake. As long as you can make it available. Hot is a way to do it. And that's what we're doing. But we're unique in that. Other people are making GTL IT and put it in a in 19 seventies and 19 eighties data format called a schema. And we avoided that because we basically make S three a hot and elected. >>So okay. So I gotta I want to, I want to land on that for a second because I think sometimes people get confused. I know I do sometimes without chaos or it's like sometimes don't know where to put you. I'm like okay observe ability that seems to be a hot space. You know of course log analytics as part of that B. I. Agile B. I. You called it but there's players like elastic search their star burst. There's data, dogs, data bricks. Dream EOS Snowflake. I mean where do you fit where what's the category and how do you differentiate from players like that? >>Yeah. So we went about it fundamentally different than everyone else. Six years ago. Um Tom hazel and his band of merry men and women came up and designed it from scratch. They may basically yesterday they purposely built make s free hot analytic environment with open A. P. I. S. By doing that. They kind of changed the game so we deliver upon the true promises. Just put it there and I'll give you access to it. No one else does that. Everyone else makes you move the data and put it in schema of some format to get to it. And they try to put so if you look at elasticsearch, why are we going after? Like it just happens to be an easy logs are overwhelming. You once you go to cloud native, you can't afford to put it in a loose seen the elk stack. L is for loosen its inverted index. Start small. Great. But once you now grow it's now not one server. Five servers, 15 servers, you lose a server, you're down for three days because you have to rebuild the whole thing. It becomes brittle at scale and expensive. So you trade off I'm going to keep less or keep less either from retention or data. So basically by doing that so elastic we're not we have no elastic on that covers but we allow you to well index the data in S. Tree and you can access it directly through a cabana interface or an open search interface. Api >>out it's just a P. >>It's open A P. I. S. It's And by doing that you've avoided a whole bunch of time cost, complexity, time of your team to do it. But also the time to results the delays of doing that cost. It's crazy. We're saving 50-80 hard dollars while giving you unlimited retention where you were dramatically limited before us. And as a managed service you have to manage that Kind of Clunky. Not when it starts small, when it starts small, it's great once at scale. That's a terrible environment to manage the scale. That's why you end up with not one elasticsearch cluster, dozens. I just talked to someone yesterday had 125 elasticsearch clusters because of the scale. So anyway, that's where elastic we're not a Mhm. If you're using elastic it scale and you're having problems with the retired off of cost time in the, in the scale, we become a natural fit and you don't change what your end users do. >>So the thing, you know, they had people here, this will go, wow, that sounds so simple. Why doesn't everybody do this? The reason is it's not easy. You said tom and his merry band. This is really hard core tech. Um and it's and it's it's not trivial what you've built. Let's talk about your secret sauce. >>Yeah. So it is a patented technology. So if you look at our, you know, component for architecture is basically a large part of the 90% of value add is actually S. Three, I gotta give S three full kudos. They built a platform that we're on shoulders of giants. Um But what we did is we purpose built to make an object storage a hot alec database. So we have an index, like a database. Um And we basically the data you bring a refinery to be able to do all the advanced type of transformation but all virtually done because we're not changing the source of record, we're changing the virtual views And then a fabric allows you to manage and be fully elastic. So if we have a big queries because we have multiple clients with multiple use cases, each multiple petabytes, we're spending up 1800 different nodes after a particular environment. But even with all that we're saving them 58%. But it's really the patented technology to do this, it took us six years by the way, that's what it takes to come up with this. I come upon it, I knew the founder, I've known tom tom a stable for a while and uh you know his first thing was he figured out the math and the math worked out. Its deep tech, it's hard tech. But the key thing about it is we've been in market now for two years, multiple use cases in production at scale. Um Now what you do is roadmap, we're adding a P. I. So now we have elasticsearch natural proofpoint. Now you're adding sequel allows you open up new markets. But the idea for the person dealing with, you know, so we believe we deliver on the true promise of Data Lakes and the promise of Data lakes was put it there, don't focus on transferring. It's just too hard. I'll get insights out and that's exactly what we do. But we're the only ones that do that everyone else makes you E. T. L. At places. And that's the innovation of the index in the refinery that allows the index in place and give virtual views in place at scale. Um And then the open api is to be honest, uh I think that's a game. Give me an open api let me go after it. I don't know what tool I'm gonna use next week every time we go into account they're not a looker shop or Tableau Sharp or quick site shop there, all of them and they're just trying to keep up with the businesses. Um and then the ability to have role based access where actually can give, hey, get them their own bucket, give them their own refinery. As long as they have access to the data, they can go to their own manipulation ends up being >>just, >>that's the true promise of data lakes. Once we come out with machine learning next year, now you're gonna rip through the same embassy and the way we structured the data matrices. It's a natural fit for things like tensorflow pytorch, but that's, that's gonna be next year just because it's a different persona. But the underlining architecture has been built, what we're doing is trying to use case that time. So we worked, our clients say it's not a big bang. Let's nail a use case that works well. Great R. O. I great business value for a particular business unit and let's move to the next. And that's how I think it's gonna be really. That's what if you think about gardener talks about, if you think about what really got successful in data, where else in the past? That's exactly it wasn't the big bang, it was, let's go and nail it for particular users. And that's what we're doing now because it's multi model, there's a bunch of different use cases, but even then we're focusing on these core things that are really hard to do with other relational only environments. Yeah, I >>can see why you're still because you know, you haven't been well, you and I have talked about the api economy for forever and then you've been in the storage world so long. You know what a nightmare is to move data. We gotta, we gotta jump. But I want to ask you, I want to be clear on this. So you are your cloud cloud Native talked to frank's Lukman maybe a year ago and I asked him about on prem and he's like, no, we're never doing the halfway house. We are cloud all the >>way. I think >>you're, I think you have a similar answer. What what's your plan on Hybrid? >>Okay. We get, there's nothing about technology, we can't go on, but we are 100 cloud native or only in the public cloud. We believe that's a trend line. Everyone agrees with us, we're sticking there. That's for the opportunity. And if you can run analytics, There's nothing better than getting to the public cloud like Amazon and he was actually, that were 100 cloud native. Uh, we love S three and what would be a better place to put this is put the next three and we just let you light it up and then I guess if I'm gonna add the commercial and buy it through amazon marketplace, which we love that business model with amazon. It's >>great. Ed thanks so much for coming back in the cube and participating in the startup showcase. Love having you and best of luck. Really exciting. >>Hey, thanks again, appreciate it. >>All right, thank you for watching everybody. This is Dave Volonte for the cube. Keep it right there.

>>from around the globe. It's the Cube with digital coverage of biz ops Manifesto unveiled. Brought to you by Biz Ops Coalition. Hey, Welcome back, everybody. Jeffrey here with the Cube. We're coming to you from our Palo Alto studios. And welcome back to this event. Is the biz Opps Manifesto unveiling? So the biz Opps manifesto and the biz Opps coalition have been around for a little while, But today's the big day. That's kind of the big public unveiling are excited to have some of the foundational people that put their put their name on the dotted line, if you will, to support this initiative to talk about why that initiative is so important. And so the next guest, we're excited to have his doctor, Mick Kirsten. He is the founder and CEO of Task Top. Make great to see you coming in from Vancouver, Canada, I think. Right. >>Yes. Great to be here, Jeff. Thank you. Absolutely. >>I hope your air is a little better out there. I know you had some of the worst air of all of us a couple a couple of weeks back, so hopefully things air, uh, getting a little better. And we get those fires under control? >>Yeah, Things have cleared up now, so yeah, it's good. It's good to be close to the U. S. And it's gonna have the Arabic clean as well. >>Absolutely. So let's let's jump into it. So you you've just been an innovation guy forever Starting way back in the day and Xerox Park. I was so excited to do an event at Xerox Park for the first time last year. I mean that that to me represents along with Bell Labs and and some other, you know, kind of foundational innovation and technology centers. That's got to be one of the greatest one. So I just wonder if you could share some perspective of getting your start there at Xerox Parc. You know, some of the lessons you learn and what you've been ableto kind of carry forward from those days. >>Yeah, I was fortunate. Joined Xerox Park in the computer science lab there at a very early point in my career, and to be working on open source programming languages. So back then, and the computer science lab where some of the inventions around programming around software development names such as Object of programming and ah, lot of what we had around really modern programming levels construct. Those were the teams that had the fortune of working with and really our goal waas. And of course, there's a Z. You know, this, uh, there's just this DNA of innovation and excitement and innovation in the water. And really, it was the model that was all about changing the way that we work was looking at for how we could make it 10 times easier to write. Code like this is back in 99 we were looking at new ways of expressing especially business concerns, especially ways of enabling people who are who want to innovate for their business, to express those concerns in code and make that 10 times easier than what that would take. So we created a new open source programming language, and we saw some benefits, but not quite quite what we expected. I then went and actually joined Charles Stephanie that former chief actor Microsoft, who is responsible for I actually got a Microsoft word as a out of Xerox Parc and into Microsoft and into the hands of Bill Gates and the company I was behind the whole office suite and his vision and the one I was trying to execute with working for him was to, you know, make Power point like a programming language, make everything completely visual. And I realized none of this was really working, that there was something else fundamentally wrong that programming languages or new ways of building software like Let's try to do with Charles around intentional programming. That was not enough. >>That was not enough. So you know, the agile movement got started about 20 years ago, and we've seen the rise of Dev ops and really this kind of embracing of of, of sprints And, you know, getting away from M. R. D s and P. R. D s and these massive definitions of what we're gonna build and long billed cycles to this iterative process. And that's been going on for a little while. So what was still wrong? What was still missing? Why the Biz Ops Coalition? Why the biz ops manifesto? >>Yeah, so I basically think we nailed some of the things that the programming language levels of teams can have. Effective languages deployed softened the club easily now right and at the kind of process and collaboration and planning level agile two decades decades ago was formed. We were adopting all the all the teams I was involved with on. It's really become a solved problem. So agile tools, agile teams actually of planning are now very mature and the whole challenges when organizations try to scale that. And so what I realized is that the way that Agile was scaling across teams and really scaling from the Technology Party organization to the business was just completely flawed. The agile teams had one set of doing things. One set of metrics, one set of tools and the way that the business was working was planning was investing in technology was just completely disconnected and using a a whole different set of measures. It's pretty interesting because I think it's >>pretty clear from the software development teams in terms of what they're trying to deliver, because they've got a feature set right and they've got bugs and it's easy. It's easy to see what they deliver, but it sounds like what you're really honing in on is is disconnect on the business side in terms of, you know, is it the right investment you know. Are we getting the right business? R o I on this investment? Was that the right feature? Should we be building another feature or shall we building a completely different products? That so it sounds like it's really a core piece of this is to get the right measurement tools, the right measurement data sets so that you can make the right decisions in terms of what you're investing, you know, limited resource is you can't Nobody has unlimited resources and ultimately have to decide what to do, which means you're also deciding what not to dio. It sounds like that's a really big piece of this of this whole effort. >>Yeah, Jeff, that's exactly it. Which is the way that the adult measures their own way of working is very different from the way that you measure business outcomes. The business outcomes are in terms of how happy your customers are. Are you innovating fast enough to keep up with the pace of, ah, rapidly changing economy, rapidly changing market and those are those are all around the customer. And so what? I learned on this long journey of supporting many organizations transformations and having them trying to apply those principles vigilant develops that those are not enough. Those measures technical practices, those measures, technical excellence of bringing code to the market. They don't actually measure business outcomes. And so I realized that really was much more around having these entwined flow metrics that are customer centric and business centric and market centric where we needed to go. So I want to shift gears >>a little bit and talk about your book because you're also a best selling author project a product, and and you you brought up this concept in your book called The Flow Framework. And it's really interesting to me because I know, you know, flow on one hand is kind of a workflow in the process flow, and you know that's how things get done and and embrace the flow. On the other hand, you know, everyone now in a little higher level, existential way is trying to get into the flow right into the workflow and, you know not be interrupted and get into a state where you're kind of your highest productivity, you know, kind of your highest comfort. Which floor you talking about in your book, or is it a little bit of both. >>That's a great question, is it's not what I gotta ask very often, cause me, it's It's absolutely both. So the thing that we want to get that we've learned how toe and, uh, master individual flow, that there's this beautiful book by me Holly teachings mentality. There's a beautiful Ted talk about him as well, about how we can take control of our own flow. So my question with the book with project surprise, How can we bring that to entire teams and really entire organizations? How come we have everyone contributing to a customer outcome? And this is really what if you go to the bazaar manifesto? It says, I focus on Out comes on using data to drive, whether we're delivering those outcomes rather than a focus on proxy metrics such as How quickly did we implement this feature? And now it's really how much value did the customs of the future and how quickly did we learn? And how quickly did you use that data to drive to that next outcome? Really, that with companies like Netflix on, like Amazon, have mastered, how do we get that every large organization, every idea, organization and make everyone be a softer innovator. So it's to bring that on the concept of flow to these entering value streams. And the fascinating thing is, we've actually seen the data. We've been able to study a lot of value streams. We see when flow increases, when organizations deliver value to a customer faster developers actually become more happy. So things like that implying that promotes course rise. And we've got empirical data for this. So that beautiful thing to me is that we've actually been able thio, combine these two things and and see the results in the data that you increased flow to the customer, your development or more happy. I >>love it. I love it, right, because we're all more. We're all happier when we're in the flow and we're all more productive winner in the flow. So I that is a great melding of two concepts. But let's jump into the into the manifesto itself a little bit. And you know, I love that you know, that took this approach really of having kind of four key values, and he gets 12 key principles and I just want to read a couple these values because when you read them, it sounds pretty brain dead, right? Of course. Right. Of course, you should focus on business outcomes. Of course, you should have trust and collaboration. Of course, you should have data based decision making processes and not just intuition or, you know, whoever is the loudest person in the room on toe, learn and respond and pivot. But >>what's the >>value of actually just putting them on a piece of paper? Because again, this is not this. These are all good positive things, right? When when somebody reads these to you or tells you these or sticks it on the wall? Of course. But unfortunately, of course, isn't always enough. >>No, I think what's happened is some of these core principles originally from the agile manifested two decades ago. The whole Dev ops movement of the last decade off flow feedback and continue learning has been key. But a lot of organizations, especially the ones undergoing transformations, have actually gone a very different way, right? The way that they measure value in technology innovation is through costs For many organizations, the way that they actually are looking at at their moving to cloud is actually is a reduction in costs, whereas the right way of looking at moving the cloud is how much more quickly can we get to the value to the customer? How quickly can we learn from that? And how could quickly can we drive the next business outcome? So, really, the key thing is to move away from those old ways of doing things that funding projects and call centers to actually funding and investing in outcomes and measuring outcomes through these flow metrics, which in the end are your fast feedback for how quickly you're innovating for your customer. So these things do seem, you know, very obvious when you look at them. But the key thing is what you need to stop doing. To focus on these, you need to actually have accurate real time data off how much value your phone to the customer every week, every month, every quarter. And if you don't have that, your decisions are not given on data. If you don't know what your bottle like, it's. And this is something that in the decades of manufacturing car manufacturers, other manufacturers master. They always know where the bottom back in their production processes you ask, uh, random. See, I all want a global 500 company where the bottleneck is, and you won't get it there. Answer. Because there's not that level of understanding. So have to actually follow these principles. You need to know exactly where you follow like is because that's what's making your developers miserable and frustrated on having them context, which on thrash So it. The approach here is important, and we have to stop doing these other things right. >>There's so much. They're a pack. I love it, you know, especially the cloud conversation, because so many people look at it wrong as a cost saving device as opposed to an innovation driver, and they get stuck, they get stuck in the literal. And, you know, I think the same thing always about Moore's law, right? You know, there's a lot of interesting riel tech around Moore's law and the increasing power of microprocessors. But the real power, I think in Moore's laws, is the attitudinal change in terms of working in a world where you know that you've got all this power and what will you build and design? E think it's funny to your your comment on the flow in the bottleneck, right? Because because we know manufacturing assumes you fix one bottleneck. You move to your next one, right, You always move to your next point of failure. So if you're not fixing those things, you know you're not. You're not increasing that speed down the line unless you can identify where that bottleneck is, or no matter how Maney improvements you make to the rest of the process, it's still going to get hung up on that one spot. >>That's exactly, and you also make it sound so simple. But again, if you don't have the data driven visibility of where the bottleneck is. And but these bottlenecks are just as you said, if it's just lack, um, all right, so we need to understand is the bottleneck, because our security use air taking too long and stopping us from getting like the customer. If it's that automate that process and then you move on to the next bottleneck, which might actually be that deploy yourself through the clouds is taking too long. But if you don't take that approach of going flow first rather than again the sort of way cost production first you have taken approach of customer centric city, and you only focus on optimizing cost. Your costs will increase and your flow will slow down. And this is just one, these fascinating things. Whereas if you focus on getting back to the customer and reducing your cycles on getting value your flow time from six months to two weeks or 21 week or two event as we see with tech giants, you actually could both lower your costs and get much more value. Of course, get that learning going. So I think I've I've seen all these cloud deployments and modernizations happen that delivered almost no value because there was such a big ball next up front in the process. And actually the hosting and the AP testing was not even possible with all of those inefficiencies. So that's why going flow first rather than costs. First, there are projects versus Sochi. >>I love that and and and and it begs, repeating to that right within a subscription economy. You know you're on the hook to deliver value every single month because they're paying you every single month. So if you're not on top of how you delivering value, you're going to get sideways because it's not like, you know, they pay a big down payment and a small maintenance fee every month. But once you're in a subscription relationship, you know you have to constantly be delivering value and upgrading that value because you're constantly taking money from the customers. It's it's such a different kind of relationship, that kind of the classic, you know, Big Bang with the maintenance agreement on the back end really important. >>Yeah, and I think in terms of industry ship, that's it. That's what catalyzed this industry shift is in this SAS that subscription economy. If you're not delivering more and more value to your customers, someone else's and they're winning the business, not you. So one way we know is that divide their customers with great user experiences. Well, that really is based on how many features you delivered or how much. How about how many quality improvements or scaler performance improvements you delivered? So the problem is, and this is what the business manifesto was was the forefront of touch on is, if you can't measure how much value delivered to a customer, what are you measuring? You just back again measuring costs, and that's not a measure of value. So we have to shift quickly away from measuring costs to measuring value to survive in in the subscription economy. Mick, >>we could go for days and days and days. I want to shift gears a little bit into data and and a data driven, um, decision making a data driven organization. Because right day has been talked about for a long time. The huge big data mean with with Hadoop over over several years and data warehouses and data lakes and data, oceans and data swamps and you go on and on, it's not that easy to do right. And at the same time, the proliferation of data is growing exponentially were just around the corner from from I, O. T and five G. So now the accumulation of data at machine scale again this is gonna overwhelm, and one of the really interesting principles that I wanted to call out and get your take right is today's organizations generate mawr data than humans can process. So informed decisions must be augmented by machine learning and artificial intelligence. I wonder if you can again, you've got some great historical perspective reflect on how hard it is to get the right data to get the data in the right context and then to deliver to the decision makers and then trust the decision makers to actually make the data and move that down. You know, it's kind of this democratization process into more and more people and more and more frontline jobs, making more and more of these little decisions every day. >>Yeah, and Jeff, I think the front part of what you said are where the promises of big data have completely fallen on their face into these swamps. As you mentioned, because if you don't have the data and the right format, you can connect, collected that the right way, you're not. Model it that way the right way. You can't use human or machine learning on it effectively. And there have been the number of data, warehouses and a typical enterprise organization, and the sheer investment is tremendous. But the amount of intelligence being extracted from those is a very big problem. So the key thing that I've known this is that if you can model your value streams so you actually understand how you're innovating, how you're measuring the delivery value and how long that takes. What is your time to value through these metrics? Like for the time you can actually use both. You know the intelligence that you've got around the table and push that balance as it the assay, far as you can to the organization. But you can actually start using that those models to understand, find patterns and detect bottlenecks that might be surprising, Right? Well, you can detect interesting bottle next one you shift to work from home. We detected all sorts of interesting bottlenecks in our own organization that we're not intuitive to me that had to do with more senior people being overloaded and creating bottlenecks where they didn't exist. Whereas we thought we were actually organization. That was very good at working from home because of our open source route. So the data is highly complex. Software Valley streams are extremely complicated, and the only way to really get the proper analysts and data is to model it properly and then to leverage these machine learning and AI techniques that we have. But that front, part of what you said, is where organizations are just extremely immature in what I've seen, where they've got data from all the tools, but not modeled in the right way. >>Well, all right, so before I let you go, you know? So you get a business leader he buys in. He reads the manifesto. He signs on the dotted line. He says, Mick, how do I get started? I want to be more aligned with With the development teams, you know, I'm in a very competitive space. We need to be putting out new software features and engage with our customers. I want to be more data driven. How do I get started? Well, you know, what's the biggest inhibitor for most people to get started and get some early winds, which we know is always the key to success in any kind of a new initiative, >>right? So I think you can reach out to us through the website. Uh, on the is a manifesto, but the key thing is just it's exactly what you said, Jeff. It's to get started and get the key wins. So take a probably value stream. That's mission critical. It could be your new mobile Web experiences, or or part of your cloud modernization platform where your analysts pipeline. But take that and actually apply these principles to it and measure the entire inflow of value. Make sure you have a volumetric that everyone is on the same page on, right. The people on the development teams that people in leadership all the way up to the CEO and one of the where I encourage you to start is actually that enter and flow time, right? That is the number one metric. That is how you measure whether you're getting the benefit of your cloud modernization. That is the one metric that even Cockcroft when people I respect tremendously put in his cloud for CEOs Metric 11 way to measure innovation. So basically, take these principles, deployed them on one product value stream measure into and flow time on. Then you'll actually you well on your path to transforming and to applying the concepts of agile and develops all the way to the business to the way in your operating model. >>Well, Mick, really great tips, really fun to catch up. I look forward to a time when we can actually sit across the table and and get into this, because I just I just love the perspective. And, you know, you're very fortunate to have that foundational, that foundational base coming from Xerox parc. And it's, you know, it's a very magical place with a magical history. So the to incorporate that and to continue to spread that wealth, you know, good for you through the book and through your company. So thanks for sharing your insight with us today. >>Thanks so much for having me, Jeff. Absolutely. >>Alright. And go to the biz ops manifesto dot org's Read it. Check it out. If you want to sign it, sign it. They'd love to have you do it. Stay with us for continuing coverage of the unveiling of the business manifesto on the Cube. I'm Jeffrey. Thanks for watching. See you next time.

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a Himalayan Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tomb of length thanks so much for coming in the queue thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to find the curve and we're grateful for being able to be protected in this way so all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get into it but here with us you know how you spend your time okay well I head up a date operations function and a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and liberate it as as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago of the Big Data move data became an asset you had a lot of shadow I people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that the banker in 1989 had to mainly just know debits credits and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all it's the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the face if data is not the main photo really um it's always been an asset I think organizations just never consciously knew that data was that okay so so what sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like self as challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data Ops what was that like what were some of the challenges that you faced and how'd you get through them okay first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like chatter IP but when you really acknowledge that data is an essay just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not abdicate its management to a separate function that's not cold to the business and oftentimes IT are seen as a support or an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability the business function it presides in business makes to product management makes to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing offense and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about they just kind of leave us to to it was was also a journey but that was kind of the first step into it in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could take us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Dave is that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our date of college and from that perspective it then brought the case for data quality management so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they then buy into into the concept so sometimes you need to just wait for a good Christ and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity all the other capabilities at the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that's when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think the the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a P&L any department worth its salt has a FDA or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities in managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that they data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely um in the sense that unless the whole business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific you say so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners will the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of of 2019 into the beginning of this year so what we did was they I'm worried about the sunlight I mean through the window you look creative to me like sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in mates 2019 coming in too early this year we we had long kind of achieved the the compliance and regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity um the one that comes to mind is we're a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to get go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a database fee from a reporting history and we were in a place where in some cases based on how our Ledger's roll up and the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements were reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can learn at any time I see the sales they've made which is very important right now at the time of covert nineteen from a form of productivity and executional competitiveness those are two great use cases of women lying so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data bills profanity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barret data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm going to ask you then so when gdpr hit were you able to very quickly leverage this capability and and apply and then maybe other of compliance edik as well well actually you know what we just now was post TDP our us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes they use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time it but we're able to do real-time reporting from a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because Dana monetization is actually the penis' t the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know it kind of happened with enterprise data warehouse but it was too slow it was complicated and it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you I mean like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're out who we the the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of ovid 19 well I mean because of of the quality of kind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government put us on lockdown relatively early in in the curve or in the cycle of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue or potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student and students with loans on our books a instant three month payment holiday assuming they were in good standing and we did that upfront though it was actually an opt-out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we had not been able to provide productivity dashboard and and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are now that people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various methods being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric but business lines care about you know into these dashboards you usually the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and in an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important stupid like I can go on for an hour talking about this topic but unfortunately we're we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to be applied in terms of the use cases that we can find the second one is really providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are really utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa to be like thanks again great having you thank you very much Dave appreciate the opportunity thank you for watching everybody that we go we are digging in the data ops we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music] you

>>from around the globe. It's the queue with digital coverage of women transforming technology brought to you by VM Ware. >>Hi, this is Lisa Martin covering fifth Annual Women Transforming Technology WT two from my home in San Jose, California Because this is the first year than WT two has gone digital. Very excited to welcome next one of the speakers from the executive track. We have Charmaine Macquarie, president of Macquarie Group, but also offer C Suite Advisor. You know, Speaker Charmaine. Nice to start with you. >>An absolute pleasure. Thank you for having me. >>So you have an incredible background. You have been for two decades working with leaders and I read 27 industries, five continents and from some pretty big, well known brands Coca Cola, Johnson and Johnson, my particular favorite Starbucks. Tell me a little bit about your background in your career and how you came to be working with potential leaders >>early on in my career, I was working in politics, actually helping politicians understand their constituency and then how to communicate effectively with them and then went on into marketing. And really, what I say is that what I do is a conglomeration of all of my life experience working with leaders either in politics, in marketing and sales on a variety of industries, including gas and oil, and coming together and helping them understand how to communicate their message effectively. How have executive presence and ensuring that they're seeing, heard and remembered >>what? One of the things. One of the things that talking about being remembered especially now during a crisis that nobody has ever experienced before, when there are so much, so much concern and so much uncertainty. Um, I e. Read that you said effective communication is more than just words and phrases, especially in today's climate. What is effective communication? >>Effective communication is making sure that people hear your value, your value proposition, and that is really essential today. One of the things you want to do is that you want to elevate your visibility and when elevate the value that you bring to your organization. There are a number of competing priorities, and what you want organization to understand is what is it that you see that others don't see, and that is a part of your value proposition. How are you going to help the organization innovate through this time, and wanting to do that is really speaking about what is the value. What is it that it's gonna make the difference for the organization today with this crises and that will also take it further into the future. >>Tell me a little bit about this session that you did at women transforming technology the other day. 35 minutes. Interactive session. Since everything for this year's event was digital, I love the name of your session. Speak up, Stand out. We heard talking a little bit about when you first learned maybe last month, that this event was going digital. Did you change anything? Were there certain elements of your expertise and your recommendations that are now more even more important? Respect to visibility and value? >>Yes, So what? I changed it. What I changed and Waas. I really wanted to make it as a conversational as possible, because in this isolation it's easy to not feel seen or heard, and I want people to be able to elevate again their visibility and their ability to add value. So a couple of things that people can do is they can actually rewrite their narrative if they need to meaning if you believe that if you do not define yourself, others well, and their definition will inevitably be inadequate. So if you know that you are seen as a very quiet person and a person that is in the background and you want to have greater visibility, this is a great opportunity for you to rewrite that narrative and make yourself more visible. Meaning, I think, the expertise that you have again the insight that you have, making sure that you bring that to the table. You can do it in a number of formats. You could do it not only on a zoom call with your colleagues, but you can. Also, your email is heightened if you're using language and the language of leadership language that really hurts. People's here, and that creates a visual. So now you want to do to really make sure that using language that is very vivid and allows a person to touch, taste and feel what it is that you're saying, so that's one of the things that you can do. The other is say, Is that what I want to make sure that my clients are not well kept secrets. I want to make sure that in this time of isolation that they're finding opportunities to reach out. So most everyone is at home sheltering in place so people have more time on their hands in terms of reading your emails. When researchers found that there is a 26% increase and say your newsletters being read your emails being written, so now is the time that you could actually heighten that kind of communication. >>That's fascinating. Look that you said about making what you're communicating in an email. Maybe it's even texture over something like slack, vivid. Say, somebody has a great idea, I think. All right, so terms have changed. My job function is difference, or it's challenging to complete certain Give me some words that you think. So now you're saying people are actually focusing more on reading what you're saying, What are some vivid words that I could use if I had an interesting finance project or a marketing project that I wanted to raise the visibility of and gets them to really feel what I'm looking at? >>So when you speak about up in a finance project, one of the things you want to do is think about what is a story that could articulate those numbers that can tell the story with those numbers. So if you were saying, um, let's just make it as simple as possible. Two plus two equals four. Well, what you want to think about is what is it that is going to be different when you finished this project, or what is it that's gonna be? It's gonna shift in the marketplace. And so you want to create that visual? What does the future look like? And using examples of things that are very basic to our life today, as opposed to using really complicated language. Now is the time to have your language simple, having very clear and having very vivid. So you >>run it, Go ahead. Sorry. >>No, please go. Right. Yet >>I'm glad that you brought up simplicity because so often I think people think maybe I'm managing a project or I'm creating a methodology, and I think, really, it's just it's the simple. But we often second guess ourselves because I think I included in this. A lot of folks think it can't be that simple. It's got to be more complex I need to show, you know, like an episode of I'm picturing an Apple sort of the Big Bang theory, and Sheldon's talking about strength there. You need to make it complex to show your value. And but sometimes it's the simplest methodology. The simplest way of communicating that is the most effective. Do you find out that sometimes spokes, regardless of their level of executive nous, are challenged to really step back and look at the simple way to communicate with the simple answer? >>Absolutely. And simplicity is best, whether it's during this time period or even beyond this pandemic, but particularly now. So I don't know if anybody's ever seen the show. The marvelous is, um, I think it's amazing. Yeah, single and one of the things that she asked her husband, She goes, Well, honey, what do you do? And so I think, in the first episode, and he says, You know, I signed papers, I do this, I do that and he says, I really don't know what the hell I do. And I remember an incident with one of my clients, and I asked her, What does she do? She gave me her job title and I said, Okay, how many people work in your company? And she said, 49,000 people work here. I said, How many people do you think have the same title issue? If she goes well, you know, I'm sure at least a couple 1000. I said yes. So what distinguishes you? And so she wanted to talk about the title, which is like talking about acronyms at a company. And I said So, Really, What do you do? What we realized is that what she does is that she was responsible the fastest growing market segment in her company that articulates your value proposition that made a very visit vivid and very brought it to life. So people are able to understand when someone asked me, What do I do? I don't say that I'm an executive coach because you may have read an article last week that says all executive coach us up, that defines May. I wanted to find myself. My value is, I hope smart people get promoted when they get promoted, they communicate the big picture. So I help smart people get promote and communicate the big picture. I provide executive coaching senior level executives. I articulated my value. You know who I work with their smart people, that they're not smart. They're not working with me when they work with me and get promoted. Why? Because it communicate the big picture. Really? Simple one sent it. So what is the value? That is what really heightens your visibility and heightens your and levels. Level up your ability to be seen and heard in organizations. >>And, you know, I was looking at your website. You've been 98% success rate of folks that have worked with you that have been promoted within the following 18 months. What are some of the both hard and soft skills that you're looking for? So when you work, when you select clients to work with that, that demonstrate they are ready to be in the six weeks >>Well, there's a couple of things. One is that person has to be open and willing and not being volunteered by the organization, meaning saying you need to do you have to do this. If it is mandatory that someone work with an executive coach, that's not a winning proposition. The winning propositions That person is open and open to change and ready to make change. As I say to my clients, if you want everything to remain the same, I am not the coach for you because you're going to see change and you're going to see significant change. So that's one the other is preparing your organization for the kind of change is going to take place so that your organization begins with C and hear what you're doing different. So, for example, I would say to a client, if you're prepared to really step up and make the commitment to making the shift, you want to let people know what kind of shift that you're taking you're making so that they can begin to look for people like to look for success. They like to be able to reward you when you're successful, but you've got to let them know that you're there >>for that shift. >>So that's one of the things that's really important is that people be open to it and they'd be ready to take their spotlight. If you want to do it and remain behind the curtain, that's wonderful. This is not the work for you. >>It requires a little bit of vulnerability that, or maybe a lot of vulnerability to be able to do that, not easy, unless you're bringing a brown fan like I am talk to me about, especially in this time with covered 19 The uncertainty in every aspect of our lives. Every single aspect is it's dense and it's an emotional challenge. So do you find that it's harder for some folks, whether they're men or women, to do what your title says? You know? Speak up, let them know I'm coming. I'm on my way. How are you advising folks from a psychological perspective, to be able to do this? >>Well, I think there's a couple of things. One is that with the three questions I ask every client and those three questions are one. How do you see yourself? How do other people see you? And the third is, How do you want to be seen? So when you're able to answer, become introspective and answer those questions from the heart from your heart, then you can get really clear about what you want the world to know about you and how you want to show up. And it does require vulnerability. It requires you to look inward first for you to make that decision on how you want the world to see you. And then once you're able to make that, get that clarity and so it's process make getting that clarity. Then you can speak about that to the world. My thing is, is again. If you don't define yourself, others will, and their definition is inadequate. So when you define yourself, you know who you are and what you stand for. You can then shout that at the top of your lungs. But you don't really have to, because your actions will speak very clearly about what it is and who you say you are and how you want the world to see you. And you're always asking, am I can grow it? >>I love that about defining yourself so that others don't do it incorrectly. Talk to me about how somebody can develop their own communication style. How what are some of the steps that they need to recognize that, for example, if you see someone, anything there too bold or there to brush, or maybe dial it back a bit, especially because messages are getting read more now, which that process internally that I would need to take to develop and effective communication style. What is it >>that you need to do to to develop that effective communication style one? As I said, being able to define what that looks like for you and what that is may not be appropriate for every organization and every corporate culture. So you need to find immediate. Make sure either evaluate whether not you're in the right corporate culture so that you can be successful and or find a new one so that you could be successful once you have that, really, um, helping the people in your organization to make it easy for them to come to you. So by extending by extending yourself first, that is one of the things that I would say it would be really important in terms of stepping up during this time frame is saying, I feel really this is really let's say, someone has been felt really shaken by this really shaken by this. But I am determined so leverage this as an opportunity to really show up as my best self and show my greatest humanity. And I think that when we let people know what did it, where we're going and where we're headed, This far more easy for people to support you and provide you with the venues in which to exhibit who you are. This is a great time for you to volunteer A so much as possible to have that visibility. Because I think one of the questions you asked me earlier is how do you get hadn't become comfortable with this? You get comfortable with it by practising, Lady Gaga says. We're born that way, but we are. The only way that it happens with people that are really successful is because they practice >>something that is so interesting. Is during this time in particular, is getting is accountability, right? It's so easy right now more than ever to lose accountability. And I like that. You said that That's what I'm hearing when you say, you know, let people know that direction that you're going in. I think for the person you set that okay, I publicly said this, I need to be held that I need to hold myself accountable so that I deliver. I think there's a lot of power in that >>there is, and when you step up and articulate to the world. Well, you're about what it is that you're going to deliver your level of excellence. You hold yourself accountable because the person who is most important for you to be accountable to is yourself. Others come second, actually, sort of like being on the airplane in the mask. You've got to do it for you first. Because if you let yourself down, that's the that is the most horrific. And so stepping up to that is so much. There's so much power. And I believe that people provide you with a lot of grace when you do that and people know they can count on you. >>And that's so important knowing demonstrating your dependability in any situation. Sherman, I wish we had more time. It's been such a pleasure talking to you. Thank you for sharing your insight. I'm gonna be visible show value and the vetted and communication and accountable. Thank you so much for joining me. >>Have a wonderful day. You >>as well. And for Charmaine McCleery. I'm Lisa Martin. You're watching the Cube's coverage of the digital version of women transforming technology 2020 for now. >>Yeah, >>Yeah, yeah, yeah, yeah, yeah

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cute conversation everybody welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is it's not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a the tumor lang Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tumor length thanks so much for coming in the cube thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine and we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to flatten the curve and we're grateful for being able to be protected in this way so we're all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get in with here with us you know how you spend your time okay well I hit up a date operations function in a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and leverage it as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago the Big Data move data became an asset you had a lot of shadow ID people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that um the banker in 1989 had to mainly just know debits credit and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back um the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all is the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the plate if data is not the main so really um it's always been an acid I think organizations just never consciously knew that data was there okay so so it sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like toughest challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data ops what was that like what were some of the challenges that you faced and how'd you get through them first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like shadow IP but when you really acknowledge that data is and si just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not add the a its management to a separate function that's not code to the business and oftentimes IT are seen as a support for an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability a business function it presides in business next to product management next to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing events and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about and they just kind of leave us to - it was was also a journey but that was kind of the first step in - in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could pick us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Davis that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our data quality and from that perspective it then brought the case for data quality management to the whole so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they stick then buy into into the concepts so sometimes you need to just wait for a good price and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity or the other capabilities of the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think that the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then um also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly it's almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a pl any department worth its salt has a FD or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities and managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that their data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed through and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely in the sense that unless the core business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific use so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners were the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of 2019 into the beginning of this year so what we did was they've am worried about the sunlight coming through the window you look crazy to me like the sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in late 2019 coming in too early this year we we had long kind of achieved the the compliance and the regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity the one that comes to mind is where a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to give go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we're constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a data perspective um we were in a place where in some cases based on how our Ledger's roll up in the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements from a reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can Logan at any time I see the sales they've made which is very important right now and the time of overt nineteen from a from a productivity and executional competitiveness listing those are two great use cases of cooling so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data stills probity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barratt data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm gonna ask you then so when GDP are hit were you able to very quickly leverage this capability and and imply and then maybe other of compliance edik as well Oh actually you know what we just now was post gdpr us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes the use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time ago were able to do real-time reporting of a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because data monetization is actually the penisy the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know kind of happened with enterprise data warehouse but it was too slow it was complicated it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you to me like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're on the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of mobit nineteen well I mean because of of the quality of mind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government and put us on lockdown relatively early in in the curve in disciple of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue for potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student um students with loans on our books a instant preman payment holiday assuming they were in good standing and we did that upfront though it was actually an up out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we have not been able to provide productivity dashboard and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are and other people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various metric being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric the business lines care about you know into these dashboards you using the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and and an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important to me like I could go on for an hour talking about this topic but unfortunately we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to to be applied in terms of the use cases that we can find the second one is really um providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are ready utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa the trooper like thanks again great having you thank you very much Dave appreciate the opportunity and thank you for watching everybody that we go we are digging in the data offs we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat dot net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music]

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hello and welcome to the cube conversation here in the Palo Alto studio I'm John four host of the cube we are here at the quarantine crew of the cube having the conversations that matter the most now and sharing that with you got a great guest here Phil Quaid was the chief information security officer of Fortinet also the author of book digital bing-bang which I just found out he wrote talking about the difference cybersecurity and the physical worlds coming together and we're living that now with kovat 19 crisis were all sheltering in place Phil thank you for joining me on this cube conversation so I want to get in this quickly that I think the main top thing is that we're all sheltering in place anxiety is high but people are now becoming mainstream aware of what we all in the industry have been known for a long time role of data cybersecurity access to remote tools and we're seeing the work at home the remote situation really putting a lot of pressure on as I've been reporting what I call at scale problems and one of them is security right one of them is bandwidth we're starting to see you know the throttling of the packets people are now living with the reality like wow this is really a different environment but it's been kind of a disruption and has created crimes of opportunity for bad guys so this has been a real thing everyone's aware of it across the world this is something that's now aware on everyone's mind what's your take on this because you guys are fighting the battle and providing solutions and we're doing for a long time around security this highlights a lot of the things in the surface area called the world with what's your take on this carbon 19 orton s been advocating for architectures and strategies that allow you to defend anywhere from the edge through the core all the way up to the cloud boom so with you know high speed and integration and so all the sudden what we're seeing not just you know in the US but the world as well is that that edge is being extended in places that we just hadn't thought about or our CV that people just hadn't planned for before so many people or telecommunication able to move that edge securely out to people's homes and more remote locations and do so providing the right type of security of privacy if those communications that are coming out of those delicate ears I noticed you have a flag in the background and for the folks that might not know you spent a lot of time at the NSA government agency doing a lot of cutting-edge work I mean going back to you know really you know post 9/11 - now you're in the private sector with Fortinet so you don't really speak with the agency but you did live through a time of major transformation around Homeland Security looking at data again different physical thing you know terrorist attacks but it did bring rise to large-scale data to bring to those things so I wanted to kind of point out I saw the flag there nice nice touch there but now that you're in the private sector it's another transformation it's not a transition we're seeing a transformation and people want to do it fast and they don't want to have disruption this is a big problem what's your reaction to that yeah I think what you're reporting out that sometimes sometimes there's catalysts that cause major changes in the way you do things I think we're in one of those right now that we're already in the midst of an evolutionary trend towards more distributed workforces and as I mentioned earlier doing so with the right type of security privacy but I would think what I think the global camp in debt endemic is showing is that we're all going to be accelerating that that thing is like it's gonna be a lot less evolutionary and a little bit more faster that's what happens when you have major world events like this being 911 fortunate tragedies it causes people to think outside the box or accelerate what they're already doing I think wearing that in that world today yeah it pulls forward a lot of things that are usually on the planning side and it makes them reality I want to get your thoughts because not only are CEOs and their employees all thinking about the new work environment but the chief information security officer is people in your role have to be more aware as more things happening what's on the minds of CISOs around the world these days obviously the pandemics there what are you seeing what are some of the conversations what are some of the thought processes what specifically is going on in the of the chief information security officer yeah I think there's probably a there's probably two different two different things there's the there's the emotional side and there's the analytic side on the emotional side you might say that some Caesars are saying finally I get to show how cyber security can be in an abler of business right I can allow you to to to maintain business continuity by allowing your workers to work from home and trying sustain business and allow you to keep paying their salary is very very important to society there's a very important time to step up as the seaso and do what's helpful to sustain mission in on the practical side you say oh my goodness my job's gotten a whole lot harder because I can rely less and less on someone's physical controls that use some of the physical benefits you get from people coming inside the headquarters facility through locked doors and there's personal congress's and personal identification authentication you need to move those those same security strategies and policies and you need to move it out to this broad eggs it's gotten a lot bigger and a lot more distributed so I want to ask you around some of the things they're on cyber screws that have been elevated to the top of the list obviously with the disruption of working at home it's not like an earthquake or a tornado or hurricane or flood you know this backup and recovery for that you know kind of disaster recovery this has been an unmitigated disaster in the sense of it's been unfor casted I was talking to an IT guy he was saying well we provisioned rvv lands to be your VPNs to be 30% and now they need a hundred percent so that disruption is causing I was an under forecast so in cyber as you guys are always planning in and protecting has there been some things that have emerged that are now top of mind that are 100 percent mindshare base or new solutions or new challenges why keep quite done what we're referring to earlier is that yep any good see so or company executive is going to prepare for unexpected things to a certain degree you need it whether it be spare capacity or the ability to recover from something an act of God as you mentioned maybe a flood or tornado or hurricane stuff like that what's different now is that we have a disruption who which doesn't have an end date meaning there's a new temporal component that's been introduced that most companies just can't plan for right even the best of companies that let's say Ronald very large data centers they have backup plans where they have spare fuel to run backup generators to provide electricity to their data centers but the amount of fuel they have might only be limited to 30 days or so it's stored on-site we might think well that's pretty that's a lot of for thinking by storing that much fuel on site for to allow you to sort of work your way through a hurricane or other natural disaster what we have now is a is a worldwide crisis that doesn't have a 30-day window on it right we don't know if it's gonna be 30 days or 120 days or or you know even worse than that so what's different now is that it's not just a matter of surging in doing something with band-aids and twine or an extra 30 days what we need to do is as a community is to prepare solutions that can be enduring solutions you know I have some things that if the absent I might like to provide a little color what those types of solutions are but that that would be my main message that this isn't just a surge for 30 days this is a surge or being agile with no end in sight take a minute explain some of those solutions what are you seeing whatever specific examples and solutions that you can go deeper on there yeah so I talked earlier about the the edge meaning the place where users interact with machines and company data that edge is no longer at the desktop down the hallway it could be 10 miles 450 miles away to where anyone where I'm telling you I'm commuting crumb that means we need to push the data confidentiality things out between the headquarters and the edge you do that with things like a secure secured tunnel it's called VPNs you also need to make sure that the user identification authentication this much is a very very secure very authentic and with high integrity so you do that with multi-factor authentication there's other things that we like that that are very very practical that you do to support this new architecture and the good news is that they're available today in the good news at least with some companies there already had one foot in that world but as I mentioned earlier not all companies had yet embraced the idea of where you're going to have a large percentage of your workforce - until a community so they're not quite so they're there they're reacting quickly to to make sure this edge is better protected by identification and authentication and begins I want to get to some of those edge issues that now translate to kind of physical digital virtualization of of life but first I want to ask you around operational technology and IT OT IT these are kind of examples where you're seeing at scale problem with the pandemic being highlighted so cloud providers etc are all kind of impacted and bring solutions to the table you guys at Foot are doing large scale security is there anything around the automation side of it then you've seen emerge because all the people that are taking care of being a supplier in this new normal or this crisis certainly not normal has leveraged automation and data so this has been a fundamental value proposition that highlights what we call the DevOps movement in the cloud world but automation has become hugely available and a benefit to this can you share your insights into how automation is changing with cyber I think you up a nice question for me is it allowed me to talk about not only automation but convergence so it's let's hit automation first right we all even even pre-crisis we need to be better at leveraging automation to do things that machines do best allow people to do higher-order things whether it's unique analysis or something else with a with a more distributed workforce and perhaps fewer resources automation is more important ever to automatically detect bad things that are about to happen automatically mitigating them before they get or they get to bad you know in the cybersecurity world you use things like agile segmentation and you use like techniques called soar it's a type of security orchestration and you want to eat leverage those things very very highly in order to leverage automation to have machines circum amount of human services but you also brought up on my favorite topics which is ot graceful technology though OTS you know are the things that are used to control for the past almost a hundred years now things in the physical world like electric generators and pipes and valves and things like that often used in our critical infrastructures in my company fort net we provide solutions that secure both the IT world the traditional cyber domain but also the OT systems of the world today where safety and reliability are about most important so what we're seeing with the co19 crisis is that supply chains transportation research things like that a lot of things that depend on OT solutions for safety and reliability are much more forefront of mine so from a cybersecurity strategy perspective what you want to do of course is make sure your solutions in the IT space are well integrated with you solutions in the OT space to the so an adversary or a mistake in cause a working to the crack in causing destruction that convergence is interesting you know we were talking before you came on camera around the fact that all these events are being canceled but that really highlights the fact that the physical spaces are no longer available the so-called ot operational technologies of events is the plumbing the face-to-face conversations but everyone's trying to move to digital or virtual eyes that it's not as easy as just saying we did it here we do it there there is a convergence and some sort of translation this new there's a new roles there's new responsibilities new kinds of behaviors and decision making that goes on in the physical and digital worlds that have to then come together and get reimagined and so what's your take on all this because this is not so much about events but although that's kind of prime time problem zooming it is not the answer that's a streaming video how do you replicate the value of physical into the business value in digital it's not a one-to-one so it's quite possible that that we might look back on this event to cover 19 experience we might look back at it in five or ten years and say that was simply a foreshadowing of our of the importance of making sure that our physical environment is appropriate in private what I mean is that with the with the rapid introduction of Internet of Things technologies into the physical world we're going to have a whole lot of dependencies on the thing inconveniences tendencies inconveniences on things an instrument our physical space our door locks or automobiles paths our temperatures color height lots of things to instrument the physical space and so there's gonna be a whole lot of data that's generated in that cyber in a physical domain increasingly in the future and we're going to become dependent upon it well what happens if for whatever reason in the in the future that's massively disruptive so all of a sudden we have a massive disruption in the physical space just like we're experiencing now with open 19 so again that's why it makes sense now to start your planning now with making sure that your safety and reliability controls in the physical domain are up to the same level security and privacy as the things in your IT delete and it highlights what's the where the value is to and it's a transformation I was just reading an article around spatial economics around distance not being together it's interesting on those points you wrote a book about this I want to get your thoughts because in this cyber internet or digital or virtualization of physical to digital whether it's events or actual equipment is causing people to rethink architectures you mentioned a few of them what's the state of the art thinking around someone who has the plan for this again is in its complex it's not just creating a gateway or a physical abstraction layer of software between two worlds there's almost a blending or convergence here what's your what's your thoughts on what's the state of the art thinking on this area yeah the book that I number of a very esteemed colleagues contribute to what we said is that it's time to start treating cybersecurity like a science let's not pretend it's a dark art that we have to relearn every couple years and what what we said in the in the digital Big Bang is that humankind started flourishing once we admitted our ignorance in ultimately our ignorance in the physical world and discovered or invented you can right word the disciplines of physics and chemistry and once we recognize that our physical world was driven by those scientific disciplines we started flourishing right the scientific age led to lots of things whether it would be transportation health care or lots of other things to improve our quality of life well if you fast forward 14 billion years after that cosmic Big Bang which was driven by physics 50 years ago or so we had a digital Big Bang where there was a massive explosion of bits with the invention of the internet and what we argue in the book is that let's start treating cybersecurity like a science or the scientific principle is that we ought to write down and follow a Rousseau's with you so we can thrive in the in the in a digital Big Bang in the digital age and one more point if you don't mind what we what we noted is that the internet was invented to do two things one connect more people or machines than ever imagined in to do so in speeds that were never imagined so the in the Internet is is optimized around speed in connectivity so if that's the case it may be a fundamental premise of cybersecurity science is make sure that your cyber security solutions are optimized around those same two things that the cyber domains are optimized around speed in integration continue from there you can you can build on more and more complex scientific principles if you focus on those fundamental things and speed and integration yeah that's awesome great insight they're awesome I wanted to throw in while you had the internet history lesson down there also was interesting was a very decentralization concept how does that factor in your opinion to some of the security paradigms is that helped or hurt or is it create opportunities for more secure or does it give the act as an advantage yeah I love your questions is your it's a very informed question and you're in a give me good segue to answer the way you know it should be answer yeah the by definition the distributed nature of the Internet means it's an inherently survivable system which is a wonderful thing to have for a critical infrastructure like that if one piece goes down the hole doesn't go down it's kind of like the power grid the u.s. the u.s. electrical power grid there's too many people who say the grid will go down well that's that's just not a practical thing it's not a reality thing the grades broken up into three major grades and there's AB ulis strategies and implementations of diversification to allow the grid to fail safely so it's not catastrophic Internet's the same thing so like my nipple like I was saying before we ought to de cyber security around a similar principle that a catastrophic failure in one partner to start cybersecurity architecture should result in cascading across your whole architecture so again we need to borrow some lessons from history and I think he bring up a good one that the internet was built on survivability so our cybersecurity strategies need to be the same one of the ways you do that so that's all great theory but one of the ways you do that of course is by making your cybersecurity solutions so that they're very well integrated they connect with each other so that you know speaking in cartoon language you know if one unit can say I'm about to fail help me out and another part of your architecture can pick up a slack and give you some more robust security in that that's what a connected the integrated cyber security architecture do for you yeah it's really fascinating insight and I think resiliency and scale are two things I think are going to be a big wave is going to be added into the transformations that going on now it's it's very interesting you know Phil great conversation I could do a whole hour with you and do a fish lead a virtual panel virtualize that our own event here keynote speech thanks so much for your insight one of things I want to get your thoughts on is something that I've been really thinking a lot lately and gathering perspectives and that is on biosecurity and I say biosecurity I'm referring to covet 19 as a virus because biology involves starting a lab or some people debate all that whether it's true or not but but that's what people work on in the biology world but it spreads virally like malware and has a similar metaphor to cybersecurity so we're seeing conversation starting to happen in Washington DC in Silicon Valley and some of my circles around if biology weapon or it's a tool like open-source software could be a tool for spreading cybersecurity Trojans or other things and techniques like malware spear phishing phishing all these things are techniques that could be deployed metaphorically to viral distribution a biohazard or bio warfare if you will will it look the same and how do you defend against the next covet 19 this is what you know average Americans are seeing the impact of the economy with the shelter in place is that what happens again and how do we prevent it and so a lot of people are thinking about this what is your thoughts because it kind of feels the same way as cybersecurity you got to see it early you got to know what's going on you got to identify it you got to respond to it time to close your contain similar concepts what's your thoughts on with BIOS we don't look with all due respect to the the the bio community let me make a quick analogy to the cyber security strategy right cyber security strategy starts with we start as an attacker so I parts of my previous career I'm an authorized had the opportunity to help develop tools that are very very precisely targeted against foreign adversaries and that's a harder job than you think I mean I think the same is true of anyone of a natural-born or a custom a buyer buyer is that not just any virus has the capability to do a lot of harm to a lot of people selling it so it's it's if that doesn't mean though you can sit back and say since it's hard it'll never happen you need to take proactive measures to look for evidence of a compromise of something whether it's a cyber cyber virus or otherwise you have to actively look for that you have to harm yourself to make sure you're not susceptible to it and once you detect one you need to make sure you have a the ability to do segmentation or quarantine very rapidly very very effectively right so in the cyber security community of course the fundamental strategy is about segmentation you keep different types of things separate that don't need to interact and then if you do have a compromise not everything is compromised and then lastly if you want to gradually say bring things back up to recover you can do some with small chunks I think it's a great analogy segmentation is a good analogy to I think what the nation is trying to do right now by warranty kneeing and gradually reopening up things in in segments in actually mention earlier that some of the other techniques are very very similar you want to have good visibility of where you're at risk and then you can automatically detect and then implement some some mitigations based on that good visibility so I agree with you that it turns out that the cyber security strategies might have a whole lot in common with biohazard I address it's interesting site reliability engineers which is a term that Google coined when they built out their large-scale cloud has become a practice that kind of mindset combined with some of the things that you're saying the cyber security mindset seemed to fit this at scale problem space and I might be an alarmist but I personally believe that we've been having a digital war for many many years now and I think that you know troops aren't landing but it's certainly digital troops and I think that we as a country and a global state and global society have to start thinking about you know these kinds of things where a virus could impact the United States shut down the economy devastating impact so I think Wars can be digital and so I may be an alarmist and a conspirators but I think that you know thinking about it and talking about it might be a good thing so appreciate your insights there Phil appreciated what one other point that might be interesting a few years back I was doing some research with the National Lab and we're looking for novel of cybersecurity analytics and we hired some folks who worked in the biology the bio the biomedical community who were studying a biome fires at the time and it was in recognition that there's a lot of commonality between those who are doing cybersecurity analytics and those reviewing bio biology or biomedical type analytics in you know there was a lot of good cross fertilization between our teams and it kind of helps you bring up one more there's one more point which is what we need to do in cybersecurity in general is have more diversity of workforces right now I don't mean just the traditional but important diversities of sex or color but diversity of experiences right some of the best people I've worked with in the cyber analytics field weren't computer science trained people and that's because they came in problems differently with a different background so one of the things that's really important to our field at large and of course the company my company fort net is to massively increase the amount of cyber security training that's available to people not just the computer scientists the world and the engineers but people in other areas as well the other degree to non-greek people and with that a you know higher level of cyber security training available to a more diverse community not only can we solve the problem of numbers we don't have enough cybersecurity people but we can actually increase our ability to defend against these things I have more greater diversity of thought experience you know that's such a great point I think I just put an exclamation point on that I get that question all the time and the skills gap is should I study computer science and like actually if you can solve problems that's a good thing but really diversity about diversity is a wonderful thing in the age of unlimited compute power because traditionally diversity whether it was protocol diversity or technical diversity or you know human you know makeup that's tend to slow things down but you get higher quality so that's a generalization but you get the point diversity does bring quality and if you're doing a data science you don't want have a blind spot I'm not have enough data so yeah I think a good diverse data set is a wonderful thing you're going to a whole nother level saying bringing diversely skill sets to the table because the problems are diverse is that what you're getting at it is it's one of our I'll say our platforms that we're talking about during the during the covered nineteen crisis which is perhaps there's perhaps we could all make ourselves a little bit better by taking some time out since we're not competing taking some time out and doing a little bit more online training where you can where you can either improve your current set of cybersecurity skills of knowledge or be introduced to them for the first time and so there's one or some wonderful Fortinet training available that can allow both the brand-new folks the field or or the the intermediate level folks with you become higher level experts it's an opportunity for all of us to get better rather than spending that extra hour on the road every day why don't we take at least you know 30 of those 60 minutes or former commute time and usually do some online soccer security treaty feel final question for you great insight great conversation as the world and your friends my friends people we don't know other members of society as they start to realize that the virtualization of life is happening just in your section it's convergence what general advice would you have for someone just from a mental model or mindset standpoint to alleviate any anxiety or change it certainly will be happening so how they can better themselves in their life was it is it thinking more about the the the experiences is it more learning how would you give advice to folks out there who are gonna come out of this post pandemic certainly it's gonna be a different world we're gonna be heightened to digital and virtual but as things become virtualized how can someone take this and make a positive outcome out of all this I I think that the future the future remains bright earlier we talked about sci-fi the integration of the cyber world in the physical world that's gonna provide great opportunities to make us more efficient gives us more free time detect bad things from happening earlier and hopefully mitigating those bad things from happening earlier so a lot of things that some people might use as scare tactics right convergence and Skynet in in robotics and things like that I believe these are things that will make our lives better not worse our responsibilities though is talking about those things making sure people understand that they're coming why they're important and make sure we're putting the right security and privacy to those things as these worlds this physical world and the soccer worlds converged I think the future is bright but we still have some work to do in terms of um making sure we're doing things at very high speeds there's no delay in the cybersecurity we put on top of these applications and make sure we have very very well integrated solutions that don't cause things to become more complex make make things easier to do certainly the winds of change in the big waves with the transformations happening I guess just summarize by saying just make it a head win I mean tailwind not a headwind make it work for you at the time not against it Phil thank you so much for your insights I really appreciate this cube conversation remote interview I'm John Ford with the cube talking about cybersecurity and the fundamentals of understanding what's going on in this new virtual world that we're living in to being virtualized as we get back to work and as things start to to evolve further back to normal the at scale problems and opportunities are there and of course the key was bringing it to you here remotely from our studio I'm John Ferrier thanks for watching [Music]

from Chicago it's the cube covering Oracle transformation day 2020 not to you by Oracle consulting welcome back everybody to this special presentation of the cube where we're covering the rebirth of Oracle consulting it's a digital event where we're going out we're extracting the signal from the noise we happen today to be in Chicago which is obviously the center of the country a lot of big customers here a lot of consultants and consulting organizations here and a lot of expertise Mike Owens is here as a group VP for cloud advisory and a general manager of Oracle elevate Mike thanks for coming on the cube I appreciate it I'm glad to be here so I could ask you elevate in your title what is Oracle elevate yeah Oracle elevate was actually announced Oracle OpenWorld last year it's the partnership that we really had to actually take her skill of the next level so we actually did it with Deloitte Consulting so the goal is to actually take the capabilities of both organizations Deloitte really has functional capabilities and expertise with an Oracle practice and obviously Oracle has Oracle technical expertise the combination the two really allows us to scale provide sort I call the one plus one equals three effort for customers now you've got a decent timeline or observation over the past several years you joined three years ago you were at some brand name companies first of all what attracted you to come to Oracle consulting yeah absolutely so Oracle was in the point where they were doing a lot of stuff around on Prem on-premise software right the old ERP type stuff they were doing cloud they sort of had to have this sort of transformational moment I was asked to come in an Oracle consulting in their early days and say hey look we're trying to transform the organization from on-prem consulting over to cloud consulting coming to help us with this stuff that you've worked from your prior to cloud companies and help us really move the organization forward and look at things differently so it's definitely been a journey over the last three years I've taken it from really 85% of the 90% of our revenue around on Prem type of engagements to now actually splitting the organization being dedicated huntersam on cloud which is just a huge transformation last three years yeah of course I work was a product company and you're you're at your then CTO Larry Ellison said we're going cloud first and that happened during your tenure you came in I'd believe prior to that what kind of effect did that have on the organization I mean we we know from a product standpoint but but just culturally and just a mindset yeah absolutely it had a huge effect in their organization they they started splitting the organization to actually have be dedicated organizations whether it's sales on product whether it's support for product pre-sales support or engineering and solutions architecture and or consulting so we've now split the organization's to primarily support those different lines of business and what that allows us to do is actually focus that and put a lot of the stuff on cloud and moving the company at cloud first at this point we still have a lot of organizations to do either on Prem type of work and things like that they can't move over that's supportive but you're gonna see a larger shift of the cloud first right to actually move our customers and our organizations and back to wheats you hear a bunch of our executives talk about we also actually use our own capabilities as well to you know whether it's a our machine learning we actually use in our own HR systems if I do my expense reporting there's actually a I bought that I can actually put stuff in there and automatically pulls it in we actually take those capabilities and consumer ourselves right because we have to believe and what we actually create as well your definition to cloud of course is different from you know the hyper scale cloud providers would say our cloud is like per scale put it in our cloud on Prem you guys don't buy into that obviously your definition is it's the experience the weather wherever your data is we're gonna bring that cloud experience clarify that if you would yeah I'll kind of give the Oracle version and what I what I've talked about for years for Oracle R for cloud consulting as a whole or cloud capabilities right so Oracle really looks at true enterprise capabilities and it's kind of what I've been talking about for years publicly as well too is cloud is really when they say cloud it's not just 100% in cloud it's a combination that you pull from on-premise systems and an engagement you know your systems of record all get created together sometimes it's an ecosystem with another company right so the more connected we are so a cloud is really an enabler to sort of pull everything together right Oracle's really focused on a lot on the enterprise capabilities some of the other cloud providers do great on some of the systems of engagement the smaller applications that that's what's sitting in someone's cell phone or hands all the time Oracle is really around foundation of the database and data so we start with that enterprise and come up versus creating that really snazzy application - coming down to make it at a prize level so we take it that approach I look at cloud computing and my definition is really different than most people it's really around it's a roundel way of doing business and what I mean by that is it's it's a business that's technology enabled specifically right so you have to change the way that you do business the way that you engage with your customers with with their customers right the actual customers on the endline cloud capabilities really are on changing your operating model it's change and change the way that you organize you govern you know you can't just if you take a great capability and move it forward and then turn around and do it in the same way in process that's where you lose the efficiency if you talk about things like business case where we see the technology itself as a standalone we'll give 30% of that business case changing the way that you operate and engage people will actually give you the bigger benefit right so if you actually go for as we talked about a cloud transformation not only is around changing the capabilities from the tools standpoint it's your people and your skills and your operating model right so if you look at an operating model potentially has seven or eight dimensions depending on what organization you kind of talk to your right Gartner or whatever right if you don't hit every single understand the impacts of everything portion of the operating model would make the change you will not reap the benefits and my limit test is that experience has to be the same whether it's in your public cloud whether it's on pram whether it's in a partner you know multi cloud and that you guys actually came up with the notion of same-same sometime to actually deliver that but but do you feel like you're you're there now with customers that yeah that buy-in and lean in yeah absolutely and so the concept of you know what I call your master alas or picking it up and moving it over there has no value it could be the first step so a cloud journey it may give you informally but it should be the first step in your journey you know so if we talk about like cloud journeys if you're gonna say you know it's the same safe you're gonna move it over there that may give you back to the sort of the slope on the business case that's going to give you a incremental that would should self fund then a go okay I'm gonna take that I'm going to decompose that okay great now I'm gonna expand on that I'm gonna take that money to actually reinvest Automation right so if you move it over to infrastructure right where you're gonna get the automation is really on the pass later all the services in the monitoring the autonomous capabilities all those kind of things that's where you drive efficiency and scale so you basically so fun with the infrastructure transformation with potentially typical journey we see customers with right as let's move it let's use that funding to actually automate it it gets more savings we use that more for innovation so it's kind of a continuous stream you want to get to the point where you can actually have a continuous innovation stream and what I mean by that is you have a mechanism or a capability if you look at our Gen 2 cloud versus our Gen 1 cloud Gen 2 cloud actually can take an inch of all the capabilities that we have from the past layer through automation right and then as you do that we actually won't have a continuous process where you constantly look for innovation and incrementally add pieces over time it's no longer it's a Big Bang it's just a continuous stream of innovation so ok so you've made the statement that the business case for Oracle gen 2 cloud is overwhelming first of all what really what's the underpinning of Gen 2 cloud can you give us throw to the bumper sticker on that yeah all the underpinning magenta to cloud is really if you look at the Gen 1 cloud was purely just an infrastructure layer Gen 2 is really based on a segmenting security which is a huge problem out in the marketplace right a so we actually have a sort of a world-class way we take a segment security outside of the actual environment itself it's completely segment which is awesome right but then they also will you actually move it forward the capability of the entire thing is built on sort of the autonomous enterprise or autonomous capabilities everything is sort of self-healing self-funding are not sorry so healing and self-aware that continually moves it forward so the goal with that is is if you have something that takes mundane tasks back to that you have people that are no longer doing those capabilities today so the underpinning of that and what that allows you do is actually take that business case and you reduce that because you're no longer having a bunch of people do things that are no value add those people can actually move on to do back to the innovation and doing those higher-level components so the so the business case is really about I mean primarily I would imagine about labor cost right IT labor cost we're very labor intensive we're doing stuff that doesn't necessarily add differentiation and value to the business you're shifting that to other tasks right yeah so the big components are really the overall cost the infrastructure what it takes to maintain the infrastructure and that's broken up into kind of two components one of it is typical power physical location of building all those kinds of things and then the people to do the automations that take care of that right at the lower level the third level is as you continue to sort of process in automation going forward the people capability that actually maintains the applications becomes easier because you can actually extend those capabilities out into the application then you require fewer people to actually do the typical day-to-day things whether it's DBAs that are like that so it kind of becomes a continuous stream there's various elements of the business case you could sort of start with just the pure infrastructure cost and then get some of the process and automations going forward and then actually go that even further right and then as organizations as a CIO one of the questions I always have is where do you want to end on this and they say well what are you talking about alright it's really I'm late ever done you're on it and you're on a journey you're on a transformation I go this is the big boy big girl conversation right do you want to have an organization that actually it stays the same from the headcount standpoint are you trying to look to a partner to do the were you trying to get our operating model what is your company trying to get you to look at right because all those inflection points takes a different step in the cloud journey so as an adviser right as a trusted adviser I asked those herbs a half a dozen or so questions I would kind of walk your organization through on sort of a cloud strategy and I'll pick the path that it kind of works with them and if they want to go to a managed service provider at the end we would actually prepare someone either bring the partner in or have a little associate a partner we've heard it off - but we put the right pieces in place to make sure that that business cake works well that's it really important point because a lot of custom customers would say I don't want to reduce headcount I want to I'm starving for people I want to retrain people you know some companies may want to say hey okay I got a reduce headcount it's a mandate but but most at least in these boom times are saying I want to ship so buy point to the business cases if you're not going to you know cut people then you have to have those people be more productive and so the example that you gave in terms of making the application developers more productive is relevant and I want to explain this is that for example very simple you're I'm inferring you're gonna be able to compress the time to value you're gonna reduce you lower your breakeven you know accelerate the time to positive cash flow if you will that's an example of a value component to the business and part of the business case the people look at that and is that absolutely that's what it is definitely the business case and one we call it the you know when you get your rate of return right the more that we can compress that and I would say back to the conversation we had earlier about elevate and some of the partnership's we have with Deloitte around that a lot of that is to actually come up with enough capabilities that we can actually take the business case and actually reduce that and have special other things we can do for our customers or on financing and things like that to make it easier for them right we have options to make customers and actually help that business case some of the business cases we've seen our entire IT organizations saving 30 plus percent well if you multiply that on a you know a large fortune 100 that may have a billion dollar budget that's real money yeah but and okay yes no doubt but then when you translate that into the business impact like you talked about the ite impact but if you look at the business impact now it becomes telephone numbers and actually CFOs often times you just don't even believe it but it's true because if you can make the entire organization just you know a half a percentage point more productive and you get a hundred thousand employees I mean that is that overwhelms actually the IT business case yeah and that's where that back to the sort of the the steps in the business case is on the business and application side is making those folks actually more productive in the business case and saving them and adding you know whether it's a financial services you're getting an application out to market that actually generates revenue right so that's it's sort of the trickle effect so when I look at I definitely look at it from a IT all the way through business I am a technically a business architect that does IT pretty damn good yeah enables that sort of business absolution how do you let's talk about this notion of you know continuous improvement how are people thinking about that because you're talking a lot about just sort of self funding and and self progressing you know sort of an organic entity that you're describing how are people thinking about that yeah and I would say they're kind of a little bit older map right but I would say that goal is what we're trying to embed back to the operating model we want to really embed is you know sort of the concept of a cloud center of excellence and as part of that at the end you have to have a set of functionality of folks that's constantly looking at the applications and/or services of the different cloud providers at capabilities you have across the board everyone's got a multi cloud environment right how do they take those services they're probably already paying for anyways and as the components get released how can you continually put little pieces in there and do little micro releases quarterly aren't sorry weekly you know every month versus a big bang twice a year right those little automation pieces continually add innovation in smaller chunks and that's really the goal of cloud computing and you know is you can actually break it up it's no longer the Big Bang Theory right and I love that concept embedding that whether you actually have a partner with some of the stuff that we're doing that actually we embed what we call like a day-to services that that's what it is is to support them but us constantly look for different ways to include capabilities that were just released to add value on an ongoing basis you don't have to go hey they're great that capability came out it'll be on next year's release no it could be next week it could be next month right well so the outcome should be you should be dramatically lowering costs really accelerating your time to value it really is what you're describing and we've been talking about in terms of the autonomous you know Enterprise it's really a prerequisite for scale isn't it it is absolutely right and so when we use the term autonomous Enterprise - I love that because that's actually the term I've been using for a few years even before Larry started talking about the autonomous database I talked about that environment of constantly look at a cloud capability and everything that you can put from a machine or into AI under basically basically let it run itself the more that you can do that the higher the value you can put those people off in a higher-level tasks right that's been going on every provider for a while Oracle just has the capabilities now within the database that takes it to the next level right so we still are the only organization with that put that on top of our gen 2 cloud where all that is built in as part of it going forward that's where we have the upper level really at the enterprise computing level right we can we can work at all types of workload but where we are niches is really those big enterprise workloads because that's where we started from data Enterprise I don't want to make it a technology discussion but you said the only organizations meaning the only technology company would that autonomous database capabilities that yes sir yes okay so I know other sort of talk about it but you know Oracle I think talks about it more forcefully will dig into that and and report back Mike thanks so much for coming on the cube really appreciate it good stuff anything thank you very much all right and thank you for watching but right back with our next guest you're watching the cube we're here in Chicago covering the rebirth of Oracle consulting I'm Dave Volante look right back

[Music] everyone welcome to this cube conversation here in Palo Alto California I'm John Fourier co-host of the cube were in the cube studios our next guest is Adam Weinstein who's the CEO of a company called cursor so introducing curse it's hot startup growing in the data analytics space doing something unique very innovative around changing the game on data data catalogs but more importantly how data is being used and consumed and also kind of revitalized so Adam welcome to the cube conversation thanks for joining us thanks for having me excited to be here so you guys are a young startup you're in a really good wave right now it's the cloud data the changing nature of data take him into explain what cursor does what's the company what's the focus how big you raise money start the update yeah yeah so I'll give you a quick background on me that sort of leads into that right so spent most of my career as an analyst I might say right so working with data living in data good the bad the ugly right and spent last couple years prior to this at LinkedIn working an analytics team there and one of the challenges we had as an organization was you know finding what was where and who worked on what so when you had literally a thousand people across the company of 10% of the business touching data on a daily basis one thing we struggled with was knowing you know who was working on what what was where what was accurate what was maybe outdated data was getting created it insane velocity was talking earlier little we were creating a trillion events a day inside the business and so you know as an analytics practitioner if you all it became increasingly difficult to get to a quick answer there was no search to go and say okay I want to look for this question as I've been asked before and if so where's the data so you know there was this new space called data cataloging at the time that seemed interesting with the cataloging was really only looking at how do we create like a yellow pages of data not necessarily how do you put it in the workflow of a person that's then taking that and acting on it and then you know feeding that insight that they may have created back into that sort of cataloging feel right so it's all an opportunity to create something new that really supported an analyst and really was you know mindful of how their day-to-day what job existed and you know that was that was cursor right what's the role of the analyst now because one of the things that's challenging the industry was this idea of and you just go back five years data science is the next big thing there are more open jobs in data science than there are people but then this also trend came on around humanizing data science and not requiring you to know hardcourt C++ or Python or having all this wrangling environments doing all this provisioning of stuff to get started to his idea of okay can we level up that and also can he make it easier almost like using Excel yeah I thought of the kind of the trend what's your thought on the current state of the data analyst role no I think that there is a lot of analytics work that maybe five years ago you know was being done and and there was no automation around it and in the next five years it'll get it wouldn't say automated away but I'll be at heavily automated away called 80% of the workload but that 20% use or 20% of data that it's really difficult to understand and may not be able to you know get an answer out of it automatically that that's not you know that needs people and someone that understands the business that's technical enough to go dive into the data and even though that may not be the hundred percent that existed before the amount of like effort that's needed to decipher it I think is is maybe even greater than it used to be because the rate of data getting created is so much greater to is the demand for more solutions how about cursor how big are you guys who's on the team what's the product is it SAS as a software sir give a quick overview now great so we're small or seven person team right now I started the company a little over a year and a half ago you know the idea was to get a solution to market that was lightweight enough that someone could come and download it and try it very quickly without having to go through a long enterprise sale cycle they could get something on their computer literally stand it up in five minutes start putting in a data and having it you know identify and help with their day-to-day job the team is is volunteering - me right so you know there's that we have folks from Salesforce where you know I came from a company called ExactTarget the tails for spot Pandora thumbtack were basically tried to bring people together that if all you know seen companies scale and data scale and and you know bring those insights alongside them so first generation data scale yet the classic you know web scale build it out on open source grow it have things break rebuild it yeah I mean we levered some open source I think you for us right now how do we get something that unique to market as quickly as possible right so there's things that we can use that that are out there that are that are available that are you know especially if they're you know standardized right we'll make use of them but other times well we've built quite a bit of stuff on our own and our solution lives you can't live in the cloud it can also live on premise and actually see a lot of customers deploy it in a hybrid manner so they may have this sort of collaboration layer live in the cloud but it's pointing at data that's both cloud-based and on-prem and even though that data may get migrated to the cloud over the next several years a lot of large enterprises are still so are you guys going to market by selling a product as freemium what's the and is it software they download on-premise is it SAS in the cloud you talk about the go to market and how people engage with the product no it's heavily SAS in the cloud right so I think sort of companies that are in a heavily regulated industry that really haven't yet figured out that cloud model you know our products SAS delivered there is a client that lives on the users local machine and the reason that exists is just for security purposes because data is still often behind the firewall so like you can ask your security guy hey poke a hole in the firewall for this company I've never heard of or you can have a tool that lives on the machine that sort of brokers that in a fall way you guys are flexible we're flexible right you don't necessarily need that right if you deploy it in your own infrastructure obviously there's there's no need to then have that client it can it can handle things so why curse or what are the market drivers for you guys what's driving your business yeah we saw this need errors I felt this needed very acutely LinkedIn which is you know with analysts are getting you know hundreds or thousands of questions as a team on a daily or weekly basis if they're within a large organization how do you address some meaningful portion of those with automation so if a questions been asked before and you've got you know great solutions like a tableau or a look or a thought spot or a power bi like you've got tons of reporting solutions around the business but there's no place to go and say hey where's the answer to this question which one of those is it in is it a Salesforce report is a tableau dashboard and and so you'd ask your friendly analysts who'd be happy to help but like that's taking them away from doing things that are new and so I I kind of became that switchboard unfortunately and so I saw an opportunity to create a solution that would sort of want to meet me and that's that's really obviously index all the questions kind of see what the frequency was the behavior you have the analytics kind of packaging it up in the catalog yeah and taking it a step further I think what are the topics how do you map topics and understand okay there's a fire in Aisle seven and that fire happens to be churn and it's q3 and why is fire on turn and how do we dig into the data behind turn and get some water they made an insight surround it and then you know but yes certainly the step one is being able to direct people on the right to the right place once you get beyond that doe to understanding what our company's data is and what the sort of you know size and shape and characteristics of it are you can actually take it a step further and you know really sort of recommend things which is what we want the alternatives I'm not having like a data catalog and a cursor is to go ask your resident analyst or hope that someone posted something on slack and then you search through slow I mean all kinds of I mean really not up not a viable no it's a hodgepodge of solutions right so one of the things we saw in this is interesting having been at LinkedIn is that you know more and more teams around organizations are hiring analyst talent they may not call it analyst I might call like a citizen data scientist they might call it a researcher they might even call it an engineer like a data engineer a lot of overlapping skills and what the real need is is like someone to be on that team that knows their data inside and out but yet can help answer like you said sort of the ad hoc question that comes up you know every day and and so for that like you know if they can use her sword answer 80% of those or you know as many as possible right we've got it's interesting I do see the same kind of knee-jerk reaction when LinkedIn and and other clients that have a similar profile where they have a lot of data I certainly see that when they get hired what's the kind of what's the marching orders go jump into the data and figure it out is there I mean because this is kind of an evolving new position that's growing very very fast what are they directed to do I mean what's this what's the job responsible it's a great question so I think one of the challenges is how do you onboard people when when there is no place to start right like it's okay here the hundred places we store data go figure it out with Lauren on your own we had built a little bit of a training and onboarding every college they really have start as a PowerPoint deck and then it expanded into some code and some additional training but you know there is no solution for that right I think our internally we had this notion that you know somewhere between three and six months the person was ramped enough to begin to be productive it was like how we how do you measure ROI on a person when you hire them right and that was LinkedIn where I think we were pretty you know we were out here we you know we have you know quite a few nerds right like I think we're pretty good at organizing things relatively speaking I can't imagine what that's like in productivity just write some Python code spit out some Angela is that good enough look yeah I guess then or sink-or-swim kind of mentality and then you know to get someone else in there yeah and the nuance of the data has gotten just because everyone's mindset is record everything right like it becomes harder and harder to actually get a quick answer so gonna give an example like you know looking at data do you know if something's you know test data if it's you know fake data if it's you know if there's something you need to be mindful of like in e-commerce how do you account for returns how do you account for you know fraud how do you account for things that you know if you look at the data and say I just want to add up all my orders and get some total amount of receipts like you would think oh that's my sales for the day but then you forget like there are all these things that if you don't know the data really well that you miss out on and so yeah multiply that by you know large corporates what's the phrasing needle in a stack of needles I'm trying to find it like everything in there so I mean data structures data cleanliness yep these are huge issues huge and you know we will address every single one of them many think we're courser wants to sit is in between a lot of best-of-breed solutions right so we're not building a new Hadoop we think we do a great job of storing data whether you want to call it a lake or you know something beyond a lake right like you know there are plenty of data stores in an organization to do a great job at storing data you know on the opposite end of the spectrum like in terms of visualizing data are actually generating you know insights they're a great bi solution to the market but in between those two sort of you know ends of the spectrum there's a lot of work that gets done and that's what we want to live Adam talked about the innovation and the tech behind cursor and then just you know innovation in general the way you see it and the team sees it because you're on the Front Range of a new trend bleeding edge cutting edge whatever you want to call it certainly you're pushing the envelope yeah yeah what's the core tech for cursor sir where's the innovation lie has it all tie together sure so we have a you know a couple different deployment models but our most common one is we have a you know a cloud layer that enables collaboration so anytime a company is using our product you know metadata we don't ever look at company data that's one promise we've made because we want to work in regulated industries we want to be in places where there are high security environments but we never pushed actual data to the cloud but met about a company's data so you know what's the name of a column you know what's the name of a database who's used often have they used it what dashboard names are using all those kinds of things could push to our cloud you know we use a language called Kotlin which is a java derivative to write most of our back-end code mostly because a lot of legacy data stores or you know designed to interoperate with Java and then you know we have a client component that lives on a user's machine and that's what facilitates a lot of the day-to-day work and we do that just for security purposes because you know because most data is behind a firewall whether it's cloud based or not is you know it gets independent of that it's oftentimes not publicly accessible we can't expect our cloud will be able to get directly to it right whether or in WSG CP or arouser we can work with any of them you know we you know expected the company's security policies requires some sort of you know local connectivity and so that's you know that that client it's actually just a product called electron that wraps you know react front ends are very very common and you know paradigms you know we try to pick packages that we think have some staying power cuz you know every time the wind blows there's a new framework that's you know the latest and greatest so that's that's awesome I talked about the marketplace and customer interactions you have up so you guys are a year and a half into this or so what's the feedback what are you seeing what are you learning what are the key signals from the marketplace that you're seeing that's supporting your company the direction you're going share some anecdotes and data around what you're seeing and hearing so we launched the the first personal product it was last May and what we were trying to do was get something out there in the wild that anybody could try and get value out of without having to go through like it's a sort of long enterprise sale cycle so download it you can use it you can share it with the guy next to you think of like an Evernote or a Google Drive style approach to actually being able to do something and you know so that that had some great success rate when we went out with announcement we announced we'd you know fun with the company we roughly we got 1500 users in the first four months just that we're trying it it was across about four to five hundred companies of four ish five ish users a company and that will let us get a bunch of feedback which was great right some of it was hey we don't like this and other words hey double down here and the key thing that we learned was they're sort of three audiences that we're serving right one is that traditional analysts which you know hopefully that was the case cuz that's where I came from and that was the goal there's also two other audiences I didn't expect as much of one being software engineers and software engineers that you know constantly pulled into you know like you said find the needle on the pile of needles and they don't want that to be their day job but they do want to like do it once and then share it with the rest organization and they don't have a place to do that today so there's a poly there's a great great you know audience of softwares and then the last one is actually business leaders that are the ones asking the questions and they want to find a place that they can go to that you know will answer the majority of them and so the feedback we've gotten is that there's probably three skins of the product that we're gonna have to build ones for that analysts the second a little bit more technical for an engineer and the third is actually very business-friendly which is just you know you don't care about sequel code you don't want Python code you don't want any code at all you just want to know the reports here or if it's not ask Danny that's interesting so the feedback of the marketplace is kind of lays out the workflow stakeholders yeah you know the analysts got to do their job and doesn't want to be coding so they bring the coder and coders once the kid put gets pulled into the project so they're doing their thing and they certainly want to get back to their coding but get pulled in for business reasons the business wants a search and discover yeah kind of all kind of coming together that seems to be the stakeholders it's the stakeholders exactly right I mean I think it's it almost lines up probably engineer analyst business leader right like in the engineer oftentimes is the one that has to go build a pipeline if that's what's needed right and the analyst is the one that consumes from it and then business leaders the one that looks the report every morning and says hope that's bad and really what you're getting down to his classic software development kind of thinking of DevOps and cloud computing which is you don't what you want to automate repetitive tasks and you don't want one offs all right so engineer doesn't want to do one office of constant one-off pipelining yep yep know that you hit the nail on the head like I think you know it like the whole notion of like self-service bi or self-service data like it it's aspirational I think it will be forever right even as you get into AI and yes automated AI and in you know a certain percentage of problems will always be able to be automated but a certain percentage won't be right it was get more point about the reporting is it's only good as the data being reported so you might feel good he's looking at a dashboard with underlying data that sucks and you're like you're dead in the water that's that's a very true thing unfortunately we saw that you know not just did like every company feels that but I talk about the environment and customer base okay as as you worked at linkedin which i think is a very acute example because you know linkedin is one of those magical companies where they really hit the data equation really well obviously it's like a resume for recruiters and it turned into a social network and then they got a treasure trove of data all kinds of gesture data they got great metadata on profiles now they've got a feed so again it's like Facebook analyst this data and so the unknowns probably got came came piling in so it's great proxy for as enterprises and businesses start thinking about how to think about the tsunami of new kinds of data not just grow the data but like hey there's all kinds of new data mobile the touch point gesture day all those kinda stuffs coming together how should they think about setting up a plan so if I'm a customer say hey you know I got a date I got Cuban of you data I got consumption data all these new things and what do I do yeah how do I create a holistic architecture yep take advantage of the different data silos or data sets but yet not screw up the operations of those days yes we can't stop right what's your advice on that cuz it seems to be a core problem it is and one of the things I think I've come to believe is that you know companies will get together and they'll spend months or even years coming up with like an architecture of the future right and and I don't believe that you can come up you know and sit in a room no matter how many days it takes and come up with something that's gonna be you know all things to all people like you're gonna basically need solutions that are nimble enough to be to be you know installed and get value very very quickly even if just a small amount of value and then grow with you over time so of course that's sort of the way we're set up right like you know you can come have a small team so take take on marketing operations D and they work with advertising data they're dealing with how do you get you know a lead and convert them into a sale they can use you know a product like cursor or I think any other good product in the marketplace should be you know you designed it this way where you you nibble on it you get some value and then you deploy it to other teams once you've learned how to how to best do that I think the like Big Bang approach of like hey this is our solution that's gonna you know work for everyone is really tough okay take an area we can get time to value quicker right and is it like a data Lake of model where you just kind of throw some data into one corpus or so we can have a base data doesn't actually live ever within cursor right we may you know if you're actually operating on it say you're an analyst you're writing some Python you're writing some sequel like yes I mean you for the sake of seeing in the UI it will temporarily be cached and encrypted there but we never actually store any company data we just point to it and when in in what we've built are these really intelligent connectors they can go mine what's there so if we're looking at a tableau instance we can say okay here all the dashboards there here all the code behind those dashboards here the table the data stores those dashboards are hitting here's are often they're consumed Oh every Monday morning at 9:00 a.m. 250 people in New York hit this dashboard and how do we learn from that and then hopefully make recommendations on it like what happens when data underlying a dashboard changes every Monday morning and all of a sudden it doesn't should that be a red flag somewhere that you know we should tell somebody that hey there's probably an issue with this so we're trying to really learn from things that are already there today as opposed to having you create new things what's next what's going on now how you going forward what's the key objectives for you guys yeah so I think there's two things really stage business like you can get sort of pulled into this hey we want to be a generic solution for everything what we found is that there probably a couple industries that are really they feel this problem really acutely and some of its financial services actually retail surprisingly just given you know dispersion of data inside retail so we've had pretty good success in both of those areas and I think our next step will be to actually probably formalize some you know sort of play books if you will and continue down that path and then integrations are that are the next thing right like we integrate with a bunch of stuff but we definitely won't agree with everything and there's you know an infinite amount of tools out there right so we want to continue to you know partner with companies that have you know Best of Breed solutions work with them to create deep integrations we're not trying to displace them what is trying to you know complement them and help drive you know the traffic to them that's looking for what's in there and so like that integration work is really never-ending why should the company keep up the old way to bring in the new way what's your what's your yeah I don't think they're actually having to give up the old way I think it's you know there are some things that you're gonna naturally be transitioning off of right there's there's always gonna be a bi solution that transitions from you know legacy to new whatever legacy may be defined as and as you're doing that there's there's there's this missing ingredient I feel like how do I track what's where when you could say that that was sort of solved by data catalog so I think the old data catalog is kind of dead and I think what's really happening is that you need something that works with you know where you are and every day whether you're an analyst a business leader or an engineer right and they can follow you along not disrupt you from your day-to-day workflow and also be intelligent about what's what what's where and that's sort of what we're trying to build well great to chat thanks for coming in spending the time talking about cursor congratulations on the venture thanks looking forward to seeing that be round coming soon yeah thanks for having you very much it's coming soon be round a round a round seed round and yeah it will definitely be on the on the near term horizon and Weinstein CEO cursor serial entrepreneur here inside the cube innovating around the data this is the new model this is what's going on it's the new wave that they're ride I'm John furry with the cube thanks for watching [Music]

>> From Palo Alto, California, it's theCUBE, covering the Conference Board's Sixth Annual Innovation Master Class. (fast techno music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Innovation Master Collab at Xerox PARC. It's put on by the Conference Board, a relatively small event, but really, a lot of high-caliber individuals giving really great presentations. And we're excited about our next guest, he kicked the whole thing off this morning, and we could go for hours. We won't go for hours, we'll go about 10 minutes. But Peter Coffee, he's the VP of Strategic Research for Salesforce. Been there a long time, but you were a media guy before that for many, many years? So Peter, great to see you. >> It's good to be with you, thanks. >> So, you talk about so many things. So many things in your opening statement, and I have a ton of notes. But let's just jump into it, I think. One of the big things is you know, the future happens faster than we expect it. And we as humans have a really hard time with exponential growth, because it's not built that way. That's the way things move. >> So how do you as a businessperson kind of deal with that reality? Because the issue is you're never going to be ready for when they come. >> Yeah, well, it's not just humans as individuals, but the institutions and processes we've built. If you look at the process of getting a college degree, it's really seriously misaligned with the timeframe of change. By the time you're a senior, half of the subject matter in your field may be new since your freshman year, and conversely four years after you've graduated, perhaps a third of what you were taught will no longer be considered to be current information. Someone at Motorola once said, "a batch process "no matter how much you accelerate it "doesn't become a continuous flow process". You have to rethink what does a continuous flow look like, and that's useful conversation to have getting back to your actual opening question. When we're talking with customers, we say what are your unvoiced assumptions about the manner in which you have succession of technology, succession of product, and so on? Can we try to see what it would look like if that were a continuous process and not a project process? Many of our partners will tell us that their most difficult conversations with their customers are about getting away from a project mentality, a succession of Big Bang changes, into a process in which transformation is a way of life and not a bold initiative that will take a big sigh of relief and congratulate yourself on having transformed. No, dude, you've gotten your running shoes tied now you can begin to run. But now the hard part begins. >> Right, and the sun comes up tomorrow and you start to run again. You talked on big shifts count on new abundance and use horsepower. >> George Gilder's phrase, "errors are punctuated "by a dramatic change from a scarcity "to an abundance" so for example, horsepower or bandwidth or intelligence. >> So now we're coming into the era of massive big data we are asymptotically approaching free compute, free storage, and free networking. So how do you get business leaders to kind of rethink in an era where they have basically infinite resources, and it always goes back, so what would you build then? Because we're heading that way even if we're not there today. >> A Jedi mind trick that I often use with them is to say, let's not talk about the next couple of quarters, I want you to imagine the next Winter Olympics. When they light the torch four years from now I want you to try to visualize the world you're pretty sure you'll be living in four years from now and work backwards from that and say well if we all agree that within four years that's going to get done, well there's some implications about things we should be doing now and some things that we should stop doing now if we know that four years from now, the world is going to look like this. It helps free your mind from the pressures of incremental improvement and meeting next quarterly goals. And instead saying, ya know, that's not going to be a thing in four years and we should stop getting better at doing something that's simply not going to be relevant in that short of a time. >> So hard though, right? Innovators still, I mean, that's the classic conundrum especially if it's something that you have paying customers and you're driving great revenue to, it's hard to face the music that that may not be so important down the path. >> The willingness to acknowledge that someone will disrupt you, so it might as well be you, you might as well disrupt yourself, the conversation was had with IBM back in the days of the IBM PC, that they thought that that might be a quarter of a million machines they would sell, but whatever you do, don't touch the bread and butter of the 3270 terminal business, right? And they did not ultimately succeed in visualizing the impact of what they had done. Ironically, because they didn't think it was that important, they opened all the technology, and so things like Microsoft becoming what it is and the fact that the bios was open and allowed the compatibles industry like Compact to emerge was a side effect of IBM failing to realize how big of a door they were opening for the world. You can start off a spinoff operation. At Salesforce we have a product line called Essentials which is specifically tasked with create versions of Salesforce that are packaged and priced and supported in a way that's suitable to that small business. And that way you can kind of uncouple from that Clayton Christensen innovators dilemma thing by acknowledging it's a separate piece of the business, it can be measured differently, rewarded differently, and it's going to convey itself maybe even through a genuinely different brand. This is an example that was used once with Disney which when it decided it wanted to get away from family and children's entertainment, and start making movies aimed at more adult audiences, fine, they created the Touchstone brand so they could do that without getting in the way of, or maybe even polluting, a brand that they spent so much time building. So branding is important. A brand is a set of promises, and if you want to make different promises to different people, have a different brand. >> Right, so I'm shifting gears 'cause you touched on so many great things. A really popular thing that's going on now is the conversion of products to services. And repackaging your product as a service. And you talked about the don't taze me bro story which has so many elements of fun and interesting but I thought the best part of it, though, was now they took it to the next step. And we're only a stones throw away from Tesla, a lot of innovation but I think one of the most kind of not reported on benefits of these connected devices and a feedback loop back to the manufacturer is how people are actually using these things, checking in from home, being able to do these updates. And you talk about how the TASER company now is doing all the services, it's not even a service, it's a process. I thought it's awesome. >> Taking a product and selling it at a subscription price does not turn it into a service, even though some people will say, well see now we're moving to a services model. If you're still delivering a product in a lumpy, change-it-every-couple-of-years way, you haven't really achieved that transformation. So you have to go back into more of a sense of I mean, look at the expectation people have of the apps on their smartphones, that they just get better all the time, that the update process is low-burden, low-complexity, low-risk, and you have to achieve that same fluidity of continuous improvement. So that's one of the differences. You can't just take the thing you sell, bill for it on a monthly subscription, and think that you achieved that transition. The thing that they folks who were once TASER and now are Axon, of which TASER is a sub-brand, they managed to elevate their view from the device in a police officer's hand to a process of which that device is a part. Which is the incident that begins, is concluded, results in a report, maybe results in a criminal prosecution, and they broadened the scope of the Axon services package to the point that now it is selling the proposition of increased peace officer productivity rather than merely the piece of hardware that's part of that. So being able to zoom out and really see the environment in which your product is used, and this relates to yet another idea which is that people are saying you got to think outside your box. It doesn't help if you get outside your box, but all of the people with whom you might want to collaborate are all still inside their boxes. And so you may actually have to invest in the transformation and interface development of partners or maybe even competitors, and isn't that a wild idea. Elon Musk at Tesla open sourced a lot of their technology with the specific goal of growing that whole ecosystem of charging stations and other things so Tesla could be a great success. And the comment that I once made is it doesn't help if you're a perfect drop of artisanal oil in a world of water. You have to make the world capable of interacting with you and supporting you if you really want to grow. Or else you're an oddity, you're Betamax, which might have been technically superior but by failing to really build the ecosystem around it, wound up losing big time to VHS for a while. I may have to explain to all of your viewers under the age of 30 what VHS and Betamax even mean. >> I was sellin' those, I could tell you the whole Panasonic factory optimization story, which is whole 'nother piece of that puzzle. So that's good, so I'm going to shift gears again. >> You have to look a big perspective, you have to be prepared to forget that your excellence is your product, and start thinking of that as just the kernel of what needs to be your real proposition which is the need you meet, the pain you address, the process of which you become an inseparable part instead of a substitutable chunk of hardware. >> Well and I think too it's embracing the ongoing relationship as part of the process, versus selling something to your distribution and off it goes you cash the check and you build another one. >> Well that's another aspect, we've got whole industries where there's been a waterfall model. Automobiles were a particular example. Where manufacturers wholesaled cars to distributors who gave them the small markup to dealers who owned the buyer customer. And dealers would be very hostile to manufacturers trying to get involved in that relationship. But now because of the connected vehicles the manufacturer may know things about the manner of use of the vehicle and about the preliminary engagement of the prospective buyer with the manufacturers website. And so improving that relationship from a futile model, or a waterfall model, into a collaborative model is really necessary if all these great digital aspects are to have any value. >> Right, right, right. And as a distribution of information that desire to get a level of knowledge is no longer the case, there's so much more. >> Well it's scary how easy it is to do it wrong. IDC just did a study about the use in retail banking of technology like apps and websites. Which that industry was congratulating itself on adopting in ways that reduce the cost of things like bank office hours. And yet J.D. Power has found that the result is that customers no longer see differentiation among banks, are less loyal, more easily seduced by $50 to open a new bank account with direct deposit. And so innovation's a vector, and if you aim it at cost reduction, you'll get one set of results. And if you aim it at customer satisfaction improvement, you'll innovate differently, and ultimately I think much more successfully. >> Right, right, so we're almost out of time here. I want to go down one more path with you which I love. You talked a lot about visualization, you brought up some old NOPs, really talked about context, right? In the right context, this particular visualization is of value. And there's a lot of conversation about visualization especially with big data. And something I've been looking for, and maybe you've got an answer is, is there a visualization of a billion data point dataset that I can actually look at the visualization and see something, and see the insight. 'Cause most of the ones we see that are examples, they're very beautiful and there's a lot of compound shapes going on, but to actually pinpoint an actionable something out of that array, often times I don't see, I wonder if you have any good examples that you've seen out there where you can actually use visualization to drive insight from a really, really big dataset. >> Well if a big data exercise produces a table of numbers, then someone's going to have to apply an awful lot of understanding to know which numbers look odd. But a billion points, to use your initial question, well what is that? That's an array that's 1,000 by 1,000 by 1,000. We look at 1,000 by 1,000 two-dimensional screens all the time, visualizing a three-dimensional 1,000 by 1,000 cube is something we could do. And if there is use of color, use of motion, superposition of one over another with highlighting of what's changed, what people need most is for their attention to be drawn to what's changing or what's out of a range. And so it's tremendously important that people who are presenting the output of a big data exercise go beyond the high-resolution snapshot, if you will, and construct at least some sense of A B. Back in the ancient days of astronomy, they had a thing called the Blink Camera which would put two pictures side-by-side and simply let you flip back-and-forth between the images, and the human eye turned out to be amazingly good. There could be thousands of stars in that picture, the one dot that's moving and represents some new object, the one dot that suddenly appears, the human brain is very good at doing that. And there's a misperception that the human eye's just a camera. The eye does a lot of pre-processing before it ever sends stuff to the brain. And understanding what human vision does, it impressed the heck out of me the first time I had a consultation on the big data program at a university where the faculty waiting to meet with me turned out to be from the schools of Computer Science, Mathematics, Business, and Visual Arts. And having people with a sense of visual understanding and human perception in the room is going to be that critical link between having data and having understanding of opportunity threat or change. And that's really where it has to go. So if you just ask yourself, how can I add an element of color, or motion, or something else that the human eye and brain have millennia of evolution to get good at detecting, do that. And you will produce something that changes behavior and doesn't just give people facts >> Right, right. Well, Peter, thank you for taking a few minutes. We could go on, and on, and on. >> Happy to do chapters two, three, and four any time you like, yeah. >> We'll do chapter two at the new tower downtown. >> Any old time, thanks so much. >> Thanks for stoppin' by. >> My pleasure. >> He's Peter, I'm Jeff, you're watching theCUBE. We're at the Master Innovation Class at Xerox PARC put on by the Conference Board. Thanks for watching. (fast techno music)

>> Announcer: Live from Las Vegas. It's theCUBE. Covering NetApp Insight, 2018. Brought to you by NetApp. >> Hey, welcome back to theCUBE's coverage of NetApp Insight 2018. From the Mandalay Bay in Las Vegas, I am Lisa Martin with Stu Miniman. And we're welcoming back one of our alumni, Brendon Howe SVP of the Cloud Volume Services at NetApp. Hey, Brendon. >> Hey. >> Thanks for taking some time to come chat with Stu and me. >> Brendon: And thank you for having me. Great to be here. >> Big event about 5,000 plus people, the keynote this morning we had a chance to go to that, and it was when we were leaving standing room only. Biggest, Jean English was saying, this is the biggest collection of customers and partners under one roof. >> That's great. >> Yeah, fantastic. You're a long time NetApp-iac. >> 12 and a half years. >> 12 and a half years young. So you've seen a lot of NetApp's transformation. >> I have. >> In the messaging and the positioning, NetApp is the data authority. We're helping customers to be hashtag data driven. Cloud is really now seeming to be at the heart of NetApp's strategy. >> Brendon: Yeah. >> Talk to us about that evolution. >> Absolutely, you always want to be positioning yourself ahead of where you are, where you want to go. Alright, you want to be perceived as the future of where you're aiming. And I think it's been clear to us for a while now that the whole dynamic and movement to cloud, is probably the most disruptive and most impactful thing that's hit traditional IT. We've lived through a lot of changes. I've been here for a lot of them, where you went to a virtualization and the way applications were deployed and the way infrastructure was deployed waves. And up and down of the economy. Those were minor speed bumps, I think, in the journey of how we get to where we want to go. The disruption of cloud, which really could be characterized as the availability of an unprecedented set of services from the biggest public clouds in the world, who happen to be the biggest companies in the world, has changed the dynamics completely. I don't know that people fully appreciate why it's been so impactful. When you talk to customers, what you hear is they go to the cloud for agility and speed. It's not really a cost discussion of where are compute instances or bits or storage cheaper, one or the other. It's an agility argument. And what cloud brings to them is unprecedented pace of change, of adoption, of speed of line of business. That they can't reproduce otherwise. So, I think it's really important, that we aim ahead of where we want to be, which is really a cloud-first, data-oriented company. And that's why you see so much of that messaging from us. >> Brendan, it's really interesting, I think back. If I turn back the clock a dozen years ago, we didn't talk about software defines. >> Brendon: No. >> Yet, there were certain companies out there that storage, it was like, okay, we're going to create software for storage. Well, no, that was some software that ran on their box and only on their box. >> Brendon: That's right. >> You know, NetApp was the hipster software defines storage company, right? They were software before anybody else was. When you talk about NetApp in this cloud world, I think it's taken a while to come into focus. I remember back at the early solutions it was like, oh, let's stick a filer in a data center direct-connect it, we can offer some services. But the nirvana we've been trying to reach is storage services, available lots of different places. Can you walk us through some of that? Philosophically where NetApp's going? >> Yup, I think that's a good observation. I would say, think back four or five years ago, which I still think most of the industry's in at the moment, the notion of working with cloud was largely a connect-to-cloud theory. As you describe, where you have systems that would interconnect into the cloud. Or even leap into that world of taking an operating system and having it run in a VM in the cloud. I think of that as a cloud-connected strategy. And customers were intrigued, but what we often heard from them is it really can't be consumed as a cloud service. And it really can't be part of my traditional build with Azure, Google, or AWS. So, it's interesting, but it's an adjacency. And what we're really looking for are native cloud services. So, we took that to heart and really retrenched our effort to figure out how to build Cloud Data Services that behaved every bit like a native cloud service from the big cloud companies. All the way through to metered billing, provisioned and managed through the native portals of those cloud companies. Other than a brand label here and there, a customer may not even know it's NetApp. That's how cloud-oriented these services are. I think that's what it's going to take to be successful in this space. And you do that across multiple clouds with a quest towards going after market share. At the end of the day, you want to be relevant in as many cloud instances as exist, so you aim at the big cloud companies and you aim at global scale. I think that was what the learnings that we had through that journey is, it's not enough to reference architectures or software ports to the cloud, you really have to think about native services. There clearly, you have to find unique value, you have to do something that's not available otherwise, which is par for the course, but you also have to look at levels of integration that make it very, very easy to consume. And in the cloud, that's an unprecedented level of simplicity. >> One of the big challenges of the multi-cloud world is, it would be really nice if it was just a utility. People always say, oh well, I'm going to choose a cloud, and I can change things. Well, as you said, there's differentiation in the cloud. If you go talk to Amazon, Google and Microsoft, they're not all saying. no longer is it the race to the bottom. >> Absolutely. >> When you talk about partnering with the clouds, how do you provide, you need to provide unique differentiation, you need to integrate with all of the different players, yet, customers would love to be able to, oh, it's just a Kubernetes service and I use this deal and I move things around. How do you balance and deal with that complicated nuanceness of what multi-cloud really is? >> I think that the starting point is being good at a cloud in something. Right, and then you build on that competency. The Big Bang theory of going in and helping a customer with a hybrid cloud scenario that extends to multi-cloud is sort of the longest term vision of where they might end up over time. So, to some extent, it's the hardest problem to take on first. So if you core that back a little bit saying, let's focus on a use case that runs on the cloud to get started, and we'll build on that. The true fashion of, start small, iterate, grow, earn monthly recurring revenue, build under success and go is really the nature of the beast of what we're trying to do. Each of the cloud environments, tend to have real core competents that leads customers there in the first place. I don't know that you can ever listen to discussions from AWS without hearing about the breadth of their platform as a service. And how attractive it's been to the development in the DevOps community. Or you swing over and talk to Google, it's all about machine learning and analytics and tensor data flow, and all of that big query type stuff. And you swing over to Azure, and you hear about linking to the enterprise with traditional applications now enabled to run natively in the cloud. You follow those paths toward use case success and figure out how to build those solution stack with real value for the customer. So, we're trying to bring Cloud Volume Services into the fold, not as infrastructure as a service that's an option as well that might be faster, but tether that to real use cases where, look people are trying to move SAP HANA environments into the cloud; can we help? People are trying to figure out how to run database in the cloud; can we help? People are trying to figure out how to run analytics on file data that may even be collected on-prem; how can we help? You get into those types of discussions and start building validation, and it gets a lot easier to begin the journey of getting involved. I do think a multi-cloud world is the reality where people end up. As I do a hybrid-cloud. But customers have to work their way through that implementation in order to achieve that outcome. I think that's a long journey for a lot of customers. And I think there's a lot of technology that still has to be built to realize that full vision, the point is we're focused on that. I think we're on the right path, and if you saw the keynote this morning Anthony gave a nice preview of some of the data fabric vision that really showed snippets of how that plays out. A lot of which is available today. Which is pretty cool. >> Last question, and about a minute left, Brendon, NetApp is very customer focused, very customer-centric >> Brendon: Always has been. >> Exactly. Massive install base, as George was addressing this morning. A lot of enterprise customers not born in the cloud, those who are digital, those who are now. And last question, how have your customers helped influence the evolution of Cloud Volume Services? >> In a variety of ways. At times the traditional NetApp customer, that runs with things on-prem, is the most complex customer for services in the cloud because they're expectations are take everything the way they run on premise, and reproduce that in the cloud. And that's just simply not practical. Because you're in a new environment with new circumstances with new economics that make that achievement for a customer near impossible to do. To some extent, you have to sort of reprogram the traditional NetApp customer to understand, the cloud is different. The compare is not against us on-premise, the compare is the services in the cloud today that we look to improve upon. So that's one aspect of it. But clearly, a lot of our customers here at the show have decades of experience in leveraging the features we have into application environments that exist in the cloud today as well. And as it turns out, efficient handling of data, still is a problem. Having a reliable and dependable way to do backup and recovery is still a problem for customers. The ability to deal with bulk data from a backup and archive perspective, it's still a problem. So, I think a lot of the themes are the same and that the technology applies, but it has to be built differently because of the ecosystems that we're going in. I think the customers here are beginning to realize that, and then you bring in the wildcards of what's happening with Kubernetes and the drive towards application provisioning and how all of that can be linked to our solution set. We bring a lot of new opportunity that is different than the way traditional on-premises worked. >> Is that just one of the biggest barriers initially, was helping these large incumbent enterprises realize that it isn't possible to just go from on-prem to cloud, poof? >> Yes, I think so. The whole notion of taking the exact configuration, by the way, they custom tuned, and said I want to do that exact same thing in the cloud. It turns out that the configuration options in global cloud services just simply aren't available to do that. So you have to rework your customer's minds set, into the proper compare, and set expectations the right way. >> Lisa: It's all an evolution. Well, Brendon thanks so much for stopping by >> Thank you. >> and having a chat with Stu and me. We appreciate it. >> Thank you, it was a pleasure. >> We want to thank you for watching theCUBE, Lisa Martin with Stu Miniman. We are at NetApp Insight 2018 from Vegas, we'll be back with our next guest shortly. 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>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. (upbeat music) >> Welcome back to theCUBE, I'm Lisa Martin with John Furrier. We are covering our second day of our event Big Data SV. We've had some great conversations, John, yesterday, today as well. Really looking at Big Data, digital transformation, Big Data, plus data science, lots of opportunity. We're excited to welcome back to theCUBE an alumni, Satyen Sangani, the co-founder and CEO of Alation. Welcome back! >> Thank you, it's wonderful to be here again. >> So you guys finish up your fiscal year end of December 2017, where in the first quarter of 2018. You guys had some really strong results, really strong momentum. >> Yeah. >> Tell us what's going on at Alation, how are you pulling this momentum through 2018. >> Well, I think we have had an enterprise focused business historically, because we solve a very complicated problem for very big enterprises, and so, in the last quarter we added customers like American Express, PepsiCo, Roche. And with huge expansions from our existing customers, some of whom, over the course of a year, I think went 12 X from an initial base. And so, we found some just incredible momentum in Q4 and for us that was a phenomenal cap to a great year. >> What about the platform you guys are doing? Can you just take a minute to explain what Alation does again just to refresh where you are on the product side? You mentioned some new accounts, some new use cases. >> Yeah. >> What's the update? Take a minute, talk about the update. >> Absolutely, so, you certainly know, John, but Alation's a data catalog and a data catalog essentially, you can think of it as Yelp or Amazon for data and information side of the enterprise. So if you think about how many different databases there are, how many different reports there are, how many different BI tools there are, how many different APIs there are, how many different algorithms there are, it's pretty dizzying for the average analyst. It's pretty dizzying for the average CIO. It's pretty dizzying for the average chief data officer. And particularly, inside of Fortune 500s where you have hundreds of thousands of databases. You have a situation where people just have too much signal or too much noise, not enough signal. And so what we do is we provide this Yelp for that information. You can come to Alation as a catalog. You can do a search on revenue 2017. You'll get all of the reports, all of the dashboards, all of the tables, all of the people that you might need to be able to find. And that gives you a single place of reference, so you can understand what you've got and what can answer your questions. >> What's interesting is, first of all, I love data. We're data driven, we're geeks on data. But when I start talking to folks that are outside the geek community or nerd community, you say data and they go, "Oh," because they cringe and they say, "Facebook." They see that data issues there. GDPR, data nightmare, where's the store, you got to manage it. And then, people are actually using data, so they're realizing how hard (laughs) it is. >> Yeah >> How much data do we have? So it's kind of like a tropic disillusionment, if you will. Now they got to get their hands on it. They've got to put it to work. >> Yeah. >> And they know that So, it's now becoming really hard (laughs) in their mind. This is business people. >> Yeah. >> They have data everywhere. How do you guys talk to that customer? Because, if you don't have quality data, if you don't have data you can trust, if you don't have the right people, it's hard to get it going. >> Yeah. >> How do you guys solve that problem and how do you talk to customers? >> So we talk a lot about data literacy. There is a lot of data in this world and that data is just emblematic of all of the stuff that's going on in this world. There's lots of systems, there's lots of complexity and the data, basically, just is about that complexity. Whether it's weblogs, or sensors, or the like. And so, you can either run away from that data, and say, "Look, I'm going to not, "I'm going to bury my head in the sand. "I'm going to be a business. "I'm just going to forget about that data stuff." And that's certainly a way to go. >> John: Yeah. >> It's a way to go away. >> Not a good outlook. >> I was going to say, is that a way of going out of business? >> Or, you can basically train, it's a human resources problem fundamentally. You've got to train your people to understand how to use data, to become data literate. And that's what our software is all about. That's what we're all about as a company. And so, we have a pretty high bar for what we think we do as a business and we're this far into that. Which is, we think we're training people to use data better. How do you learn to think scientifically? How do you go use data to make better decisions? How do you build a data driven culture? Those are the sorts of problems that I'm excited to work on. >> Alright, now take me through how you guys play out in an engagement with the customer. So okay, that's cool, you guys can come in, we're getting data literate, we understand we need to use data. Where are you guys winning? Where are you guys seeing some visibility, both in terms of the traction of the usage of the product, the use cases? Where is it kind of coming together for you guys? >> Yeah, so we literally, we have a mantra. I think any early stage company basically wins because they can focus on doing a couple of things really well. And for us, we basically do three things. We allow people to find data. We allow people to understand the data that they find. And we allow them to trust the data that they see. And so if I have a question, the first place I start is, typically, Google. I'll go there and I'll try to find whatever it is that I'm looking for. Maybe I'm looking for a Mediterranean restaurant on 1st Street in San Jose. If I'm going to go do that, I'm going to do that search and I'm going to find the thing that I'm looking for, and then I'm going to figure out, out of the possible options, which one do I want to go to. And then I'll figure out whether or not the one that has seven ratings is the one that I trust more than the one that has two. Well, data is no different. You're going to have to find the data sets. And inside of companies, there could be 20 different reports and there could be 20 different people who have information, and so you're going to trust those people through having context and understanding. >> So, trust, people, collaboration. You mentioned some big brands that you guys added towards the end of calendar 2017. How do you facilitate these conversations with maybe the chief data officer. As we know, in large enterprises, there's still a lot of ownership over data silos. >> Satyen: Yep. >> What is that conversation like, as you say on your website, "The first data catalog designed for collaboration"? How do you help these organizations as large as Coca-Cola understand where all the data are and enable the human resources to extract values, and find it, understand it, and trust it? >> Yeah, so we have a very simple hypothesis, which is, look, people fundamentally have questions. They're fundamentally curious. So, what you need to do as a chief data officer, as a chief information officer, is really figure out how to unlock that curiosity. Start with the most popular data sets. Start with the most popular systems. Start with the business people who have the most curiosity and the most demand for information. And oh, by the way, we can measure that. Which is the magical thing that we do. So we can come in and say, "Look, "we look at the logs inside of your systems to know "which people are using which data sets, "which sources are most popular, which areas are hot." Just like a social network might do. And so, just like you can say, "Okay, these are the trending restaurants." We can say, "These are the trending data sets." And that curiosity allows people to know, what data should I document first? What data should I make available first? What data do I improve the data quality over first? What data do I govern first? And so, in a world where you've got tons of signal, tons of systems, it's totally dizzying to figure out where you should start. But what we do is, we go these chief data officers and say, "Look, we can give you a tool and a catalyst so "that you know where to go, "what questions to answer, who to serve first." And you can use that to expand to other groups in the company. >> And this is interesting, a lot of people you mentioned social networks, use data to optimize for something, and in the case of Facebook, they they use my data to target ads for me. You're using data to actually say, "This is how people are using the data." So you're using data for data. (laughs) >> That's right. >> So you're saying-- >> Satyen: We're measuring how you can use data. >> And that's interesting because, I hear a lot of stories like, we bought a tool, we never used it. >> Yep. >> Or people didn't like the UI, just kind of falls on the side. You're looking at it and saying, "Let's get it out there and let's see who's using the data." And then, are you doubling down? What happens? Do I get a little star, do I get a reputation point, am I being flagged to HR as a power user? How are you guys treating that gamification in this way? It's interesting, I mean, what happens? Do I become like-- >> Yeah, so it's funny because, when you think about search, how do you figure out that something's good? So what Google did is, they came along and they've said, "We've got PageRank." What we're going to do is we're going to say, "The pages that are the best pages are the ones "that people link to most often." Well, we can do the same thing for data. The data sources that are the most useful ones are the people that are used most often. Now on top of that, you can say, "We're going to have experts put ratings," which we do. And you can say people can contribute knowledge and reviews of how this data set can be used. And people can contribute queries and reports on top of those data sets. And all of that gives you this really rich graph, this rich social graph, so that now when I look at something it doesn't look like Greek. It looks like, "Oh, well I know Lisa used this data set, "and then John used it "and so at least it must answer some questions "that are really intelligent about the media business "or about the software business. "And so that can be really useful for me "if I have no clue as to what I'm looking at." >> So the problem that you-- >> It's on how you demystify it through the social connections. >> So the problem that you solve, if what I hear you correctly, is that you make it easy to get the data. So there's some ease of use piece of it, >> Yep. >> cataloging. And then as you get people using it, this is where you take the data literacy and go into operationalizing data. >> Satyen: That's right. >> So this seems to be the challenge. So, if I'm a customer and I have a problem, the profile of your target customer or who your customers are, people who need to expand and operationalize data, how would you talk about it? >> Yeah, so it's really interesting. We talk about, one of our customers called us, sort of, the social network for nerds inside of an enterprise. And I think for me that's a compliment. (John laughing) But what I took from that, and when I explained the business of Alation, we start with those individuals who are data literate. The data scientists, the data engineers, the data stewards, the chief data officer. But those people have the knowledge and the context to then explain data to other people inside of that same institution. So in the same way that Facebook started with Harvard, and then went to the rest of the Ivies, and then went to the rest of the top 20 schools, and then ultimately to mom, and dad, and grandma, and grandpa. We're doing the exact same thing with data. We start with the folks that are data literate, we expand from there to a broader audience of people that don't necessarily have data in their titles, but have curiosity and questions. >> I like that on the curiosity side. You spent some time up at Strata Data. I'm curious, what are some of the things you're hearing from customers, maybe partners? Everyone used to talk about Hadoop, it was this big thing. And then there was a creation of data lakes, and swampiness, and all these things that are sort of becoming more complex in an organization. And with the rise of myriad data sources, the velocity, the volume, how do you help an enterprise understand and be able to catalog data from so many different sources? Is it that same principle that you just talked about in terms of, let's start with the lowest hanging fruit, start making the impact there and then grow it as we can? Or is an enterprise needs to be competitive and move really, really quickly? I guess, what's the process? >> How do you start? >> Right. >> What do people do? >> Yes! >> So it's interesting, what we find is multiple ways of starting with multiple different types of customers. And so, we have some customers that say, "Look, we've got a big, we've got Teradata, "and we've got some Hadoop, "and we've got some stuff on Amazon, "and we want to connect it all." And those customers do get started, and they start with hundreds of users, in some case, they start with thousands of users day one, and they just go Big Bang. And interestingly enough, we can get those customers enabled in matters of weeks or months to go do that. We have other customers that say, "Look, we're going to start with a team of 10 people "and we're going to see how it grows from there." And, we can accommodate either model or either approach. From our prospective, you just have to have the resources and the investment corresponding to what you're trying to do. If you're going to say, "Look, we're going to have, two dollars of budget, and we're not going to have the human resources, and the stewardship resources behind it." It's going to be hard to do the Big Bang. But if you're going to put the appropriate resources up behind it, you can do a lot of good. >> So, you can really facilitate the whole go big or go home approach, as as well as the let's start small think fast approach. >> That's right, and we always, actually ironically, recommend the latter. >> Let's start small, think fast, yeah. >> Because everybody's got a bigger appetite than they do the ability to execute. And what's great about the tool, and what I tell our customers and our employees all day long is, there's only metric I track. So year over year, for our business, we basically grow in accounts by net of churn by 55%. Year over year, and that's actually up from the prior year. And so from my perspective-- >> And what does that mean? >> So what that means is, the same customer gave us 55 cents more on the dollar than they did the prior year. Now that's best in class for most software businesses that I've heard. But what matters to me is not so much that growth rate in and of itself. What it means to me is this, that nobody's come along and says, "I've mastered my data. "I understand all of the information side of my company. "Every person knows everything there is to know." That's never been said. So if we're solving a problem where customers are saying, "Look, we get, and we can find, and understand, "and trust data, and we can do that better last year "than we did this year, and we can do it even more "with more people," we're going to be successful. >> What I like about what you're doing is, you're bringing an element of operationalizing data for literacy and for usage. But you're really bringing this notion of a humanizing element to it. Where you see it in security, you see it in emerging ecosystems. Where there's a community of data people who know how hard it is and was, and it seems to be getting easier. But the tsunami of new data coming in, IOT data, whatever, and new regulators like GDPR. These are all more surface area problems. But there's a community coming together. How have you guys seen your product create community? Have you seen any data on that, 'cause it sounds like, as people get networked together, the natural outcome of that is possibly usage you attract. But is there a community vibe that you're seeing? Is there an internal collaboration where they sit, they're having meet ups, they're having lunches. There's a social aspect in a human aspect. >> No, it's humanal, no, it's amazing. So in really subtle but really, really powerful ways. So one thing that we do for every single data source or every single report that we document, we just put who are the top users of this particular thing. So really subtly, day one, you're like, "I want to go find a report. "I don't even know "where to go inside of this really mysterious system". Postulation, you're able to say, "Well, I don't know where to go, but at least I can go call up John or Lisa," and say, "Hey, what is it that we know about this particular thing?" And I didn't have to know them. I just had to know that they had this report and they had this intelligence. So by just discovering people in who they are, you pick up on what people can know. >> So people of the new Google results, so you mentioned Google PageRank, which is web pages and relevance. You're taking a much more people approach to relevance. >> Satyen: That's right. >> To the data itself. >> That's right, and that builds community in very, very clear ways, because people have curiosity. Other people are in the mechanism why in which they satisfy that curiosity. And so that community builds automatically. >> They pay it forward, they know who to ask help for. >> That's right. >> Interesting. >> That's right. >> Last question, Satyen. The tag line, first data catalog designed for collaboration, is there a customer that comes to mind to you as really one that articulates that point exactly? Where Alation has come in and really kicked open the door, in terms of facilitating collaboration. >> Oh, absolutely. I was literally, this morning talking to one of our customers, Munich Reinsurance, largest reinsurance customer or company in the world. Their chief data officer said, "Look, three years ago, "we started with 10 people working on data. "Today, we've got hundreds. "Our aspiration is to get to thousands." We have three things that we do. One is, we actually discover insights. It's actually the smallest part of what we do. The second thing that we do is, we enable people to use data. And the third thing that we do is, drive a data driven culture. And for us, it's all about scaling knowledge, to centers in China, to centers in North America, to centers in Australia. And they've been doing that at scale. And they go to each of their people and they say, "Are you a data black belt, are you a data novice?" It's kind of like skiing. Are you blue diamond or a black diamond. >> Always ski in pairs (laughs) >> That's right. >> And they do ski in pairs. And what they end up ultimately doing is saying, "Look, we're going to train all of our workforce to become better, so that in three, 10 years, we're recognized as one of the most innovative insurance companies in the world." Three years ago, that was not the case. >> Process improvement at a whole other level. My final question for you is, for the folks watching or the folks that are going to watch this video, that could be a potential customer of yours, what are they feeling? If I'm the customer, what smoke signals am I seeing that say, I need to call Alation? What are some of the things that you've found that would tell a potential customer that they should be talkin' to you guys? >> Look, I think that they've got to throw out the old playbook. And this was a point that was made by some folks at a conference that I was at earlier this week. But they basically were saying, "Look, the DLNA's PlayBook was all about providing the right answer." Forget about that. Just allow people to ask the right questions. And if you let people's curiosity guide them, people are industrious, and ambitious, and innovative enough to go figure out what they need to go do. But if you see this as a world of control, where I'm going to just figure out what people should know and tell them what they're going to go know. that's going to be a pretty, a poor career to go choose because data's all about, sort of, freedom and innovation and understanding. And we're trying to push that along. >> Satyen, thanks so much for stopping by >> Thank you. >> and sharing how you guys are helping organizations, enterprises unlock data curiosity. We appreciate your time. >> I appreciate the time too. >> Thank you. >> And thanks John! >> And thank you. >> Thanks for co-hosting with me. For John Furrier, I'm Lisa Martin, you're watching theCUBE live from our second day of coverage of our event Big Data SV. Stick around, we'll be right back with our next guest after a short break. 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>> [Voiceover] Live, from Los Angeles, it's theCUBE. Covering Open Source Summit, North America 2017. Brought to you by the Linux Foundation, and Red Hat.>> Hello everyone, welcome back to our special Cube live coverage of Linux Foundation's Open Source Summit North America here in LA, I'm John Furrier your co-host with Stu Mitiman. Our next guest is Christine Corbett Moran, Ph.D. at astronomy, astrophysics post-doctoral fellow at Caltech.>> That's right, it's a mouthful.>> Welcome to theCUBE, a mouthful but you're also keynoting, gave one of the talks opening day today after Jim Zemlin, on tech and culture and politics.>> That's right, yeah.>> Which I thought was fantastic. A lot of great notes there. Connect the dots for us metaphorically speaking, between Caltech and tech and culture. Why did you take that theme?>> Sure. So I've been involved in programming since I was an undergraduate in college. I studied computer science and always attending more and more conferences. hacker cons, security conferences, that sort of stuff. Very early on what attracted me to technology was not just the nitty gritty nuts and bolts of being able to solve a hard technical problem That was a lot of fun, but also the impact that it could have. So even as I went on a very academic track, I continued to make open source contributions. Really seeking that kind of cultural impact. And it wasn't something that I was real vocal about. Talking about. More talking about the technology side of things than the politics side of things. But in the past few years, I think with the rise of fake news, with the rise of various sorts of societal problems that we're seeing as a consequence of technology, I decided I was going to try to speak more to that end of things. So that we can focus on that as a technology community on what are we going to do with this enormous power that we have.>> And looking at that, a couple of direct questions for you, it was awesome talk. You get a lot in there. You were riffing some good stuff there with Jim as well. But you had made a comment that you originally wanted to be lawyer, you went to MIT, and you sort of got pulled in to the dark side>> That's right, yeah.>> In programming. As a former computer scientist myself, what got the bug take us through that moment. Was it you just started coding and said damn I love coding? What was the moment?>> Sure, so I was always talented in math and science. That was part of the reason why I was admitted to MIT and chose to go there. My late father was a lawyer. I didn't really have an example of a technologist in my life. So, to me, career wise I was going to be a lawyer, but I was interested in technology. What kind of lawyer is that? Patent attorney. So that was my career path. MIT, some sort of engineering, then a patent attorney. I got to MIT and realized I didn't have to be a attorney. I could just do the fun stuff. For some people that's the fun part. For me it ended up being when I took my first computer science class. Something that was fun, that I was good at, and that I really got addicted to kind of the feedback loop of you always have a problem you're trying to solve. It doesn't work, it doesn't work. Then you get it to work and then it's great for a minute and then there's a new problem to solve.>> That's a great story. I think it was very inspirational. A lot of folks of watching will be inspired by that. The other thing that inspired me in the key note was your comment about code and culture.>> [Christine] Yeah.>> I love this notion that code is now at a point where open source is a global phenomenon. You mentioned Earth and space.>> [Christine] Yeah.>> You know and all this sort of space is now Linux based now. But coding can shape culture. Explain what you mean by that, because I think it's one of those things that people might not see happening right now, but it is happening. You starting to see the more inclusionary roles and the communities are changing. Code is not just a tech thing. Explain what you mean by code-shaping culture.>> Well we can already that in terms of changing corporate culture. So, for example, 10 or 15, 20 years ago it might be inconceivable to make contributions that might benefit your corporate competitor. And we all have corporate competitors whether that's a nation, the US having competitors. Whether that's your local sports rivalry. We all have competitors, but open source has really shown that you're relying on things that you as a group, no matter what entity you are, you can't do as much as you can if you share your contributions and benefit from people around the globe. So that's one big way I've seen corporate culture in just every day culture change that people have recognized. Whether it's science, or corporate success, you can't do it alone. There's no lone genius. You really have to do it as a community.>> As a collective too you mentioned some of the ruling class and you kind of referring to not ruling class and open source, but also politics. In that gerrymandering was a word you used. We don't hear that often at conferences, but the idea of having more people exposed creates more data. Talk about what you mean by that because this is interesting. This truly is a democratization opportunity.>> [Christine] Absolutely.>> If not handled properly could go away.>> Yeah, I think am a little, I don't know if there's any Game of Thrones fans out there, but you know at some point this season and previous seasons you know Daenerys Targaryen is there and they're like well if you do this you're going to be the same evil person just new face. I think there's a risk of that in the open source community that if it ends up just being a few people it's the same oligarchy. The same sort of corruption just a different face to it. I don't think open source will go that way just based on the people that I've met in the community. It is something that we actively have to guard against and make sure that that we have as many people contributing to open source so that it's not just a few people who are capable of changing the world and have the power to decide whether it's going to be A or B, but as many people as possible.>> Christine, the kind of monetization of open source is always an interesting topic at these kind of shows. You had an interesting piece talking about young people contributing. You know contributing to open source. It's not just oh yeah do it for free and expect them to do it. Same thing in academia a lot of times. Like oh hey, you're going to do that research and participate and write papers and you know money is got to come somewhere to help fund this. How does kind of the money fit into this whole discussion of open source?>> So I think that's been one of the big successes of open source and we heard that from Jim as well today. It isn't you know some sort of unattainable in terms of achieving value for society. When you do something of value, money is a reward for that. The only question is how to distribute that award effectively to the community. What I see sometimes in the community is there's this myth of everyone in open source getting involved for just the fun of it and there's a huge amount of that. I have done a bunch of contributions for free on the side, but I've always in the end gotten some sort monetary reward for that down the line. And someone talked today about that makes you more employable, et cetera. That has left me with the time and freedom to continue that development. I think it's a risk that as a young person who is going into debt for college to not realize that that monetary reward will come or have it be so out of sync with their current life situation that they're unable to get the time to develop the skills. So, I don't think that money is a primary motivating factor for most people in the community, but certainly as Linus said today as well. When you don't have to worry about money that's when you do the really cool nitty-gritty things that might be a risk that then grow to be that next big project.>> It's an interesting comment you made about the US how they couldn't do potentially Linux if it wasn't in the US. It opens up your eyes and you say hmm we got to do better.>> Yeah.>> And so that brings up the whole notion of the radical comment of open source has always been kind of radical and then you know when I was growing up it was a tier two alternative to the big guys. Now it's tier one. I think the stakes are higher and the thing I'd like you to get your comment or reaction to is how does the community take it to the next level when it's bigger than the United States. You have China saying no more ICOs, no more virtual currencies. That's a potential issue there's a data point of many other things that can be on the global scale. Security, the Equifax hack, identity theft, truth in communities is now an issue, and there's more projects more than ever. So I made a comment on Twitter. Whose shoulders do we stand on in the expression of standing on the shoulders before you.>> [Christine] Yeah, you're standing on a sea.>> So it's a discovery challenge of what do we do and how do we get to the truth. What's your thoughts on that?>> That is a large question. I don't know if I can answer it in the short amount of time. So to break it down a little bit. One of the issues is that we're in this global society and we have different portions trying to regulate what's next in technology. For example, China with the ICOs, et cetera. One of the phrases I used in my talk was that the math was on the people's side and I think it is the case still with a lot of the technologies that are distributed. It's very hard for one particular government, or nation state, to say hey we're going to put this back in the box. It's Pandora's box. It's out in the open. So that's a challenge as well for China and other people, the US. If you have some harmful scenario, how to actually regulate that. I don't know how that's going to work out moving forward. I think it is the case in our community how to go to the next level, which is another point that you brought up. One thing that Linus also brought up today, is one of the reasons why it's great to collaborate with corporations is that often they put kind of the finishing touches on a product to really make it to the level that people can engage with it easily. That kind of on ramping to new technology is very easy and that's because of corporations is very incentivized monetarily to do that, whereas the open source community isn't necessarily incentivized to do that. Moreover, a lot of that work that final 1% of a project for the polish is so much more difficult. It's not the fun technical element. So a lot of the open source contributors, myself included, aren't necessarily very excited about that. However, what we saw in Signal, which is a product that it is a non-profit it is something that isn't necessarily for corporate gain, but that final polish and making it very usable did mean that a lot more people are using the product. So in terms of we as a community I think we have to figure out how keeping our radical governance structure, how to get more and more projects to have that final polish. And that'll really take the whole community.>> Let them benefit from it in a way that they're comfortable with now it's not a proprietary lock and it's more of only 10% of most of the applications are uniquely differentiated with open source. Question kind of philosophic thought experiment, or just philosophical question, I'll say astronomy and astrophysics is an interesting background. You've got a world of connected devices, the IoT, Internet of Things, includes people. So, you know I'm sitting there looking at the stars, oh that's the Apache Project, lots of stars in that one. You have these constellations of communities, if you will out there to kind of use the metaphor. And then you got astrophysics, the Milky Way, a lot of gravity around me. You almost take a metaphor talks to how communities work. So let's get your thoughts. How does astrophysics and astronomy relate to some of the dynamics in how self-governing things work?>> I'd love to see that visualization by the way, of the Apache Project and the Milky Way,>> [John] Which one's the Big Dipper?>> That sounds gorgeous, you guys should definitely pursue that.>> John you're going to find something at Caltech, you know our next fellowship.>> Argued who always did the Big Dipper or not, but you know.>> I think some of the challenges are similar in the sciences in that people initially get into it because it's something they're curious about. It's something they love and that's an innate human instinct. People have always gazed up at the stars. People have always wondered how things work. How your computer works? You know let me figure that out. That said, ultimately, they need to eat and feed their families and that sort of stuff. And we often see in the astrophysics community incredibly talented people at some stage in their career leaving for some sort of corporate job. And retaining talent is difficult because a lot of people are forced to move around the globe, to different centers in academia, and that lifestyle can be difficult. The pay often isn't as rewarding as it could be. So to make some sort of parallel between that community and the open source community, retaining talent in open source, if you want people to not necessarily work in open source under Microsoft, under a certain corporation only, but to kind of work more generally. That is something that ultimately, we have to distribute the rewards from that to the community.>> It's kind of interesting. The way I always thought the role of the corporation and open source was always trying to change the game. You know, you mentioned gerrymandering. The old model was we got to influence a slow that down so that we can control it.>> So John we've had people around the globe and even that have made it to space on theCUBE before. I don't know that we've ever had anybody that's been to the South Pole before on theCUBE. So Christine, maybe tell us a little about how's technology you know working in the South Pole and what can you tell our audience about it?>> Sure. So I spent 10 and half months at the South Pole. Not just Antarctica, but literally the middle of the continent, the geographic South Pole. There the US has a research base that houses up to about 200 people during the austral summer months when it's warm that is maybe minus 20 degrees or so. During the cold winter months, it gets completely dark and planes have a very difficult time coming in and out so they close off the station to a skeleton crew to keep the science experiments down there running. There are several astrophysical experiments, several telescopes, as well as many research projects, and that skeleton crew was what I was a part of. 46 people and I was tasked with running the telescope down there and looking at some of the echoes of the Big Bang. And I was basically a telescope doctor. So I was on call much like a sys-admin might be. I was responsible for the kind of IT support for the telescope, but also just physical, something physically broke, kind of replacing that. And that meant I could be woken up in the middle of night because of some kind of package update issue or anything like that and I'd have to hike out in minus a 100 degrees to fix this, sometimes. Oftentimes, there was IT support on the station so we did have internet running to the telescope which was about a kilometer away. It took me anywhere from 20 to 30 minutes to walk out there. So if it didn't require on-site support sometimes I could do the work in my pajamas to kind of fix that. So it was a kind of traditional computer support role in a very untraditional environment.>> That's an IoT device isn't it.>> Yeah.>> Stu and I are always interested in the younger generation as we both have kids who are growing up in this new digital culture. What's your feeling in terms of the younger generation that are coming up because people going to school now, digital natives, courseware, online isn't always the answer, people learn differently. Your thoughts on onboarding the younger generation and for the inclusion piece which is super important whether it's women in tech and/or just people just getting more people into computer science. What are some of things that you see happening that excite you and what are some of the things that get you concerned?>> Yeah, so I had the chance I mentioned a little in my talk to teach 12 high school students how to computer program this summer. Some of them have been through computer programming classes at their colleges, or at their high schools, some not. What I saw when I was in high school was a huge variety of competence in the high school teachers that I had. Some were amazing and inspiring. Others because in the US you need a degree in education, but not necessarily a degree in the field that you're teaching. I think that there's a huge lack of people capable of teaching the next generation who are working at the high school level. It's not that there's a huge lack of people who are capable, kind of anyone at this conference could sit down and help a high schooler get motivated and self-study. So I think teacher training is something that I'm concerned about. In terms of things I'm very excited about, we're not quite there yet with the online courses, but the ability to acquire that knowledge online is very, very exciting. In addition, I think we're waking up as a society to the fact that four year college isn't necessarily the best preparation for every single field. For some fields it's very useful. For other fields, particularly engineering, maybe even computer science engineering, apprenticeships or practical experience could be as valuable if not more valuable for less expense. So I'm excited about new initiatives, these coding bootcamps. I think there's a difficulty in regulation in that you don't know for a new coding bootcamp. Is it just trying to get people's money? Is it really going to help their careers? So we're in a very frothy time there, but I think ultimately how it will shake out is it's going to help people enter technology jobs quicker.>> You know there's a percentage of jobs that aren't even invented yet. So there's AI. You see self-driving cars. These things are easy indicators that hey society's changing.>> Yeah. And it's also good to be helpful for a professionals like us, older professionals who want to keep up in this ever growing field and I don't necessarily want to go back for a second Ph.D, but I'll absolutely take an online course in something I didn't see in my undergrad.>> I mean you can get immersed in anything these days online. It's great, there's a lot of community behind it. Christine thanks so much for sharing. Congratulations on a great keynote. Thanks for spending some time with us.>> [Christine] Yeah, thanks for having me.>> It's theCUBE live coverage here in LA for Open Source Summit in North America. I'm John Furrier, Stu Miniman, and we'll be right back with more live coverage after this short break.