>> Live from Boston, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back, everyone, to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillin. We have two guests for this session, we have Rebecca Shockley, she is executive consultant and IBM Global Business Services, and Alfred Essa, vice president analytics and R&D at McGraw-Hill Education. Rebecca and Alfred, thanks so much for coming on theCUBE. >> Thanks for having us. >> So I'm going to start with you, Rebecca. You're giving a speech tomorrow about the AI ladder, I know you haven't finished writing it-- >> Shh, don't tell. >> You're giving a speech about the AI ladder, what is the AI ladder? >> So, when we think about artificial intelligence, or augmented intelligence, it's very pervasive, we're starting to see it a lot more in organizations. But the AI ladder basically says that you need to build on a foundation of data, so that data and information architecture's your first rung, and with that data, then you can do analytics, next rung, move into machine learning once you're getting more comfortable, and that opens up the whole world of AI. And part of what we're seeing is organizations trying to jump to the top of the ladder or scramble up the ladder really quickly and then realize they need to come back down and do some foundational work with their data. I've been doing data and analytics with IBM for 21 years, and data governance is never fun. It's hard. And people would just as soon go do something else than do data governance, data security, data stewardship. Especially as we're seeing more business-side use of data. When I started my career, data was very much an IT thing, right. And part of my early career was basically just getting IT and business to communicate in a way that they were saying the same things. Well now you have a lot more self-service analytics, and business leaders, business executives, making software decisions and various decisions that impact the data, without necessarily understanding the ripples that their decisions can have throughout the data infrastructure, because that's not their forte. >> So what's the outcome, what's the result of this? >> Well, you start to see organizations, it's similar to what we saw when organizations first started making data lakes, right? The whole concept of a data lake, very exciting, interesting, getting all the data in together, whether it's virtual or physical. What ended up happening is without proper governance, without proper measures in place, you ended up with a data swamp instead of a data lake. Things got very messy very quickly, and instead of creating opportunities you were essentially creating problems. And so what we're advising clients, is you really have to make sure that you're focused on taking care of that first rung, right? Your data architecture, your information architecture, and treating the data with the respect as a strategic asset that it is, and making sure that you're dealing with that data in a proper manner, right? So, basically telling them, yes we understand that's fun up there, but come back down and deal with your foundation. And for a lot of organizations, they've never really stepped into data governance, because again, data isn't what they think makes the company run, right? So banks are bankers, not data people, but at the same time, how do you run a bank without data? >> Well exactly. And I want to bring you into this conversation, Alfred, as McGraw-Hill, a company that is climbing the ladder, in a more steady fashion. What's your approach? How do you think about bringing your teams of data scientists together to work to improve the company's bottom line, to enhance the customer experience? >> First I'd sort of like to start with laying some of the context of what we do. McGraw-Hill Education has been traditionally a textbook publisher, we've been around for over a hundred years, I started with the company over a hundred years ago. (all laughing) >> You've aged well. >> But we no longer think of ourselves as a textbook publisher. We're in the midst of a massive digital transformation. We started that journey over five years ago. So we think of ourselves as a software company. We're trying to create intelligent software based on smart data. But it's not just about software and AI and data, when it comes to education it's a tale of two cities. This is not just the U.S., but internationally. Used to be, we were born, went to school, got a job, raised a family, retired, and then we die. Well now, education is not episodic. People need to be educated, it's life-long learning. It's survival, but also flourishing. So that's created a massive problem and a challenge. It's a tale of two cities, by that I mean there's an incredible opportunity to apply technology, AI, we see a lot of potential in the new technologies. In that sense, it's the best of times. The worst of times is, we're faced with massive problems. There's a lot of inequity, we need to educate a people who have largely been neglected. That's the context. So I think in now answering your question about data science teams, first and foremost, we like to get people on the teams excited about the mission. It's like, what are we trying to achieve? What's the problem that we're trying to achieve? And I think the best employees, including data scientists, they like solving hard problems. And so, first thing that we try to do is, it's not what skills you have, but do you like solving really, really hard problems. And then taking it next step, I think the exciting thing about data science is it's an interdisciplinary field. It's not one skill, but you need to bring together a combination of skills. And then you also have to excel and have the ability to work in teams. >> You said that the AI has potential to improve the education process. Now, people have only so much capacity to learn, how can AI accelerate that process? >> Yeah, so if we stand back a little bit and look at the traditional model of education, there's nothing wrong with it but it was successful for a certain period of years, and it works for some people. But now the need for education is universal, and life long. So what our basic model, current model of education is lecture mode and testing. Now from a learning perspective, learning science perspective, all the research indicates that that doesn't work. It might work for a small group of people, but it's not universally applicable. What we're trying to do, and this is the promise of AI, it's not AI alone, but I think this is a big part of AI. What we can do is begin to customize and tailor the education to each individual's specific needs. And just to give you one quick example of that, different students come in with different levels of prior knowledge. Not everyone comes into a class, or a learning experience, knowing the same things. So what we can do with AI is determine, very, very precisely, just think of it as a brain scan, of what is it each student need to know at every given point in time, and then based on that we can determine also, this is where the models and algorithms are, what are you ready to learn next. And what you might be ready to learn next and what I might be ready to learn next is going to be very different. So our algorithms also help route delivery of information and knowledge at the right time to the right person, and so on. >> I mean, you're talking about these massive social challenges. Education as solving global inequity, and not every company has maybe such a high-minded purpose. But does it take that kind of mission, that kind of purpose, to unite employees? Both of you, I'm interested in your perspectives here. >> I don't think it takes, you know, a mission of solving global education. I do firmly agree with what Al said about people need a mission, they need to understand the outcome, and helping organizations see that outcome as being possible, gives them that rally point. So I don't disagree, I think everybody needs a mission to work towards but it doesn't have to be solving-- >> You want to extract that mission to a higher level, then. >> Exactly. >> Making the world a better place. >> Exactly, or at least your little corner of the world. Again what we're seeing, the difficulty is helping business leaders or consumers or whomever understand how data plays into that. You may have a goal of, we want better relationship with our customer, right? And at least folks of my age think that's a personal one-on-one kind of thing. Understanding who you are, I can find that much more quickly by looking at all your past transactions, and all of your past behaviors, and whether you clicked this or that. And you should expect that I remember things from one conversation to the next. And helping people understand that, you know, helping the folks who are doing the work, understand that the outcome will be that we can actually treat our customers the way that you want to be treated as a person, gives them that sense of purpose, and helps them connect the dots better. >> One of the big challenges that we hear CDOs face is getting buy-in, and what you're proposing about this new model really appending the old sage on the stage model, I mean, is there a lot of pushback? Is it difficult to get the buy-in and all stakeholders to be on the same page? >> Yeah, it is, I think it's doubly difficult. The way I think about it is, it's like a shift change in hockey, where you have one shift that's on the ice and another one that's about to come on the ice, that's a period of maximum vulnerability. That's where a lot of goals are scored, people get upset, start fighting. (all laughing) That's hockey. >> That's what you do. >> Organizations and companies are faced with the same challenge. It's not that they're resisting change. Many companies have been successful with one business model, while they're trying to bring in a new business model. Now you can't jettison the old business model because often that's paying the bills. That's the source of the revenue. So the real challenge is how are you going to balance out these two things at the same time? So that's doubly difficult, right. >> I want to ask you quickly, 'cause we have to end here, but there's a terrible shortage of cybersecurity professionals, data science professionals, the universities are simply not able to keep up with demand. Do you see the potential for AI to step in and fill that role? >> I don't think technology by itself will fill that role. I think there is a deficit of talented people. I think what's going to help fill that is getting people excited about really large problems that can be solved with this technology. I think, actually I think the talent is there, what I see is, I think we need to do a better job of bringing more women, other diverse groups, into the mix. There are a lot of barriers in diversity in bringing talented people. I think they're out there, I think we could do a much better job with that. >> Recruiting them, right. Alfred, Rebecca, thanks so much for coming on theCUBE, it was a pleasure. >> Thank you so much for having us. >> I'm Rebecca Knight, for Paul Gillin, we will have more from theCUBE's live coverage of the IBM CDO Summit here in Boston coming up in just a little bit.

(pulsating music) >> Live from Fisherman's Wharf in San Francisco, it's theCUBE! Covering IBM Chief Data Officer Strategy Summit, Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit, Spring 2017. It's a mouthful, it's a great event, and it's one of many CDO summits that IBM's putting in around the country, and soon around the world. So check it out. We're happy to be here and really talk to some of the thought leaders about getting into the nitty gritty detail of strategy and execution. So we're excited to be joined by our next guest, Rebecca Shockley. She's an Analytics Global Research Leader for the IBM Institute for Business Value. Welcome, Rebecca. I didn't know about the IBM Institute for Business Value. >> Thank you. >> Absolutely. And Priya V. She said Priya V's good, so you can see the whole name on the bottom, but Priya V. is the CTO of Cognitive/IOT/Watson Health at IBM. Welcome, Priya. >> Thank you. >> So first off, just impressions of the conference? It's been going on all day today. You've got 170 or some-odd CDO's here sharing best practices, listening to the sessions. Any surprising takeaways coming out of any of the sessions you've been at so far? >> On a daily basis I live and breathe data. That's what I help our customers to get better at it, and today is the day where we get to talk about how can we adopt something which is emerging in that space? We talk about data governance, what we need to look at in that space, and cognitive as being the fabric that we are integrating into this data governance actually. It's a great day, and I'm happy to talk to over, like you said, 170 CDO's representing different verticals. >> Excellent. And Rebecca, you do a lot of core research that feeds a lot of the statistics that we've seen on the keynote slides, this and that. And one of the interesting things we talked about off air, was really you guys are coming up with a playbook which is really to help CDO's basically execute and be successful CDO's. Can you tell us about the playbook? >> Well, the playbook was born out of a Gartner statistic that came out I guess two or three years ago that said by 2016 you'll have 90% of organizations will have a CDO and 50% of them will fail. And we didn't think that was very optimistic. >> Jeff: 90% will have them and 50% will fail? >> Yes, and so I can tell you that based on our survey of 6,000 global executives last fall, the number is at 41% in 2016. And I'm hoping that the playbook kept them from being a failure. So what we did with the playbook is basically laid out the six key questions that an organization needs to think about as they're either putting in a CDO office or revamping their CDO offices. Because Gartner wasn't completely unfounded in thinking a lot of CDO offices weren't doing well when they made that prediction. Because it is very difficult to put in place, mostly because of culture change, right? It's a very different kind of way to think. So, but we're certainly not seeing the turnover we were in the early years of CDO's or hopefully the failure rate that Gartner predicted. >> So what are the top two or three of those six that they need to be thinking about? >> So they need to think about their objectives. And one of the things that we found was that when we look at CDO's, there's three different categories that you can really put them in. A data integrator, so is the CDO primarily focused on getting the data together, getting the quality of the data, really bringing the organization up to speed. The next thing that most organizations look at is being a business optimizer. So can they use that data to optimize their internal processes or their external relationships? And then the third category is market innovator. Can they use that data to really innovate, bring in new business models, new data monetization strategies, things like that. The biggest problem we found is that CDO's that we surveyed, and we surveyed 800 CDO's, we're seeing that they're being assessed on all three of those things, and it's hard to do all three at once, largely because if you're still having to focus on getting your data in a place where you can start doing real science against it you're probably not going to be full-time market innovator either. You can't be full-time in two different places. That's not to say as a data integrator you can't bring in data scientists, do some skunk works on some of the early work, find... and we've seen organizations really, like Bank Itau down in Brazil, really in that early stages still come up with some very innovative things to do, but that's more of a one-off, right. If you're being judged on all three of those, that I think is where the failure rate comes in. >> But it sounds like those are kind of sequential, but you can't operate them sequentially cause in theory you never finish the first phase, right? >> You never finish, you're always keeping up with the data. But for some organizations, they really need to, they're still operating with very dirty, very siloed data that you really can't bring together for analytics. Now once you're able to look at that data, you can be doing the other two, optimizing and innovating, at the same time. But your primary focus has to be on getting the data straight. Once you've got a functioning data ecosystem, then the level of attention that you have to put there is going to go down, and you can start working on, focusing on innovation and optimization more as your full-time role. But no, data integrator never goes away completely. >> And cleanser. Then, that's a great strategy. Then, as you said, then the rubber's got to hit the road. And Priya, that's where you play in, the execution point. Like you say, you like to get your hands dirty with the CDO's. So what are you seeing from your point of view? In terms of actually executing, finding early wins, easy paths to success, you know, how to get those early wins basically, right? To validate what you're doing. That's right. Like you said, it's become a universal fact that data governance and things, everything around consolidating data and the value of insights we get off it, that's been established fact. Now CDO's and the rest of the organization, the CIO's and the CTO's, have this mandate to start executing on them. And how do we go about it? That's part of my job at IBM as well. As a CTO, I work with our customers to identify where are the dominant business value? Where are those things which is completely data-driven? Maybe it is cognitive forecasting, or your business requirement could be how can I maximize 40% of my service channel? Which in the end of the day could be a cognitive-enabled data-driven virtual assistant, which is automating and bringing a TCO of huge incredible value. Those are some of the key execution elements we are trying to bring. But like we said, yes, we have to bring in the data, we have to hire the right talent, and we have to have a strategy. All those great things happen. But I always start with a problem, a problem which actually anchors everything together. A problem is a business problem which demonstrates key business values, so we actually know what we are trying to solve, and work backwards in terms of what is the data element to it, what are the technologies and toolkits that we can put on top of it, and who are the right people that we can involve in parallel with the strategy that we have already established. So that's the way we've been going about. We have seen phenomenal successes, huge results, which has been transformative in nature and not just these 170 CDO's. I mean, we want to make sure every one of our customers is able to take advantage of that. >> But it's not just the CDO, it's the entire business. So the IBM Institute on Business Value looks at an enormous amount of research, or does an enormous amount of research and looks at a lot of different issues. So for example, your CDO report is phenomenal, I think you do one for the CMO, a number of different chief officers. How are other functions or other roles within business starting to acculturate to this notion of data as a driver of new behaviors? And then we can talk about, what are some of those new behaviors? The degree to which the leadership is ready to drive that? >> I think the executive suite is really starting to embrace data much more than it has in the past. Primarily because of the digitization of everything, right. Before, the amount of data that you had was somewhat limited. Often it was internal data, and the quality was suspect. As we started digitizing all the business processes and being able to bring in an enormous amount of external data, I think organizationally executives are getting much more comfortable with the ability to use that data to further their goals within the organization. >> So in general, the chief groups are starting to look at data as a way of doing things differently. >> Absolutely. >> And how is that translating into then doing things differently? >> Yeah, so I was just at the session where we talked about how organizations and business units are even coming together because of data governance and the data itself. Because they are having federated units where a certain part of business is enabled and having new insights because we are actually doing these things. And new businesses like monetizing data is something which is happening now. Data as a service. Actually having data as a platform where people can build new applications. I mean the whole new segment of people as data engineers, full stack developers, and data scientists actually. I mean, they are incubated and they end up building lots of new applications which has never been part of a typical business unit. So these are the cultural and the business changes we are starting to see in many organizations actually. Some of them are leading the way because they just did it without knowing actually that's the way they should be doing it. But that's how it influences many organizations. >> I think you were looking for kind of an example as well, so in the keynote this morning one of the gentlemen was talking about working with their CFO, their risk and compliance office, and were able to take the ability to identify a threat within their ecosystem from two days down to three milliseconds. So that's what can happen once you really start being able to utilize the data that's available to an organization much more effectively, is that kind of quantum leap change in being able to understand what's happening in the marketplace, bing able to understand what's happening with consumers or customers or clients, whichever flavor you have, and we see that throughout the organization. So it's not just the CFO, but the CMO, and being able to do much more targeted, much more focused on the consumer side or the client customer side, that's better for me, right. And the marketing teams are seeing 30, 40% increase in their ability to execute campaigns because they're more data-driven now. >> So has the bit flipped where the business units are now coming to the CDO's office and pounding on the door, saying "I need my team"? As opposed to trying to coerce that you no longer use intuition? >> So it depends upon where you are, where the company is. Because what we call that is the snowball effect. It's one of the reasons you have to have the governance in place and get things going kind of in parallel. Because what we see is that most organizations go in skeptically. They're used to running on their gut instinct. That's how they got their jobs mostly, right? They had good instincts, they made good decisions, they got promoted. And so making that transition to being a data-driven organization can be very difficult. What we find though, is that once one section, one segment, one flavor, one good campaign happens, as soon as those results start to mount up in the organization, you start to see a snowball effect. And what I was hearing particularly last year when I was talking to CDO's was that it had taken them so long to get started, but now they had so much demand coming from the business that they want to look at this, and they want to look at that, and they want to look at the other thing, because once you have results, everybody else in the organization wants those same kind of results. >> Just to add to that, data is not anymore viewed as a commodity. If you have seen valuable organizations who know what their asset is, it's not just a commodity. So the parity of... >> Peter: Or even a liability is what it used to be, right? >> Exactly. >> Peter: It's expensive to hold it and store it, and keep track of it. >> Exactly. So the parity of this is very different right now. So people are talking about, how can I take advantage of the intelligence? So business units, they don't come and pound the door rather they are trying to see what data that I can have, or what intelligence that I can have to make my business different shade, or I can value add something more. That's a type of... So I feel based on the experiences that we work with our customers, it's bringing organizations together. And for certain times, yes sometimes the smartness and the best practices come in place that how we can avoid some of the common mistakes that we do, in terms of replicating 800 times or not knowing who else is using. So some of the tools and techniques help us to master those things. It is bringing organizations and leveraging the intelligence that what you find might be useful to her, and what she finds might be useful. Or what we all don't know, that we go figure it out where we can get it. >> So what's the next step in the journey to increase the democratization of the utilization of that data? Because obviously Chief Data Officers, there aren't that many of them, their teams are relatively small. >> Well, 41% of businesses, so there's a large number of them out there. >> Yeah, but these are huge companies with a whole bunch of business units that have tremendous opportunity to optimize around things that they haven't done yet. So how do we continue to kind of move this democratization of both the access and the tools and the utilization of the insights that they're all sitting on? >> I have some bolder expectations on this, because data and the way in which data becomes an asset, not anymore a liability, actually folds up many of the layers of applications that we have. I used to come from an enterprise background in the past. We had layers of application programming which just used data as one single layer. In terms of opportunities for this, there is a lot more deserving silos and deserving layers of IT in a typical organization. When we build data-driven applications, this is all going to change. It's fascinating. This role is in the front and center of everything actually, around data-driven. And you also heard enough about cognitive computing these days, because it is the key ingredient for cognitive computing. We talked about full ease of cognitive computing. It has to start first learning, and data is the first step in terms of learning. And then it goes into process re-engineering, and then you reinvent things and you disrupt things and you bring new experiences or humanize your solution. So it's on a great trajectory. It's going tochange the way we do things. It's going to give new and unexpected things both from a consumer point and from an enterprise point as well. It'll bring effects like consumerization of enterprises and what-not. So I have bolder and broader expectations out of this fascinating data world. >> I think one of the things that made people hesitant before was an unfamiliarity with thinking about using data, say a CSR on the front line using data instead of the scripts he or she had been given, or their own experience. And I think what we're seeing now is A, everybody's personal life is much more digital than it was before, therefore everybody's somewhat more comfortable with interacting. And B, once you start to see those results and they realize that they can move from having to crunch numbers and do all the background work once we can automate that through robotic process automation or cognitive process automation, and let them focus on the more interesting, higher value parts of their job, we've seen that greatly impact the culture change. The culture change question comes whether people are thinking they're going to lose their job because of the data, or whether it's going to let them do more interesting things with their jobs. And I think hopefully we're getting past that "it's me or it" stage, into the, how can I use data to augment the work that I'm doing, and get more personal satisfaction, if not business satisfaction, out of the work that I'm doing. Hopefully getting rid of some of the mundane. >> I think there's also going to be a lot of software that's created that's going to be created in different ways and have different impacts. The reality is, we're creating data incredibly fast. We know that is has enormous value. People are not going to change that rapidly. New types of algorithms are coming on, but many of the algorithms are algorithms we've had for years, so in many respects it's how we render all of that in some of the new software that's not driven by process but driven by data. >> And the beauty of it is this software will be invisible. It will be self-healing, regeneratable software. >> Invisible to some, but very very highly visible to others. I think that's one of the big challenges that IT organizations face, and businesses face. Is how do they think through that new software? So you talked about today, or historically, you talked about your application stack, where you have stacks which would have some little view of the data, and in many respects we need to free that data up, remove it out of the application so we can do new things with it. So how is that process going to either be facilitated, or impeded by the fact that in so many organizations, data is regarded as a commodity, something that's disposable. Do we need to become more explicit in articulating or talking about what it means to think of data as an asset, as something that's valuable? What do you think? >> Yeah, so in the typical application world, when we start, if you really look at it, data comes at the very end of it. Because people start designing what is going to be their mockups, where are they going to integrate with what sources, am I talking to the bank as an API, et cetera. So the data representation comes at the very end. In the current generation of applications, the cognitive applications that we are building, first we start with the data. We understand what are we working on, and we start applying, taking advantage of machines and all these algorithms which existed like you said, many many decades ago. And we take advantage of machines to automate them to get the intelligence, and then we write applications. So you see the order has changed actually. It's a complete reversal. Yes we had typical three-tier, four-tier architecture. But the order of how we perceive and understand the problem is different. But we are very confident. We are trying to maximize 40% of your sales. We are trying to create digital connected dashboards for your CFO where the entire board can make decisions on the fly. So we know the business outcome, but we are starting with the data. So the fundamental change in how software is built, and all these modules of software which you are talking about, why I mentioned invisible, is some are generatable. The AI and cognitive is advanced in such a way that some are generatable. If it understands the data underlying, it can generate what it should do with the data. That's what we are teaching. That's what ontology and all this is about. So that's why I said it's limitless, it's pretty bold, and it's going to change the way we have done things in the past. And like she said, it's only going to complement humans, because we are always better decision-makers, but we need so much of cognitive capability to aid and supplement our decision-making. So that's going to be the way that we run our businesses. >> All right. Priya's painting a pretty picture. I like it. You know, some people see only the dark side. That's clearly the bright side. That's a terrific story, so thank you. So Priya and Rebecca, thanks for taking a few minutes. Hope you enjoy the rest of the show, surrounded by all this big brain power. And I appreciate you stopping by. >> Thanks so much. >> Thank you. >> All right. Jeff Frick and Peter Burris. You're watching theCUBE from the IBM Chief Data Officers Summit, Spring 2017. We'll be right back after this short break. Thanks for watching. (drums pound) (hands clap rhythmically) >> [Computerized Voice] You really crushed it. (quiet synthesizer music) >> My name is Dave Vellante, and I'm a long-time industry analyst. I was at IDC for a number of years and ran the company's largest and most profitable business. I focused on a lot of areas, infrastructure, software, organizations, the CIO community. Cut my teeth there.

>>Okay, welcome back everyone. To the cubes coverage here, coop con cloud native con 2021 in person. The Cuba's here. I'm John farrier hosted the queue with Dave Nicholson, my cohost and cloud analyst, man. It's great to be back, uh, in person. We also have a hybrid event. We've got two great guests here, the founders of deep fence, sham, Krista Swami, C co-founder and CTO, and said deep line founder. It's great to have you on. This is a super important topic. As cloud native is crossed over. Everyone's talking about it mainstream, blah, blah, blah. But security is driving the agenda. You guys are in the middle of it. Cutting edge approach and news >>Like, like we were talking about John, we had operating at the intersection of the awesome desk, right? Open source security and cloud cloud native, essentially. Absolutely. And today's a super exciting day for us. We're launching something called track pepper, Apache V2, completely open source. Think of it as an x-ray or MRI scan for your cloud scan, you know, visualize this cloud at scale, all of the modalities, essentially, we look at cloud as a continuum. It's not a single modality it's containers. It's communities, it's William to settle we'll list all of them. Co-exist side by side. That's how we look at it and threat map. It essentially allows you to visualize all of this in real time, think of fed map, but as something that you, that, that takes over the Baton from the CIS unit, when the lift shift left gets over, that's when the threat pepper comes into picture. So yeah, super excited. >>It's like really gives that developer and the teams ops teams visibility into kind of health statistics of the cloud. But also, as you said, it's not just software mechanisms. The cloud is evolving, new sources being turned on and off. No one even knows what's going on. Sometimes this is a really hidden problem, right? Yeah, >>Absolutely. The basic problem is, I mean, I would just talk to, you know, a gentleman 70 of this morning is two 70 billion. Plus public cloud spent John two 70 billion plus even 3 billion, 30 billion they're saying right. Uh, projected revenue. And there is not even a single community tool to visualize all the clouds and all the cloud modalities at scale, let's start there. That's what we sort of decided, you know what, let's start with utilizing everything else there. And then look for known badness, which is the vulnerabilities, which still remains the biggest attack vector. >>Sure. Tell us about some of the hood. How does this all work cloud scale? Is it a cloud service managed service it's code? Take us out, take us through product. Absolutely. >>So, so, but before that, right, there's one small point that Sandeep mentioned. And Richard, I'd like to elaborate here, right? He spoke about the whole cloud spending such a large volume, right? If you look at the way people look at applications today, it's not just single clone anymore. It's multicloud multi regions across diverse plants, right? What does the solution to look at what my interests are to this point? That is a missing piece here. And that is what we're trying to tackle. And that is where we are going as open source. Coming back to your question, right? How does this whole thing work? So we have a completely on-prem model, right? Where customers can download the code today, install it. It can bill, we give binary stool and Shockley just as the exciting announcement that came out today, you're going to see somewhat exciting entrepreneurs. That's going to make a lot more easy for folks out there all day. Yeah, that's fine. >>So how does this, how does this all fit into security as a micro service and your, your vision of that? >>Absolutely. Absolutely. You know, I'll tell you, this has to do with the one of the continual conferences I would sort of when I was trying to get an idea, trying to shape the whole vision really? Right. Hey, what about syncretism? Microservice? I would go and ask people. They mentioned that sounds, that makes sense. Everything is becoming a microservice. Really. So what you're saying is you're going to deploy one more microservice, just like I deploy all of my other microservices. And that's going to look after my microservices. That compute back makes logical sense, essentially. That was the Genesis of that terminology. So defense essentially is deployed as a microservice. You go to scale, it's deployed, operated just like you to your microservices. So no code changes, no other tool chain changes. It just is yet another microservice. That's going to look after you talk about >>The, >>So there's one point I would like to add here, which is something very interesting, right? The whole concept of microservice came from, if you remember the memo from Jeff Bezos, that everybody's going to go, Microsoft would be fired. That gave rise to a very conventional unconditionally of thinking about their applications. Our deep friends, we believe that security should be. Now. You should bring the same unconventional way of thinking to security. Your security is all bottom up. No, it has to start popping up. So your applications on microservice, your security should also be a micro. >>So you need a microservice for a microservice security for the security. You're starting to get into a paradigm shift where you starting to see the API economy that bayzos and Amazon philosophy and their approach go Beanstream. So when I got to ask you, because this is a trend we've been watching and reporting on the actual application development processes, changing from the old school, you know, life cycle, software defined life cycle to now you've got machine learning and bots. You have AI. Now you have people are building apps differently. And the speed of which they want to code is high. And then other teams are slowing them down. So I've heard security teams just screw people over a couple of days. Oh my God, I can wait five days. No, it used to be five weeks. Now it's five days. They think that's progress. They want five minutes, the developers in real time. So this is a real deal optimum. >>Well, you know what? Shift left was a good thing. Instill a good thing. It helps you sort of figure out the issues early on in the development life cycle, essentially. Right? And so you started weaving in security early on and it stays with you. The problem is we are hydrating. So frequently you end up with a few hundred vulnerabilities every time you scan oftentimes few thousand and then you go to runtime and you can't really fix all these thousand one. You know? So this is where, so there is a little bit of a gap there. If you're saying, if look at the CIC cycle, the in financial cycle that they show you, right. You've got the far left, which is where you have the SAS tools, snake and all of that. And then you've got the center where, which is where you hand off this to ops. >>And then on the right side, you've got tech ops defense essentially starts in the middle and says, look, I know you've had thousand one abilities. Okay. But at run time, I see only one of those packages is loaded in memory. And only that is getting traffic. You go and fix that one because that's going to heart. You see what I'm saying? So that gap is what we're doing. So you start with the left, we come in in the middle and stay with you throughout, you know, till the whole, uh, she asks me. Yeah, well that >>Th that, that touches on a subject. What are the, what are the changes that we're seeing? What are the new threats that are associated with containerization and kind of coupled with that, look back on traditional security methods and how are our traditional security methods failing us with those new requirements that come out of the microservices and containerized world. And so, >>So having, having been at FireEye, I'll tell you I've worked on their windows products and Juniper, >>And very, very deeply involved in. >>And in fact, you know what I mean, at the company, we even sold a product to Palo Alto. So having been around the space, really, I think it's, it's, it's a, it's a foregone conclusion to say that attackers have become more sophisticated. Of course they have. Yeah. It's not a single attack vector, which gets you down anymore. It's not a script getting somewhere shooting who just sending one malicious HTP request exploiting, no, these are multi-vector multi-stage attacks. They, they evolve over time in space, you know? And then what happens is I could have shot a revolving with time and space, one notable cause of piling up. Right? And on the other side, you've got the infrastructure, which is getting fragmented. What I mean by fragmented is it's not one data center where everything would look and feel and smell similar it's containers and tuberosities and several lessons. All of that stuff is hackable, right? So you've got that big shift happening there. You've got attackers, how do you build visibility? So, in fact, initially we used to, we would go and speak with, uh, DevSecOps practitioner say, Hey, what is the coalition? Is it that you don't have enough scanners to scan? Is it that at runtime? What is the main problem? It's the lack of visibility, lack of observability throughout the life cycle, as well as through outage, it was an issue with allegation. >>And the fact that the attackers know that too, they're exploiting the fact that they can't see they're blind. And it's like, you know what? Trying to land a plane that flew yesterday and you think it's landing tomorrow. It's all like lagging. Right? Exactly. So I got to ask you, because this has comes up a lot, because remember when we're in our 11th season with the cube, and I remember conversations going back to 2010, a cloud's not secure. You know, this is before everyone realized shit, the club's better than on premises if you have it. Right. So a trend is emerged. I want to get your thoughts on this. What percentage of the hacks are because the attackers are lazier than the more sophisticated ones, because you see two buckets I'm going to get, I'm going to work hard to get this, or I'm going to go for the easy low-hanging fruit. Most people have just a setup that's just low hanging fruit for the hackers versus some sort of complex or thought through programmatic cloud system, because now is actually better if you do it. Right. So the more sophisticated the environment, the harder it is for the hackers, AK Bob wire, whatever you wanna call it, what level do we cross over? >>When does it go from the script periods to the, the, >>Katie's kind of like, okay, I want to go get the S3 bucket or whatever. There's like levels of like laziness. Yeah. Okay. I, yeah. Versus I'm really going to orchestrate Spearfish social engineer, the more sophisticated economy driven ones. Yeah. >>I think, you know what, this attackers, the hacks aren't being conducted the way they worked in the 10, five years ago, isn't saying that they been outsourced, there are sophisticated teams for building exploiters. This is the whole industry up there. Even the nation, it's an economy really. Right. So, um, the known badness or the known attacks, I think we have had tools. We have had their own tools, signature based tools, which would know, look for certain payloads and say, this is that I know it. Right. You get the stuff really starts sort of, uh, getting out of control when you have so many sort of different modalities running side by side. So much, so much moving attack surfaces, they will evolve. And you never know that you've scanned enough because you never happened because we just pushed the code. >>Yeah. So we've been covering the iron debt. Kim retired general, Keith Alexander, his company. They have this iron dome concept where there's more collective sharing. Um, how do you see that trend? Because I can almost imagine that the open-source man is going to love what you guys got. You're going to probably feed on it, like it's nobody's business, but then you start thinking, okay, we're going to be open. And you have a platform approach, not so much a tool based approach. So just give me tools. We all know that when does it, we cross over to the Nirvana of like real security sharing. Real-time telemetry data. >>And I want to answer this in two parts. The first part is really a lot of this wisdom is only in the community. It's a tribal knowledge. It's their informal feeds in from get up tickets. And you know, a lot of these things, what we're really doing with threat map, but as we are consolidating that and giving it out as a sort of platform that you can use, I like to go for free. This is the part you will never go to monetize this. And we are certain about disaster. What we are monetizing instead is you have, like I said, the x-ray or MRI scan of the cloud, which tells you what the pain points are. This is feel free. This is public collective good. This is a Patrick reader. This is for free. It's shocking. >>I took this long to get to that point, by the way, in this discussion. >>Yeah, >>This is this timing's perfect. >>Security is collective good. Right? And if you're doing open source, community-based, you know, programs like this is for the collector group. What we do look, this whole other set map is going to be open source. We going to make it a platform and our commercial version, which is called fetch Stryker, which is where we have our core IP, which is basically think about this way, right? If you figured out all the pain points and using tech map, or this was a free, and now you wanted the remedy for that pain feed to target a defense, we targeted quarantining of those statin workloads and all that stuff. And that's what our IP is. What we really do there is we said, look, you figured out the attack surface using tech fabric. Now I'm going to use threat Stryker to protect their attacks and stress >>Free. Not free to, or is that going to be Fort bang? >>Oh, that's for, okay. >>That's awesome. So you bring the goodness to the party, the goods to the party, again, share that collective, see where that goes. And the Stryker on top is how you guys monetize. >>And that's where we do some uniquely normal things. I would want to talk about that. If, if, if, if you know public probably for 30 seconds or so unique things we do in industry, which is basically being able to monitor what comes in, what goes out and what changes across time and space, because look, most of the modern attacks evolve over time and space, right? So you go to be able to see things like this. Here's a party structure, which has a vulnerability threats. Mapper told you that to strike. And what it does is it tells you a bunch of stress has a vulnerable again, know that somebody is sending a Melissa's HTP request, which has a malicious payload. And you know what, tomorrow there's a file system change. And there is outbound connection going to some funny place. That is the part that we're wanting this. >>Yeah. And you give away the tool to identify the threats and sell the hammer. >>That's giving you protection. >>Yeah. Yeah. Awesome. I love you guys love this product. I love how you're doing it. I got to ask you to define what is security as a microservice. >>So security is a microservice is a deployment modality for us. So defense, what defense has is one console. So defense is currently self posted by the customers within the infrastructure going forward. We'll also be launching a SAS version, the cloud version of it. But what happens as part of this deployment is they're running the management console, which is the gooey, and then a tiny sensor, which is collecting telemetric that is deployed as a microservice is what I'm saying. So you've got 10 containers running defenses level of container. That's, that's an eight or the Microsoft risk. And it utilizes, uh, EDP F you know, for tracing and all that stuff. Yeah. >>Awesome. Well, I think this is the beginning of a shift in the industry. You start to see dev ops and cloud native technologies become the operating model, not just dev dev ops are now in play and infrastructure as code, which is the ethos of a cloud generation is security is code. That's true. That's what you guys are doing. Thanks for coming on. Really appreciate it. Absolutely breaking news here in the queue, obviously great stuff. Open source continues to grow and win in the new model. Collaboration is the cube bringing you all the cover day one, the three days. I'm Jennifer, your host with Dave Nicholson. Thanks for watching.