>> From the Cube Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> Hi everyone, this is Dave Vellante and welcome back to the Cube's coverage of the MIT CDOIQ. God, we've been covering this show since probably 2013, really trying to understand the intersection of data and organizations and data quality and how that's evolved over time. And with me to discuss these issues is Krishna Cheriath, who's the Vice President and Chief Data Officer, Bristol-Myers Squibb. Krishna, great to see you, thanks so much for coming on. >> Thank you so much Dave for the invite, I'm looking forward to it. >> Yeah first of all, how are things in your part of the world? You're in New Jersey, I'm also on the East coast, how you guys making out? >> Yeah, I think these are unprecedented times all around the globe and whether it is from a company perspective or a personal standpoint, it is how do you manage your life, how do you manage your work in these unprecedented COVID-19 times has been a very interesting challenge. And to me, what is most amazing has been, I've seen humanity rise up and so to our company has sort of snap to be able to manage our work so that the important medicines that have to be delivered to our patients are delivered on time. So really proud about how we have done as a company and of course, personally, it has been an interesting journey with my kids from college, remote learning, wife working from home. So I'm very lucky and blessed to be safe and healthy at this time. So hopefully the people listening to this conversation are finding that they are able to manage through their lives as well. >> Obviously Bristol-Myers Squibb, very, very strong business. You guys just recently announced your quarter. There's a biologics facility near me in Devon's, Massachusetts, I drive by it all the time, it's a beautiful facility actually. But extremely broad portfolio, obviously some COVID impact, but you're managing through that very, very well, if I understand it correctly, you're taking a collaborative approach to a COVID vaccine, you're now bringing people physically back to work, you've been very planful about that. My question is from your standpoint, what role did you play in that whole COVID response and what role did data play? >> Yeah, I think it's a two part as you rightly pointed out, the Bristol-Myers Squibb, we have been an active partner on the the overall scientific ecosystem supporting many different targets that is, from many different companies I think. Across biopharmaceuticals, there's been a healthy convergence of scientific innovation to see how can we solve this together. And Bristol-Myers Squibb have been an active participant as our CEO, as well as our Chief Medical Officer and Head of Research have articulated publicly. Within the company itself, from a data and technology standpoint, data and digital is core to the response from a company standpoint to the COVID-19, how do we ensure that our work continues when the entire global workforce pivots to a kind of a remote setting. So that really calls on the digital infrastructure to rise to the challenge, to enable a complete global workforce. And I mean workforce, it is not just employees of the company but the all of the third-party partners and others that we work with, the whole ecosystem needs to work. And I think our digital infrastructure has proven to be extremely resilient than that. From a data perspective, I think it is twofold. One is how does the core book of business of data continue to drive forward to make sure that our companies key priorities are being advanced. Secondarily, we've been partnering with a research and development organization as well as medical organization to look at what kind of real world data insights can really help in answering the many questions around COVID-19. So I think it is twofold. Main summary; one is, how do we ensure that the data and digital infrastructure of the company continues to operate in a way that allows us to progress the company's mission even during a time when globally, we have been switched to a remote working force, except for some essential staff from lab and manufacturing standpoint. And secondarily is how do we look at the real-world evidence as well as the scientific data to be a good partner with other companies to look at progressing the societal innovations needed for this. >> I think it's a really prudent approach because let's face it, sometimes one shot all vaccine can be like playing roulette. So you guys are both managing your risk and just as I say, financially, a very, very successful company in a sound approach. I want to ask you about your organization. We've interviewed many, many Chief Data Officers over the years, and there seems to be some fuzziness as to the organizational structure. It's very clear with you, you report in to the CIO, you came out of a technical bag, you have a technical degree but you also of course have a business degree. So you're dangerous from that standpoint. You got both sides which is critical, I would think in your role, but let's start with the organizational reporting structure. How did that come about and what are the benefits of reporting into the CIO? >> I think the Genesis for that as Bristol-Myers Squibb and when I say Bristol-Myers Squibb, the new Bristol-Myers Squibb is a combination of Heritage Bristol-Myers Squibb and Heritage Celgene after the Celgene acquisition last November. So in the Heritage Bristol-Myers Squibb acquisition, we came to a conclusion that in order for BMS to be able to fully capitalize on our scientific innovation potential as well as to drive data-driven decisions across the company, having a robust data agenda is key. Now the question is, how do you progress that? Historically, we had approached a very decentralized mechanism that made a different data constituencies. We didn't have a formal role of a Chief Data Officer up until 2018 or so. So coming from that realization that we need to have an effective data agenda to drive forward the necessary data-driven innovations from an analytic standpoint. And equally importantly, from optimizing our execution, we came to conclusion that we need an enterprise-level data organization, we need to have a first among equals if you will, to be mandated by the CEO, his leadership team, to be the kind of an orchestrator of a data agenda for the company, because data agenda cannot be done individually by a singular CDO. It has to be done in partnership with many stakeholders, business, technology, analytics, et cetera. So from that came this notion that we need an enterprise-wide data organization. So we started there. So for awhile, I would joke around that I had all of the accountabilities of the CDO without the lofty title. So this journey started around 2016, where we create an enterprise-wide data organization. And we made a very conscious choice of separating the data organization from analytics. And the reason we did that is when we look at the bowl of Bristol-Myers Squibb, analytics for example, is core and part of our scientific discovery process, research, our clinical development, all of them have deep data science and analytic embedded in it. But we also have other analytics whether it is part of our sales and marketing, whether it is part of our finance and our enabling functions they catch all across global procurement et cetera. So the world of analytics is very broad. BMS did a separation between the world of analytics and from the world of data. Analytics at BMS is in two modes. There is a central analytics organization called Business Insights and Analytics that drive most of the enterprise-level analytics. But then we have embedded analytics in our business areas, which is research and development, manufacturing and supply chain, et cetera, to drive what needs to be closer to the business idea. And the reason for separating that out and having a separate data organization is that none of these analytic aspirations or the business aspirations from data will be met if the world of data is, you don't have the right level of data available, the velocity of data is not appropriate for the use cases, the quality of data is not great or the control of the data. So that we are using the data for the right intent, meeting the compliance and regulatory expectations around the data is met. So that's why we separated out that data world from the analytics world, which is a little bit of a unique construct for us compared to what we see generally in the world of CDOs. And from that standpoint, then the decision was taken to make that report for global CIO. At Bristol-Myers Squibb, they have a very strong CIO organization and IT organization. When I say strong, it is from this lens standpoint. A, it is centralized, we have centralized the budget as well as we have centralized the execution across the enterprise. And the CDO reporting to the CIO with that data-specific agenda, has a lot of value in being able to connect the world of data with the world of technology. So at BMS, their Chief Data Officer organization is a combination of traditional CDO-type accountabilities like data risk management, data governance, data stewardship, but also all of the related technologies around master data management, data lake, data and analytic engineering and a nascent AI data and technology lab. So that construct allows us to be a true enterprise horizontal, supporting analytics, whether it is done in a central analytics organization or embedded analytics teams in the business area, but also equally importantly, focus on the world of data from operational execution standpoint, how do we optimize data to drive operational effectiveness? So that's the construct that we have where CDO reports to the CIO, data organization separated from analytics to really focus around the availability but also the quality and control of data. And the last nuance that is that at BMS, the Chief Data Officer organization is also accountable to be the Data Protection Office. So we orchestrate and facilitate all privacy-related actions across because that allows us to make sure that all personal data that is collected, managed and consumed, meets all of the various privacy standards across the world, as well as our own commitments as a company from across from compliance principles standpoint. >> So that makes a lot of sense to me and thank you for that description. You're not getting in the way of R&D and the scientists, they know data science, they don't need really your help. I mean, they need to innovate at their own pace, but the balance of the business really does need your innovation, and that's really where it seems like you're focused. You mentioned master data management, data lakes, data engineering, et cetera. So your responsibility is for that enterprise data lifecycle to support the business side of things, and I wonder if you could talk a little bit about that and how that's evolved. I mean a lot has changed from the old days of data warehouse and cumbersome ETL and you mentioned, as you say data lakes, many of those have been challenging, expensive, slow, but now we're entering this era of cloud, real-time, a lot of machine intelligence, and I wonder if you could talk about the changes there and how you're looking at and thinking about the data lifecycle and accelerating the time to insights. >> Yeah, I think the way we think about it, we as an organization in our strategy and tactics, think of this as a data supply chain. The supply chain of data to drive business value whether it is through insights and analytics or through operation execution. When you think about it from that standpoint, then we need to get many elements of that into an effective stage. This could be the technologies that is part of that data supply chain, you reference some of them, the master data management platforms, data lake platforms, the analytics and reporting capabilities and business intelligence capabilities that plug into a data backbone, which is that I would say the technology, swim lane that needs to get right. Along with that, what we also need to get right for that effective data supply chain is that data layer. That is, how do you make sure that there is the right data navigation capability, probably you make sure that we have the right ontology mapping and the understanding around the data. How do we have data navigation? It is something that we have invested very heavily in. So imagine a new employee joining BMS, any organization our size has a pretty wide technology ecosystem and data ecosystem. How do you navigate that, how do we find the data? Data discovery has been a key focus for us. So for an effective data supply chain, then we knew that and we have instituted our roadmap to make sure that we have a robust technology orchestration of it, but equally important is an effective data operations orchestration. Both needs to go hand in hand for us to be able to make sure that that supply chain is effective from a business use case and analytic use standpoint. So that has led us on a journey from a cloud perspective, since you refer that in your question, is we have invested very heavily to move from very disparate set of data ecosystems to a more converse cloud-based data backbone. That has been a big focus at the BMS since 2016, whether it is from a research and development standpoint or from commercialization, it is our word for the sales and marketing or manufacturing and supply chain and HR, et cetera. How do we create a converged data backbone that allows us to use that data as a resource to drive many different consumption patterns? Because when you imagine an enterprise of our size, we have many different consumers of the data. So those consumers have different consumption needs. You have deep data science population who just needs access to the data and they have data science platforms but they are at once programmers as well, to the other end of the spectrum where executives need pre-packaged KPIs. So the effective orchestration of the data ecosystem at BMS through a data supply chain and the data backbone, there's a couple of things for us. One, it drives productivity of our data consumers, the scientific researchers, analytic community or other operational staff. And second, in a world where we need to make sure that the data consumption appalls ethical standards as well as privacy and other regulatory expectations, we are able to build it into our system and process the necessary controls to make sure that the consumption and the use of data meets our highest trust advancements standards. >> That makes a lot of sense. I mean, converging your data like that, people always talk about stove pipes. I know it's kind of a bromide but it's true, and allows you to sort of inject consistent policies. What about automation? How has that affected your data pipeline recently and on your journey with things like data classification and the like? >> I think in pursuing a broad data automation journey, one of the things that we did was to operate at two different speed points. In a historically, the data organizations have been bundled with long-running data infrastructure programs. By the time you complete them, their business context have moved on and the organization leaders are also exhausted from having to wait from these massive programs to reach its full potential. So what we did very intentionally from our data automation journey is to organize ourselves in two speed dimensions. First, a concept called Rapid Data Lab. The idea is that recognizing the reality that the data is not well automated and orchestrated today, we need a SWAT team of data engineers, data SMEs to partner with consumers of data to make sure that we can make effective data supply chain decisions here and now, and enable the business to answer questions of today. Simultaneously in a longer time horizon, we need to do the necessary work of moving the data automation to a better footprint. So enterprise data lake investments, where we built services based on, we had chosen AWS as the cloud backbone for data. So how do we use the AWS services? How do we wrap around it with the necessary capabilities so that we have a consistent reference and technical architecture to drive the many different function journeys? So we organized ourselves into speed dimensions; the Rapid Data Lab teams focus around partnering with the consumers of data to help them with data automation needs here and now, and then a secondary team focused around the convergence of data into a better cloud-based data backbone. So that allowed us to one, make an impact here and now and deliver value from data to the dismiss here and now. Secondly, we also learned a lot from actually partnering with consumers of data on what needs to get adjusted over a period of time in our automation journey. >> It makes sense, I mean again, that whole notion of converged data, putting data at the core of your business, you brought up AWS, I wonder if I could ask you a question. You don't have to comment on specific vendors, but there's a conversation we have in our community. You have AWS huge platform, tons of partners, a lot of innovation going on and you see innovation in areas like the cloud data warehouse or data science tooling, et cetera, all components of that data pipeline. As well, you have AWS with its own tooling around there. So a question we often have in the community is will technologists and technology buyers go for kind of best of breed and cobble together different services or would they prefer to have sort of the convenience of a bundled service from an AWS or a Microsoft or Google, or maybe they even go best of breeds for all cloud. Can you comment on that, what's your thinking? >> I think, especially for organizations, our size and breadth, having a converged to convenient, all of the above from a single provider does not seem practical and feasible, because a couple of reasons. One, the heterogeneity of the data, the heterogeneity of consumption of the data and we are yet to find a single stack provider who can meet all of the different needs. So I am more in the best of breed camp with a few caveats, a hybrid best of breed, if you will. It is important to have a converged the data backbone for the enterprise. And so whether you invest in a singular cloud or private cloud or a combination, you need to have a clear intention strategy around where are you going to host the data and how is the data is going to be organized. But you could have a lot more flexibility in the consumption of data. So once you have the data converged into, in our case, we converged on AWS-based backbone. We allow many different consumptions of the data, because I think the analytic and insights layer, data science community within R&D is different from a data science community in the supply chain context, we have business intelligence needs, we have a catered needs and then there are other data needs that needs to be funneled into software as service platforms like the sales forces of the world, to be able to drive operational execution as well. So when you look at it from that context, having a hybrid model of best of breed, whether you have a lot more convergence from a data backbone standpoint, but then allow for best of breed from an analytic and consumption of data is more where my heart and my brain is. >> I know a lot of companies would be excited to hear that answer, but I love it because it fosters competition and innovation. I wish I could talk for you forever, but you made me think of another question which is around self-serve. On your journey, are you at the point where you can deliver self-serve to the lines of business? Is that something that you're trying to get to? >> Yeah, I think it does. The self-serve is an absolutely important point because I think the traditional boundaries of what you consider the classical IT versus a classical business is great. I think there is an important gray area in the middle where you have a deep citizen data scientist in the business community who really needs to be able to have access to the data and I have advanced data science and programming skills. So self-serve is important but in that, companies need to be very intentional and very conscious of making sure that you're allowing that self-serve in a safe containment sock. Because at the end of the day, whether it is a cyber risk or data risk or technology risk, it's all real. So we need to have a balanced approach between promoting whether you call it data democratization or whether you call it self-serve, but you need to balance that with making sure that you're meeting the right risk mitigation strategy standpoint. So that's how then our focus is to say, how do we promote self-serve for the communities that they need self-serve, where they have deeper levels of access? How do we set up the right safe zones for those which may be the appropriate mitigation from a cyber risk or data risk or technology risk. >> Security pieces, again, you keep bringing up topics that I could talk to you forever on, but I heard on TV the other night, I heard somebody talking about how COVID has affected, because of remote access, affected security. And it's like hey, give everybody access. That was sort of the initial knee-jerk response, but the example they gave as well, if your parents go out of town and the kid has a party, you may have some people show up that you don't want to show up. And so, same issue with remote working, work from home. Clearly you guys have had to pivot to support that, but where does the security organization fit? Does that report separate alongside the CIO? Does it report into the CIO? Are they sort of peers of yours, how does that all work? >> Yeah, I think at Bristol-Myers Squibb, we have a Chief Information Security Officer who is a peer of mine, who also reports to the global CIO. The CDO and the CSO are effective partners and are two sides of the coin and trying to advance a total risk mitigation strategy, whether it is from a cyber risk standpoint, which is the focus of the Chief Information Security Officer and whether it is the general data consumption risk. And that is the focus from a Chief Data Officer in the capacities that I have. And together, those are two sides of a coin that the CIO needs to be accountable for. So I think that's how we have orchestrated it, because I think it is important in these worlds where you want to be able to drive data-driven innovation but you want to be able to do that in a way that doesn't open the company to unwanted risk exposures as well. And that is always a delicate balancing act, because if you index too much on risk and then high levels of security and control, then you could lose productivity. But if you index too much on productivity, collaboration and open access and data, it opens up the company for risks. So it is a delicate balance within the two. >> Increasingly, we're seeing that reporting structure evolve and coalesce, I think it makes a lot of sense. I felt like at some point you had too many seats at the executive leadership table, too many kind of competing agendas. And now your structure, the CIO is obviously a very important position. I'm sure has a seat at the leadership table, but also has the responsibility for managing that sort of data as an asset versus a liability which my view, has always been sort of the role of the Head of Information. I want to ask you, I want to hit the Escape key a little bit and ask you about data as a resource. You hear a lot of people talk about data is the new oil. We often say data is more valuable than oil because you can use it, it doesn't follow the laws of scarcity. You could use data in infinite number of places. You can only put oil in your car or your house. How do you think about data as a resource today and going forward? >> Yeah, I think the data as the new oil paradigm in my opinion, was an unhealthy, and it prompts different types of conversations around that. I think for certain companies, data is indeed an asset. If you're a company that is focused on information products and data products and that is core of your business, then of course there's monetization of data and then data as an asset, just like any other assets on the company's balance sheet. But for many enterprises to further their mission, I think considering data as a resource, I think is a better focus. So as a vital resource for the company, you need to make sure that there is an appropriate caring and feeding for it, there is an appropriate management of the resource and an appropriate evolution of the resource. So that's how I would like to consider it, it is a personal end of one perspective, that data as a resource that can power the mission of the company, the new products and services, I think that's a good, healthy way to look at it. At the center of it though, a lot of strategies, whether people talk about a digital strategy, whether the people talk about data strategy, what is important is a company to have a pool north star around what is the core mission of the company and what is the core strategy of the company. For Bristol-Myers Squibb, we are about transforming patients' lives through science. And we think about digital and data as key value levers and drivers of that strategy. So digital for the sake of digital or data strategy for the sake of data strategy is meaningless in my opinion. We are focused on making sure that how do we make sure that data and digital is an accelerant and has a value lever for the company's mission and company strategy. So that's why thinking about data as a resource, as a key resource for our scientific researchers or a key resource for our manufacturing team or a key resource for our sales and marketing, allows us to think about the actions and the strategies and tactics we need to deploy to make that effective. >> Yeah, that makes a lot of sense, you're constantly using that North star as your guideline and how data contributes to that mission. Krishna Cheriath, thanks so much for coming on the Cube and supporting the MIT Chief Data Officer community, it was a really pleasure having you. >> Thank you so much for Dave, hopefully you and the audience is safe and healthy during these times. >> Thank you for that and thank you for watching everybody. This is Vellante for the Cube's coverage of the MIT CDOIQ Conference 2020 gone virtual. Keep it right there, we'll right back right after this short break. (lively upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering UiPath Forward Americas 2019. Brought to you by UiPath. >> We're back, UiPath Forward III from Las Vegas at the Bellagio, you're watching theCUBE, the leader in live tech coverage, my name is Dave Vellante. Marie Myers is here, she's the CFO of the rocket-ship known as UiPath. Welcome to theCUBE, thanks for coming on. >> Thank you. >> So, wow. You must be under a lot of pressure to keep the ship moving in a fast direction. But I was just talking to Daniel, he said, you know, when we started the company, we had basic finance systems, kind of like every other startup, but that obviously has changed, so. Well, congratulations, I know you got a lot more work to do but how are you spending your time these days? >> Doing a lot of work, is what I would say. So, as you've kind of seen, that tremendous growth, so huge pressure to just scale the company and ensure that the company has the ability to meet the growth that we're experiencing. So right now I've been really focused on building the operational backbone and actually building a lot of robots for UiPath, actually. That's something I wasn't expecting but came into the role and really help build our own ecosystem around robotics as well. >> I was asking Daniel how much dogfooding, champagne sipping you guys have done and, if it has contributed to the growth and it sounds like quite a bit, actually. >> Absolutely, I'd say we're really hitting the gas pedal right now in terms of building out our own competency and kind of to your point, eating the dog food, drinking the champagne and starting to push the envelope on how we actually use automation and AI to really scale our own business. You asked me where I was spending my time and where I was focused, I literally moved my family to Bucharest for the summer to really focus in on helping to scale the infrastructure. >> So, CFOs usually have a philosophy, a framework, that they like to work with. Obviously you got to stay flexible. How would you describe your philosophy as to how you'd like to manage this company? >> Well clearly, for us we're at an incredible stage of momentum in the market, and the ability for us to continue to build distance in terms of being number one is critical, so in terms of strategy, supporting that number one position, being agile. Able to scale for growth and ultimately do so profitably is certainly the ambition that we have in mind. And that requires turning a lot of different dials, right? And being able to turn them at the right time but at the same time ensure that we've got enough, let's just say, cushion underneath to scale that growth, because the growth is happening very very quickly. >> So CFOs, today's CFO is definitely, I would say more strategic than when I first got into the business, we used to joke that the cheap financial officer. But, I think of CFOs that I really admire, guys like Mike Scarpelli, who was at ServiceNow, now he's at Snowflake. I think he was at Data Domain too, Tom Sweet at Dell, whole different example, they're doing crazy financial engineering. But, much more of a strategic focus. Want to throw gasoline in the fire, and drive growth, but at the same time, thinking about efficiency, so. How have you seen that role evolving and how does that apply to what you guys are doing? >> So I think your comments about the role of the CFO are really right on, I mean, what's perhaps even more interesting, I think, for CFOs that are in software and maybe in a space like we're in is that you ultimately also get involved in being an advocate for your business. In robotics process automation, almost 40% of the first use cases are in finance. So, you're out there supporting the business case with other CFOs who want to understand how does efficiency really, why they should buy from us and what's the business proposition? So you've got to balance the demands of the business with running the business and so, I think that does give you the very unique lens because you understand how this product, to your point, drives operational efficiency. And obviously all CFOs really care, that's right on the list of the top three. >> You know, that's interesting, Marie, because the tech company CIOs are always being pulled in. Because they're early users of some technology. It's not common anyway, that the CFO is one of the lead sales go-to people but it sounds like it is in your case. How much time do you spend in the field? >> I try to balance my time, because you could get pulled very heavily I feel because of the nature of our business into that but I think because robotics process automation has been a key entry point into finance, there's a lot of work for CFOs to do there. So I try to balance my time, but it is, I think, a very important part of our own learning for our company, we get a lot of feedback from our customers. And, even helps me in my role because I get use cases from customers that I apply internally to drive our own efficiency. >> Well, plus, you know, you can see what's happening in the field, you can feel the pain of the sales reps, you can tell which ones are kind of sandbagging, >> You're right, absolutely! >> 'cause they're all sandbaggers! >> You're right about that, so it's been great being at this event, I know a lot of the great reps and so you really understand, you've got a good pulse on what's happening in terms of the business and where the risks are in the quarter. So that's one advantage. >> What're the metrics that you're driving? I mean, obviously the conventional ones, throw those in, but. >> Yeah, I mean obviously productivity, very important for us, we've got a lot of folks we've hired so really understanding what that productivity looks like. The usual cast of characters, AR. Customer acquisition costs, really focused on, what is that first customer costing and then how we're managing our land and expand. What our upsell looks like, so I think the usual cast of characters. >> And then eventually, as all these M and As happen, you'll get cohort sales coming in and the like. So, is everything that you guys sell recognized on a deferred revenue basis? >> No, we're in the midst of converting to 606 right now so we're kind of like subscription one year on prem. So pretty conventional software, red rack. >> Okay, but as you move to the cloud model. >> That gives us a different model, yeah. And we have it, we're just starting that journey. >> It seems like, you see different models. You know, Adobe bit the bullet, Splunk sort of peeled the Band-Aid off very slowly and they both can work. But it seems like a lot of the, I'll call it game, maybe it's the wrong word. But that's what came to mind, is educating the street. On that metric, on that transition. You certainly see it, for instance, in Oracle's case. Putting a lot of emphasis on helping the street understand that transition. That's not your primary focus right now, I'm sure you're spending some time with the analysts, I saw many buzzing around here. >> There was a lot here in the last few days. >> Dave: Yeah, they all want your business! >> (she laughs) They all want your business! I got a lot of texts in the last 48 hours. >> Well, it's an exciting time. And you know, eventually you guys are going to do an IPO and why wouldn't they? Be smart to be here, but what are your thoughts on that? Is that something that you really don't pay attention to right now, are you preparing for that? >> I'd say we're just getting total transparency, we're just moving through 606. So we're digesting that transition first and we're just starting down the whole cloud migration path. So as we start to think that through it's going to be I'd say a priority for 2020. And it's going to be important, I mean, for this business we expect, who's to say what the uptake rate is as customers move to the cloud? But I suspect it's going to be fairly aggressive in our business just because of the nature of bots and how customers think about bots. >> Yeah, so, Daniel said on the previous segment, he said, look, IPO's in our future, probably not 2020, we need at least a year to get our act together. So we're looking at 2021 but it depends on what the climate is, et cetera. My question is, and I've talked to, I see you orange here, Pure Storage is a high flyer in the infrastructure business, they're all orange, so they paint the town orange. >> Seems orange is very popular right now. >> It's a great color, recognizable. But I was talking about, they're all about growth. Not about optimizing profit right now and that's the right play because the street's rewarding growth. You guys, clearly, all about growth. >> We've got the growth story buttoned down, yeah. >> Yeah, you've got that down. But you still want to put gas on the fire, right? So, right now you're still optimized for growth. >> Absolutely, you see what's happening here, right? So, yeah, I think that kind of-- >> And you're well capitalized, so that's not the issue. So the strategy, I presume, is keep growing, get escape velocity, because, the company that gets escape velocity and is the leader in this business, you guys are the leader right now. You're not going to rest, you're going to stay paranoid, I'm sure. But the one that leads is going to make the most money. That always happens. >> Well, extending that leadership role is part of our core strategy, right? Maintaining number one, putting distance. I think you've seen the products that we announced here the last couple of days, adding to the portfolio or giving us incremental TAM so we can grow across the space. I think growing both down the stack and up the stack is critically important for us as we think forward to the future, too, right? We just don't want to be a pure robotics process automation company. We want to look across AI, down the stack into process mining. >> How do you think about your TAM? >> That's a great question. So I've been studying up a little over the last few days preparing for the board meeting tomorrow. I mean, robotics process automation, TAM next year is about two and a half, or two and change in terms of revenue, two billion. I've been looking at it a lot more broadly because I do believe that it is defined today quite narrowly in terms of very traditional RPE. And that started very much in the back office. As we've spread automation and kind of created that platform mentality, the TAM becomes additive. You've got now the process mining TAM which I think we can clearly start to play in that space. And then also the BPMs and now, obviously, AI. So, I was just doing our own back of the envelope in the last few days and you can get, easily, I think now, above that $10 billion mark and it depends on how you start to think about AI as you go forward and that just adds incremental TAM. >> Well, and you throw in services, you're already there. >> Yeah, exactly. >> Probably be there by next year. I think generally, I'll just give you my quick opinion. I think the market's undercounting the TAM potential. And I haven't done a detailed TAM analysis of the, I don't even want to say RPA 'cause that's the core. >> Exactly. >> But I could see this thing expanding dramatically, we talked about cohort sales. Just talking to customers, you're like one to 2% penetrated and there's so many more use cases. As you bring in AI, which, I really think of AI as a horizontal. But if you start applying AI and bringing in automation as an adjacency to you guys, I think that TAM are going to be many many tens of billions beyond what you're thinking. >> That's exactly how I like to think about it, of course, I go back to my IDC friends and try to use some of their benchmarks. But I think they're somewhat conservative. And I think as the market matures and people understand the breadth of the category, I just think that when RPA started it was kind of pigeonholed as a back office opportunity. >> Yeah, I mean, I was at IDC for a long time and we were really crappy at long-term TAM analysis. And you saw it with, Craig LeClair was awesome today. >> Yeah, I love Craig. >> Love him, fantastic. >> Very witty. >> His forecast, however, and same with IDC, we were there, we used to do these linear forecasts and that's not how these markets grow. It's an ogive and a steep S-curve and I think that's my prediction. >> Marie: I couldn't agree more with you. >> We heard predictions this morning, I summarized the predictions and gave my own. And that's one that I see. I'd like to see a longer-term forecast. Maybe we'll work on that. >> Well, we'd love that, I think that's going to be important. I think, part of it's just the maturity of this category. And as folks are starting to understand the breadth of the application, if you think about it, that's why there was so much early work in finance. Now you're starting to see the business spread across the enterprise, right? And I think as it spreads across the enterprise it just adds that incremental TAM and it becomes a gateway to AI. >> I've been using ServiceNow as an example, even though a totally different business, they had a much heavier lift, they started in IT, and went on, so it took longer for adoption. But there's a lot of similarities that I see just in terms of extending beyond just the core of the business, growing the ecosystem, I think is a critical part of that but as far as the customer adoption and the applicability of your technology, I think it's got a lot of legs, so. Like you say, Marie, we'll work on that a little bit. >> I'd love that, thank you. >> Dave: Appreciate you coming on, it was great to have you and wonderful to meet you. >> Enjoyed it. You too, thank you very much. >> You're welcome. Alright, keep right there, buddy, we'll be back to wrap up UiPath Forward III right after this short break. You're watching theCUBE. (electronic music)

>> Life from Las Vegas, it's theCube, covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back everyone to theCube's live coverage of the AWS Executive Summit. I'm your host, Rebecca Knight. And I'm joined by Kathleen Natriello. She is the vice president and the head of IT, digital design at Bristol Myers Squibb. And Shalu Chadha, senior technology services lead at Accenture. Thank you so much for coming on theCube. >> Sure. >> Thank you for having us. >> So we're going to talk about Bristol Myers Squibb's journey to the cloud today, but I want. Bristol Myers Squibb is a household name, but I would love you to just start out, Kathleen, by telling our viewers a little bit about Bristol Myers Squibb. Just how big a global pharma company you are. >> Sure. We're a global company, as you said. We have about 23,000 employees all over the world. And we're very focused on our immuno oncology therapies. And the way that they work is that they boost the immune system to fight cancer. So it's a really exciting development that we've had over the years. >> And so what was it, sort of, in the trajectory of Bristol Myers Squibb, that made you realize, as an organization, we need to do things differently? What challenges were you facing? >> So, we're very science focused in terms of developing treatments for our patients. And so our highest priority was our scientists' productivity. And so we started our cloud journey about 10 years ago. And our initial focus was on leveraging burst computing in AWS, which enabled us to spin up enough capacity for our scientists to do research with very large volumes of data. That's one of the things about biopharma. We use very large volumes for genomics research. >> And also, with this partnership, using AWS, you also partner with Accenture. So, can you describe a little bit, Shalu, how the partnership evolved? >> Right. And so that journey that Kathy mentioned, We've been part of that journey for the last two years now. And I think it's this nice partnership between AWS, BMS, and Accenture. And the teams have gone on with a lot of quick successes and early successes. And I think, going forward, the focus is really now businesses is going to look for a lot more demand and agility. Clouded adoption is going to be key in how we actually expand on that. And I know we're talking amongst us to say, how do we get there faster now? >> A little less conversation, a little more action please. >> Yes. (inaudible speech and laughter) >> Exactly. So, let's talk about this journey. So you're not only migrating existing applications, you're also building your own applications. >> Yes. >> What's the, sort of the wisdom behind that strategy? >> A couple of things. So I mentioned earlier that we started our journey with our scientists and we've continued because that's where AWS really delivers significant value for Bristol Myers Squibb. So, what we have done is implemented several AWS cloud services that enable our scientists to use machine learning, artificial intelligence, a lot of computational approaches and simulations that significantly reduce the amount of time it takes them to do an experiment, as well as the cost. Because they no longer have to use actual physical material, or patients, or investigators. They can do it all through simulation and modeling, which is exciting. >> So, I mean, we all know that the drug discovery process takes a long time, and it's tedious, um, cumbersome. So can you actually bring it back down to earth a little bit and say, what have you seen? What are your scientists? In terms of how the drug discovery process is going. >> Yeah. Our scientists are our biggest advocates of the cloud and the capabilities it delivers. And they will report back to us that they are doing things with machine learning and artificial intelligence with these simulations, that they're doing in a few hours, that used to take them weeks and months. And so that's how it's really shortening that cycle. >> And are the patients feeling the benefits yet, too? >> The patients will feel the benefits with our focus on clinical trials. And so, being able to speed up a clinical trial is very helpful. And both from the patient experience, as well as the investigators. >> Shalu, can you talk about some of the other innovation and automation capabilities? >> Yeah. So, BMS is really on this really exciting journey, and now that they've, like Kathy said, extended some of those capabilities and actually building and enabling for the scientists, of the commercial, the brand sites. It's now about, really, what do you do next and how you bring that next wave of innovation. And so, what's been nice at Bristol Myers Squibb and the partnership we have with Accenture here, is really looking at taking some of the learnings we had in the back office, in the finance and the procurement. Where we've actually brought a lot of process efficiency through our bots taking some of that learnings and bringing that across in many other different ways. And now we have bots across legal, compliance, and moving into the clinical area that adverse events. And we're looking at really that part which is how do you actually get quicker with how the patients are going to see both responses to the adverse events, as well as how do you actually accelerate the clinical trial process. And all of those innovations are really possible with what Kathy has set up in her organization. And actually having that digital acceleration competency and be able to take this span enterprise. >> One of the things that's so interesting about these partnerships is how you work together. >> (in unison) Yes. >> And is it that you're focusing on the science and Accenture is thinking about the technology? I mean, are you, sort of, two different groups? Or how are you coming together to collaborate and build a relationship? >> I really see it as three groups. So it's Bristol Myers Squibb that's focused on science as well as the technology. And if I take an example of how that partnership works, when we were doing our migration to the cloud, the more aggressive plan that we have in place right now, Amazon partnered with us on a migration readiness program. And that enabled us to move as much as 400 plus workloads into the cloud and to other locations. And then Accenture partnered with us, as well, to actually move the applications and migrate them to the cloud and the two other locations. So, I really see it as a three way partnership. And part of the way, one of the reasons it's so successful is it's not just BMS partnering with Accenture, and BMS partnering with Amazon, But it's Amazon and Accenture partnering together. And they would come up with ideas on here's what we think will make BMS even more successful. >> And how, and how is that? Is it because you were really grasping their business challenges? Or, I mean, how are you able to come up with? You're not a life science person. >> Right. >> It's, how are you doing that? >> It's a good question, and I think when I reflect on what I experience with other clients, I think what's so tremendously making us successful here is everything is about interest based. And it's about how we start the conversation. The patient in the center. And then it's about who's interests are we serving. Let's be clear. And let's try and try trigress into what's the solution that actually needs that. So, I think, whether, Kathy mentioned it in the cloud cumulus work, or even with the SAPS four journey right now. It's the combination of AWS, BMS, and Accenture in that journey of how we going to solve this together. Those critical and complex programs. >> Kathy, you said that scientists were some of your biggest advocates for going cloud native. I'm curious about the rest of the work force. I mean, has it been, sometimes introducing new technologies and new ways of doing things can cause consternation among your employees. >> Yeah., but in my organization, we bring a lot of change to the rest of the company. And your right. Sometimes it's well received. But I think when it is well received, is when across the company they can see the productivity gain with our robotics process automation. At a digital workforce, people are able to have, they are able to get a lot more done. And so there is acceptance of that. And very often, the business functions are the ones that introduce the new technologies because they're really interested in it and curious. So it works out well. >> So they're getting more done so >> Yes >> So then they're more satisfied with their work and life >> Yes >> And, exactly. So tell our viewers a little bit more about what's next for this partnership, for this relationship, in terms of new technologies. In terms of what you hope to be able to accomplish in the years to come. >> So, I can start. I really think that's what is next for us is to move a little faster. So, in our cloud journey, as I mentioned, we started 10 years ago and then, we've build on what we've learned. So, as an example, we put our commercial data warehouse into a Amazon Redshift. And then that laid the foundation for us to do, for example, rapid data labs. We started by building some data lakes in HR and R and D. And then, by the time we got to doing that for manufacturing, we did it serverless. And so we've had a nice progression based on learning and going the next step. But I think, we're to the point where the technology's evolving so quickly we can move a lot faster and get the benefits faster. So for me, that's what I view as what's next. >> Shalu, anything? >> Yeah. I would just add that I think analytics set the core. I think there is such a strong foundation set here that now it's about how are we going to extrapolate from there. And really look at bot machine learning and what that could do for us. And that, and we will take a lot from what we've learned here today about actually evolving that journey. And I think the best part is the foundation is set strong. And now it's about accelerating into those specific business areas as well. So I would say analytics and really extending our machine learning capabilities. >> So move faster, analytics machine learning. Great. So we're going to be talking about it next year's summit. Well, Kathy and Shalu, thank you so much for coming on theCube. This was a lot of fun. >> Yes. It was. >> (in unison) Thank you. >> I'm Rebecca Knight. We will have more of theCube's live coverage of the AWS Executive Summit coming up in just a little bit.

>> Live from Washington D.C., it's theCUBE covering Inforum DC 2018. Brought to you by Infor. >> Welcome back to Washington D.C., we are live here at theCUBE at Inforum '18. I'm John Walls along with Dave Vallante and it's a pleasure now to welcome to the show from Capgemini couple of folks, Rachel Myers, who's Director of Alliances at Capgemini. (laughing) And John Clark, who's the VP of info-practice at Capgemini and Dave put your phone away, would you please. >> We're off to a good start. >> We are. (laughing) >> Who are you guys again? >> I think it was givin' him directions for dinner tonight. I think what you're doing. It's down at K Street take a right. >> Don't drive scooters without a helmet. >> That's right. Inside story. Rachel and John, thanks for being with us. We appreciate the time here. >> Thanks for having us. >> Let's talk about the partnership with Infor. Where it's coming from. What you are adding to that. How you view it and what you're gettin' out of it. And John, if you would? >> Yeah absolutely. First, hello from D.C., he said. The relationship that Capgemini has had with Infor goes back over 20 years. But we formalized it really two years ago and had a strategic partnership defined around several of the products that Infor has with a big focus on digital and cloud. So Capgemini sees that Infor is really leading the charge in a lot of native cloud products out there and we know that, that is certainly something our clients are looking for. So formalized relationship and extremely excited to be lead partners and sponsors here at Inforum. >> And so Rachel, where do you come into play here then as far as Director of Alliances goes? I think the job title probably speaks for itself, but in terms of how the Infor relationship works and where it comes in to your portfolio onto your plate, how does that work? >> So I manage the relationship with Infor as our customers are looking at cloud and all the options out there. I manage the relationship into Infor bringing the right folks to bear to our customers and joining at the hip where we need to in support of our customers. >> Okay, so you mentioned John, that its been a 20 year relationship. So that means it goes back probably to the loss and software days, right? The whole early days of ERP. Now we come into the modern era, cloud. We're hearing all about AI. We're also hearing about, sort of, micro-verticals and industry expertise. >> Yes, yes. >> So square that circle for me because you guys have deep industry expertise. How do you mesh with Infor? >> Yeah great question. We absolutely, as you said, go to market from a sector perspective, so everything we do has some tent of an industry or a sector verticalisation and it matches exactly well with how Infor goes to market with last model functionality. So what we do for example, is look at where Infor and our sector team see gaps like on food processing companies and we'll build out that solution and take that to market. So really kind of extending the last malfunctionality with Infor and having Capgemini's solutions as well. >> So does that functionality ultimately make it back into Infor code or not necessarily? >> Not necessarily. >> Okay, all right. So it's like last inch function-- >> Right exactly. That's a pretty good analogy for it. >> Okay so, well, it's always the hardest part, right? I mean you think of cable, you think of all the-- >> Telephone whatever. >> Sort of examples, right? So, you know the old story is if you're here and you want to get to the wall and you go half way, you never get there, right? >> Exactly. >> So that's kind of the process that you're in. There's always more to do, right? >> Right. >> Okay, so what's hot these days in your space? >> Well we're here at Inforum talking to customers and our partners about many things. But we actually are speaking about Industry 4.0 which is a big hot topic. Supply chain and EAM, Enterprise Asset Management. We have practices and expertise in all of those, so we can bring the best to our customers from a system integration partner capability which would be us along with Infor and the products that they bring to bear. >> So what's the 101 on 4.0? Presumably a lot of automation, more efficiency, driving business value. How would you describe Industry 4.0 Next Gen? >> It's the next evolution, I would say, to automation of processees. We're getting closer, I would think, and people are definitely piloting to get there, but building a road map and helping them really see the value is what we're trying to do with our customers these days and making it real and really producing some ROI beyond that with automation. >> So AI is a piece of that? How about, have you seen like blockchain hit yet? Or is that sort of on people's road maps? >> I think it's definitely a road map item. I think there's some experimentation, but what we're definitely seeing become real is robotics process automation, RPA. We're doin' a lot of that with our customers and taking it beyond experimentation to actual ROI. >> And the RPA is exploding. I was actually impressed and surprised to hear so much RPA talk this morning. I didn't realize that Infor had quasi out of the box capbilities there. So what are you seeing? A lot of, sort of back office functions getting automated, software robots getting trained to do mundane tasks? What's the experience there? >> I think as we are implementing ERPs like Infor's, there is a need to take processes that customers are doing today manual and automate those to see the extension and the ROI beyond just the ERP software. >> We do see a lot of it start in the back office, so a lot of finance and HR functions is kind of the first place that companies look for 'cause on thing that we do see on RPA projects is don't try to tackle everything, but get focused and get some quick wins, if you will and that's really where we built our library and where we work with Infor. >> Is it fair the automation of it is coming from the lines of business which is kind of your wheelhouse, right? >> Right. >> It's not, sort of an IT thing so much. IT is probably a little afraid of it, but is that the way you see it? >> Yes it is. >> Okay and so talk about Capgemini's strategy as the world sort of evolves. You know, you always hear small projects, small wins are the way to go and for years it was like the big SAP implementation >> Yeah. >> Or the big Oracle implementation. How are you guys changing your business to accommodate that new thinking? >> So really on several fronts. One is definitely the methodology that we have and we see on projects is shifting from a waterfall to an agile. So much quicker iterations and cycles on the projects themselves and usually the scope. It will start off with a line of business and again, if it's looking for, hey, I just need to improve the digital relationship I have with my customer. Which can a lot of times just mean start a digital relationship with my customer. So it's really, you kind of keep a tight focus on the scope and just have an agile approach which, again, is what we have changed our methodologies for. >> So digital obviously is real. I mean, every CEO that we talk to is trying to get digital right. A lot of experimentation going on. Like you said a lot of, hey we have to have a digital strategy then you throw AI into the mix. You throw things like blockchain. It's a complicated situation for a lot of firms. What are the discussions like with customers? Where are you seeing the most success or early traction? >> I think having the vision and the scope of where you want to go three years, five years down the road and being able to prioritize against that road map what's going to give you the biggest benefit first, so that it's not just haphazardly trying out these technology enablers like RPA and AI, it is a clear vision and strategy of where we're trying to go and solely hitting some of that ROI and seeing value. >> Are you seeing more of a save money, make money kind of a mix? What are you seeing there? I would say probably a mix, save money for the right reasons and spend money to get the ROI that we're planning for in that road map. >> Just to amplify on the point that you're making Dave. Just from the customer side of the fence on this, for people who aren't, you're just introducing them to the cloud, right? To begin with and they're trying to embrace or understand a concept that they don't have any experience with and now you think of all these other capabilities you have down the road or all these other opportunities whether it's artificial intelligence or whether it's RPA, whatever it is. It's got to be mind-blowing. A little bit, doesn't it? And how do you, I guess, calm 'em down if they realize we are that far behind. We're never going to get there. We're always going to be three, five, 10 years behind because we're that far behind right now. So how do you, I guess, allay their concerns and then get them up to speed at such a way that they feel like they can catch up? >> Yeah, say one of the key things that we can provide is various maturity models. So we have kind of a keepin' it simple of a two by two grid of where do you fall from digital enablement? A, do you even know what that means? Do you do it within divisions or certain lines of business? And then, is that a part of the strategy for your customer acquisition, customer retention, employee retention, et cetera. And start with kind of a fit there and then we basically have offerings that then go from okay, if you're starting out then the approach can be let's go through what cloud is. Like I said, there are absolutely still discussions that we have now on, hey what is the difference between cloud and on-prem? Is it the same software version? Is it a different software? What are the security features and the data center? Some of those questions are still out there as you said and we've got to look at the maturity model to get 'em there. >> So let's go through the simple, I like simple, the two dimensional, one of the buckets, so it's like, hey, we're not even thinkin' about it, it's kind of lower left. Upper left would be line of business focus sort of narrow. Lower right would be at strategic, but we're not acting on it yet. >> Right, in a division or a single line of business or I may have a cross functional solution with a great digital road map, but it's in one plant, you know, 'cause then you get into, okay, well that's probably because you either had a champion locally or you had some trigger such as some customer issues or production issues or something that forced the issue, so to speak, there. And then the top right is, yeah, it's part of the strategy. It's built in to where the budget is allocated as well and it's a part of all the conversations we're having with business and IT. >> Were you guys seeing particular, thinking about sticking on digital for a minute, you think particular industry uptake, I mean, obviously retail's been disrupted, publishing, you know the music industry's been disrupted. But there's certain industries that really haven't been dramatically disrupted yet, financial services, healthcare, defense, really to date, these high risk businesses. What are you guys seeing and kind of where's the greatest familiarity or affinity to digital? >> Where we're starting and where we've been focused with Infor and the market place is consumer products and distribution as well as manufacturing. That's really been a focus area for us and we didn't get into this, but John's team has capability in Infor and is skilled in Infor and there are some focus areas for us with the customers in those industry segments. >> Do you think that automation, AI, improvements in the supply chain, you know robotics even software robots will reverse the trend toward offshore manufacturing tariffs, I guess maybe help too, but I mean, are you seeing any evidence of that automation sort of making the pendulum swing back or are the cost advantages so attractive and is the supply chain so intrenched? >> I'll let John elaborate, but I would say that there is still a fit for purpose for offshoring certain things and for automating certain things and that's why I think it's important to build a plan and a strategy for which things will be solved for in which ways. >> Yeah and the one thing I want to add is as you see some plant go from, it took 200, 300 people to operate a facility to I can do it with 10. That changes the economics of now the labor cost and labor arbitrage isn't as much a function, but yes, what about the rent, facilities and transportation? So we are seeing the economic calculation change a bit from the point of just go offshore for labor. Well if labor is not a big a point, we are seeing a shift there. >> Right, so the labor component's shrinking. And then you can automate that. Is there a quality aspect or is that kind of a myth? >> We think that's a myth from what we're seeing. >> Quality can improve a little bit. >> Exactly. >> Won't go down. Won't go down. >> You're saying coming back on-shoring? Or are you saying offshoring? >> Or automating. Automating whether it's on or off. >> Oh regardless of the location, right? >> Right. >> Automation's going to drive quality up. Lower re-work, right? Okay. >> Robots do it a little bit better than us especially if it's repetitive. >> They don't get tired. (laughing) How about some of your favorite kind of joint examples with Infor, any kind of customer wins you can talk about? >> We're actually working together in a lot of spaces, but one of the biggest ones that we are actually talking about a case study here on the floor at Inforum is at Coke Industries, one of it's companies Flint Hills Resources. We're actually in the middle of an EAM implementation with Flint Hills and working together collaboratively with Infor at the client. >> And is that the or bigger picture, you said 20 year relationship formalized much more recently than that. Ultimately what does that deliver for the client? You think at the end of the day? What's the power of that partnership? >> So I think that there's several things, one is that with the experience and history of a Capgemini with 50 years of consulting experience and strategy work. We now specifically bring Infor and Infor's technology into the conversations that it was not a structure before two years ago. So now we specifically have, where does Infor fit in the road map from a software agnostic industry perspective? And then from a just a plain and simple support and keeping your customer's Infor environment running that's additional strength that we have that we didn't have before. >> So you guys are known for being technology agnostic even though you've got an affinity of going to market with a company in this case Infor. How are they doing? What's on the to do list? If you're talking to customers saying, hey this is the sweet spot," here's where some of the items we want them to improve on. What would you say? >> I'd say for, I can at least say tactically with my team we are looking to enhance our solution is around burst and analytics. So that's definitely a best debris tool in the marketplace and so where we can integrate that into more products 'cause it's, Infor acquired it year and a half ago. So we're trying to fold it in with each product and keeping that trajectory. Where again a customer only has one platform to support for-- >> So that's kind of infusing that modern BI into the platforms. Functionally you're kind of happy with it. >> Oh absolutely. >> And it's just a matter of getting the function into-- >> Right. >> The sweet. >> Have it the defacto. >> Right. >> That's where we want to get. >> Right, right. >> But honestly if you just look at the floor out there, you know from our perspective, the great showing and the excitement and just the conversations that we have around Infor. There's been some confusion, I would say, from, without naming names, other competitors of Infor's on what is our cloud and digital road map and then when we look at Infor with cloud native, you know from the ground up, it makes that back to one of the questions you had on, depending on where customers are starting, if you can go from the beginning like Infor has done with some of their products, natively built cloud up. Then those are great conversations and we're seeing more of that in the market right now. >> When we talk to customers, when you talk to the sort of, traditional vendors, they'll say it's a hybrid world, which seems to be. >> It's true. >> When you talk to other cloud guys, it's like, cloud, cloud, cloud. Now even AWS has somewhat capitulated, they've made some announcements to do stuff on-prem. But logically it makes sense that if the data is in some data center location, it's probably going to stay there for a while if it's working and it's a lot of it and you don't necessarily want to move it to the cloud, so do you buy that? Is it a hybrid world? Will it stay a hybrid world? Or do you feel like the pendulum really is swinging into the cloud or not because of IoT, it's more sort of a decentralized world. What do you guys think? >> I think it's a customer choice. Sometimes we have some federally regulated customers that are concerned about data and security and not necessarily there yet in terms of the cloud and we have some customers that are wanting to go 100% cloud so I think it is definitely customer choice and we are there to advise them whether cloud is the right answer and even to help them implement and support them on their journey. So I think we've seen all, every which flavor of cloud, hybrid. >> From your stand point, whatever you want, you're going to-- >> Yeah, I'd say in the past two or three years there's definitely more clients, I would say most now will look at some, when they're doing their TCO and software selection, they absolutely will lead with, hey at least the core part, ERP part, for example, what can I do for cloud with that? 'Cause there's just so much-- >> Considerationalities. >> Yeah the consideration versus three, five years ago no you wouldn't look at that, but I do think there absolutely will be a hybrid foot print going forward. >> Well, if there's an affinity to cloud, presumably Infor has an advantage there, 'cause they're born on the cloud, or at least for that part of the business and other entrenched ERP is not going to be so easy to move to the cloud. In fact that's what you want to do. >> And I think we share the vision with Infor and talking to customers with the cloud first approach. It makes sense to move to the cloud. There is value in the cloud and we can help build that story for them. >> Charles Philips pretty smooth spokesperson, he's a clear thinker, he laid out the strategy. The strategy of, this is my fourth Inforum, I mean, it's grown, but it's consistent, you know, he presents it in a manner that I think is pretty compelling, so that's got to make you feel good, right? You got a leader that's committed, been here for a while. >> Yeah absolutely and one other thing that I really do like about coming to Inforum to see Charles is he actually gets it. If you think of it from CEO of a large software company with hundreds of products, he knows where they actually fit and can go through kind of the road map and the story. So very credible. >> The partnership's a win-win for sure. It certainly sounds like you've painted a very good picture and we appreciate the time. >> Yeah. >> Thanks for being with us and good luck the next couple of days here at the show. Have fun. >> Thank you. >> Appreciate the time. >> Should be, right? (laughing) Back with more live in Washington D.C., you're watching theCUBE. (upbeat music)

>> This is George Gilbert, we're on the ground at Demandbase, the B2B CRM company, based on AI, one of uh, a very special company that's got some really unique technology. We have the privilege to be with Seth Myers today, Senior Data Scientist and resident wizard, and who's going to take us on a journey through some of the technology Demandbase is built on, and some of the technology coming down the road. So Seth, welcome. >> Thank you very much for having me. >> So, we talked earlier with Aman Naimat, Senior VP of Technology, and we talked about some of the functionality in Demandbase, and how it's very flexible, and reactive, and adaptive in helping guide, or react to a customer's journey, through the buying process. Tell us about what that journey might look like, how it's different, and the touchpoints, and the participants, and then how your technology rationalizes that, because we know, old CRM packages were really just lists of contact points. So this is something very different. How's it work? >> Yeah, absolutely, so at the highest level, each customer's going to be different, each customer's going to make decisions and look at different marketing collateral, and respond to different marketing collateral in different ways, you know, as the companies get bigger, and their products they're offering become more sophisticated, that's certainly the case, and also, sales cycles take a long time. You're engaged with an opportunity over many months, and so there's a lot of touchpoints, there's a lot of planning that has to be done, so that actually offers a huge opportunity to be solved with AI, especially in light of recent developments in this thing called reinforcement learning. So reinforcement learning is basically machine learning that can think strategically, they can actually plan ahead in a series of decisions, and it's actually technology behind AlphaGo which is the Google technology that beat the best Go players in the world. And what we basically do is we say, "Okay, if we understand "you're a customer, we understand the company you work at, "we understand the things they've been researching elsewhere "on third party sites, then we can actually start to predict "about content they will be likely to engage with." But more importantly, we can start to predict content they're more likely to engage with next, and after that, and after that, and after that, and so what our technology does is it looks at all possible paths that your potential customer can take, all the different content you could ever suggest to them, all the different routes they will take, and it looks at ones that they're likely to follow, but also ones they're likely to turn them into an opportunity. And so we basically, in the same way Google Maps considers all possible routes to get you from your office to home, we do the same, and we choose the one that's most likely to convert the opportunity, the same way Google chooses the quickest road home. >> Okay, this is really, that's a great example, because people can picture that, but how do you, how do you know what's the best path, is it based on learning from previous journeys from customers? >> Yes. >> And then, if you make a wrong guess, you sort of penalize the engine and say, "Pick the next best, "what you thought was the next best path." >> Absolutely, so the way, the nuts and bolts of how it works is we start working with our clients, and they have all this data of different customers, and how they've engaged with different pieces of content throughout their journey, and so the machine learning model, what it's really doing at any moment in time, given any customer in any stage of the opportunity that they find themselves in, it says, what piece of content are they likely to engage with next, and that's based on historical training data, if you will. And then once we make that decision on a step-by-step basis, then we kind of extrapolate, and we basically say, "Okay, if we showed them this page, or if they engage with "this material, what would that do, what situation would "we find them in at the next step, and then what would "we recommend from there, and then from there, "and then from there," and so it's really kind of learning the right move to make at each time, and then extrapolating that all the way to the opportunity being closed. >> The picture that's in my mind is like, the Deep Blue, I think it was chess, where it would map out all the potential moves. >> Very similar, yeah. >> To the end game. >> Very similar idea. >> So, what about if you're trying to engage with a customer across different channels, and it's not just web content? How is that done? >> Well, that's something that we're very excited about, and that's something that we're currently really starting to devote resources to. Right now, we already have a product live that's focused on web content specifically, but yeah, we're working on kind of a multi-channel type solution, and we're all pretty excited about it. >> Okay so, obviously you can't talk too much about it. Can you tell us what channels that might touch? >> I might have to play my cards a little close to my chest on this one, but I'll just say we're excited. >> Alright. Well I guess that means I'll have to come back. >> Please, please. >> So, um, tell us about the personalized conversations. Is the conversation just another way of saying, this is how we're personalizing the journey? Or is there more to it than that? >> Yeah, it really is about personalizing the journey, right? Like you know, a lot of our clients now have a lot of sophisticated marketing collateral, and a lot of time and energy has gone into developing content that different people find engaging, that kind of positions products towards pain points, and all that stuff, and so really there's so much low-hanging fruit by just organizing and leveraging all of this material, and actually forming the conversation through a series of journeys through that material. >> Okay, so, Aman was telling us earlier that we have so many sort of algorithms, they're all open source, or they're all published, and they're only as good as the data you can apply them to. So, tell us, where do companies, startups, you know, not the Googles, Microsofts, Amazons, where do they get their proprietary information? Is it that you have algorithms that now are so advanced that you can refine raw information into proprietary information that others don't have? >> Really I think it comes down to, our competitive advantage I think is largely in the source of our data, and so, yes, you can build more and more sophisticated algorithms, but again, you're starting with a public data set, you'll be able to derive some insights, but there will always be a path to those datasets for, say, a competitor. For example, we're currently tracking about 700 billion web interactions a year, and then we're also able to attribute those web interactions to companies, meaning the employees at those companies involved in those web interactions, and so that's able to give us an insight that no amount of public data or processing would ever really be able to achieve. >> How do you, Aman started to talk to us about how, like there were DNS, reverse DNS registries. >> Reverse IP lookups, yes. >> Yeah, so how are those, if they're individuals within companies, and then the companies themselves, how do you identify them reliably? >> Right, so reverse IP lookup is, we've been doing this for years now, and so we've kind of developed a multi-source solution, so reverse IP lookups is a big one. Also machine learning, you can look at traffic coming from an IP address, and you can start to make some very informed decisions about what the IP address is actually doing, who they are, and so if you're looking at, at the account level, which is what we're tracking at, there's a lot of information to be gleaned from that kind of information. >> Sort of the way, and this may be a weird-sounding analogy, but the way a virus or some piece of malware has a signature in terms of its behavior, you find signatures in terms of users associated with an IP address. >> And we certainly don't de-anonymize individual users, but if we're looking at things at the account level, then you know, the bigger the data, the more signal you can infer, and so if we're looking at a company-wide usage of an IP address, then you can start to make some very educated guesses as to who that company is, the things that they're researching, what they're in market for, that type of thing. >> And how do you find out, if they're not coming to your site, and they're not coming to one of your customer's sites, how do you find out what they're touching? >> Right, I mean, I can't really go into too much detail, but a lot of it comes from working with publishers, and a lot of this data is just raw, and it's only because we can identify the companies behind these IP addresses, that we're able to actually turn these web interactions into insights about specific companies. >> George: Sort of like how advertisers or publishers would track visitors across many, many sites, by having agreements. >> Yes. Along those lines, yeah. >> Okay. So, tell us a little more about natural language processing, I think where most people have assumed or have become familiar with it is with the B2C capabilities, with the big internet giants, where they're trying to understand all language. You have a more well-scoped problem, tell us how that changes your approach. >> So a lot of really exciting things are happening in natural language processing in general, and the research, and right now in general, it's being measured against this yardstick of, can it understand languages as good as a human can, obviously we're not there yet, but that doesn't necessarily mean you can't derive a lot of meaningful insights from it, and the way we're able to do that is, instead of trying to understand all of human language, let's understand very specific language associated with the things that we're trying to learn. So obviously we're a B2B marketing company, so it's very important to us to understand what companies are investing in other companies, what companies are buying from other companies, what companies are suing other companies, and so if we said, okay, we only want to be able to infer a competitive relationship between two businesses in an actual document, that becomes a much more solvable and manageable problem, as opposed to, let's understand all of human language. And so we actually started off with these kind of open source solutions, with some of these proprietary solutions that we paid for, and they didn't work because their scope was this broad, and so we said, okay, we can do better by just focusing in on the types of insights we're trying to learn, and then work backwards from them. >> So tell us, how much of the algorithms that we would call building blocks for what you're doing, and others, how much of those are all published or open source, and then how much is your secret sauce? Because we talk about data being a key part of the secret sauce, what about the algorithms? >> I mean yeah, you can treat the algorithms as tools, but you know, a bag of tools a product does not make, right? So our secret sauce becomes how we use these tools, how we deploy them, and the datasets we put them again. So as mentioned before, we're not trying to understand all of human language, actually the exact opposite. So we actually have a single machine learning algorithm that all it does is it learns to recognize when Amazon, the company, is being mentioned in a document. So if you see the word Amazon, is it talking about the river, is it talking about the company? So we have a classifier that all it does is it fires whenever Amazon is being mentioned in a document. And that's a much easier problem to solve than understanding, than Siri basically. >> Okay. I still get rather irritated with Siri. So let's talk about, um, broadly this topic that sort of everyone lays claim to as their great higher calling, which is democratizing machine learning and AI, and opening it up to a much greater audience. Help set some context, just the way you did by saying, "Hey, if we narrow the scope of a problem, "it's easier to solve." What are some of the different approaches people are taking to that problem, and what are their sweet spots? >> Right, so the the talk of the data science community, talking machinery right now, is some of the work that's coming out of DeepMind, which is a subsidiary of Google, they just built AlphaGo, which solved the strategy game that we thought we were decades away from actually solving, and their approach of restricting the problem to a game, with well-defined rules, with a limited scope, I think that's how they're able to propel the field forward so significantly. They started off by playing Atari games, then they moved to long term strategy games, and now they're doing video games, like video strategy games, and I think the idea of, again, narrowing the scope to well-defined rules and well-defined limited settings is how they're actually able to advance the field. >> Let me ask just about playing the video games. I can't remember Star... >> Starcraft. >> Starcraft. Would you call that, like, where the video game is a model, and you're training a model against that other model, so it's almost like they're interacting with each other. >> Right, so it really comes down, you can think of it as pulling levers, so you have a very complex machine, and there's certain levers you can pull, and the machine will respond in different ways. If you're trying to, for example, build a robot that can walk amongst a factory and pick out boxes, like how you move each joint, what you look around, all the different things you can see and sense, those are all levers to pull, and that gets very complicated very quickly, but if you narrow it down to, okay, there's certain places on the screen I can click, there's certain things I can do, there's certain inputs I can provide in the video game, you basically limit the number of levers, and then optimizing and learning how to work those levers is a much more scoped and reasonable problem, as opposed to learn everything all at once. >> Okay, that's interesting, now, let me switch gears a little bit. We've done a lot of work at WikiBound about IOT and increasingly edge-based intelligence, because you can't go back to the cloud for your analytics for everything, but one of the things that's becoming apparent is, it's not just the training that might go on in a cloud, but there might be simulations, and then the sort of low-latency response is based on a model that's at the edge. Help elaborate where that applies and how that works. >> Well in general, when you're working with machine learning, in almost every situation, training the model is, that's really the data-intensive process that requires a lot of extensive computation, and that's something that makes sense to have localized in a single location which you can leverage resources and you can optimize it. Then you can say, alright, now that I have this model that understands the problem that's trained, it becomes a much simpler endeavor to basically put that as close to the device as possible. And so that really is how they're able to say, okay, let's take this really complicated billion-parameter neural network that took days and weeks to train, and let's actually derive insights at the level, right at the device level. Recent technology though, like I mentioned deep learning, that in itself, just the actual deploying the technology creates new challenges as well, to the point that actually Google invented a new type of chip to just run... >> The tensor processing. >> Yeah, the TPU. The tensor processing unit, just to handle what is now a machine learning algorithm so sophisticated that even deploying it after it's been trained is still a challenge. >> Is there a difference in the hardware that you need for training vs. inferencing? >> So they initially deployed the TPU just for the sake of inference. In general, the way it actually works is that, when you're building a neural network, there is a type of mathematical operation to do a whole bunch, and it's based on the idea of working with matrices and it's like that, that's still absolutely the case with training as well as inference, where actually, querying the model, but so if you can solve that one mathematical operation, then you can deploy it everywhere. >> Okay. So, one of our CTOs was talking about how, in his view, what's going to happen in the cloud is richer and richer simulations, and as you say, the querying the model, getting an answer in realtime or near realtime, is out on the edge. What exactly is the role of the simulation? Is that just a model that understands time, and not just time, but many multiple parameters that it's playing with? >> Right, so simulations are particularly important in taking us back to reinforcement learning, where you basically have many decisions to make before you actually see some sort of desirable or undesirable outcome, and so, for example, the way AlphaGo trained itself is basically by running simulations of the game being played against itself, and really what that simulations are doing is allowing the artificial intelligence to explore the entire possibilities of all games. >> Sort of like WarGames, if you remember that movie. >> Yes, with uh... >> Matthew Broderick, and it actually showed all the war game scenarios on the screen, and then figured out, you couldn't really win. >> Right, yes, it's a similar idea where they, for example in Go, there's more board configurations than there are atoms in the observable universe, and so the way Deep Blue won chess is basically, more or less explore the vast majority of chess moves, that's really not the same option, you can't really play that same strategy with AlphaGo, and so, this constant simulation is how they explore the meaningful game configurations that it needed to win. >> So in other words, they were scoped down, so the problem space was smaller. >> Right, and in fact, basically one of the reasons, like AlphaGo was really kind of two different artificial intelligences working together, one that decided which solutions to explore, like which possibilities it should pursue more, and which ones not to, to ignore, and then the second piece was, okay, given the certain board configuration, what's the likely outcome? And so those two working in concert, one that narrows and focuses, and one that comes up with the answer, given that focus, is how it was actually able to work so well. >> Okay. Seth, on that note, that was a very, very enlightening 20 minutes. >> Okay. I'm glad to hear that. >> We'll have to come back and get an update from you soon. >> Alright, absolutely. >> This is George Gilbert, I'm with Seth Myers, Senior Data Scientist at Demandbase, a company I expect we'll be hearing a lot more about, and we're on the ground, and we'll be back shortly.

>>so erin you're a technique for a Cloudera, you're a whiz kid from Brown, you have, how many Brown people are engineers here at Cloudera >>as of monday, we have five full timers and two interns at the moment and we're trying to hire more all the time. >>Mhm. So how many interns? >>Uh two interns from Brown this this summer? A few more from other schools? Cool, >>I'm john furry with silicon angle dot com. Silicon angle dot tv. We're here in the cloud era office in my little mini studio hasn't been built out yet, It was studio, we had to break it down for a doctor, ralph kimball, not richard Kimble from uh I called him on twitter but coupon um but uh the data warehouse guru was in here um and you guys are attracting a lot of talent erin so tell us a little bit about, you know, how Claudia is making it happen and what's the big deal here, people smart here, it's mature, it's not the first time around this company, this company has some some senior execs and there's been a lot, a lot of people uh in the market who have been talking about uh you know, a lot of first time entrepreneurs doing their startups and I've been hearing for some folks in in the, in the trenches that there's been a frustration and start ups out there, that there's a lot of first time entrepreneurs and everyone wants to be the next twitter and there's some kind of companies that are straddling failure out there? And and I was having that conversation with someone just today and I said, they said, what's it like Cloudera and I said, uh, this is not the first time crew here in Cloudera. So, uh, share with the folks out there, what you're seeing for Cloudera and the management team. >>Sure. Well, one of the most attractive parts about working Cloudera for me, one of the reasons I, I really came here was have been incredibly experienced management team, Mike Charles, they've all there at the top of this Oregon, they have all done this before they founded startups, Growing startups, old startups and uh, especially in contrast with my, the place where I worked previously. Uh, the amount of experience here is just tremendous. You see them not making mistakes where I'm sure others would. >>And I mean, Mike Olson is veteran. I mean he's been, he's an adviser to start ups. I know he's been in some investors. Amer was obviously PhD candidates bolted out the startup, sold it to yahoo, worked at, yahoo, came back finish his PhD at stanford under Mendel over there in the PhD program over this, we banged in a speech. He came back entrepreneur residents, Excel partners. Now it does Cloudera. Um, when did you join the company and just take us through who you are and when you join Cloudera, I want your background. >>Sure. So I, I joined a little over a year ago is about 30 people at the time. Uh, I came from a small start up of the music online music store in new york city um uh, which doesn't really exist all that much anymore. Um but you know, I I sort of followed my other colleagues from Brown who worked here um was really sold by the management team and also by the tremendous market opportunity that that Hadoop has right now. Uh Cloudera was very much the first commercial player there um which is really a unique experience and I think you've covered this pretty well before. I think we all around here believe that uh the markets only growing. Um and we're going to see the market and the big data market in general get bigger and bigger in the next few years. >>So, so obviously computer science is all the rage and and I'm particularly proud of hangout, we've had conversations in the hallway while you're tweeting about this and that. Um, but you know, silicon angles home is here, we've had, I've had a chance to watch you and the other guys here grow from, you know, from your other office was a san mateo or san Bruno somewhere in there. Like >>uh it was originally in burlingame, then we relocate the headquarters Palo Alto and now we have a satellite up in san Francisco. >>So you guys bolted out. You know, you have a full on blow in san Francisco office. So um there was a big busting at the seams here in Palo Alto people commuting down uh even building their burning man. Uh >>Oh yeah sure >>skits here and they're constructing their their homes here, but burning man, so we're doing that in san Francisco, what's the vibe like in san Francisco, tell us what's going on >>in san Francisco, san Francisco is great. It's, I'm I live in san Francisco as do a lot of us. About half the engineering team works up there now. Um you know we're running out of space there certainly. Um and you're already, oh yeah, oh yeah, we're hiring as fast as we absolutely can. Um so definitely not space to build the burning man huts there like like there is down, down in Palo Alto but it's great up there. >>What are you working on right now for project insurance? The computer science is one of the hot topics we've been covering on silicon angle, taking more of a social angle, social media has uh you know, moves from this pr kind of, you know, check in facebook fan page to hype to kind of a real deal social marketplace where you know data, social data, gestural data, mobile data geo data data is the center of the value proposition. So you live that every day. So talk about your view on the computer science landscape around data and why it's such a big deal. >>Oh sure. Uh I think data is sort of one of those uh fundamental uh things that can be uh mind for value across every industry, there's there's no industry out there that can't benefit from better understanding what their customers are doing, what their competitors are doing etcetera. And that's sort of the the unique value proposition of, you know, stuff like Hadoop. Um truly we we see interest from every sector that exists, which is great as for what the project that I'm specifically working on right now, I primarily work on H. D. F. S, which is the Hadoop distributed file system underlies pretty much all the other um projects in the Hadoop ecosystem. Uh and I'm particularly working with uh other colleagues at Cloudera and at other companies, yahoo and facebook on high availability for H. D. F. S, which has been um in some deployments is a serious concern. Hadoop is primarily a batch processing system, so it's less of a concern than in others. Um but when you start talking about running H base, which needs to be up all the time serving live traffic than having highly available H DFS is uh necessity and we're looking forward to delivering that >>talk about the criticism that H. D. F. S has been having. Um Well, I wouldn't say criticism. I mean, it's been a great, great product that produced the HDs, a core parts of how do you guys been contributing to the standard of Apache, that's no secret to the folks out there, that cloud area leads that effort. Um but there's new companies out there kind of trying a new approach and they're saying they're doing it better, what are they saying in terms and what's really happening? So, you know, there's some argument like, oh, we can do it better. And what's the what, why are they doing it, that was just to make money do a new venture, or is that, what's your opinion on that? Yeah, >>sure. I mean, I think it's natural to to want to go after uh parts of the core Hadoop system and say, you know, Hadoop is a great ecosystem, but what if we just swapped out this part or swapped out that part, couldn't couldn't we get some some really easy gains. Um and you know, sometimes that will be true. I have confidence that that that just will not simply not be true in in the very near future. One of the great benefits about Apache, Hadoop being open source is that we have a huge worldwide network of developers working at some of the best engineering organizations in the world who are all collaborating on this stuff. Um and, you know, I firmly believe that the collaborative open source process produces the best software and that's that's what Hadoop is at its very core. >>What about the arguments are saying that, oh, I need to commercialize it differently for my installed base bolt on a little proprietary extensions? Um That's legitimate argument. TMC might take that approach or um you know, map are I was trying to trying to rewrite uh H. T. F. >>S. To me, is >>it legitimate? I mean is there fighting going on in the standards? Maybe that's a political question you might want to answer. But give me a shot. >>I mean the Hadoop uh isn't there's no open standard for Hadoop. You can't say like this is uh this is like do compatible or anything like that. But you know what you can say is like this is Apache Hadoop. Uh And so in that sense there's no there's no fighting to be had there. Um Yeah, >>so yeah. Who um struggling as a company. But you know, there's a strong head Duke D. N. A. At yahoo, certainly, I talked with the the founder of the startup. Horton works just announced today that they have a new board member. He's the guy who's the Ceo of Horton works and now on bluster, I'm sorry, cluster announced they have um rob from benchmark on the board. Uh He's the Ceo of Horton works and and one of my not criticisms but points about Horton was this guy's an engineer, never run a company before. He's no Mike Olson. Okay, so you know, Michaelson has a long experience. So this guy comes into running and he's obviously in in open source, is that good for Yahoo and open sources. He they say they're going to continue to invest in Hadoop? They clearly are are still using a lot of Hadoop certainly. Um how is that changing Apache, is that causing more um consolidation, is that causing more energy? What's your view on the whole Horton works? Think >>um you know, yahoo is uh has been and will continue to be a huge contributor. Hadoop, they uh I can't say for sure, but I feel pretty confident that they have more data under management under Hadoop than anyone else in the world and there's no question in my mind that they'll continue to invest huge amounts of both key way effort and engineering effort and uh all of the things that Hadoop needs to to advance. Um I'm sure that Horton works will continue to work very closely with with yahoo. Um And you know, we're excited to see um more and more contributors to to Hadoop um both from Horton works and from yahoo proper. >>Cool, Well, I just want to clarify for the folks out there who don't understand what this whole yahoo thing is, It was not a spin out, these were key Hadoop core guys who left the company to form a startup of which yahoo financed with benchmark capital. So, yahoo is clearly and told me and reaffirm that with me that they are clearly investing more in Hadoop internally as well. So there's more people inside, yahoo that work on Hadoop than they are in the entire Horton's work company. So that's very clear. So just to clear that up out there. Um erin. so you're you're a young gun, right? You're a young whiz like Todd madam on here, explain to the folks out there um a little bit older maybe guys in their thirties or C IOS a lot of people are doing, you know, they're kicking the tires on big data, they're hearing about real time analytics, they're hearing about benefits have never heard before. Uh Dave a lot and I on the cube talk about, you know, the transformations that are going on, you're seeing AMC getting into big data, everyone's transforming at the enterprise level and service provider. What explains the folks why Hadoop is so important. Why is that? Do if not the fastest or one of the fastest growing projects in Apache ever? Sure. Even faster than the web server project, which is one of the better, >>better bigger ones. >>Why is the dupes and explain to them what it is? Well, you know, >>it's been it's pretty well covered that there's been an explosion of data that more data is produced every every year over and over. We talk about exabytes which is a quantity of data that is so large that pretty much no one can really theoretically comprehend it. Um and more and more uh organizations want to store and process and learn from, you know, get insights from that data um in addition to just the explosion of data um you know that there is simply more data, organizations are less willing to discard data. One of the beauties of Hadoop is truly that it's so very inexpensive per terabyte to store data that you don't have to think up front about what you want to store, what you want to discard, store it all and figure out later what is the most useful bits we call that sort of schema on read. Um as opposed to, you know, figuring out the schema a priority. Um and that is a very powerful shift in dynamics of data storage in general. And I think that's very attractive to all sorts of organizations. >>Your, I'll see a Brown graduate and you have some interns from Brown to Brown um, Premier computer science program almost as good as when I went to school at Northeastern University. >>Um >>you know, the unsung heroes of computer science only kidding Brown's great program, but you know, cutting edge computer science areas known as obviously leading in a lot of the computer science areas do in general is known that you gotta be pretty savvy to be either masters level PhD to kind of play in this area? Not a lot of adoption, what I call the grassroots developers. What's your vision and how do you see the computer science, younger generation, even younger than you kind of growing up into this because those tools aren't yet developed. You still got to be, you're pretty strong from a computer science perspective and also explained to the folks who aren't necessarily at the browns of the world or getting into computer science, what about, what is that this revolution about and where is it going? What are some of the things you see happening around the corner that that might not be obvious. >>Sure there's a few questions there. Um part of it is how do people coming out of college get into this thing, It's not uh taught all that much in school, How do how do you sort of make the leap from uh the standard computer science curriculum into this sort of thing? And um you know, part of it is that really we're seeing more and more schools offering distributed computing classes or they have grids available um to to do this stuff there there is some research coming out of Brown actually and lots of other schools about Hadoop proper in the behavior of Hadoop under failure scenarios, that sort of stuff, which is very interesting. Google uh actually has classes that they teach, I believe in conjunction with the University of Washington um where they teach undergraduates and your master's level, graduate students about mass produced and distributed computing and they actually use Hadoop to do it because it is the architecture of Hadoop is modeled after um >>uh >>google's internal infrastructure. Um So you know that that's that's one way we're seeing more and more people who are just coming out of college who have distributed systems uh knowledge like this? Um Another question? the other part of the question you asked is how does um how does the ordinary developer get into this stuff? And the answer is we're working hard, you know, we and others in the hindu community are working hard on making it, making her do just much easier to consume. We released, you cover this fair bit, the ECM Express project that lets you install Hadoop with just minimal effort as close to 11 click as possible. Um and there's lots of um sort of layers built on top of Hadoop to make it more easily consumed by developers Hive uh sort of sequel like interface on top of mass produce. And Pig has its own DSL for programming against mass produce. Um so you don't have to write heart, you don't have to write straight map produced code, anything like that. Uh and it's getting easier for operators every day. >>Well, I mean, evolution was, I mean, you guys actually working on that cloud era. Um what about what about some of the abstractions? You're seeing those big the Rage is, you know, look back a year ago VM World coming up and uh little plugs looking angle dot tv will be broadcasting live and at VM World. Um you know, he has been on the Q XV m where um Spring Source was a big announcement that they made. Um, Haruka brought by Salesforce Cloud Software frameworks are big, what does that look like and how does it relate to do and the ecosystem around Hadoop where, you know, the rage is the software frameworks and networks kind of collide and you got the you got the kind of the intersection of, you know, software frameworks and networks obviously, you know, in the big players, we talk about E M C. And these guys, it's clear that they realize that software is going to be their key differentiator. So it's got to get to a framework stand, what is Hadoop and Apache talking about this kind of uh, evolution for for Hadoop. >>Sure. Well, you know, I think we're seeing very much the commoditization of hardware. Um, you just can't buy bigger and bigger computers anymore. They just don't exist. So you're going to need something that can take a lot of little computers and make it look like one big computer. And that's what Hadoop is especially good at. Um we talk about scaling out instead of scaling up, you can just buy more relatively inexpensive computers. Uh and that's great. And sort of the beauty of Hadoop, um, is that it will grow linearly as your data set as your um, your your scale, your traffic, whatever grows. Um and you don't have to have this exponential price increase of buying bigger and bigger computers, You can just buy more. Um and that that's sort of the beauty of it is a software framework that if you write against it. Um you don't have to think about the scaling anymore. It will do that for you. >>Okay. The question for you, it's gonna kind of a weird question but try to tackle it. You're at a party having a few cocktails, having a few beers with your buddies and your buddies who works at a big enterprise says man we've got all this legacy structured data systems, I need to implement some big data strategy, all this stuff. What do I do? >>Sure, sure. Um Not the question I thought you were going to ask me that you >>were a g rated program here. >>Okay. I thought you were gonna ask me, how do I explain what I do to you know people that we'll get to that next. Okay. Um Yeah, I mean I would say that the first thing to do is to implement a start, start small, implement a proof of concept, get a subset of the data that you would like to analyze, put it, put Hadoop on a few machines, four or five, something like that and start writing some hive queries, start writing some some pig scripts and I think you'll you know pretty quickly and easily see the value that you can get out of it and you can do so with the knowledge that when you do want to operate over your entire data set, you will absolutely be able to trivially scale to that size. >>Okay. So now the question that I want to ask is that you're at a party and I want to say, what do you >>do? You usually tell people in my hedge fund manager? No but seriously um I I tell people I work on distributed supercomputers. Software for distributed supercomputers and that people have some idea what distributed means and supercomputers and they figure that out. >>So final question for I know you gotta go get back to programming uh some code here. Um what's the future of Hadoop in the sense of from a developer standpoint? I was having a conversation with a developer who's a big data jockey and talking about Miss kelly gets anything and get his hands on G. O. Data, text data because the data data junkie and he says I just don't know what to build. Um What are some of the enabling apps that you may see out there and or you have just conceiving just brainstorming out there, what's possible with with data, can you envision the next five years, what are you gonna see evolve and what some of the coolest things you've seen that might that are happening right now. >>Sure. Sure. I mean I think you're going to see uh just the front ends to these things getting just easier and easier and easier to interact with and at some point you won't even know that you're interacting with a Hadoop cluster that will be the engine underneath the hood but you know, you'll you'll be uh from your perspective you'll be driving a Ferrari and by that I mean you know, standard B. I tool, standard sequel query language. Um we'll all be implemented on top of this stuff and you know from that perspective you could implement, you know, really anything you want. Um We're seeing a lot of great work coming out of just identifying trends amongst masses of data that you know, if you tried to analyze it with any other tool, you'd either have to distill it down so far that you would you would question your results or that you could only run the very simplest sort of queries over um and not really get those like powerful deep insights, those sort of correlative insights um that we're seeing people do. So I think you'll see, you'll continue to see uh great recommendations systems coming out of this stuff. You'll see um root cause analysis, you'll see great work coming out of the advertising industry um to you know to really say which ad was responsible for this purchase. Was it really the last ad they clicked on or was it the ad they saw five weeks ago they put the thought in mind that sort of correlative analysis is being empowered by big data systems like a dupe. >>Well I'm bullish on big data, I think people I think it's gonna be even bigger than I think you're gonna have some kids come out of college and say I could use big data to create a differentiation and build an airline based on one differentiation. These are cool new ways and, and uh, data we've never seen before. So Aaron, uh, thanks for coming >>on the issue >>um, your inside Palo Alto Studio and we're going to.

>>Hey, welcome back everyone. Day three of eight of us Reinvent 2022. I'm John Farmer with Dave Volante, co-host the q Dave. 10 years for us, the leader in high tech coverage is our slogan. Now 10 years of reinvent day. We've been to every single one except with the original, which we would've come to if Amazon actually marketed the event, but they didn't. It's more of a customer event. This is day three. Is the machine learning ai keynote sws up there. A lot of announcements. We're gonna break this down. We got, we got Andy Thra here, vice President, prince Constellation Research. Andy, great to see you've been on the cube before one of our analysts bringing the, bringing the, the analysis, commentary to the keynote. This is your wheelhouse. Ai. What do you think about Swami up there? I mean, he's awesome. We love him. Big fan Oh yeah. Of of the Cuban we're fans of him, but he got 13 announcements. >>A lot. A lot, >>A lot. >>So, well some of them are, first of all, thanks for having me here and I'm glad to have both of you on the same show attacking me. I'm just kidding. But some of the announcement really sort of like a game changer announcements and some of them are like, meh, you know, just to plug in the holes what they have and a lot of golf claps. Yeah. Meeting today. And you could have also noticed that by, when he was making the announcements, you know, the, the, the clapping volume difference, you could say, which is better, right? But some of the announcements are, are really, really good. You know, particularly we talked about, one of that was Microsoft took that out of, you know, having the open AI in there, doing the large language models. And then they were going after that, you know, having the transformer available to them. And Amazon was a little bit weak in the area, so they couldn't, they don't have a large language model. So, you know, they, they are taking a different route saying that, you know what, I'll help you train the large language model by yourself, customized models. So I can provide the necessary instance. I can provide the instant volume, memory, the whole thing. Yeah. So you can train the model by yourself without depending on them kind >>Of thing. So Dave and Andy, I wanna get your thoughts cuz first of all, we've been following Amazon's deep bench on the, on the infrastructure pass. They've been doing a lot of machine learning and ai, a lot of data. It just seems that the sentiment is that there's other competitors doing a good job too. Like Google, Dave. And I've heard folks in the hallway, even here, ex Amazonians saying, Hey, they're train their models on Google than they bring up the SageMaker cuz it's better interface. So you got, Google's making a play for being that data cloud. Microsoft's obviously putting in a, a great kind of package to kind of make it turnkey. How do they really stand versus the competition guys? >>Good question. So they, you know, each have their own uniqueness and the we variation that take it to the field, right? So for example, if you were to look at it, Microsoft is known for as industry or later things that they are been going after, you know, industry verticals and whatnot. So that's one of the things I looked here, you know, they, they had this omic announcement, particularly towards that healthcare genomics space. That's a huge space for hpz related AIML applications. And they have put a lot of things in together in here in the SageMaker and in the, in their models saying that, you know, how do you, how do you use this transmit to do things like that? Like for example, drug discovery, for genomics analysis, for cancer treatment, the whole, right? That's a few volumes of data do. So they're going in that healthcare area. Google has taken a different route. I mean they want to make everything simple. All I have to do is I gotta call an api, give what I need and then get it done. But Amazon wants to go at a much deeper level saying that, you know what? I wanna provide everything you need. You can customize the whole thing for what you need. >>So to me, the big picture here is, and and Swami references, Hey, we are a data company. We started, he talked about books and how that informed them as to, you know, what books to place front and center. Here's the, here's the big picture. In my view, companies need to put data at the core of their business and they haven't, they've generally put humans at the core of their business and data. And now machine learning are at the, at the outside and the periphery. Amazon, Google, Microsoft, Facebook have put data at their core. So the question is how do incumbent companies, and you mentioned some Toyota Capital One, Bristol Myers Squibb, I don't know, are those data companies, you know, we'll see, but the challenge is most companies don't have the resources as you well know, Andy, to actually implement what Google and Facebook and others have. >>So how are they gonna do that? Well, they're gonna buy it, right? So are they gonna build it with tools that's kind of like you said the Amazon approach or are they gonna buy it from Microsoft and Google, I pulled some ETR data to say, okay, who are the top companies that are showing up in terms of spending? Who's spending with whom? AWS number one, Microsoft number two, Google number three, data bricks. Number four, just in terms of, you know, presence. And then it falls down DataRobot, Anaconda data icu, Oracle popped up actually cuz they're embedding a lot of AI into their products and, and of course IBM and then a lot of smaller companies. But do companies generally customers have the resources to do what it takes to implement AI into applications and into workflows? >>So a couple of things on that. One is when it comes to, I mean it's, it's no surprise that the, the top three or the hyperscalers, because they all want to bring their business to them to run the specific workloads on the next biggest workload. As you was saying, his keynote are two things. One is the A AIML workloads and the other one is the, the heavy unstructured workloads that he was talking about. 80%, 90% of the data that's coming off is unstructured. So how do you analyze that? Such as the geospatial data. He was talking about the volumes of data you need to analyze the, the neural deep neural net drug you ought to use, only hyperscale can do it, right? So that's no wonder all of them on top for the data, one of the things they announced, which not many people paid attention, there was a zero eight L that that they talked about. >>What that does is a little bit of a game changing moment in a sense that you don't have to, for example, if you were to train the data, data, if the data is distributed everywhere, if you have to bring them all together to integrate it, to do that, it's a lot of work to doing the dl. So by taking Amazon, Aurora, and then Rich combine them as zero or no ETL and then have Apaches Apaches Spark applications run on top of analytical applications, ML workloads. That's huge. So you don't have to move around the data, use the data where it is, >>I, I think you said it, they're basically filling holes, right? Yeah. They created this, you know, suite of tools, let's call it. You might say it's a mess. It's not a mess because it's, they're really powerful but they're not well integrated and now they're starting to take the seams as I say. >>Well yeah, it's a great point. And I would double down and say, look it, I think that boring is good. You know, we had that phase in Kubernetes hype cycle where it got boring and that was kind of like, boring is good. Boring means we're getting better, we're invisible. That's infrastructure that's in the weeds, that's in between the toes details. It's the stuff that, you know, people we have to get done. So, you know, you look at their 40 new data sources with data Wrangler 50, new app flow connectors, Redshift Auto Cog, this is boring. Good important shit Dave. The governance, you gotta get it and the governance is gonna be key. So, so to me, this may not jump off the page. Adam's keynote also felt a little bit of, we gotta get these gaps done in a good way. So I think that's a very positive sign. >>Now going back to the bigger picture, I think the real question is can there be another independent cloud data cloud? And that's the, to me, what I try to get at my story and you're breaking analysis kind of hit a home run on this, is there's interesting opportunity for an independent data cloud. Meaning something that isn't aws, that isn't, Google isn't one of the big three that could sit in. And so let me give you an example. I had a conversation last night with a bunch of ex Amazonian engineering teams that left the conversation was interesting, Dave. They were like talking, well data bricks and Snowflake are basically batch, okay, not transactional. And you look at Aerospike, I can see their booth here. Transactional data bases are hot right now. Streaming data is different. Confluence different than data bricks. Is data bricks good at hosting? >>No, Amazon's better. So you start to see these kinds of questions come up where, you know, data bricks is great, but maybe not good for this, that and the other thing. So you start to see the formation of swim lanes or visibility into where people might sit in the ecosystem, but what came out was transactional. Yep. And batch the relationship there and streaming real time and versus you know, the transactional data. So you're starting to see these new things emerge. Andy, what do you, what's your take on this? You're following this closely. This seems to be the alpha nerd conversation and it all points to who's gonna have the best data cloud, say data, super clouds, I call it. What's your take? >>Yes, data cloud is important as well. But also the computational that goes on top of it too, right? Because when, when the data is like unstructured data, it's that much of a huge data, it's going to be hard to do that with a low model, you know, compute power. But going back to your data point, the training of the AIML models required the batch data, right? That's when you need all the, the historical data to train your models. And then after that, when you do inference of it, that's where you need the streaming real time data that's available to you too. You can make an inference. One of the things, what, what they also announced, which is somewhat interesting, is you saw that they have like 700 different instances geared towards every single workload. And there are some of them very specifically run on the Amazon's new chip. The, the inference in two and theran tr one chips that basically not only has a specific instances but also is run on a high powered chip. And then if you have that data to support that, both the training as well as towards the inference, the efficiency, again, those numbers have to be proven. They claim that it could be anywhere between 40 to 60% faster. >>Well, so a couple things. You're definitely right. I mean Snowflake started out as a data warehouse that was simpler and it's not architected, you know, in and it's first wave to do real time inference, which is not now how, how could they, the other second point is snowflake's two or three years ahead when it comes to governance, data sharing. I mean, Amazon's doing what always does. It's copying, you know, it's customer driven. Cuz they probably walk into an account and they say, Hey look, what's Snowflake's doing for us? This stuff's kicking ass. And they go, oh, that's a good idea, let's do that too. You saw that with separating compute from storage, which is their tiering. You saw it today with extending data, sharing Redshift, data sharing. So how does Snowflake and data bricks approach this? They deal with ecosystem. They bring in ecosystem partners, they bring in open source tooling and that's how they compete. I think there's unquestionably an opportunity for a data cloud. >>Yeah, I think, I think the super cloud conversation and then, you know, sky Cloud with Berkeley Paper and other folks talking about this kind of pre, multi-cloud era. I mean that's what I would call us right now. We are, we're kind of in the pre era of multi-cloud, which by the way is not even yet defined. I think people use that term, Dave, to say, you know, some sort of magical thing that's happening. Yeah. People have multiple clouds. They got, they, they end up by default, not by design as Dell likes to say. Right? And they gotta deal with it. So it's more of they're inheriting multiple cloud environments. It's not necessarily what they want in the situation. So to me that is a big, big issue. >>Yeah, I mean, again, going back to your snowflake and data breaks announcements, they're a data company. So they, that's how they made their mark in the market saying that, you know, I do all those things, therefore you have, I had to have your data because it's a seamless data. And, and Amazon is catching up with that with a lot of that announcements they made, how far it's gonna get traction, you know, to change when I to say, >>Yeah, I mean to me, to me there's no doubt about Dave. I think, I think what Swamee is doing, if Amazon can get corner the market on out of the box ML and AI capabilities so that people can make it easier, that's gonna be the end of the day tell sign can they fill in the gaps. Again, boring is good competition. I don't know mean, mean I'm not following the competition. Andy, this is a real question mark for me. I don't know where they stand. Are they more comprehensive? Are they more deeper? Are they have deeper services? I mean, obviously shows to all the, the different, you know, capabilities. Where, where, where does Amazon stand? What's the process? >>So what, particularly when it comes to the models. So they're going at, at a different angle that, you know, I will help you create the models we talked about the zero and the whole data. We'll get the data sources in, we'll create the model. We'll move the, the whole model. We are talking about the ML ops teams here, right? And they have the whole functionality that, that they built ind over the year. So essentially they want to become the platform that I, when you come in, I'm the only platform you would use from the model training to deployment to inference, to model versioning to management, the old s and that's angle they're trying to take. So it's, it's a one source platform. >>What about this idea of technical debt? Adrian Carro was on yesterday. John, I know you talked to him as well. He said, look, Amazon's Legos, you wanna buy a toy for Christmas, you can go out and buy a toy or do you wanna build a, to, if you buy a toy in a couple years, you could break and what are you gonna do? You're gonna throw it out. But if you, if you, if part of your Lego needs to be extended, you extend it. So, you know, George Gilbert was saying, well, there's a lot of technical debt. Adrian was countering that. Does Amazon have technical debt or is that Lego blocks analogy the right one? >>Well, I talked to him about the debt and one of the things we talked about was what do you optimize for E two APIs or Kubernetes APIs? It depends on what team you're on. If you're on the runtime gene, you're gonna optimize for Kubernetes, but E two is the resources you want to use. So I think the idea of the 15 years of technical debt, I, I don't believe that. I think the APIs are still hardened. The issue that he brings up that I think is relevant is it's an end situation, not an or. You can have the bag of Legos, which is the primitives and build a durable application platform, monitor it, customize it, work with it, build it. It's harder, but the outcome is durability and sustainability. Building a toy, having a toy with those Legos glued together for you, you can get the play with, but it'll break over time. Then you gotta replace it. So there's gonna be a toy business and there's gonna be a Legos business. Make your own. >>So who, who are the toys in ai? >>Well, out of >>The box and who's outta Legos? >>The, so you asking about what what toys Amazon building >>Or, yeah, I mean Amazon clearly is Lego blocks. >>If people gonna have out the box, >>What about Google? What about Microsoft? Are they basically more, more building toys, more solutions? >>So Google is more of, you know, building solutions angle like, you know, I give you an API kind of thing. But, but if it comes to vertical industry solutions, Microsoft is, is is ahead, right? Because they have, they have had years of indu industry experience. I mean there are other smaller cloud are trying to do that too. IBM being an example, but you know, the, now they are starting to go after the specific industry use cases. They think that through, for example, you know the medical one we talked about, right? So they want to build the, the health lake, security health lake that they're trying to build, which will HIPPA and it'll provide all the, the European regulations, the whole line yard, and it'll help you, you know, personalize things as you need as well. For example, you know, if you go for a certain treatment, it could analyze you based on your genome profile saying that, you know, the treatment for this particular person has to be individualized this way, but doing that requires a anomalous power, right? So if you do applications like that, you could bring in a lot of the, whether healthcare, finance or what have you, and then easy for them to use. >>What's the biggest mistake customers make when it comes to machine intelligence, ai, machine learning, >>So many things, right? I could start out with even the, the model. Basically when you build a model, you, you should be able to figure out how long that model is effective. Because as good as creating a model and, and going to the business and doing things the right way, there are people that they leave the model much longer than it's needed. It's hurting your business more than it is, you know, it could be things like that. Or you are, you are not building a responsibly or later things. You are, you are having a bias and you model and are so many issues. I, I don't know if I can pinpoint one, but there are many, many issues. Responsible ai, ethical ai. All >>Right, well, we'll leave it there. You're watching the cube, the leader in high tech coverage here at J three at reinvent. I'm Jeff, Dave Ante. Andy joining us here for the critical analysis and breaking down the commentary. We'll be right back with more coverage after this short break.

>>Good afternoon everyone, and thank you so much for joining us. My name is Savannah Peterson, joined by my co-host Paul for the afternoon. Very excited. Oh, Savannah. Hello. I'm, I'm pumped for this. This is our first bit together. Exactly. >>It's gonna be fun. Yes. We have a great guest to kick off with. >>We absolutely do. We're at Supercomputing 2022 today, and very excited to talk to our next guest. We're gonna be talking about data at scale and data that really matters to us joining us. Kelly Gayer, thank you so much for being here and you are with tech. Tell everyone what TAC is. >>Tech is the Texas Advanced Computing Center at the University of Texas at Austin. And thank you so much for having me here. >>It is wonderful to have you. Your smile's contagious. And one of the themes that's come up a lot with all of our guests, and we just talked about it, is how good it is to be back in person, how good it is to be around our hardware, community tech. You did some very interesting research during the pandemic. Can you tell us about that? >>I can. I did. So when we realized sort of mid-March, we realized that, that this was really not normal times and the pandemic was statement. Yes. That pandemic was really gonna touch everyone. I think a lot of us at the center and me personally, we dropped everything to plug in and that's what we do. So UT's tagline is what starts here changes the world and tax tagline is powering discoveries that change the world. So we're all about impact, but I plugged in with the research group there at UT Austin, Dr. Lauren Myers, who's an epidemiologist, and just we figured out how to plug in and compute so that we could predict the spread of, of Covid 19. >>And you did that through the use of mobility data, cell phone signals. Tell us more about what exactly you were choreographing. >>Yeah, so that was really interesting. Safe graph during the pandemic made their mobility data. Typically it was used for marketing purposes to know who was going into Walmart. The offenses >>For advertising. >>Absolutely, yeah. They made all of their mobility data available for free to people who were doing research and plugging in trying to understand Covid. 19, I picked that data up and we used it as a proxy for human behavior. So we knew we had some idea, we got weekly mobility updates, but it was really mobility all day long, you know, anonymized. I didn't know who they were by cell phones across the US by census block group or zip code if we wanted to look at it that way. And we could see how people were moving around. We knew what their neighbor, their home neighborhoods were. We knew how they were traveling or not traveling. We knew where people were congregating, and we could get some idea of, of how people were behaving. Were they really, were they really locking down or were they moving in their neighborhoods or were they going outside of their neighborhoods? >>What a, what a fascinating window into our pandemic lives. So now that you were able to do this for this pandemic, as we look forward, what have you learned? How quickly could we forecast? What's the prognosis? >>Yeah, so we, we learned a tremendous amount. I think during the pandemic we were reacting, we were really trying. It was a, it was an interesting time as a scientist, we were reacting to things almost as if the earth was moving underneath us every single day. So it was something new every day. And I've told people since I've, I haven't, I haven't worked that hard since I was a graduate student. So it was really daylight to dark 24 7 for a long period of time because it was so important. And we knew, we, we knew we were, we were being a part of history and affecting something that was gonna make a difference for a really long time. And, and I think what we've learned is that indeed there is a lot of data being collected that we can use for good. We can really understand if we get organized and we get set up, we can use this data as a means of perhaps predicting our next pandemic or our next outbreak of whatever. It is almost like using it as a canary in the coal mine. There's a lot in human behavior we can use, given >>All the politicization of, of this last pandemic, knowing what we know now, making us better prepared in theory for the next one. How confident are you that at least in the US we will respond proactively and, and effectively when the next one comes around? >>Yeah, I mean, that's a, that's a great question and, and I certainly understand why you ask. I think in my experience as a scientist, certainly at tech, the more transparent you are with what you do and the more you explain things. Again, during the pandemic, things were shifting so rapidly we were reacting and doing the best that we could. And I think one thing we did right was we admitted where we felt uncertain. And that's important. You have to really be transparent to the general public. I, I don't know how well people are gonna react. I think if we have time to prepare, to communicate and always be really transparent about it. I think those are three factors that go into really increasing people's trust. >>I think you nailed it. And, and especially during times of chaos and disaster, you don't know who to trust or what to believe. And it sounds like, you know, providing a transparent source of truth is, is so critical. How do you protect the sensitive data that you're working with? I know it's a top priority for you and the team. >>It is, it is. And we, we've adopted the medical mantra, do no harm. So we have, we feel a great responsibility there. There's, you know, two things that you have to really keep in mind when you've got sensitive data. One is the physical protection of it. And so that's, that's governed by rule, federal rules, hipaa, ferpa, whatever, whatever kind of data that you have. So we certainly focus on the physical protection of it, but there's also sort of the ethical protection of it. What, what is the quote? There's lies, damn lies and statistics. >>Yes. Twain. >>Yeah. So you, you really have to be responsible with what you're doing with the data, how you're portraying the results. And again, I think it comes back to transparency is is basically if people are gonna reproduce what I did, I have to be really transparent with what I did. >>I, yeah, I think that's super important. And one of the themes with, with HPC that we've been talking about a lot too is, you know, do people trust ai? Do they trust all the data that's going into these systems? And I love that you just talked about the storytelling aspect of that, because there is a duty, it's not, you can cut data kind of however you want. I mean, I come from marketing background and we can massage it to, to do whatever we want. So in addition to being the deputy director at Tech, you are also the DEI officer. And diversity I know is important to you probably both as an individual, but also in the work that you're doing. Talk to us about that. >>Yeah, I mean, I, I very passionate about diversity, equity and inclusion in a sense of belongingness. I think that's one of the key aspects of it. Core >>Of community too. >>I got a computer science degree back in the eighties. I was akin to a unicorn in a, in an engineering computer science department. And, but I was really lucky in a couple of respects. I had a, I had a father that was into science that told me I could do anything I, I wanted to set my mind to do. So that was my whole life, was really having that support system. >>He was cheers to dad. >>Yeah. Oh yeah. And my mom as well, actually, you know, they were educators. I grew up, you know, in that respect, very, very privileged, but it was still really hard to make it. And I couldn't have told you back in that time why I made it and, and others didn't, why they dropped out. But I made it a mission probably back, gosh, maybe 10, 15 years ago, that I was really gonna do all that I could to change the needle. And it turns out that there are a number of things that you can do grassroots. There are certainly best practices. There are rules and there are things that you really, you know, best practices to follow to make people feel more included in an organization, to feel like they belong it, shared mission. But there are also clever things that you can do with programming to really engage students, to meet people and students where they are interested and where they are engaged. And I think that's what, that's what we've done over, you know, the course of our programming over the course of about maybe since 2016. We have built a lot of programming ATAC that really focuses on that as well, because I'm determined the needle is gonna change before it's all said and done. It just really has to. >>So what, what progress have you made and what goals have you set in this area? >>Yeah, that, that's a great question. So, you know, at first I was a little bit reluctant to set concrete goals because I really didn't know what we could accomplish. I really wasn't sure what grassroots efforts was gonna be able to, you're >>So honest, you can tell how transparent you are with the data as well. That's >>Great. Yeah, I mean, if I really, most of the successful work that I've done is both a scientist and in the education and outreach space is really trust relationships. If I break that trust, I'm done. I'm no longer effective. So yeah, I am really transparent about it. But, but what we did was, you know, the first thing we did was we counted, you know, to the extent that we could, what does the current picture look like? Let's be honest about it. Start where we are. Yep. It was not a pretty picture. I mean, we knew that anecdotally it was not gonna be a great picture, but we put it out there and we leaned into it. We said, this is what it is. We, you know, I hesitated to say we're gonna look 10% better next year because I'm, I'm gonna be honest, I don't always know we're gonna do our best. >>The things that I think we did really well was that we stopped to take time to talk and find out what people were interested in. It's almost like being present and listening. My grandmother had a saying, you have two errors in one mouth for a reason, just respect the ratio. Oh, I love that. Yeah. And I think it's just been building relationships, building trust, really focusing on making a difference, making it a priority. And I think now what we're doing is we've been successful in pockets of people in the center and we are, we are getting everybody on board. There's, there's something everyone can do, >>But the problem you're addressing doesn't begin in college. It begins much, much, that's right. And there's been a lot of talk about STEM education, particularly for girls, how they're pushed out of the system early on. Also for, for people of color. Do you see meaningful progress being made there now after years of, of lip service? >>I do. I do. But it is, again, grassroots. We do have a, a, a researcher who was a former teacher at the center, Carol Fletcher, who is doing research and for CS for all we know that the workforce, so if you work from the current workforce, her projected workforce backwards, we know that digital skills of some kind are gonna be needed. We also know we have a, a, a shortage. There's debate on how large that shortage is, but about roughly about 1 million unmet jobs was projected in 2020. It hasn't gotten a lot better. We can work that problem backwards. So what we do there is a little, like a scatter shot approach. We know that people come in all forms, all shapes, all sizes. They get interested for all different kinds of reasons. We expanded our set of pathways so that we can get them where they can get on to the path all the way back K through 12, that's Carol's work. Rosie Gomez at the center is doing sort of the undergraduate space. We've got Don Hunter that does it, middle school, high school space. So we are working all parts of the problem. I am pretty passionate about what we consider opportunity youth people who never had the opportunity to go to college. Is there a way that we can skill them and get, get them engaged in some aspect and perhaps get them into this workforce. >>I love that you're starting off so young. So give us an example of one of those programs. What are you talking to kindergartners about when it comes to CS education? >>You know, I mean, gaming. Yes. Right. It's what everybody can wrap their head around. So most kids have had some sort of gaming device. You talk in the context, in the context of something they understand. I'm not gonna talk to them about high performance computing. It, it would go right over their heads. And I think, yeah, you know, I, I'll go back to something that you said Paul, about, you know, girls were pushed out. I don't know that girls are being pushed out. I think girls aren't interested and things that are being presented and I think they, I >>Think you're generous. >>Yeah. I mean, I was a young girl and I don't know why I stayed. Well, I do know why I stayed with it because I had a father that saw something in me and I had people at critical points in my life that saw something in me that I didn't see. But I think if we ch, if we change the way we teach it, maybe in your words they don't get pushed out or they, or they won't lose interest. There's, there's some sort of computing in everything we do. Well, >>Absolutely. There's also the bro culture, which begins at a very early >>Age. Yeah, that's a different problem. Yeah. That's just having boys in the classroom. Absolutely. You got >>It. That's a whole nother case. >>That's a whole other thing. >>Last question for you, when we are sitting here, well actually I've got, it's two parter, let's put it that way. Is there a tool or something you wish you could flick a magic wand that would make your job easier? Where you, you know, is there, can you identify the, the linchpin in the DEI challenge? Or is it all still prototyping and iterating to figure out the best fit? >>Yeah, that is a, that's a wonderful question. I can tell you what I get frustrated with is that, that >>Counts >>Is that I, I feel like a lot of people don't fully understand the level of effort and engagement it takes to do something meaningful. The >>Commitment to a program, >>The commitment to a program. Totally agree. It's, there is no one and done. No. And in fact, if I do that, I will lose them forever. They'll be, they will, they will be lost in the space forever. Rather. The engagement is really sort of time intensive. It's relationship intensive, but there's a lot of follow up too. And the, the amount of funding that goes into this space really is not, it, it, it's not equal to the amount of time and effort that it really takes. And I think, you know, I think what you work in this space, you realize that what you gain is, is really more of, it's, it really feels good to make a difference in somebody's life, but it's really hard to do on a shoer budget. So if I could kind of wave a magic wand, yes, I would increase understanding. I would get people to understand that it's all of our responsibility. Yes, everybody is needed to make the difference and I would increase the funding that goes to the programs. >>I think that's awesome, Kelly, thank you for that. You all heard that. More funding for diversity, equity, and inclusion. Please Paul, thank you for a fantastic interview, Kelly. Hopefully everyone is now inspired to check out tac perhaps become a, a Longhorn, hook 'em and, and come deal with some of the most important data that we have going through our systems and predicting the future of our pandemics. Ladies and gentlemen, thank you for joining us online. We are here in Dallas, Texas at Supercomputing. My name is Savannah Peterson and I look forward to seeing you for our next segment.

>>We're back with the cube at Falcon 2022, Dave ante and Dave Nicholson. We're at the aria. We do of course, a lot of events in Las Vegas. It's the, it's the place to do events. Dave, I think is my sixth or seventh time here this year. At least. I don't know. I lose track. Jeff Swain is here. He's the vice president of global programs store and tech alliances at CrowdStrike. Jeff. Good to see you again. We saw each other at reinvent in July in Boston. >>Yes. Yeah, it was great to see you again, Dave, thank >>Very much. And we talked about making this happen so thrilled to be here at, at, at CrowdStrike Falcon. We're gonna talk today about the CrowdStrike XDR Alliance partners. First of all, what's XDR >>Well, I hope you were paying attention to George's George's keynote this morning. I guess. You know, the one thing we know is that if you ask 10, five people, what XDR is you'll get 10 answers. >>I like this answer a holistic approach to endpoint security. I, that was, >>It was good. Simple. >>That was a good one at black hat. So, but tell us about the XDR Alliance partners program. Give us the update there. >>Yeah, so I mean, we spoke about it reinforced, you know, the XDR program is really predicated on having a robust ecosystem of partners to help us share that telemetry across all of the different parts of our customers' environment. So we've done a lot of work over the last few weeks and trying to bolster that environment specifically, putting a lot of focus on firewall. You'll see that Cisco and fortunate have both joined the XD XDR Alliance. So we're working on that right now. A lot of customer demand for firewall data into the telemetry set. You know, obviously it's a very rich data environment. There's a lot of logs on firewalls. And so it drives a lot of, of, of information that we can, we can leverage. So we're continuing to grow that. And what we're doing is building out different content packs that support different use cases. So firewall is one CAS B is another emails another and we're building, building out the, the partner set right across the board. So it's, it's, it's been a, a great set of >>Activity. So it's it's partners that have data. Yep. There's probably some, you know, Joe Tuchi year old boss used to say that that overlap is better than gaps. So there's sometimes there's competition, but that's from a customer standpoint, overlap is, is better than gaps. So as gonna mention Cisco forte and there are a number of others, they've got data. Yes. And they're gonna pump it into your system, our platform, and you've got the, your platform. You've got the ability to ingest. You've got the cloud native architecture, you've got the analytics and you've got the near real time analysis capability. Right, right. >>Augmented by people as well, which is a really important part of our value proposition. You know, we, it's not just relying purely on AI, but we have a human, a human aspect to it as well to make sure we're getting extremely accurate responses. And then there's the final phase is the response phase. So being able to take action on a CASB, for example, when we have a known bad actor operating in the cloud is a really important, easy action for our customer to take. That's highly valuable. You're >>Talking about your threat hunting capability, right? >>So it's threat hunting and our Intel capability as well. We use all of that information as well as the telemetry to make sure we're making good, actionable >>Decisions, Intel being machine intelligence or, or human and machine >>Human and human and machine intelligence that we have. We have a whole business that's out there gathering Intel. I believe you think to Adam Myers who runs that business. And you know, that Intel is critical to making good decisions for our customers. >>So the X and XDR is extended, correct. Extending to things like firewalls. That's pretty obvious in the security space. Are there some less obvious data sources that you look to extend to at some point? >>Yeah, I think we're gonna continually go with where the customer demand is. And firewalls is one of the first and is very significant. Other one, you'll see that we're announcing support for Microsoft 365 as well as part of this, this announcement, but then we'll still grow out into the other areas. NDR is, you know, a specific area where we've already got a number of partners in that, in that space. And, and we'll grow that as we go. I think one of the really exciting additional elements is the, the OCS F announcement that we made at at, at, at, at reinforced, which also is a shared data scheme across a number of vendors as well. So talking to Mike's point, Microsoft ST's point this morning in his keynote, it's really about the industry getting together to do better job for our customers. And XDR is the platform to do that. And crowd strikes it way of doing it is the only really true, visible way for a customer to get their hands on all that information, make the decision, see the good from the bad and take the action. So I feel like we're really well placed to help our customers in >>That space. Well, Kevin mania referenced this too today, basically saying the industry's doing a better job of collaborations. I mean, sometimes I'm skeptical because we've certainly seen people try to, you know, commercialize private information, private reports. Yeah. But, but, but you're talking about, you know, some of your quasi competitors cooperatives, you know, actually partnering with you now. So that's a, that's a good indicator. Yeah. I want to step back a little bit, talk about the macro, the big conversation on wall street. Everybody wants to talk about the macro of course, for obvious reasons, we just published our breaking analysis, talking about you guys potentially being a generational company and sort of digging into that a little bit. We've seen, you know, cyber investments hold up a little bit better, both in terms of customer spending and of course the stock market better than tech broadly. Yeah. So in that case it would, it would suggest that cyber investments are somewhat non-discretionary. So, but that is my question are cyber investments non-discretionary if, if so, how, >>You know, I think George George calls that out directly in our analyst reports as well that, you know, we believe that cyber is a non-discretionary spend, but I, I actually think it's more than that. I think in this current macro or economic environment where CIOs and CSOs are being asked to sweat their assets for significantly longer period of time, that actually creates vulnerabilities because they have older kit, that's running for a longer period that they normally, you know, round out or churn out of their environment. They're not getting the investment to replace those laptops. They're not getting the, I placement to replace those servers. We have to sweat them for a little bit longer, longer, which means they need to be on top of the security posture of those devices. So that means that we need the best possible telemetry that we can get to protect those in the best possible way. So I actually think not only is it makes it non-discretionary, it actually increases the, the business case for, for, for taking on a, a cyber project. >>And I buy that. I buy that the business case is better potentially for cyber business case. And cyber is about, about risk reduction, right? It's about, it's about reducing expected loss. I, I, I, I, but the same time CISOs don't have an open wallet. They have to compete with other P and L managers. I also think the advantage for CrowdStrike I'm, I'm getting deeper into the architecture and beginning to understand the power of a lightweight agent that can do handle. I think you're up to 22 modules now, correct? Yes. I've got questions on how you keep that lightweight, but, but nonetheless, if you can consolidate the point tools, which is, you know, one of the biggest challenges that, that SecOps teams face that strengthens the ROI as well. >>Absolutely. And if you look at what George was saying this morning in the keynote, the combination of being able to provide tools, not only to the SecOps team, but the it ops team as well, being able to give the it ops team visibility on how many assets they have. I mean, these simple, these are simple questions that we should be able to answer. But often when we ask, you know, an operations leader, can you answer it? It sometimes it's hard for them. We actually have a lot of that information. So we are able to bring that into the platform. We're able to show them, we're able to show them where the assets are, where the vulnerabilities are against those assets and help it ops do a better job as well as SecOps. So the, the strength, the case strengthens, as you said, the CSO can also be talking to the it ops budget. >>The edge is getting more real. We're certainly hearing a lot about it now we're seeing a lot more and you kind of got the, the near edge, like the home Depot and the lows, you know, stores. Yeah. Okay. That I, I can get a better handle on, okay. How do I secure that? I've got some standards, but that's the far edge. It's, it's the, the OT yes. Piece of it. That's sort of the brave new world. What are you seeing there? How do you protect those far flowing estates? >>I think this gets back to the question of what's what's new or what's coming and where do we see the, the next set of workloads that we have to tackle? You know, when we came along first instance, we were really doing a lot of the on-prem on-prem and, and, and known cloud infrastructure suites. Then we started really tackling the broader crowd market with tools and technology to give visibility and control of the overall cloud environment. OT represents that next big addressable market for us, because there are so many questions around devices where they are, how old they are, what they're running. So visibility into the OT network is extremely, extremely important. And, you know, the, the wall that has existed again between the CISO and the OT environments coming down, we're seeing that's closer, closer alignment between the security on both those worlds. So the announcement that we've made around extending our Falcon discover product, to be able to receive and understand device information from the OT network and bring it into the same console as the, the it and the OT in the same console to give one cohesive picture of, of visibility of all of our devices is a major step forward for our customers and for, for the industry as well. >>And we see that being, being able to get the visibility will then lead us to a place of being able to build our AI models, build our response frameworks. So then we can go to a full EDR and then beyond that, there's, you know, all the other things that CrowdStrike do so well, but this is the first step to really the first step on control is visibility. And >>The OT guys are engineers. So they're obviously conscious of this stuff. It's, it's more it's again, you're extending that culture, isn't >>It? Yeah, yeah, yeah. Now when you're looking at threats, great, you want to do things to protect against those threats, but how much, how much of CrowdStrike's time is spent thinking about the friction that's involved in transactions? If I wanna go to the grocery store, think of me as an end point. If I wanna go to the grocery store, if I had to drive through three DUI checkpoints or car safety inspections. Yeah. Every time I went to the grocery store, I wouldn't be happy as an end point as an end user in this whole thing. Ideally, we'd be able just to be authenticated and then not have to worry about anything moving forward. Do you see that as your role, reducing friction 1%, >>That's again, one of the core tenants of, of, of why George founded the company. I mean, he tells the story of sitting on an airplane and seeing an executive who was also on the airplane, trying to boot their machine up and try and get an email out before the plane took off and watching the scanning happen, you know, old school virus scanning happening on the laptop and, and that executive not making it because, and he is like in this day and age, how can we be holding people back with that much friction in their day to day life? So that's one of the, again, founding principles of what we do at CrowdStrike was the security itself needs to support business growth, support, user growth, and actually get out of the way of how people do things. And we've seen progression along that lines. I think the zero trust work that we're doing right now really helps with that as well. >>Our integrations into other companies that play within the zero trust space makes that frictionless experience for the user, because yeah, we, we, we want to be there. We want to know everything that's happening, but we don't wanna see where we always want control points, but that's the value of the telemetry we take. We're taking all the data so we can see everything. And then we pick what we want to review rather than having to do the, the checkpoint approach of stop here. Now, let me see your credentials. Stop here. Let me see your credentials because we have a full field of, of knowledge and information on what the device is doing and what the user is doing. We're able to then do the trust with verify style approach. >>So coming back to the, to the edge in IOT, you know, bringing that zero trust concept to the, to the edge you've got, you've got it. And OT. Okay. So that's a new constituency, but you're consolidating that view. Your job gets harder. Doesn't it? So, so, so talk about how you resolve that. Do do the, do the concepts that you apply to traditional it endpoints apply at the edge. >>So first things we have to do is gain the visibility. And, and so the way in which we're doing that is effectively drawing information out from the OT environment at, by, by having a collector that's sitting there and bringing that into our console, which then will give us the ability to run our AI models and our other, you know, indications of attack or our indicators of misconfiguration into the model. So we can see whether something's good or bad whilst we're doing that. Obviously we're also working on building specific senses that will then sit in OT devices down, you know, one layer down from rather being collected and pulled and brought into the platform, being collected at the individual sensor level when we have that completed. And that requires a whole different ecosystem for us, it means that we have to engage with organizations like Rockwell and Siemens and Schneider, because they're the people who own the equipment, right? Yeah. And we have to certify with them to make sure that when we put technology onto their equipment, we're not going to cause any kind of critical failure that, you know, that could have genuine real world physical disastrous consequences. So we have to be super careful with how we build that, which we're we're in the process of >>Doing are the IOA signatures indicator as a tax. So I don't have to throw a dollar in the jar. Are the IOA signatures substantially similar at, at the edge, or >>I think we learn as we go, you know, first we have to gain the information and understand what good and bad looks like, what the kind of behaviors are there. But what we will see is that, you know, as someone's trying to, there's an actor, you know, making an attack, you know, will be able to see how they're affecting each of those endpoints individually, whether they're trying to take some form of control, whether they're switching them on and off in the edge and the far edge, it's a little bit more binary in terms of the kind of function of the device. It is the valve open or is the valve closed? It's is the production line running or is the production not line running, not running. So we need to be able to see that it's more about protecting the outcomes there as well. But again, you know, it's about first, we have to get the information. That's what this product will help us do, get it into the platform, get our teams over the top of it, learn more about what's going on there and then be able to take action. >>But the key point is the architecture will scale. And that's where the cloud native things comes >>Into. Yeah, it'll, it'll it'll scale. But to your, to your point about the lack of investment and infrastructure means older stuff means potentially wider gaps, bigger security holes, more opportunity for the security sector. Yep. I buy that. That makes sense. I think if it's a valid argument, when you, when you, when you know, we, we loosely talk about internet of things, edge, a lot of those things on the edge, there's probably a trillion dollars worth of a hundred year old garbage, and I'm only slightly exaggerating on the trillion and the a hundred years old, a lot of those critical devices that need to be sensed that are controlling our, our, our, our electrical grid. For example, a lot of those things need to be updated. So, so as you're pushing into that frontier, are you, you know, are, are you extending out developer kits and APIs to those people as they're developing those new things? Well, because some of the old stuff will never work. >>And that's what we're we're seeing is that there is a movement within the industrial control side of things to actually start, you know, doing this. Some, some simple things like removing the air gap from certain systems because you, now we can build a system around it. That's trustable and supportable. So now we can get access there over, over and over a network over the internet to, to, to kind of control a valve set that's down a pipeline or something like that. So there is, there is, there is willingness within the ecosystem, the, the IOT provider ecosystem to give us access to some of those, those controls, which, which wasn't there, which has led to some of some of these issues. Are we gonna be able to get to all of them? No, we're gonna have to make decisions based on customer demand, based on where the big, the big rock lie. And, and so we will continue to do that based on customer feedback on again, on what we see >>And the legacy air gaps in the OT worlds were by design for security reasons, or just sort of >>Mostly because there was no way to, to do before. Right. So it was, was like black >>Connectivity is >>So, so, so it was, people felt more comfortable sending an engineer route to the field truck roll. Yeah, yeah, yeah. To do it rather than expensive, rather. And, and exactly that, again, going back to our macro economic situation, you know, it's a very expensive way of managing and maintaining your fleet if you have to send someone to it every time. So there is a lot of there's, there's a lot of customer demand for change, and we're engaging in that change. And we want, we see a huge opportunity there >>Coming back to the X XDR Alliance, cuz that's kind of where we started. Where do you wanna see that go? What's your vision for that? >>So the Alliance itself has been fundamental in terms of now where we go with the overall platform. We are always constantly looking for customer feedback on where we go next on what additional elements to add that the Alliance members have been this fantastic time and effort in terms of engaging with us so that we can build in responses to their platforms, into, you know, into, into what we do. And they're seeing the value of it. I, I feel that over the next, you know, over the next two year period, we're gonna see those, our XDR Alliance and other XDR alliances growing out to get to each other and they will they'll touch each other. We will have to do it like the OSF project at AWS. And as that occurs, we're gonna be able to focus on customer outcomes, which is, you know, again, if you listen to George, you listen to Mike protecting the customers, the mission of CrowdStrike. So I think that's core to that, to, to that story. What we will see now is it's a great vehicle for us to give a structured approach to partnership. So we'll continue to invest in that. We've, we've got, we've got a pipeline of literally hundreds of, of partners who want to join. We've just gotta do that in a way that's consumable for us and consumable for the customer. >>Jeff Swain. Thanks so much for coming back in the cube. It's great to have you. Yeah. Thanks guys. Thank you. Okay. And thank you for watching Dave Nicholson and Dave ante. We'll be back right after this short break. You're watching the cube from Falcon 22 in Las Vegas, right back.

>> We're back at the ARIA Las Vegas. We're covering CrowdStrike's Fal.Con 22. First one since 2019. Dave Vellante and Dave Nicholson on theCUBE. Adam Meyers is here, he is the Senior Vice President of Intelligence at CrowdStrike. Adam, thanks for coming to theCUBE. >> Thanks for having me. >> Interesting times, isn't it? You're very welcome. Senior Vice President of Intelligence, tell us what your role is. >> So I run all of our intelligence offerings. All of our analysts, we have a couple hundred analysts that work at CrowdStrike tracking threat actors. There's 185 threat actors that we track today. We're constantly adding more of them and it requires us to really have that visibility and understand how they operate so that we can inform our other products: our XDR, our Cloud Workload Protections and really integrate all of this around the threat actor. >> So it's that threat hunting capability that CrowdStrike has. That's what you're sort of... >> Well, so think of it this way. When we launched the company 11 years ago yesterday, what we wanted to do was to tell customers, to tell people that, well, you don't have a malware problem, you have an adversary problem. There are humans that are out there conducting these attacks, and if you know who they are what they're up to, how they operate then you're better positioned to defend against them. And so that's really at the core, what CrowdStrike started with and all of our products are powered by intelligence. All of our services are our OverWatch and our Falcon complete, all powered by intelligence because we want to know who the threat actors are and what they're doing so we can stop them. >> So for instance like you can stop known malware. A lot of companies can stop known malware, but you also can stop unknown malware. And I infer that the intelligence is part of that equation, is that right? >> Absolutely. That that's the outcome. That's the output of the intelligence but I could also tell you who these threat actors are, where they're operating out of, show you pictures of some of them, that's the threat intel. We are tracking down to the individual persona in many cases, these various threats whether they be Chinese nation state, Russian threat actors, Iran, North Korea, we track as I said, quite a few of these threats. And over time, we develop a really robust deep knowledge about who they are and how they operate. >> Okay. And we're going to get into some of that, the big four and cyber. But before we do, I want to ask you about the eCrime index stats, the ECX you guys call it a little side joke for all your nerds out there. Maybe you could explain that Adam >> Assembly humor. >> Yeah right, right. So, but, what is that index? You guys, how often do you publish it? What are you learning from that? >> Yeah, so it was modeled off of the Dow Jones industrial average. So if you look at the Dow Jones it's a composite index that was started in the late 1800s. And they took a couple of different companies that were the industrial component of the economy back then, right. Textiles and railroads and coal and steel and things like that. And they use that to approximate the overall health of the economy. So if you take these different stocks together, swizzle 'em together, and figure out some sort of number you could say, look, it's up. The economy's doing good. It's down, not doing so good. So after World War II, everybody was exuberant and positive about the end of the war. The DGI goes up, the oil crisis in the seventies goes down, COVID hits goes up, sorry, goes down. And then everybody realizes that they can use Amazon still and they can still get the things they need goes back up with the eCrime index. We took that approach to say what is the health of the underground economy? When you read about any of these ransomware attacks or data extortion attacks there are criminal groups that are working together in order to get things spammed out or to buy credentials and things like that. And so what the eCrime index does is it takes 24 different observables, right? The price of a ransom, the number of ransom attacks, the fluctuation in cryptocurrency, how much stolen material is being sold for on the underground. And we're constantly computing this number to understand is the eCrime ecosystem healthy? Is it thriving or is it under pressure? And that lets us understand what's going on in the world and kind of contextualize it. Give an example, Microsoft on patch Tuesday releases 56 vulnerabilities. 11 of them are critical. Well guess what? After hack Tuesday. So after patch Tuesday is hack Wednesday. And so all of those 11 vulnerabilities are exploitable. And now you have threat actors that have a whole new array of weapons that they can deploy and bring to bear against their victims after that patch Tuesday. So that's hack Wednesday. Conversely we'll get something like the colonial pipeline. Colonial pipeline attack May of 21, I think it was, comes out and all of the various underground forums where these ransomware operators are doing their business. They freak out because they don't want law enforcement. President Biden is talking about them and he's putting pressure on them. They don't want this ransomware component of what they're doing to bring law enforcement, bring heat on them. So they deplatform them. They kick 'em off. And when they do that, the ransomware stops being as much of a factor at that point in time. And the eCrime index goes down. So we can look at holidays, and right around Thanksgiving, which is coming up pretty soon, it's going to go up because there's so much online commerce with cyber Monday and such, right? You're going to see this increase in online activity; eCrime actors want to take advantage of that. When Christmas comes, they take vacation too; they're going to spend time with their families, so it goes back down and it stays down till around the end of the Russian Orthodox Christmas, which you can probably extrapolate why that is. And then it goes back up. So as it's fluctuating, it gives us the ability to really just start tracking what that economy looks like. >> Realtime indicator of that crypto. >> I mean, you talked about, talked about hack Wednesday, and before that you mentioned, you know, the big four, and I think you said 185 threat actors that you're tracking, is 180, is number 185 on that list? Somebody living in their basement in their mom's basement or are the resources necessary to get on that list? Such that it's like, no, no, no, no. this is very, very organized, large groups of people. Hollywood would have you believe that it's guy with a laptop, hack Wednesday, (Dave Nicholson mimics keyboard clacking noises) and everything done. >> Right. >> Are there individuals who are doing things like that or are these typically very well organized? >> That's a great question. And I think it's an important one to ask and it's both it tends to be more, the bigger groups. There are some one-off ones where it's one or two people. Sometimes they get big. Sometimes they get small. One of the big challenges. Have you heard of ransomware as a service? >> Of course. Oh my God. Any knucklehead can be a ransomwarist. >> Exactly. So we don't track those knuckleheads as much unless they get onto our radar somehow, they're conducting a lot of operations against our customers or something like that. But what we do track is that ransomware as a service platform because the affiliates, the people that are using it they come, they go and, you know, it could be they're only there for a period of time. Sometimes they move between different ransomware services, right? They'll use the one that's most useful for them that that week or that month, they're getting the best rate because it's rev sharing. They get a percentage that platform gets percentage of the ransom. So, you know, they negotiate a better deal. They might move to a different ransomware platform. So that's really hard to track. And it's also, you know, I think more important for us to understand the platform and the technology that is being used than the individual that's doing it. >> Yeah. Makes sense. Alright, let's talk about the big four. China, Iran, North Korea, and Russia. Tell us about, you know, how you monitor these folks. Are there different signatures for each? Can you actually tell, you know based on the hack who's behind it? >> So yeah, it starts off, you know motivation is a huge factor. China conducts espionage, they do it for diplomatic purposes. They do it for military and political purposes. And they do it for economic espionage. All of these things map to known policies that they put out, the Five Year Plan, the Made in China 2025, the Belt and Road Initiative, it's all part of their efforts to become a regional and ultimately a global hegemon. >> They're not stealing nickels and dimes. >> No they're stealing intellectual property. They're stealing trade secrets. They're stealing negotiation points. When there's, you know a high speed rail or something like that. And they use a set of tools and they have a set of behaviors and they have a set of infrastructure and a set of targets that as we look at all of these things together we can derive who they are by motivation and the longer we observe them, the more data we get, the more we can get that attribution. I could tell you that there's X number of Chinese threat groups that we track under Panda, right? And they're associated with the Ministry of State Security. There's a whole other set. That's too associated with the People's Liberation Army Strategic Support Force. So, I mean, these are big operations. They're intelligence agencies that are operating out of China. Iran has a different set of targets. They have a different set of motives. They go after North American and Israeli businesses right now that's kind of their main operation. And they're doing something called hack and lock and leak. With a lock and leak, what they're doing is they're deploying ransomware. They don't care about getting a ransom payment. They're just doing it to disrupt the target. And then they're leaking information that they steal during that operation that brings embarrassment. It brings compliance, regulatory, legal impact for that particular entity. So it's disruptive >> The chaos creators that's.. >> Well, you know I think they're trying to create a they're trying to really impact the legitimacy of some of these targets and the trust that their customers and their partners and people have in them. And that is psychological warfare in a certain way. And it, you know is really part of their broader initiative. Look at some of the other things that they've done they've hacked into like the missile defense system in Israel, and they've turned on the sirens, right? Those are all things that they're doing for a specific purpose, and that's not China, right? Like as you start to look at this stuff, you can start to really understand what they're up to. Russia very much been busy targeting NATO and NATO countries and Ukraine. Obviously the conflict that started in February has been a huge focus for these threat actors. And then as we look at North Korea, totally different. They're doing, there was a major crypto attack today. They're going after these crypto platforms, they're going after DeFi platforms. They're going after all of this stuff that most people don't even understand and they're stealing the crypto currency and they're using it for revenue generation. These nuclear weapons don't pay for themselves, their research and development don't pay for themselves. And so they're using that cyber operation to either steal money or steal intelligence. >> They need the cash. Yeah. >> Yeah. And they also do economic targeting because Kim Jong Un had said back in 2016 that they need to improve the lives of North Koreans. They have this national economic development strategy. And that means that they need, you know, I think only 30% of North Korea has access to reliable power. So having access to clean energy sources and renewable energy sources, that's important to keep the people happy and stop them from rising up against the regime. So that's the type of economic espionage that they're conducting. >> Well, those are the big four. If there were big five or six, I would presume US and some Western European countries would be on there. Do you track, I mean, where United States obviously has you know, people that are capable of this we're out doing our thing, and- >> So I think- >> That defense or offense, where do we sit in this matrix? >> Well, I think the big five would probably include eCrime. We also track India, Pakistan. We track actors out of Columbia, out of Turkey, out of Syria. So there's a whole, you know this problem is getting worse over time. It's proliferating. And I think COVID was also, you know a driver there because so many of these countries couldn't move human assets around because everything was getting locked down. As machine learning and artificial intelligence and all of this makes its way into the cameras at border and transfer points, it's hard to get a human asset through there. And so cyber is a very attractive, cheap and deniable form of espionage and gives them operational capabilities, not, you know and to your question about US and other kind of five I friendly type countries we have not seen them targeting our customers. So we focus on the threats that target our customers. >> Right. >> And so, you know, if we were to find them at a customer environment sure. But you know, when you look at some of the public reporting that's out there, the malware that's associated with them is focused on, you know, real bad people, and it's, it's physically like crypted to their hard drive. So unless you have sensor on, you know, an Iranian or some other laptop that might be target or something like that. >> Well, like Stuxnet did. >> Yeah. >> Right so. >> You won't see it. Right. See, so yeah. >> Well Symantec saw it but way back when right? Back in the day. >> Well, I mean, if you want to go down that route I think it actually came from a company in the region that was doing the IR and they were working with Symantec. >> Oh, okay. So, okay. So it was a local >> Yeah. I think Crisis, I think was the company that first identified it. And then they worked with Symantec. >> It Was, they found it, I guess, a logic controller. I forget what it was. >> It was a long time ago, so I might not have that completely right. >> But it was a seminal moment in the industry. >> Oh. And it was a seminal moment for Iran because you know, that I think caused them to get into cyber operations. Right. When they realized that something like that could happen that bolstered, you know there was a lot of underground hacking forums in Iran. And, you know, after Stuxnet, we started seeing that those hackers were dropping their hacker names and they were starting businesses. They were starting to try to go after government contracts. And they were starting to build training offensive programs, things like that because, you know they realized that this is an opportunity there. >> Yeah. We were talking earlier about this with Shawn and, you know, in the nuclear war, you know the Cold War days, you had the mutually assured destruction. It's not as black and white in the cyber world. Right. Cause as, as Robert Gates told me, you know a few years ago, we have a lot more to lose. So we have to be somewhat, as the United States, careful as to how much of an offensive posture we take. >> Well here's a secret. So I have a background on political science. So mutually assured destruction, I think is a deterrent strategy where you have two kind of two, two entities that like they will destroy each other if they so they're disinclined to go down that route. >> Right. >> With cyber I really don't like that mutually assured destruction >> That doesn't fit right. >> I think it's deterrents by denial. Right? So raising the cost, if they were to conduct a cyber operation, raising that cost that they don't want to do it, they don't want to incur the impact of that. Right. And think about this in terms of a lot of people are asking about would China invade Taiwan. And so as you look at the cost that that would have on the Chinese military, the POA, the POA Navy et cetera, you know, that's that deterrents by denial, trying to, trying to make the costs so high that they don't want to do it. And I think that's a better fit for cyber to try to figure out how can we raise the cost to the adversary if they operate against our customers against our enterprises and that they'll go someplace else and do something else. >> Well, that's a retaliatory strike, isn't it? I mean, is that what you're saying? >> No, definitely not. >> It's more of reducing their return on investment essentially. >> Yeah. >> And incenting them- disincening them to do X and sending them off somewhere else. >> Right. And threat actors, whether they be criminals or nation states, you know, Bruce Lee had this great quote that was "be like water", right? Like take the path of least resistance, like water will. Threat actors do that too. So, I mean, unless you're super high value target that they absolutely have to get into by any means necessary, then if you become too hard of a target, they're going to move on to somebody that's a little easier. >> Makes sense. Awesome. Really appreciate your, I could, we'd love to have you back. >> Anytime. >> Go deeper. Adam Myers. We're here at Fal.Con 22, Dave Vellante, Dave Nicholson. We'll be right back right after this short break. (bouncy music plays)

>>We're back with the cube at Falcon 2022, Dave ante and Dave Nicholson. We're at the aria. We do obvious of course, a lot of events in Las Vegas. It's the, it's the place to do events. Dave, I think is my sixth or seventh time here this year. At least. I don't know. I lose track. Jeff Swayne is here. He's the vice president of global programs store and tech alliances at CrowdStrike. Jeff. Good to see again. We saw each other at reinvent in July in Boston. >>Yes. Have it's great to see you again, Dave. Thank you very >>Much. And we talked about making this happen, so it's thrilled to be here at, at, at CrowdStrike Falcon. We're gonna talk today about the CrowdStrike XDR Alliance partners. First of all, what's XDR >>Well, I hope you were paying attention to George's George's keynote this morning. I guess. You know, the one thing we know is that if you ask 10, five people, what XDR is you'll get 10 answers. >>I like this answer a holistic approach to endpoint security. I, that was a, >>It was good. Simple. That >>Was a good one at black hat. So, but tell us about the XDR Alliance partners program. Give us the update there. >>Yeah, so I mean, we spoke about it reinforced, you know, the XDR program is really predicated on having a robust ecosystem of partners to help us share that telemetry across all of the different parts of our customers' environment. So we've done a lot of work over the last few weeks and trying to bolster that environment, specifically, putting a, a lot of focus on firewall. You'll see that Cisco and fortunate have both joined the XD XDR Alliance. So we're working on that right now. A lot of customer demand for firewall data into the telemetry set. You know, obviously it's a very rich data environment. There's a lot of logs on firewalls. And so it drives a lot of, of, of information that we can, we can leverage. So we're continuing to grow that. And what we're doing is building out different content packs that support different use cases. So firewall is one CAS B is another emails another and we're building, building out the, the partner set right across the board. So it's, it's, it's been a, a great set of >>Activity. So it's it's partners that have data. Yep. There's probably some, you know, Joe, Tuchi your old boss used to say that that overlap is better than gaps. So there's sometimes there's competition, but that's from a customer standpoint, overlap is, is better than gaps. So you gonna mention Cisco forte and there are a number of others. They've got data. Yes. And they're gonna pump it into your system, our platform, and you've got the, your platform. You've got the ability to ingest. You've got the cloud native architecture, you've got the analytics and you've got the near real time analysis capability, right. >>Augmented by people as well, which is a really important part of our value proposition. You know, we, it's not just relying purely on AI, but we have a human, a human aspect to it as well to make sure we're getting extremely accurate responses. And then there's the final phase is the response phase. So being able to take action on a CASB, for example, when we have a known bad actor operating in the cloud is a really important, easy action for our customer to take. That's highly valuable. You're >>Talking about your threat hunting capability, right? >>So threat hunting and our Intel capability as well. We use all of that information as well as the telemetry to make sure we're making good, actionable >>Decisions, Intel being machine intelligence or, or human in >>Machine human and human and machine intelligence that we have. We have a whole business that's out there gathering Intel. I believe you're thinking to Adam Myers who runs that business. And you know, that Intel is critical to making good decisions for our customers. >>So the X and XDR is extended, correct. Extending to things like firewalls. That's pretty obvious in the security space. Are there some less obvious data sources that you look to extend to at some point? >>Yeah, I think we're gonna continually go with where the customer demand is. Firewalls is one of the first and email is very significant. Other one, you'll see that we're announcing support for Microsoft 365 as well as part of this, this announcement, but then we'll still grow out into the other areas. NDR is, you know, a specific area where we've already got a number of partners in that, in that space. And, and we'll grow that as we go. I think one of the really exciting additional elements is the, the OCS F announcement that we made at at, at, at, at reinforced, which also is a shared data scheme across a number of vendors as well. So talking to Mike's point Microsoft's point this morning in his keynote, it's really about the industry getting together to do better job for our customers. And XDR is the platform to do that. And crowd strikes it way of doing it is the only really true, visible way for a customer to get their hands on all that information, make the decision, see the good from the bad and take the action. So I feel like we're really well placed to help our customers in >>That space. Well, Kevin, Mandy referenced this too today, basically saying the industry's doing a better job of collaboration. I mean, sometimes I'm skeptical because we've certainly seen people try to, you know, commercialize private information, private reports. Yeah. But, but, but you're talking about, you know, some of your quasi competitors cooperatives, you know, actually partnering with you now. So that's a, that's a good indicator. Yeah. I want to step back a little bit, talk about the macro, the big conversation on wall street. Everybody wants to talk about the macro of course, for obvious reasons, we just published our breaking analysis, talking about you guys potentially being a generational company and sort of digging into that a little bit. We've seen, you know, cyber investments hold up a little bit better, both in terms of customer spending and of course the stock market better than tech broadly. Yeah. So in that case it would, it would suggest that cyber investments are somewhat non-discretionary. So, but that's is my question are cyber investments non-discretionary if so, how, >>You know, I think George George calls that out directly in our analyst reports as well that, you know, we believe that cyber is a non-discretionary spend, but I, I actually think it's more than that. I think in this current macro of economic environment where CIOs and CSOs are being asked to sweat their assets for a significantly longer period of time, that actually creates vulnerabilities because they have older kit, that's running for a longer period that they normally, you know, round out or churn out of their environment. They're not getting the investment to replace those laptops. They're not getting the investment to replace those servers. We have to sweat them for a little bit longer, longer, which means they need to be on top of the security posture of those devices. So that means that we need the best possible telemetry that we can get to protect those in the best possible way. So I actually think not only is it makes it non-discretionary, it actually increases the, the business case for, for, for taking on a, a cyber project. >>And I buy that. I buy that the business case is better potentially for cyber business case. And cyber is about, about risk reduction, right? It's about, it's about reducing expected loss. I, I, I, I, but the same time CISOs don't have an open wallet. They have to compete with other P and L managers. I also think the advantage for CrowdStrike I'm, I'm getting deeper into the architecture and beginning to understand the power of a lightweight agent that can do handle. I think you're up to 22 modules now, correct? Yes. I've got questions on how you keep that lightweight, but, but nonetheless, if you can consolidate the point tools, which is, you know, one of the biggest challenges that, that SecOps teams face that strengthens the ROI as well. >>Absolutely. And if you look at what George was saying this morning in the keynote, the combination of being able to provide tools, not only to the SecOps team, but the it ops team as well, being able to give the it ops team visibility on how many assets they have. I mean, these simple, these are simple questions that we should be able to answer. But often when we ask, you know, an operations leader, can you answer it? It sometimes it's hard for them. We actually have a lot of that information. So we are able to bring that into the platform. We're able to show them, we're able to show them where the assets are, where the vulnerabilities are against those assets and help it ops do a better job as well as SecOps. So the, the strength, the case strengths, as you said, the CSO can also be talking to the it ops budget. >>The edge is getting more real. We're certainly hearing a lot about it. Now we're seeing a lot more and you kind of got the, the near edge. It's like the home Depot and the lows, you know, stores okay. That I, I can get a better handle on, okay. How do I secure that? I've got some standards, but that's the far edge. It's, it's the, the OT yes. Piece of it. That's sort of the brave new world. What are you seeing there? How do you protect those far flung estates? >>I think this gets back to the question of what's what's new what's coming and where do we see the, the next set of workloads that we have to tackle? You know, when we came along first instance, we were really doing a lot of the on-prem on-prem and, and, and known cloud infrastructure suites. Then we started really tackling the broader cloud market with tools and technology to give visibility and control of the overall cloud environment. OT represents that next big addressable market for us, because there are so many questions around devices where they are, how old they are, what they're running. So visibility into the OT network is extremely, extremely important. And, you know, the, the wall that has existed again between the CISO and the OT environments coming down, we're seeing that's closer, closer alignment between the security on both those worlds. So the announcement that we've made around extending our Falcon discover product, to be able to receive and understand device information from the OT network and bring it into the same console as the, the it and the OT in the same console to give one cohesive picture of, of visibility of all of our devices is a major step forward for our customers and for, for the industry as well. >>And we see that being, being able to get the visibility will then lead us to a place of being able to build our AI models, build our response frameworks. So then we can go to a full EDR and then beyond that, there's, you know, all the other things that CrowdStrike do so well, but this is the first step to really the first step on control is visibility. And >>The OT guys are engineers. So they're obviously conscious of this stuff. It's, it's more it's again, you're extending that culture, isn't it? >>Yeah, yeah, yeah. Now when you're looking at threats, great, you want to do things to protect against those threats, but how much, how much of CrowdStrike's time is spent thinking about the friction that's involved in transactions? If I wanna go to the grocery store, think of me as an end point. If I wanna go to the grocery store, if I had to drive through three DUI checkpoints or car safety inspections, every time I went to the grocery store, I wouldn't be happy as an end point as an end user in this whole thing. Ideally, we'd be able just to be authenticated and then not have to worry about anything moving forward. Do you see that as your role, reducing friction >>100%, that's again, one of the core tenants of, of, of why George founded the company. I mean, he tells the story of sitting on an airplane and seeing an executive who was also on the airplane, trying to boot their machine up and trying, and get an email out before the plane took off and watching the scanning happen, you know, old school virus scanning happening on the laptop and, and that executive not making it because, and he is like in this day and age, how can we be holding people back with that much friction in their day to day life? So that's one of the, again, founding principles of what we do at CrowdStrike was the security itself needs to support business growth, support, user growth, and actually get out of the way of how people do things. And we've seen progression along that lines. I think the zero trust work that we're doing right now really helps with that as well. >>Our integrations into other companies that play within the zero trust space makes that frictionless experience for the user, because yeah, we, we, we want to be there. We want to know everything that's happening, but we don't want to see where we always want control points, but that's the value of the telemetry we take. We're taking all the data so that we can see everything. And then we pick what we want to review rather than having to do the, the checkpoint approach of stop here. Now, let me see your credentials stop here. And let me see your credentials because we have a full field of, of knowledge and information on what the device is doing and what the user is doing. We're able to then do the trust with verify style approach. >>So coming back to the, to the edge and IOT, you know, bringing that zero trust concept to the, to the edge you've got, you've got it and OT. Okay. So that's a new constituency, but you're consolidating that view. Your job gets harder. Doesn't it? So, so, so talk about how you resolve that. Do do the, do the concepts that you apply to traditional it endpoints apply at the edge. >>So first things we have to do is gain the visibility. And, and so the way in which we're doing that is effectively drawing information out from the OT environment at, by, by having a collector that's sitting there and bringing that into our console, which then will give us the ability to run our AI models and our other, you know, indications of attack or our indications of misconfiguration into the model. So we can see whether something's good or bad whilst we're doing that. Obviously we're also working on building specific sensors that will then sit in OT devices down, you know, one layer down from rather being collected and pulled and brought into the platform, being collected at the individual sensor level when we have that completed. And that requires a whole different ecosystem for us, it means that we have to engage with organizations like Rockwell and Siemens and Schneider, because they're the people who own the equipment, right? Yeah. And we have to certify with them to make sure that when we put technology onto their equipment, we're not going to cause any kind of critical failure that, you know, that could have genuine real world physical disastrous consequences. So we have to be super careful with how we build that, which we're we're in the process of doing >>Are the IOA signatures indicator as a tax. So I don't have to throw a dollar in the jar, are the IOA signatures substantially similar at, at the edge? I think >>We learn as we go, you know, first we have to gain the information and understand what good and bad looks like, what the kind of behaviors are there. But what we will see is that, you know, as someone's trying to make, if there's an actor, you know, making an attack, you know, we'll be able to see how they're affecting each of those end points individually, whether they're trying to take some form of control, whether they're switching them on and off in the edge and the far edge, it's a little bit more binary in terms of the kind of function of the device. It is the valve open or is the valve closed? It's is the production line running or is the production not line running, not running. So we need to be able to see that it's more about protecting the outcomes there as well. But again, you know, it's about first, we have to get the information. That's what this product will help us do. Get it into the platform, get our teams over the top of it, learn more about what's going on there and then be able to take action. >>But the key point is the architecture will scale. That's where the cloud native things >>Comes into. Yeah, it'll, it'll it'll scale. But to your, to your point about the lack of investment and infrastructure means older stuff means potentially wider gaps, bigger security holes, more opportunity for the security sector. Yep. I buy that. That makes sense. I think if it's a valid argument, when you, when you, when you know, we, we loosely talk about internet of things, edge, a lot of those things on the edge, there's probably a trillion dollars worth of a hundred year old garbage, and I'm only slightly exaggerating on the trillion and the a hundred years old, a lot of those critical devices that need to be sensed that are controlling our, our, our, our electrical grid. For example, a lot of those things need to be updated. So, so as you're pushing into that frontier, are you, you know, are, are you extending out developer kits and APIs to those people as they're developing those new things, right? Because some of the old stuff will never work. >>And that's what we're we're seeing is that there is a movement within the industrial control side of things to actually start, you know, doing this. Some, some simple things like removing the air gap from certain systems, because now we can build a system around it, that's trustable and supportable. So now we can get access there over, over and over a network over the internet to, to, to kind of control a valve set that's down a pipeline or something like that. So there is a, there is, there is willingness within the ecosystem, the, the IOT provider ecosystem to give us access to some of those, those controls, which, which wasn't there, which has led to some of some of these issues. Are we gonna be able to get to all of them? No, we're gonna have to make decisions based on customer demand, based on where the big, the big rock lie. And, and so we will continue to do that based on customer feedback on again, on what we see >>And the legacy air gaps in the OT worlds were by design for security reasons, or just sort of, >>I see. Because there was no way to, to do before. Right. So it was, was like >>Lack connectivity is, >>Yeah. So, so, so it was, people felt more comfortable sending an engineer route to the field truck roll. Yeah, yeah, yeah. To do it rather than expensive, rather. And, and exactly that, again, going back to our macro economic situation, you know, it's a very expensive way of managing and maintaining your fleet if you have to send someone to it every time. So there is a lot of there's, there's a lot of customer demand for change, and we're engaging in that change. And we want to see a huge opportunity there >>Coming back to the XDR Alliance, cuz that's kind of where we started. Where do you wanna see that go? What's your vision for that? >>So the Alliance itself has been fundamental in terms of now where we go with the overall platform. We are always constantly looking for customer feedback on where we go next on what additional elements to add. The, the Alliance members have video this fantastic time and effort in terms of engaging with us so that we can build in responses to their platforms, into, you know, into, into what we do. And they're seeing the value of it. I, I feel that over the next, you know, over the next two year period, we're gonna see those, our XDR Alliance and other XDR alliances growing out to get to each other and they will they'll touch each other. We will have to do it like this O project at AWS. And as that occurs, we're gonna be able to focus on customer outcomes, which is, you know, again, if you listen to George, you listen to Mike protecting the customers, the mission of CrowdStrike. So I think that's core to that, to, to that story. What we will see now is it's a great vehicle for us to give a structured approach to partnership. So we'll continue to invest in that. We've, we've got, we've got a pipeline of literally hundreds of, of partners who want to join. We've just gotta do that in a way that's consumable for us and consumable for the customer. >>Jeff Swain. Thanks so much for coming back in the cube. It's great to have you. Yeah. Thanks guys. Thank you. Okay. And thank you for watching Dave Nicholson and Dave ante. We'll be back right to this short break. You're watching the cube from Falcon 22 in Las Vegas, right back.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Welcome back to HPE. Discover 2022. You're watching the cubes, uh, coverage where day two here at Dave ante with John furrier, HaCo Myers here. He's the executive vice president and chief sales officer, newly minted, relatively newly minted chief sales officer at HPE at HPE and Paul Hunter. Who's the senior vice president and managing director of north America for Hewlett Packard enterprise gentlemen. Welcome to the cube. >>Thank you. Thanks having us. >>Hi coach. This is the first time back in Vegas in a while three years. I think it's been. And your first as the chief sales officer. Yep. What's the vibe like how how's, how's >>It feel? I can tell you. It's so cool. Is it you, you walk down the hallway, everybody's smiling and you see people from, you have seen three years ago or in this format on your screen the last three years, I think, uh, what is amazing. We had exactly three years ago, we had this event and Antonio mentioned, Hey, and by end of 20, 22, you will see everything being available as a service. Yeah. And nobody thought about that. We will not meet in person until 2022 at that point in time. Yeah, indeed. And that's what happened that I can tell you, what's the best decision to make an in person event here in vigor with so many people, uh, because it's about, Hey, the change in the market, the demand, the transition. And, uh, so I think it, I, couldn't be more happy to see the last two days and looking for, for the, to the rest of the event, >>Paul, you have a, a, a background in the, the channel, um, and now you're heading north America. What are you seeing in the ecosystem? Is it, is there a difference as HaCo was saying from 2019, is there a different, you know, feeling different conversations? What are you seeing? Yeah. >>Well, the good thing is like, because we haven't been here for three years, you've got a really marked moment of comparison. So you cast your mind back. What were the conversations like? I think three years ago we were talking about cloud services and partners were nodding their heads and thinking, yeah, but the world is gonna continue as normal and we fast forward three years and, uh, the partners are really talking about, uh, proactively how do they build up that cloud services? And, uh, they're also talking about customer experiences as well. We've landed and won new customers. So, uh, that's really sort of thrilling to hear that they're really excited about the journey on with us. >>You know, I'd like to get your perspective on the, what happened during the pandemic, because we saw, um, first of all, you know, zoom and video com saved the internet, uh, had meetings, but the partner, the partners delivered a lot of value. Um, customers had to pivot, or if they had a tailwind, they had, they took advantage of it. Some had headwinds with the pandemic everyone's working at home. So a lot of disruptions for all the companies, but a lot of the partners had success during the pandemic. And because they have that solution. What was the, uh, uh, the learnings that you guys saw during the pandemic, because now with cloud cloud scale hybrid, mainstream, and now steady state people lived it and partners delivered a lot of solutions in hybrid mode. Yeah. In virtual mode. What was the learnings for you guys out, coming out of that with customers and partners? >>I think first of all, we, we all learned during the pandemic that, uh, you can business, uh, do business in a different way, but as well, you learned, uh, how to pivot faster in the digital transformation. This makes a difference. And this creates value. And I think together with our partner ecosystem, we were able to develop faster solutions there while we developed everything as a service and came up with more and more cloud services. The good thing is it resonates. And our history with the partners is I donors, as long as I can, uh, think back in my career. And you only can do that together with the channel partners. And I think they appreciate that. We learn from each other. We do the same enablement from my guys, like from the partner guys and this close relation, I think made a difference, >>You know, in 2019 GreenLake, as a service was really a financial vehicle right now that's, that's evolved. And now, you know, two years on three years on it's actually a cloud service. Absolutely. And so what's the resonance been with customers because I mean, every everybody says they want that cloud experience. They may not all want OPEX. Yeah. But so what have you hearing from customers? So >>First of all, what I hear is, um, not the, if, so the strategy is clear, the customer they'll love it. They like it. They have, they want to have the cloud-like experience and guess what? We have 70 cloud services now. Yeah. And we have announced a lot of new one the last couple of days, but it's not so much that if they should do this, it's more the question, how can we help me to scale faster? Yeah. And, uh, that's the, the, the, the feedback I got the last couple of days, and for us, it's a motivation. We are on the right track. There is, this is a moment where you have a demand from the market and a strategy that fits, and this is so strong and you can do this with the partner through the partners and you see the, the customers, they love it. I have never seen an event where I got so many requests the last two days where I say, I thought that, can you help me to get there faster? It's perfect. >>Yeah. I think, I think it was also a landmark moment when we presented the cloud platform as part of the Antonio's keynote, I've had a lot of partners say this was sort of really marked the moment where we felt there was there's real substance to the offering now. And, uh, I had one of the sales guys relate to me a story where they have a, a, a client in the audience. And, uh, they're thinking about how they might, um, have a relationship with us and through seeing the kind of significance of it for us, we're able to close deals. So that's also, you know, a really exciting thing. We're actually know we're closing deals and, and winning new >>Customers, Hey, being agile and closing deals fast is a good thing. Right? I mean, that's what you guys like. Yeah. >>I mean, that's >>What it, so I, so I love the channel conversation partners because one of the things that I've observed and, and, and, and, and knowing the HP channels so strong, they're obviously they want make money. Gross margin is all about the profit, the profit motive, but the enablement that you guys have, how is that translated into this, this, this shift everyone's aligned behind GreenLake and as a service, cuz this seems to be a good fit for partner. Cause they're gonna go to the customer, the ultimate end customer and bolt on services. >>Yeah. >>How is that going? Cuz this is, to me, seems like a dream scenario for services, which we all know is high gross margin. >>Yeah. Yeah. I think it's a journey. What's a journey for our sales organization. Like it is for the partners, but it's a journey worth to do that. And um, so what, what is our, our strategy to have this together with our channel partners in mind, uh, to, to combine their strengths and they can, we, we have a kind of modular approach so that they can plug in their strengths, their IP, or as well, their services, which makes them sticky and, uh, relevant to the customer. And it drives profitability. And I think that's the, the, the secret behind the model, working with the channel, not, uh, separate to the channel. And I think this resonates this story, it's, it's a journey. And, uh, we learned a lot the last three years how to sell it. We, in the past we were selling, uh, transactional hardware. Yeah. Now we are selling services, cloud services, like you mentioned different game and this is an enablement. And we, we, um, we offer the same trainings we are doing with our folks to our channel partners because we are together in this journey. >>Yeah. It, you, you described it really well. And uh, so did, Hico essentially, this is, it requires a lot of persistence because you, you're not gonna get it right the first time. And so we now seen partners try and fail several times, but now try fail and succeed. So that's exciting. Um, and also I think what we're also seeing is partners is doing quite a good job of building services that integrate into the cloud services. So they're right into the APIs. I was, I was with a meeting with a partner called CBTS and they talked about the whole of their services portfolio now is embedded in, in GreenLake. So that certainly was not the case three years ago. >>Yeah. And the other, the big tailwind too, as you got the open source software movement, you're seeing, you know, the ability for partners and ultimately the channel being software enabled they're adding services, not just professional services, but cloud services where they have the domain expertise. Yeah. They're close to the customer. Yeah. And they could really be, um, customizing solutions. Um, and that's gonna always be great for the customer. The question I have for you guys is do you see that domain specialism with machine learning and with software, do you see partners start to get vertically focused and like, and start, get more targeted towards save verticals? >>Yeah. >>You go, no, go first. Yeah. >>Well, again, I was, uh, it's funny, your questions are completely resonating with the conversations we've been having all day. Like I was with our partner called connection and they're talking about how do they build practices in four areas? And they're, I'm quite closely allowed to aligned to our areas of edge cloud and data. Um, they have another one which is also workplace transformation. So, and they're thinking, how do we add expertise? How do we hire, recruit and retain the best talent? And, uh, that again, that wasn't a conversation we were having two, three years ago. So where partners really add value to us is through their services and their expertise and progressive partners are hiring and doing that. >>Yeah. And this transformation I mentioned earlier, it's selling outcomes, business outcomes for the end customer. And, uh, I think selling outcomes means you need to be specialized in something, be it on a domain area or be on a vertical. And I think, uh, when you focus on that, uh, that's the best way you can add value to a customer. This creates this trust, this trust relationship. Yeah. >>So edge cloud and data, obviously, I, I think edge, you guys, you got sending stuff and outta space, that's the ultimate edge. So you got some proof points there. Deep edge, I think. Deep edge. Yeah. >><laugh> I is very good. >>I think cloud, you showed the console Alma. It was very, had a very clear and strong platform message say, okay, now go build the data piece to me is the least mature when I walk around. Although I did see Starburst. Yeah, yeah. Out there. I think Starbursts a very advanced leading edge thinker. So that was a good sign. What do you see as having to happen to really build out that data ecosystem now? >>So I think what is important, this, this is all connected to each other edge cloud and data. And at the end, it's about, uh, how we can create insights out of the data, uh, and uh, where they, they live, where they come up, the data, how we structure them, how we get insights out of the data. So I think this is an area we see much more. It's not only about AI, but it's about having a data strategy as a customer. This is one of those areas. We have customer advisory bots that tell us, Hey, help us. We want to create our data strategy. And this is something where I think we can play together with our partners to really create value, get these insights out of the data. >>Are you hearing conversations where cus customers or partners are saying, okay, I wanna get insights out of the data, but I actually want to build a data business. I wanna build data products on, on, on GreenLake. Are you hearing that yet? >>Yeah, we are. Um, particularly the sort of, we, we think of them as sort of information, um, modern companies, um, they're building out new service lines. I mean, you, you, we see it in a lot of industries. Now you can see like how car manufacturers are increasingly thinking about how do they monetize their data, they're getting from it. And, uh, so there are new businesses being established, like in lots of different verticals. Pharmaceuticals will be another one where, um, traditional players are really being challenged and there are big businesses growing very rapidly based off data. So we're seeing it quite extensively. And, and we have to think about how do we access those new customers? How do we intersect them? And it's not just the people that we've been dealing with for 10, 20 years. They're very new companies, >>Which, which announcement's got the most buzz in your conversations with customers, partners. >><laugh>, it's funny. So I, I had, uh, when, when, when I started my conversation at a couple of, uh, meetings now, the last two days always started and said, what, what resonates? Yeah. And first of all, the funny thing is everybody told me the clarity of the message, the strategy second, uh, the consistency that we do, what we promise to do. Um, a couple of them, I know that down ISS, private cloud enterprise, uh, it's a great solution here. And, uh, then, uh, what, what I hear as well with our clarity and the strategy we are leapfrogging the competition. That's what I get out of these meetings. And I think that's the best compliment we can get for the two days. Yeah. >>Yeah. And I think the platform and the conversations around machine learning, AI, we even had an HP executive talk about quantum. Yeah. So you guys are already starting to think about what's around the corner. And I think if the platform works, the test will be, and the results will be enablement ecosystem will be flourishing, and we're gonna watch that. So I wanna get your, your take on the early, um, shift. Cause I think this year with GreenLake and the platform it's, it's maturing enough to the right. No doubt about it. We see the momentum, but there's still a lot more to do than go. So how do you guys envision the ecosystem developing? Because that'll be the true test, the flourishing, cuz if you enable people will get value out of it and it's gotta be a step function, not incremental value. >>Yeah. I think we, we, we, we always talk about, Hey, we landed and then we expand from there. That's the beauty of the model. And the good thing is there's no window looking for the customer. So they are free. That's a modular system. And what we see it's uh, really first of all, to understand the customer digital journey, where are the journey and they're all in a different place. And we have this digital, uh, uh, next advisor workshops when we have this anchor point mm-hmm, <affirmative>, you start there, you really can grow. And then you add workloads based on where the customer sits, what are the partnerships we have to bring to that? So it's really a model which starts and is, uh, designed for the future. >>The field must love it. The folks in the field, we love it. Yeah. You guys love that. Absolutely. Yeah. Yeah. Give it the customer plan future and >>I can tell you the partners love it the, well, yeah, >>Got it. When I talk to CIOs, I, I, and I ask them, you know, what's changed, you know, with Ukraine and supply chain and inflation and rising interest rate, what's changed in terms of your assumption from the beginning of the year, you know, let's say, you know, in, in terms of it spend and they're saying, well, not a lot, actually we're gonna continue to spend, we are reprioritizing. You know, we got, we're taking Robb a little bit from over here to put it into security. Yeah. Okay. But generally speaking, it's, it's the same as we expected, let's call it six, 7% growth, which is pretty good on top of last year. Um, and, and maybe there's some dry powder there, depending on how business goes. It also seems like there's, there's a lot of headwinds at the macro and B to C you know, some of the consumer companies, but B2B is booming. >>So I think >>That what do you guys are seeing? >>Absolutely. I, I completely agree that the demand will continue. Mm-hmm <affirmative> for different reasons. It could be a little bit shift within the demand as you described, but, uh, they know exactly they're on a journey in the digital transformation. If they stop now, they have a competitive disadvantage. So they are wisely in investing. So I think that the, the demand will stay here. Yes. Everybody talks about macroeconomics recession. Uh, we are confident we will see in our B2B >>Part continued demand and they're well capitalized as are a lot of the ecosystem partners. >>Yeah. And it's not a nice to have. It's a must have, I mean, I dunno of any customers that are deinvest in technology, deinvest in the life blood of their business >>Business. Exactly. Guys, thanks so much for coming on the cube. Great. Great to see you. Yeah. Congratulations on being here and, and, and best of luck with all the follow up from the show. I'm sure that lot we're gonna update next year. You see how it turned out? Yeah. >><laugh> numbers >><laugh> thanks for having us. Thank you for watching this segment. This is Dave ante for John furrier, the cubes coverage of HPE discover 22 from Las Vegas. We'll be right back.

>> Welcome to theCUBEs, continuing coverage of Splunk's dot conf 21. I'm Dave Nicholson, and I am joined by Chris Faulk, director, cybersecurity policy, and strategic partnerships at MITRE corporation; As well as Mohan Koo, the co-founder and chief technology officer at tech systems. Now, uh, gentlemen, we've heard this before, but I think this is going to be the best example of a conversation on this subject I've ever had. Security is a team sport. So let's talk about how that applies, where MITRE and D techs and Splunk all come together and work as a team. Uh, starting with you, Chris. miter published the, the attack framework. And, just so people are clear on that Ca- all caps, ATT, Ampersand or, AndSign, I should say. Capital C, capital K looks like attack. That's how you say it. Their framework was created by MITRE. Uh, It's a bit of a game changer. Now, enterprise security teams use that pretty religiously. So, so tell us about that, and tell us what we can expect next from MITRE. >> So thank you David, uh, pleasure to be here. You know, I think that the, um, what made attack resonate with users is it's based on data; It started with data that we observed in our networks and organized around at that time, the emergent principle that Lockheed Martin had put out on the kill chain. Uh, so it gave it structure. And we have, we have been lucky that the community has sort of embraced that concept of what we started off. We got the numbers completely wrong. Uh, we, we started off with like 41 TTPs. And, um, that was because that was based on a small subset of data that we had, uh, and what's been powerful and what's made it truly wonderful as the community's adopted it. And it's, that's, what's it's added to it. It's an additive approach. Um, and but it's all based on data and it's all just a fabulous, um, opportunity for the community to come together. So, what Myers really focused on is understanding how data, and those, uh, problems come together. And then, we surround the ecosystem of that problem with things like language. So we give it a framework and we give it, um, we give it operational data so that it actually has resonance with the users of that community. >> So give me an example, uh, of the language that's used. You know, there are, there are things that are, that are under the heading of tactics as an example. Give me an example of some of those things. What did, what's the term in plain English, and what does it mean? >> So tactics are a way for, um, an adversary to go about taking care of their business. So, in the day, uh, when we were first thinking about this, we thought about it as, um, the old cartoons where you'd have the-the-the coyote and the-the sheep would check in, you know, the coyote was given his lunchbox. He was given it, um, if you think about it, as a, uh, the adversary target list. And he was given his tools, he was, he would open up his toolbox, and he would go after those targets for the day. And he would use those tools. What we realized is that in most cases, a lot of those tools were expensive to create. They were, uh, hard to, um, train up on. And so they tended to use the same basic toolkit over and over again. What changed was, perhaps one little thing that they would exploit that was always changing. And so what, you know, what I likened it to was a burglar. A burglar would show up with his bag of- of, uh, tools. He would have a crowbar, and he would have a flashlight, and he would have a bag. And what he would do is he sometimes choose to go in through the windows. Sometimes they choose to go in through the door. Sometimes he choose to go in through the basement. It didn't matter. But once he got in the house, he had that flashlight, he had that bag, and he had that crowbar, I could figure out through my sensors, what he had in his bag or with, with him, I could catch that. And then I could alert on that, and find the other pieces of that. And so that's what really tactics, um, are about and getting that-that concept boiled down to a language that, uh, cyber defenders could readily understand and put into practice in their businesses. >> So Mohan, tell us about Dtex; And I'm particularly interested in the, in the connection between DTex and what Chris was just talking about; That MITRE has provided us, uh, this language that attack provides us. Um, essentially, you're- You're looking- you're listening for those things that go bump in the night. Chris has given us a language to describe them. Tell- tell us how Dtex fits here. >> Yeah. So, so what we're doing, David, um, and thank you for having me as well, um, what we're doing is we're bringing to the table a whole different type of telemetry, and it's all around human behavior. And, and how we got together with MITRE, um, is actually a direct connection to how we got together with Splunk as well. I'm actually sitting here in Adelaide, in Australia, at the Australian Cyber Collaboration Center. And this is an initiative we put together with the state government of South Australia, and federal government as well, um, to actually bring everybody onto one trusted group. So we could break down the silos and collaborate a hell of a lot better. As we all know, the bad guys collaborate extremely well. You know, they share everything, including their IP and their tactics, and their techniques, everything is shared. And that puts them at an extreme advantage to the good guys, and girls, right? And-and so we have to do a much better job at that collaboration. And-and when we came together and were introduced to MITRE here at the Australian Cyber Collaboration Center, we decided that taking MITREs expertise, and they've got like 15, more than 15 years, worth of dedicated experience around behavioral science, and how it contributes to insider threats and studying that in some depth. Putting that together with the data that we're collecting for our enterprise customers was something that was really, really important, and actually, you know, it was here in the Australian Cyber Collaboration Center that we first kept locked together with Splunk. And Splunk started to identify a problem statement amongst their customers too, That, you know, the data that exists out there for security operations teams just doesn't have that cleanliness and, it doesn't have the context when it comes to human behavior. And that's really what we're bringing to the, to the table here. >> So give me an example of a human behavior that you're looking for, or, you know, so, so Splunk is- Splunk is providing this data that's being gathered from logs. These events are being rolled up and, uh, and-and DTex is analyzing them. Can you give us an example that doesn't educate adversaries of-of behaviors that you look at? >> Yeah, absolutely. And I'll-I'll just touch on it. And then I'll hand over to Chris cause, cause uh, MITRE are truly the experts of this stuff. But- but what I will say is that a lot of organizations, when they think about human behavior and the insider threat, per se, they always think about the malicious actor, right? The, the Snowden type character that's, that's maliciously, and intentionally, trying to get access to take stuff. But it's, it's much more than that. It's, it's also insiders that do negligent things, and it's insider's that are victims of-of their own lack of understanding of things that they're facing. And when outsiders are cleverer, or more technically proficient, they can find ways to-to usurp the insider, and get them to do bad things without them even knowing they're doing it. And so understanding intent, and we call it, at Dtex, we call it, indicators of intent, are really important for us to know. Those indicators are what we've been working with MITRE on for the last year or so; Kind of understanding what the newest, most complicated indicators of intent are. And how do we determine those to be able to know the difference between a malicious insider, versus somebody that's just doing the wrong thing without even knowing about it? I-I don't know, Chris, if-if you wanted to touch on that a little bit. >> Yeah, yeah, yeah Chris, absolutely. You've, you know, uh, Mohan's joining us from Australia, Chris, you and MITRE have done a ton of work with the U.S. Federal Government around detection, and prevention of those insider threats. Talk to us, talk us through that. And, and more specifically, tell us how that is applicable to nongovernmental agencies. >> Yeah, well, so I mean, think at the, at the core of it, human behavior is human cue and behavior. And whether those are being applied to, uh, critical infrastructures, whether they're being applied to working at a federal government organization, or a state, local, uh, government organization, it doesn't matter. Humans, humans have behaviors. Every human has behaviors. What makes them unique, is understanding the context behind those behaviors. And then looking for, uh, indicators that are distinguishable from an individual doing his, or her, job. Right? So, one of the challenges that you have with insider behavior is that, you know, data collection is everyone's job, at every organization, right? You're always trying to put together the numbers for the spreadsheet to-to brief to your boss. Well, when you're doing that data collection, it can look like normal work. And you can't trigger on something like that, because otherwise you're going to be triggering, uh, every individual doing their job every day. So you have to add additional context, and behavioral indicators to that, to understand how the individual is doing that differently in a case where they are up to-up to no good, we'll say, as opposed to under circumstances of doing their job in a regular course of action. So, what we have long held as beliefs about how people behave are actually manifesting themselves differently in online behavior; How fast they click, um, what kinds of tools they use to do legitimate work, versus the kinds of tools that they do-to do, uh, I'll call it elicit collection. Uh, literally those kinds of subtle nuances. So while they might do the same collection activities, how fast they do it, um, where they put that information, um, how they, how often they go back to the same site, those are indicators that when taken with that behavioral context really matter. And that's what distinguishes them from just normal, typical user behavior. >> So how much does that context vary between private entities, governmental entities, and across private entities? Is this the classic 80/20 situation where, you know, 80-80% of it's the same, 20% very different? What, what does that look like? >> Yeah, I would say that, you know, an 80/20 is a very good rule. I'd probably put it up closer to 90 to 95 to five, right? So behaviors work the same. Now, the protocols that organizations have are going to drive some of that, right? So a-a government organization is going to have certain things in place that a private company may or may not. So, you know, how, how locked down the systems are, the kinds of access, um, things that, that you allow. So do you allow USB drives? Do you allow, um, those kinds of-of capabilities in your organization? So, if you're a private sector organization, but even within a private sector organization, they'd run the gamut, right? You have very locked down environments like banks, and regulated industries and then, you have very unregulated industries as well. So it really isn't about government and industry. It's about the kind of, um, protocols that are already in place for other reasons that really drive the differences between that. And then you have, again, you have those additional safeguards that you have, say with a-with a government organization and that you've got, uh, security vetting, right? So you've done security vetting of a lot of your employees, whether even if it's not security clearance, it's a- it's a personnel vetting. And so, it's an additional level, um, but all it does is change the-the emphasis of-of where you place the value in your security mechanisms. >> So, you mentioned a variety of contexts. Mohan, We've had a mass shift to remote working, obviously. Um, Splunk has shared with us that, uh, that the customers are concerned about, you know, giving- giving people visibility without compromising privacy. And I, and I-I say Splunk like Splunk is a person (man laughing) We like to personalize everything here at theCUBE, but how is DTex helping with this challenge, this challenge of not being intrusive, yet, uh, getting the important work done that needs to be done? >> Yeah, that's a, that's a great question. And-and for us, you know, we, as DTex, we kind of grew up in-in Europe, that's kind of where we became an international organization. So, employee privacy is at the heart of everything that we do. And-and, we make privacy by designing into everything that we do. So, we're actually able to, uh, pseudo anonymize every bit of data that we're collecting, so that you're actually really, truly looking for bad behaviors or unusual behaviors. You're not looking for bad people or unusual people, right? Like it's, it's a very clear distinction; and being able to do it in a way that gives you the visibility, gives the organization, the visibility to prevent against risk and to de-risk the organization without infringing on anyone's privacy is, is really critical. And, you know, as Chris was mentioning, even if you go to the private sector, you know, you've got those very regulated banks or healthcare organizations that are typically quite locked down, but we're dealing more and more with, with high-tech companies, right? A lot of bay area firms, Silicon valley companies, which have always required the flexibility for their workforce, right? They want them to be innovative. They want them to do different things. And in order to do that, they need the ability to have any tools they need to get their job done. But in those environments, you can't have too many hard and fast controls. So how do we actually provide that visibility to the organization without infringing privacy? That is absolutely what the game is about. And so, you know, not kind of having to scrape screens, and type key strokes and type video capture, you know, that's the old school way of doing it. You know, in some cases maybe you do need that level of surveillance, but in most cases you absolutely do not. And so, you know, for many, many years, a lot of enterprise security organizations have been collecting way more data than they need to and taking way more intrusive approaches. And we're about backing that off and kind of getting the right balance between security and privacy, because what we truly believe is where you overlap security and privacy, that Venn diagram that you get in the middle is where you get safety. And we really see it as, as an extension of health and safety. >> So Mohan, if we do all of these things correctly, between Splunk, MITRE, and DTex, you get the perfect scenario where you're catching bad actors and you're not inconveniencing good actors. So what's your view of this? Dystopian future, Utopian future, a mix of both? >> Well, uh, look, I think-I think that the future really is, you know, as the title to this discussion is it's a team sport, right? Like, and, and I think the, the approach that Splunk is taking right now is absolutely the right one. Like we, we need to all come together. We can't be everything to everyone. I don't think there is a one size fits all solution in enterprise security today. And those organizations that understand that and recognize that, but neither is it, are we able to continue just kind of investing in hundreds of point solutions across the enterprise and layering them across the business. Like, band-aids, we need that consolidation, but we do need to take best of breed solution providers to, to focus on those integrations and doing it properly. And that's what we've really enjoyed about working with Splunk over the last couple of years is kind of taking a very holistic approach and realizing that we all need to come together to play these teams sport because, you know, we, as detects, we bring together a very clean data set that gives you that human telemetry and then MITRE brings to get brings the behavioral science capability and behavioral science understanding. And Splunk provides that big data platform to bring everything together and show it and visualize it. And, and really that's, that's, that's, that's one way of looking at it. And I, and I think, you know, going forward those vendors or those organizations that don't recognize that that proper integration actual true integration has to be done collectively. And it has to be done in a way that's light and easy for anybody to consume. >> Perfect way to wrap this cube conversation. Thank you, Mohan. Thank you, Chris. And thank all of you for joining us on this cube conversation or continuing coverage of splunk.com 21 continues. I'm Dave Nicholson. Thanks for joining.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Global Partner Network. Hello, everyone. This is Dave Balanta. And welcome to the cubes Virtual coverage of AWS reinvent 2020 with a special focus on the A p N partner experience. I'm excited to have two great guests on the program. Fernando Castillo is the head s a p on AWS Partner Network and s A P Alliance and AWS and Stephen Jones is the general manager s a p E c to enterprise that aws Gentlemen, welcome to the Cube. Great to see you. >>I'm here. >>So guys ASAP on AWS. It's a core workload for customers. I call it the poster child for mission Critical workloads and applications. Now a lot has happened since we last talked to you guys. So So tell us it. Maybe start with Stephen. What's going on with Sapna Ws? Give us the update. >>I appreciate the question Day. Look, a lot of customers continue to migrate. These mission critical workloads State of us on a good example is the U. S. Navy right? Who moved their entire recipe landscape European workload AWS. This is a very large system of support. Over 72,000 users across 66 different navy commands. They estimate that 70 billion worth of parts and goods actually transact through the system every year. Just just massive. Right? And this this type of adoptions continued to accelerate a very rapid clip. And today, over 5000 customers now are running SFP workloads. I need to be us on there really trusting us, uh, to to manage and run these workloads. And another interesting stat here is that more than half of these customers are actually running asap, Hana, which is a safe He's flagship in memory database. >>Right, Fernando, can you add to that? >>Sure. So definitely about, you know, the customs are also SCP themselves continue to lose a dollar less to run their own offerings. Right? So think about conquer SCP platform. SCP analytics were when new offers like Hannah Cloud. In addition to that, we continue to see the P and L despondent network to grow at an accelerated pace. Today we have over 60 SNP company partners all over the world helping SFP customers s O that customers are my green. There s appeal asking CW's. They only look for reduced costs, improved performance but also toe again access to new capabilities. So innovate around their core business systems and transform their businesses. >>So for now, I wonder if I could stay with you for a minute. I mean, the numbers that Steve was putting out there, it's just massive scale. So you obviously have a lot of data. So I'm wondering when you talk to these customers, Are you discerning any common patterns that are emerging? What are some of the things that you're hearing or seeing when you analyze the data? >>Sure. So just to give a couple example right. Our biggest customers are doing complete ASAP. Transformations on Toe s four Hana. Their chance they're going to these new S a p r p code nine All customers have immediate needs, and they're taking their existing assets to AWS, so looking to reduce costs and improve performance, but also to sell them apart for innovation. This innovation is something that operation or something that they can wait. They need it right now. It's they This time to innovate is now right on some of these customers saying that while s and P has nice apart. So that is a multi year process on most organizations and have a look from waiting for this just before they start innovating. So instead of that, they focus on bringing what they have on start innovating right away on Steve has some great stories around here, so maybe Steve can share with that. Goes with that? >>Yeah, that'd be great, Steve. >>Yeah. Look, I think a good example here on and Fernando touched it, touched on it. Well, right. So customers coming from all kind of different places in their journey aws as it relates to this this critical workload and some are looking to really reap the benefits of the investments they made over the last couple decades sometimes. And Vista is a really good example Here, um there a subsidiary of Cook Industries, they migrated and moved their existing S a P r P solution called E c C. To AWS. They estimate that this migration alone from an infrastructure cost savings perspective, has netted them about two million per year. Additionally, you know, they started to bring some of the other issues they were trying to solve from a business perspective, together now that they were on the on the on the business on the AWS platform. And one thing that recognizes they had different data silos, that they had been operating in an on premises world. Right? So massive factories solution and bringing all of that data together on a single platform on AWS and enriching that with the SCP data has allowed them to actually improve their forecasting supply chain processes across multiple data sources and the estimate that that is saving them additional millions per year. So again, customers are not necessarily waiting to innovate. Um, but actually moving forward now. >>All right, so I gotta ask, you don't hate me for asking this question, but but everybody talks about how great they are. Supporting s a P is It's one of the top, of course, because s a p, you know, huge player in the in the application space. So I want you guys to address how aws specifically compares Thio some of your competitors that are, you know, the hyper scaler specifically as it relates to supporting S a P workloads. What's the rial differential value that you guys bring? Maybe Steve, you could start >>Sure, you're probably getting to know us a little bit. Way don't focus a lot on competition, Aziz mentioned week We continue to see customers adopt AWS for S a p a really rapid clip. And that alone actually brings a lot of feedback back into how we consider our own service offerings as it relates to this particular workload on that, that's it. That's important signal right for what we're building. But customers do tell us the security performance availability matters, especially for this workload, which, you know, to be honest, is the backbone of many, many organizations. Right? And we understand why. And there was a study that was done recently about a. D. C. Where they found that even a single hour of unplanned downtime as a released this particular workload could cost millions. And so it's it's super important. And if you look at, um, you know, publicly available data from an average perspective, um, it has considerably less downtime than the other hyper scale is out there way. Take the performance and availability of oh, our entire global footprint and in this workload in particular, super important. >>Well, you know, that's a great point, Steve. I mean, if you got critical mission critical applications like ASAP supporting the business, that's driving revenue. It's driving productivity. The higher the value of the application, the greater the impact when it's down, I wonder, Fernando, you know, Steve said, You guys don't focus on the competition. Well, is an analyst. You know, I always focused on the competition, So I wonder if you're gonna add anything to that. >>Sure. So again, as you can imagine, multiple analyst called Space right. And, uh, everybody shares information. And analysts have agreed that Italy's clean infrastructure services, including the three quite a for CP across the globe. So we feel very humble and honor about this recognition on this encourages to continue to improve ourselves to give you a couple examples for a 10 year in a row. Italy's US evaluated as a leader in the century Gardner Magic Quadrant, right for cloud infrastructure from services. And, as you know, the measure to access right they measure very execute on complete, insufficient were the highest, both of them. Another third party, just not keep with one is icy, right? You know, technology research dreamers, you already you might know advice for famous Well, the reason they publisher s a p on infrastructure service provider lands reports long name which, basically, the analyzers providers were best suited to host s a. P s four hana workloads on more broadly s a p Hannah landscapes, you know, very large scape ASAP 100 landscapes. So they recognize it, at least for the third year in a row. And conservative right, the best class enterprise. Great infrastructure towards security performances, Steve mentioned, but also making the panic community secure. Differentiation. Andi, they posted. They mentioned it all us as a little position in quadrant for the U. S. U K France, Germany, the Nordics in Brazil. So again, really honor and humble on discontinued in court just to continue to improve. >>You know, Steve, I just wrote a piece on Cloud 2030 trying to project what the next 10 years is gonna look like in one of the I listed a lot of things, but one of the things I talked about was some of the technical factors like alternative processors, specialized networks, and you guys have have have really, always done a good job of sort of looking at purpose built, you know, stuff that that can run workloads faster. How relevant is that in the the S A P community? >>Oh, that's a great question, David. It's It's absolutely relevant. You take a look at what? What we've done over the years with nitro and how we've actually brought the ability for customers to run on environmental infrastructure but still have that integrated, uh, native cloud experience. Uh, that is absolutely applicable to Unless if you workload and we're actually able toe with that technology, bring the capability to customers to run thes mission critical workloads on instances with up to 24 terabytes of brand, albeit bare metal, but fully integrated into the AWS network fabric, >>right? I mean, a lot of people, you know, need that bare metal raw performance on, and that makes sense that you've been, you know, prioritize such an important class of workload. I'm not surprised that that I mean, the numbers that you threw out a pretty impressive eso. It's clear you're leading the charge here. Maybe you could share a little glimpse of what's coming in the future. Show us a little leg, Steve. >>Yeah, well, look, uh, we know that infrastructure is super important. Thio. Our customers and in particular the customers are running these mission critical workloads. But there's a lot of heavy lifting, uh, that that we also want to simplify. And so you've seen some indications of what we've done here over the years, uh, ice G that Fernando mentioned actually called out. AWS is differentiating here, right? So for for many years, we've actually been leading in releasing tools for customers to actually orchestrate and automate the deployment of these types of worthless so ASAP in particular. I mean, if you think about it a customer who is coming to a to a hyper scale platforms like AWS and having to learn what that means, Plus understand all the best practices from S, A, P and AWS to make that thing really shine from a performance and availability perspective, that's a heavy asked. Right? So we put a lot of work from a tooling perspective into into automating this and making this super simple not just for customers, but also partners. >>Anything you wanna chime in on that particular the partner side, Fernando. >>Sure. So this is super important for public community, right? As you can imagine, the tooling that we're bringing together toe. The market is helping the Spanish to move quicker, right? So they don't have to reinvent. They will all the time. They will just take this and move and take it and move forward. Give an example. One of our parents in New York, three hosts. Thanks for lunch. We start with Steve just reference right. They want to create work clothes in an automated way. Speeding up the delivery time. 75% corporation is every environments. So it just imagine the the impact of these eso a thing here that is important is our goal is to help customers and partners move quicker, removing any undifferentiated heavy lifting, right, Andi, that's kind of the mantra of this group. >>You know, when you think about what Doug Young was saying is in the keynote, um, the importance of partners and I've been on this kick about we've moved in this industry from products to platforms, and the next 10 years is gonna be about leveraging ecosystems. The power of many versus the resource is of a few or even one is large is a W s so so partners air critical on I wonder if you could talk toe the role that that the network partners air playing in affecting S a p customer outcomes and strategies. Maybe Steve, you could take that first. >>Yeah, but look, we recognize that the migration on the management of these systems it's complex, right? And for years, we've invested in a global community of partners many partners who have been fundamental to s a p customer success over over a couple decades, Right? And so, um, that there are some nuances that that need to be realized when it comes to running ASAP on on a hyper scale platforms like AWS. And so we put a lot of work into making sure these partners are equipped to ensure customers have have a really good experience. And I mean, in a recent conversation I had with a CEO of a large, uh, CPG company, he told me he reflected that the partners really are the glue. That kind of brings it all together for them. And, uh, you know, just to share something with you today, our partners, our partner community network for S. If he is actually helping over 90% of net new customers who are coming toe migrate as if you were close to AWS, so they're just absolutely critical. >>So, Fernando, there's the m word, the migration, you know, it's you don't want to unless you have to, but people have to move to the cloud. So So what can you add to this conversation? >>Sure, they So again, just to echo what Steve mentioned, right? Uh, migration. Super important. We have ah group of partners that are right now specializing in migration projects. And they have built migration factories. You may have seen some of them. They have been doing press releases through the whole year saying that they're part of these and their special cells they're bringing to the helping customers adopt AWS. So they go through the next, you know, very detailed process. We call them map for ASAP partners. So they have these incremental value on top of being SCP competent funds, which I referred earlier on. This group has, as mentioned, you know, show additional capability to safeguard these migrations on. Of course, we appreciate and respect and we have put investment programs for them to help them support their own customers right in those in these migrations. But because the SNP ecosystem on it. But it's not about only migrations, right? One important topic that we need technologies as you as Steve mentioned, we have these great set of partner of customers have trusted us or 5000 through a year on these, uh, these customers asking for innovation right there, asking us how come the ecosystem help us innovate faster? So these partners are using a dollars a plan off innovation, creating new solutions that are relevant for SCP. So basically helping customers modernize their business processes so you can take an example like Accenture Data Accelerator writers taking SCP information and data legs Really harm is the power of data there or the Lloyd you know, kinetic finances helping, you know, deploy Central finance, which is a key component of SCP, or customer like partners like syntax that has created our industrial i o. T. Offering that connects with the SNP core. So more and more you will see thes ecosystem partners innovating on AWS to support SNP customers. >>You know, I think that's such an important point because for for decades have been around for a while. It's the migrations air like this. Oftentimes there's forced March because maybe a vendor is not going to support it anymore. Or you're just trying to, you know, squeeze Mawr costs out of the lemon. What you guys are talking about is leveraging an ecosystem for innovation and again that ties into the themes that we're talking about about Cloud 2030 in the next decade of innovation. Let's close, guys. What can customers ASAP customers AWS customers expect from reinvent this year? Um, you know, maybe more broadly, what can they expect from A W S in the coming 12 months? Maybe, Steve, you could give us a sense, and then Fernando could bring us home. >>You bet. Look, um, this year we've really tried to focus on customer stories, right? So we've we've optimized. There's a number of sessions here agreement this year. We want customers and partners to learn from other from other customer experiences, so customers will be able to listen to Bristol Myers Squibb talk about their performance, their their experiences, Alando Newmont's and Volkswagen. And I'll be talking about kind of different places where they are on this, this journey to cloud and this innovation life cycle, right, because it really is about choice and what's right for their business. So we're pretty excited about that. >>Yeah. Nice mix of representative Industries there. I Fernando bring us home, please. >>Sure. So, again, we think about 21 in the future. Rest assured, we'll continue to invest heavily to make sure it values remains the platform innovation. Right on choice for recipe customers where a customer wants to move their existing investments on continue to add value. So what they have already done for years or goto export transformation. We're here to support their choice. Right? And we're committed to that as part of our customers Asian culture. So we're super excited about the future. And we're thankful for you to spend time with us today. >>Great, guys, Look, these are the most demanding workloads we're seeing that that rapid movement to the cloud is just gonna accelerate over the coming years. Thanks so much for coming on The Cube. Really appreciate it. >>Our pleasure. Thank >>you. All >>right. Thank you for watching everyone keep it right there from or great content. You're watching the cube aws reinvent 2020

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> We're back live >> here on the Cube Of'em world 2019 that Mosconi Center, Downtown San Francisco along with stew Minimum. I'm John Walton. Thanks for joining us here Day one of our three days of coverage here via World 2019. We're now joined by the CEO of equity. Ex Charles Myers is with us and a cube rookie. We >> love that. Nice to have you on the ship here. Pleasure. Thanks for >> being here with us. Let's let's talk about first big picture here from the higher level, the whole multi cloud hybrid cloud movement. What's going on now with the Enterprise? Your perspective on kind of where we are in that shift, if you will, or that transformation and what's what's driving it? What what's what's creating all the. >> But you get that question a lot, right? People ask me what inning O'Ryan question. Um, you know, it's a regular >> is so what way? Well, >> you know, said I would say a couple of years ago, you know, people said, I don't think that I think the national anthem is still being played kind of thing, you know? And, uh, I think the game has probably started to know, but But I still think we're very early innings. Um and, uh, you know, I think I'd actually bring it up to even a higher level and talk about what's happening in terms of how companies were thinking about digital transformation and what I what I think is happening is it's becoming a board level priority for cos they can't afford to ignore it. Um, you know, digital is changing the U no basis for competitive advantage in most industries around the globe. Um and so they're investing in digital transformation. And I think they're gonna do that, frankly, independent of whatever macro economic climate we operated, Um, and so Ah, and I think you know the big driving force. Probably, you know, individual transformation today. So the cloud on DSO and what we're seeing is there is that, you know, is a particular architecture of choice that's emerging for customers. >> So, Charles, give us a little >> bit of a scope of your world because, you know, there was a move many years ago. We used to say in the I t industry, you know, friends don't let friends build data center because there's only a handful of companies in the world that are good at it. I believe your company's one of s O and not only, you know, even, you know, you talk about the megastar providers like, you know, Google and Amazon. They actually don't build many of their own data centers. They partner with certain companies and and you're one of the first companies that I talked to that was, You know, when you talk about how we position multi cloud today, well, you know, let me put some gear in an equinox environment, you know, have that direct fiber you know, into AWS or Azure in the lake s O. That was early, and we've been talking for a while, so it gives a little bit that that that broad look, you know, because from the big public cloud, you know, they're spending tens of billions of dollars a year to build that out. So, you know, and often your real estates a big piece of your world's >> absolutely and well, we certainly like to think we're pretty damn good to build an operating data centers. But >> there actually are a lot of >> people to build, not break data centers and and, of course, the clouds Dubai from third parties. But they, uh, you know, they build some of their own, and they do buy from third parties as well. We think we occupy a pretty special place in the overall data center landscape because, candidly, people, you know can buy credible data center capacity from a number of players what they can't but they really want, though, is not so much a data center as they want to connect to somebody specifically, Um, and that's where Equinox is really different. You know, with 10,000 you know, customers inside of our digital ecosystems, you know, And we operate in 200 data centers across 52 markets around the world. And, you know, we represent something very special. And it's that interconnection piece there really differentiates at clinics. From the rest. >> You've had some, I guess expansion news in terms of partnerships with the, um, where that you announced talk about that a little bit if you would, but how you've grown that relationship. And what do you think that'll take you? >> Sure. And it bridges a little bit back to suit earlier question to which is, you know, kind of What what role do we play and how is it, you know, frame in the overall cloud landscape? What was announced today was a preferred partnership with between ourselves and and, uh, and now Veum wear and and also Del to deliver the VMC on Del um you know, offering which is really aimed at the sort of hybrid cloud requirements for enterprises, customers who have workload, a set of workloads, some of which may be very well suited to public cloud. And they may go either native on AWS or with of'em CNW s type solution. But a >> lot of >> times they, for a variety of reasons, are looking for a hybrid cloud solution on, and they want to implement that on private infrastructure. But they would like to get the benefits of clout they would get, like to get the simplicity, that flexibility as a service convenience. But they need the control, the compliance, the predictability and the performance that private infrastructure allows. And so where that's what that's what the solution is all about. And were there were the preferred global cola partner for that solution. >> And do companies have a pretty good idea when they come to you about what they want to do and where they want to do it? Or do you have to shepherd them through that a little bit? Because there are a number of factors that would think that go into that consideration? >> Absolutely. And >> I would say it's more typically the ladder. There are certainly >> some who come with a well developed, you know, sort of view on >> things, but it that often >> changes to some degree, and and we we like to think of ourselves. As you know, it's probably an overused term in I T. But it's as a trusted advisor in terms of helping a customer think through. It's >> really one of the great things that I think >> both of'em where and Equinox are positioned, as which is somebody who doesn't bring, say, here's the answer. Instead, they come and say, Look, the answer probably depends on a lot of factors, and so you may want a private cloud solution. You may want a public cloud solution. You probably want a hybrid cloud solution and a hybrid multi cloud solution. So let's talk through what you're trying to accomplish and how we can get you there. >> Yeah, Charles, you know, we know that things were going to change, and the advice we always give to practitioners is whatever you deploy, you need to be able to have the agility and have options. So that a decision you make today is not going to freeze you from doing something in there. Absolutely. A lot gets talked about in the multi cloud world. What is portable and what things were moving. And, you know, we know KUBERNETES is not magic. Right? Um, your your company must have actually really good view of things going from the public cloud to my own racks, too. Moving sideways because many times moving between clouds is just moving between Rose and your data centers, right? Or over some connection gives a little insight what you're seeing. Yeah. What's the trend along >> that line? You bring up a really great point and one, Frankly, I think our you know, our sales teams and are are, you know, solution. Architects are constantly talking to our customers about which is fruit future proofing your architecture because you don't know kind of what your needs are going to be tomorrow, Um, and so being able to deploy infrastructure in a way that has greater agility and flexibility is really critically important. And that's why putting private infrastructure immediately proximate to the cloud, being able to get to the performance benefits the economic benefits of that is really key. So that's that's definitely something we're seeing, you know, as a critical part of the conversation with our customers. >> How about EJ computing? That's something that touched on a little bit this morning. But, you know, I'm sure you've got some strong feelings about where we are >> today. You know, it's funny because I always I always telling everybody inside my company around. I said, Be careful about the word edge because one person's edge in another person's court, right, you know? And so, um, you know, we actually talk about eh? Quinyx as really the best manifestation of the digital edge today, and perhaps that sounds somewhat self serving. But I would say that when you look at people who want to place infrastructure geo geographically distributed way and they want to interconnected with clouds with networks with other members of their sort of supply chain. Equinox is really best solution for that in many, many cases. And so we really talk about EJ oriented solutions with our customers inside of our are, you know, sort of population of 200 data centers across 52 markets today. Now, when I when typically I think when you're hearing edge today people are talking about an even more geographically distributed footprint that is out, You know, closer I ot sensors or closer to, you know, customer endpoints and those kind of things, Um >> and I I think that will happen over time. And I >> think people talk about compute storage moving closer to that edge. But >> I think that's gonna, you know, >> take place over a long period of time. I think five g once it's fully dense, ified and deployed. I think we'll start to drive some of those applications. But we're seeing today is the current digital edge at a quinyx works very well for most of these edge related applications. >> So what would you call it then, if it's not edge? Because you said one >> man's, we do call it yet. Yeah, right. We call it a vigil. Some people might operate out there as a >> core business right into them. That's the core you raise. An interesting point Depends on your perspective and how you see it. So we called the digital and you think from the telco side of that slate mobile applications, mobile devices. You know, we all know about the usage trends. What you see in the last 10 15 years, that's good. Just explode. So how are you preparing for that on slot? Because, you know, five G's coming >> it is. Well, we're actively >> involved. In fact, we haven't We've had real success in a number of I would call him EJ sensitive Reg related ecosystems, digital payments, you know, connected car these things and people love to talk about autonomous driving. The reality is that most autonomous driving, Um, you know, interactions are done on boards. You you don't even have time to go out and making a request to the cloud. Right? You know, But other connected car value propositions that do interact, you know, with, you know, with of farther edge are things that we've actually been working really closely with equipment providers and service providers on, and they're having great success in implementing those things. Using at clinics is part of the architecture. All right, >> Charles, how about security? You know, when you live in this multi cloud world, you know I need security that can living across the environment. How does a clinic make sure that it's a trusted partner in that? That whole security store? >> There's a variety of sort of layers to it, you know, you are the biggest response to be we have specifically is physical security because people are trusting their infrastructure to reside in one of our facilities, and it needs to be physically secure. So there's five layers of security between the front door. I know you've toured one of our facilities and have gotten the full experience of all the biometrics and all the checks and balances that occur in terms of being able to someone to being able to gain access to the facility. So there's the >> physical side. Then there's >> really, you know, sort of virtual or, you know, ah, digital security. And you know what we're doing there is really cultivating the ecosystem of providers. We have a number of really sophisticated customers who are delivering cloud based security solutions. VM. Where is one example of that? But you know, there's a variety of other customers that have a sort of, you know, security oriented value proposition companies like C Scale and other people that are really doing that well for customers. So I think that, you know, we're really more about cultivating that full ecosystem so that customers have access to the full portfolio of security tools that they need. >> Charles, Thanks for the time. We appreciate that. And I do want to congratulate you on having probably the strongest team showing >> of the Cube so far. Take, they have Charles do today. Everybody All right, That's the equities culture, all right? Trust me, they're clapping. I expected a little more of around next time we'll work on it. A good deal. Thanks for being with us side your baby. Thank you very much for big connects. Back >> with more where we're alive. Here in San Francisco at Veum World 2019