>> 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.

>>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

>> Hey, welcome back everybody. Jeff Rick here with the Cube. We are live in downtown San Jose at the historic Pagoda Lounge, part of Big Data SV, which is part of Strata + Hadoop Conference, which is part of Big Data Week because everything big data is pretty much in San Jose this week. So we're excited to be here. We're here with George Gilbert, our big data analyst from Wikibon, and a great guest, Gaurav Dhillon, Chairman and CEO of SnapLogic. Gaurav, great to see you. >> Pleasure to be here, Jeff. Thank you for having me. George, good to see you. >> You guys have been very busy since we last saw you about a year ago. >> We have. We had a pretty epic year. >> Yeah, give us an update, funding, and customers, and you guys have a little momentum. >> It's a good thing. It's a good thing, you know. A friend and a real mentor to us, Dan Wormenhoven, the Founder and CEO of NetApp for a very long time, longtime CEO of NetApp, he always likes to joke that growth cures all startup problems. And you know what, that's the truth. >> Jeff: Yes. >> So we had a scorching year, you know. 2016 was a year of continuing to strengthen our products, getting a bunch more customers. We got about 300 new customers. >> Jeff: 300 new customers? >> Yes, and as you know, we don't sell to small business. We sell to the enterprise. >> Right, right. >> So, this is the who's who of pharmaceuticals, continued strength in high-tech, continued strength in retail. You know, all the way from Subway Sandwich to folks like AstraZeneca and Amgen and Bristol-Myers Squibb. >> Right. >> So, some phenomenal growth for the company. But, you know, we look at it very simply. We want to double our company every year. We want to do it in a responsible way. In other words, we are growing our business in such a way that we can sail over to cash flow break-even at anytime. So responsibly doubling your business is a wonderful thing. >> So when you look at it, obviously, you guys are executing, you've got good products, people are buying. But what are some of the macro-trends that you're seeing talking to all these customers that are really helping push you guys along? >> Right, right. So what we see is, and it used to be the majority of our business. It's now getting to be 50/50. But still I would say, historically, the primary driver for 2016 of our business was a digital transformation at a boardroom level causing a rethinking of the appscape and people bringing in cloud applications like Workday. So, one of the big drivers of our growth is helping fit Workday into the new fabric in many enterprises: Vassar College, into Capital One, into finance and various other sectors. Where people bring in Workday, they want to make that work with what they have and what they're going to buy in the future, whether it's more applications or new types of data strategies. And that is the primary driver for growth. In the past, it was probably a secondary driver, this new world of data warehousing. We like to think of it as a post-modern era in the use of data and the use of analytics. But this year, it's trending to be probably 50/50 between apps and data. And that is a shift towards people deploying in the same way that they moved from on-premise apps to SAS apps, a move towards looking at data platforms in the cloud for all the benefits of racking and stacking and having the capability rather than being in the air-conditioning, HVAC, and power consumption business. And that has been phenomenal. We've seen great growth with some of the work from Microsoft Azure with the Insights products, AWS's Redshift is a fantastic growth area for us. And these sorts of technologies, we think are going to be of significant impact to the everyday, the work clothing types of analytics. Maybe the more exotic stuff will stay on prem, but a lot of the regular business-like stuff, you know, stuff in suits and ties is moving into the cloud at a rapid pace. >> And we just came off the Google Next show last week. And Google really is helping continue to push kind of ML and AI out front. And so, maybe it's not the blue suit analytics. >> Gaurav: Indeed, yes. >> But it does drive expectations. And you know, the expectations of what we can get, what we should get, what we should be moving towards is rapidly changing. >> Rapidly changing, for example, we saw at The New York Times, which as many of Google's flagship enterprise customers are media-related. >> Jeff: Right. >> No accident, they're so proficient themselves being in the consumer internet space. So as we encountered in places like The New York Times, is there's a shift away from a legacy data warehouse, which people like me and others in the last century, back in my time in Informatica, might have sold them towards a cloud-first strategy of using, in their case, Google products, Bigtable, et cetera. And also, they're doing that because they aspirationally want to get at consumer prices without having to have a campus and the expense of Google's big brain. They want to benefit from some of those things like TensorFlow, et cetera, through the machine learning and other developer capabilities that are now coming along with that in the cloud. And by the way, Microsoft has amazing machine learning capability in its Azure for Microsoft Research as well. >> So Gaurav, it's interesting to hear sort of the two drivers. We know PeopleSoft took off starting with HR first and then would add on financials and stumble a little bit with manufacturing. So, when someone wants to bring in Workday, is it purely an efficiency value prop? And then, how are you helping them tie into the existing fabric of applications? >> Look, I think you have to ask Dave or Aneel or ask them together more about that dynamic. What I know, as a friend of the firm and as somebody we collaborate with, and, you know, this is an interesting statistic, 20 percent of Workday's financial customers are using SnapLogic, 20 percent. Now, it's a nascent business for them and you and I were around in the last century of ERP. We saw the evolution of functional winners. Some made it into suites and some didn't. Siebel never did. PeopleSoft at least made a significant impact on a variety of other things. Yes, there was Bonn and other things that prevented their domination of manufacturing and, of course, the small company in Walldorf did a very good job on it too. But that said, what we find is it's very typical, in a sense, how people using TIBCO and Informatica in the last century are looking at SnapLogic. And it's no accident because we saw Workdays go to market motion, and in a sense, are following, trying to do the same thing Dave and Aneel have done, but we're trying to do the same thing, being a bunch of ex-Informatica guys. So here's what it is. When you look at your legacy installation, and you want to modernize it, what are your choices? You can do a big old upgrade because it's on-premise software. Or you can say, "You know what? "For 20% more, I could just get the new thing." And guess what? A lot of people want to get the new thing. And that's what you're going to see all the time. And that's what's happening with companies like SnapLogic and Workday is, you know, someone. Right here locally, Adobe, it's an icon in technology and certainly in San Jose that logo is very big. A few years ago, they decided to make the jump from legacy middleware, TIBCO, Informatica, WebMethods, and they've replaced everything globally with SnapLogic. So in that same way, instead of trying to upgrade this version and that version and what about what we do in Japan, what do we do in Sweden, why don't you just find a platform as a service that lets you elevate your success and go towards a better product, more of a self-service better UX, millennial-friendly type of product? So that's what's happening out there. >> But even that three-letter company from Walldorf was on-stage last week. You can now get SAP on the Google Cloud Platform which I thought was pretty amazing. And the other piece I just love but there's still a few doubters out there on the SAS platform is now there's a really visual representation. >> Gaurav: There is. >> Of the dominance of that style going up in downtown San Francisco. It's 60 stories high, and it's taken over the landscape. So if there's ever any a doubt of enterprise adaptation of SAS, and if anything, I would wonder if kind of the proliferation of apps now within the SAS environment inside the enterprise starts to become a problem in and of its own self. Because now you have so many different apps that you're working on and working. God help if the internet goes down, right? >> It's true, and you know, and how do you make e pluribus unim, out of many one, right? So it's hilarious. It is almost at proliferation at this point. You know, our CFO tapped me the other day. He said, "Hey, you've got to check this out." "They're using a SAS application which they got "from a law firm to track stock options "inside the company." I'm like, "Wow, that is a job title and a vertical." So only high growth private venture backed companies need this, and typically it's high tech. And you have very capable SAS, even in the small grid squares in the enterprise. >> Jeff: Right, right. >> So, a sign, and I think that's probably another way to think about the work that we do at SnapLogic and others. >> Jeff: Right, right. >> Other people in the marketplace like us. What we do essentially is we give you the ERP of one. Because if you could choose things that make sense for you and they could work together in a very good way to give you very good fabric for your purposes, you've essentially bought a bespoke suit at rack prices. Right? Without that nine times multiplier of the last century of having to have just consultants without end, darkened the sky with consultants to make that happen. You know? So that, yes, SAS proliferation is happening. That is the opportunity, also the problem. For us, it's an opportunity where that glass is half-full we come in with SnapLogic and knit it together for you to give you fabric back. And people love that because the businesses can buy what they want, and the enterprise gets a comprehensive solution. >> Jeff: Right, right. >> Well, at the risk of taking a very short tangent, that comment about darkening the skies, if I recall, was the battle of the Persians threatening the 300 Greeks at the battle of Thermopylae. >> Gaurav: Yes. >> And they said, "We'll darken the skies with our arrows." And so the Greek. >> Gaurav: Come and get 'em. >> No, no. >> The famous line was, he said, "Give us your weapons." And the guy says, "Come and get 'em." (laughs) >> We got to that point, the Greek general says, "Well, we'll fight in the shade." (all laughing) But I wanted to ask you. >> This is the movie 300 as well, right? >> Yes. >> The famous line is, "Give us your weapons." He said, "Come and get 'em." (all laughing) >> But I'm thinking also of the use case where a customer brings in Workday and you help essentially instrument it so it can be a good citizen. So what does that make, or connect it so it can be a good citizen. How much easier does that mean or does that make fitting in other SAS apps or any other app into the fabric, application fabric? >> Right, right. Look, George. As you and I know, we both had some wonderful runs in the last century, and here we are doing version 2.0 in many ways, again, very similar to the Workday management. The enterprise is hip to the fact that there is a Switzerland nature to making things work together. So they want amazing products like Workday. They want amazing products like the SAP Cloud Suite, now with Concur, SuccessFactors in there. Some very cool things happening in the analytics world which you'll see at Sapphire and so on. So some very, very capable products coming from, I mean, Oracle's bought 80 SAS companies or 87 SAS companies. And so, what you're seeing is the enterprise understands that there's going to be red versus blue and a couple other stripes and colors and that they want their businesspeople to buy whatever works for them. But they want to make them work together. All right? So there is a natural sort of geographic or structural nature to this business where there is a need for Switzerland and there is a need for amazing technology, some of which can only come from large companies with big balance sheets and vertical understanding and a legacy of success. But if a customer like an AstraZeneca where you have a CIO like Dave Smoley who transformed Flextronics, is now doing the same thing at AstraZeneca bringing cloud apps, is able to use companies like SnapLogic and then deploy Workday appropriately, SAP appropriately, have his own custom development, some domestic, some overseas, all over the world, then you've got the ability again to get something very custom, and you can do that at a fraction of the cost of overconsulting or darkening the skies in the way that things were done in the last century. >> So, then tell us about maybe the convergence of the new age data warehousing, the data science pipeline, and then this bespoke collection of applications, not bespoke the way Oracle tried it 20 years ago where you had to upgrade every app tied into every other app on prem, but perhaps the integration, more from many to one because they're in the cloud. There's only one version of each. How do you tie those two worlds together? >> You know, it's like that old bromide, "Know when to hold 'em. "Know when to fold them." There is a tendency when programming becomes more approachable, you have more millennials who are able to pick up technology in a way. I mean, it's astounding what my children can do. So what you want to do is as a enterprise, you want to very carefully build those things that you want to build, make sure you don't overbuild. Or, say, if you have a development capability, then every problem looks like a development nail and you have a hammer called development. "Let's hire more Java programmers." That's not the answer. Conversely, you don't want to lose sight of the fact that to really be successful in this millennium, you have to have a core competence around technology. So you want to carefully assemble and build your capability. Now, nobody should ever outsource management. That's a bad idea. (chuckles) But what you want to do is you want to think about those things that you want to buy as a package. Is that a core competence? So, there are excellent products for finance, for human capital management, for travel expense management. Coupa just announced today their for managing your spend. Some of the work at Ariba, now the Ariba Cloud at SAP, are excellent products to help you do certain job titles really well. So you really shouldn't be building those things. But what you should be doing is doing the right element of build and buy. So now, what does that mean for the world of analytics? In my view, people building data platforms or using a lot of open source and a lot of DevOps labor and virtualization engineering and all that stuff may be less valuable over time because where the puck is going is where a lot of people should skate to is there is a nature of developing certain machine language and certain kind of AI capabilities that I think are going to be transformational for almost every industry. It is hard to imagine anything in a more mechanized back office, moving paper, manufacturing, that cannot go through a quantum of improvement through AI. There are obviously moral and certain humanity dystopia issues around that to be dealt with. But what people should be doing is I think building out the AI capabilities because those are very custom to that business. Those have to do with the business's core competence, its milieu of markets and competitors. But there should be, in a sense, stroking a purchase order in the direction of a SAS provider, a cloud data provider like Microsoft Azure or Redshift, and shrinking down their lift-and-shift bill and their data center bill by doing that. >> It's fascinating how long it took enterprises to figure out that. Just like they've been leveraging ADP for God knows how many years, you know, there's a lot of other SAS applications you can use to do your non-differentiated heavy lifting, but they're clearly all in now. So Gaurav, we're running low on time. I just want to say, when we get you here next year, what's top of your plate? What's top of priorities for 2017? Cause obviously you guys are knocking down things left and right. >> Thank you, Jeff. Look, priority for us is growth. We're a growth company. We grow responsibly. We've seen a return to quality on the part of investors, on the part of public and private investors. And you know, you'll see us continue to sort of go at that growth opportunity in a manner consistent with our core values of building product with incredible success. 99% of our customers are new to our products last quarter. >> Jeff: Ninety-nine percent? >> Yes sir. >> That says it all. >> And in the world of enterprise software where there's a lot of snake oil, I'm proud to say that we are building new product with old-fashioned values, and that's what you see from us. >> Well 99% customer retention, you can't beat that. >> Gaurav: Hard to beat! There's no way but down from there, right? (laughing) >> Exactly. Alright Gaurav, well, thanks. >> Pleasure. >> For taking a few minutes out of your busy day. >> Thank you, Jeff. >> And I really appreciate the time. >> Thank you, Jeff, thank you, George. >> Alright, he's George Gilbert. I'm Jeff Rick. You're watching the Cube from the historic Pagoda Lounge in downtown San Jose. Thanks for watching.

this one minute I'm here with my co-host Jeff Frick who we just fresh off of the AWS summit the Amazon event Jeff and I covered that and we're here at knowledge 13 now this conference is all about the notion of going from IT as a service organization changing high teas mantra from no to now that really is the theme of this conference and we're here with Frank's luton who's the president and CEO of service now Frank welcome back to the cube thanks good to be here that's good to see you again we had you on that vm world is great story when we first introduced service now to our community you just fresh off the keynote fantastic keynote by the way thank you you had strong themes i mentioned the from no to now you talked about itu gave a little little tongue-in-cheek joke about the line outside the the rmv the Registry of Motor Vehicles and that's sort of the the idea is you guys are transforming IT from an organization that is trying to manage demand push off demand saying no we'll get it in six months it'll cost you five million dollars to one that really is redesigning IT processes around the globe so first of all welcome back congratulations how do you feel after that keynote I have to work a lot of energy in that room and it was electrifying it was awesome well one of the one of the guys in the panel stopped when you had asking the question I think was the guy from NY yes he said even stop you looked at the audience said i love this crowd that was a great crowd we gave a little goop out to the audience so talk about from know to now how'd you come up with that theme and you know give us a little color behind you know it's it's actually not easy for for us to communicate about service now desk to to lay people in sight unless you have lived in sight I t you just most people don't even know what I t really does on the day-to-day basis right so we've lived a fairly insular existence because you know everybody knows what sales people do and to some degree about HR doesn't finance people but I t it's a bit of a you know a bit of a mystery to what most folks do right but most people do know however is that the service experience with IT has been and challenging what's all we say I mean it's been you know sort of a service experience where if you have to ask the answer was going to be no right because IT organizations have been super preoccupied with infrastructure rapid change in the infrastructure for the last 30 40 years nothing ever set still long enough for us to really master the architecture and the platforms are really stabilizing mature our systems and they have to keep moving so you get pretty cranky it's back to your organization having to live that kind of life so their their their reputation for service has not been stellar and I love making the joke during the keynote their ranking right down there with legal in the basement you know of the corporate enterprise you know so well so talk a little bit about sort of how you guys you know go into an organism's you start with the IT organization right in helping them sort of automated processes connect all these different processes but you've been through your platform expanding out to other parts of the organization the irony is that I T which is the most technology savvy organization in the price as the least management sophistication in terms of managing their own activity which you know I duck to the CIO of a very large consumer gets company he said where does she make her son it's inexcusable right here here we are running milk that going in dollar budgets and staffs with tens of thousands of people and we're running it on spreadsheets email excel project management tools this is ridiculous right we don't have real information in near real time and show that we can drive our business as opposed to being driven by it right i key executives have a tendency to run from one crisis to another with their hair on fire and that's sort of the mental model and a note of now message is about out of a get these people out of this you know reactive crisis mode to where they become full-blown business partners and they start you know bring your guide to enterprise and in a very transformative way or they become the people that bring innovation to the enterprise you know here's so much Frank about shadow I teach my colleague Jeff Frick and I were at the AWS some of the few weeks and you see a lot of these cloud companies you mentioned your keynote Salesforce the salespeople workday talk to HR people they sort n run IT certainly amazon is the poster child for shadow IT but you know Jeff we have that sort of notion where IT people are not the center of the new cloud universe but that's different for service now yes it's very different but the other thing brought up amazon your keynote and how they've kind of fine what kind of a user expectation experiences with an application on the web a level of service a level of delivery and then you've got AWS its kind of the girl child of shadow IT but you guys are coming in really as the enabler to let the internal IT guys actually have the tools to compete with with guys trying to go around it really exact with delivery platform I mean we're trying to turn the tables here right because the entire history of IT is one big end around righty the many computer was an end-around of the glasshouse client-server was really pcs you know dribbling into departmental environments suffer as a service was an incredible end around people in there didn't realize it was seeping into the enterprise right now things like 80 lbs now infrastructure right is actually finding its way so we're saying look you know worthy Enterprise IT cloud company right we are going to empower and enable IT to be driving rather than just being driven and being taken over and run over by by events because that's what's been happening here's the goodness IT can start withdrawing and getting out of the business of infrastructure which is what they've been doing forever infrastructure is very challenging pretty soon that's going to be somebody else's problem right infrastructure goes behind the cooking all you have to do is in network connection so that means that the role of IT is moving from you know keeping the lights on to you know we're going to be the people who are experts at defining structuring and automating service relationships and so does relationship management I mean at this and I make a joke about you know your hole in the inbox of email you know it's full of basically service relationships that are unstructured and unlimited and undefined right right and there is this incredible opportunity to go aptet with record-keeping workflow systems and that's what we want to enable and empower IT to do right we had to give you a quick example actually very interesting we talked to our one of our very large retail customers and the supply chain office unbeknownst to us went to IT and said hey we want to build this app what should we use and Ikey said no you should try and do that on service now what's the app a supply chain office in a retail environment what they do is they take requests all day long stores distribution centers suppliers and they're rebalancing you know product right place right time right right product and they were doing that everybody running spreadsheets and emails and people constantly calling what's the update on my request and they decide no we're going to go to a record-keeping workflow system and from the moment you know they started using that system all of a sudden they had full visibility to a what the volume was of issues that was coming in but the nature of the volume was how well they were doing on their SOS relative to their storage and distribution centers and they were able to structurally go after you know the things that were a constant them grief because they just didn't know right so very simply in very short period of time you know they transformed themselves from the supply chain all those Devils running around like a chicken with his head cut off the people that were actually driving to supply chain now now supply chain management in the retail organization it's super mission-critical right because their results are directly impacted by having right product right time right place simple example where we moving from email and Excel to a record-keeping workflow system any impact with literally within 30 40 days is enormous yeah you hear that a lot of people just using Excel using email we talked to we talking some customers last night we talked to some perspective customers that were in so to check it out and they were big Lotus no shop and is describing sort of the difficulties and challenges of it you will sign them up I can almost see it but the other thing so so this notion of your customer base is very powerful in fact I tweeted out I said the service now has a sick logo basis and we said is that a typo said no sick like that sick touchdown catch it isn't good yeah sick is it good but I mean which I we hear from land o lakes Red Hat metropcs KPM nor Brent I mean just on and on and on at Facebook Intel google or customers what are some other favorite customer stories you hear a lot of the same themes Frank you know we used to use spreadsheets with using email or reliant on all these disparate processes bringing them all together getting some some other you know favorite stories of yours for customers I I relayed a bunch of him on stage this morning right beasties it's just extraordinary to me the the corporate America I mean you mentioned some of them but you know the people we had on stage you know AIG you know coca-cola company's general electric demand this is United States Army right and they owe is yeah New York Stock Exchange eli lilly big pharmaceuticals bristol-myers squibb they all have the same set of issues they have a completely fractured fragmented sprawled acti environment right and here's the interesting history we have not had CIOs that long you know I T used to report into a division next sag or a regional exact and there really wasn't one person that was responsible for running IT throughout the global enterprise because it was just a decentralized function by the way example when you in Europe yeah I ray mighty and I certainly wasn't IT guy stuff and by the way it wasn't my priority either you know it was just by the way that's for some of the history you know comes from so CIO comes in and they are now charged with you're going to run this thing they're not running anything they're being run by it right so until you get to global IT processes I mean City another you know big name they set to as rogue global bank that we don't have global IT right it is the inefficiency and the lack of ability to drive and manage is unacceptable for these very sophisticated large institutions it's embarrassing really you know yeah I mean you really can't go global as a come you can't scale your business not having all these surprises so to me it's about global scaling and it's about the business value of both having ITB accountable but also have the metrics and the visibility to be able to demonstrate the value to the organization you see i SAT with our executive sponsor from bristol-myers squibb last night and she said i got data and i got it in real time and i know it's good so I'm not putting my service providers on their heels you know before they were you know everything was you know in the realm of you know interpretation and fuzzy fuzzy right and now it's like I have data and I'm driving and I'm changing behavior right so the empowering effective it has mighty organizations it's just stomach right I thought that empowering note that came up in your keynote was interesting how the IT organizations themselves and their presentation now to their internal customers are looking more like a company you know they're they're being cute there yeah I'm taking branding they're there they're not just button pushers in and as you said you know infrastructure operators they are trying to be contributors to the business and keeping some this automobile shade of nail them to it's even stronger than now yes they want to be contributors to the business but they want to be the playmakers they wanted me to go to guys give me the ball you know that that's where we want to you know take itt there that people that really understand how to change how work gets done the enterprise I thought you characterize the dwelling experience in IT people have been running from crisis to crisis and they need to be more proactive so talk about how your system allows them to be more proactive well it's all about going from a message oriented environment to a system or an a message or environment is the one way l know it's email it's text you know it's voice right that doesn't work because you know we're just talking right systems have the ability to drive behavior because you know every time you send an email you should think to yourself could i create a service request instead right because a service request has a defined data ship it goes into a database it gets assigned you know in a workflow operation it has metrics around it if it doesn't get responded to a certain amount of time it gets accelerated to the escalator to the next level or management right so the process is defined structure to automate it is going to run its course right whether you know people are participating in it or not with this great example one of our customers equinix delilah or Brian Lily's here actually is a CIO and he said they will sell funny you know we have a system that all my life cycle application where our developers check-in fixes and enhancement to a particular software release for an application and he says because they know to work flows is completely structured an automated everybody knows that they don't get their fixes enhancement in by a certain time poof the dashboards pop the higher-ups see you know who's behind and who's not and that the threat alone of the transparency and visibility that the process introduces causes everybody there run harder right so people won't have to run around with the whip like where are you you know the process is driving is like a hamster on a treadmill you know so Freki used amazon as an example of the user experience that you know you covet as a CEO of this company and you believe you're your customer base desires at the back end also when you talk about companies like Amazon and Facebook and Google they are super highly automated you also talked about lights out automation yeah now normally IT organizations are managed now they're managed by humans they're not highly automated are you are you seeing your customers able to get to that sort of vision that you're talking about that lights-out automation almost like the hyperscale guys you know it's a super important custody I said during the cleanup or were overstaffed and under automated NIT we have reams of people on staff any large financial institutions have tens of thousands of people on staff they're bigger than any technology company right why is that it's because things are very laborious laborious and manual right the processes that they run require so many touch points I mean one of the things that we always tell our customers when you can reimplement these processes do not take your legacy forward because your legacy is very manual you remember the inbox in the outbox when we have physical in boxes and other boxes and now we know we have our laptop why do we have an inbox and outbox right does this message really this cross why are you even involved in this process right so we have to invert the process it's not like wouldn't it be nice for you to be involved in this process there'd better be a very good reason for you to touch this process because the moment you touch it you know we're going from the speed of light to you know the speed of the dirt road that Franco so service now is really in a rocket ship right now and you've demonstrated you've got a track record of being able to be sometimes call jump three myself throwing gasoline on the fire you look very good at that you got 1,600 customers you're growing like crazy but you're under penetrated in your target which is the global 2000 you're only fourteen percent penetrated in the global 2000 so get a long way to go in this journey we're very excited to be you know covering this event really appreciate you guys having us here Frank's loot Minh will give you the last word and then we'll wrap you know this is actually one of the great things that we are so on the front hood and they're penetrated because our investors are like wow you've got a lot of runway you know considering the size company that we we already are and you know the rate of monetization of our business is is extraordinarily I in other words the share of wallet that service now represents and the enterprise is so much larger than people had ever considered or thought because it was not an existing category that was fully metastasized and visible it's new it's emergent it is really transforming how people you know look at technology and process automation and so on now we're gonna be here all week covering knowledge we've got it we're going to double-click on so how is it that service now is able to deliver this cloud functionality the secret is in the single system of record the CMDB and that is not a trivial thing to do we didn't talk about that with Frankie could talk about it but we don't want to steal you know the name of thunder yeah fred muddies going to be on RNA Justin who's the CTO we're going to go deep into sort of how service now actually accomplishes this architecture Lee what their vision is so Frank thanks very much for spending so much time I know you're busy you got to run but appreciate you coming on terrific thanks for having me alright thanks for watching everybody keep it right there we'll be right back with more we're live from Las Vegas ServiceNow knowledge we'll be right back this is the Q cute baby rock and roll

>>we're back. >>This is Dave Volante. I'm with Wiki bond dot Oregon. This is Silicon angle's the cube where we extract the signal from the noise. We go into the events, we're bringing you the best guests that we can find. And we're here at the AWS summit. Amazon is taking the cloud world by storm. He was on, invented the cloud in 2006. They've popularized it very popular of course with developers. Everybody knows that story. Uh, Amazon appealing to the web startups, but what's most impressive is the degree to which Amazon is beginning to enter the enterprise markets. I'm here with my cohost Jeff Frick and Jeff, we heard Andy Jassy this morning just laying out the sort of marketing messaging and progress and strategies of AWS. One of the things that was most impressive was the pace at which they put forth innovations. We talked about that earlier, but also the pace at which they proactively reduce prices. Uh, that's different than what you'd see in the normal sort of enterprise space. Talk about that a little bit. >>Yeah. Again, I think it really speaks to their strategy to lock up the customer. It's really a lifetime value of the customer and making sure that they don't have a really an opportunity or a reason to go anywhere else. So as we discussed a little bit earlier, they leverage, you know, kind of the pure hardware economics of, of decreasing a computing power, decreasing storage, decreasing bandwidth, but then they also get all the benefits of scale. And I think what's in one of the interesting things that Andy talked about and kind of his six key messages was that it's actually cheaper to rent from them because of the scale than it is to buy yourself. And I know that's a pretty common knock between kind of a build or buy, um, kind of process you go through and usually you would think renting at some scale becomes less economical than if you just did it yourself. But because their scale is so massive because of the flexibility that you can bring, uh, computing resources to bear based on what you're trying to accomplish really kind of breaks down the, uh, the old age old thought that, you know, at scale we need to do it ourselves. >>Well, and that's the premise. Um, I think, and, uh, let's Brits break down a little bit about that, that analysis and, and Andy's keynote. So he put forth some data from IDC which showed that, uh, the Amazon cloud is cheaper than the, uh, a, a so-called private cloud or an in house on premise installation. You know, I certainly, there's, it's, it's a, it's an, it's depends, right? It really depends on the workload. That's somewhat of an apples to orange is going on here and the types of workloads that are going down in the AWS cloud, granted he's right and that they're running Oracle, they're running SAP, but the real mission critical workloads, what he calls mission critical aren't the same as what, you know, Citi would call mission critical. Right? So to replicate that level of mission criticality, uh, would probably almost most certainly be more expensive rental versus owning the real Achilles heel of, of, of any cloud, not just Amazon. >>Cloud really is getting data out. Um, moving data, right? Amazon's going to charge you not to get data in. They're gonna charge you to store it there to exercise, you know, compute. Uh, and then, but they're also gonna charge it if you wanted to take it out. That's expensive. The bandwidth costs and the extrication costs are expensive. Uh, the other issue with cloud again is data movement. It takes a long time to move a terabyte, let alone multiple terabytes. So those are sort of the two sort of Achilles heels of, of cloud. But that's not specific to Amazon's cloud. That's any cloud. Yeah. So we've got a great lineup today. Um, let's see. We've got Ariel Kelman coming on, uh, and I believe he's in the house. So we're going to take a quick break. Quick break. Right now we right back with Ariel Kelman, who's the head of marketing at AWS. Keep right there. This is the cube right back. >>we lift out all the programs out there and identified a gap in tech news coverage. Those shows are just the tip of the iceberg and we're here for the deep dive, the market beg for our program to fill that void. We're not just touting off headlines. We also want to analyze the big picture and ask the questions that no one else is asking. We work with analysts who know the industry from the inside out. So what do you think was the source of this missing? So you mentioned briefly there are, that's the case then why does the world need another song? We're creating a fundamental change in news coverage, laying the foundation and setting the standard, and this is just the beginning. We looked on all the programs out there and identified a gap in tech news coverage. There are plenty of tech shows that provide new gadgets and talk about the latest in gaming, but those shows aren't just the tip of the iceberg. And we're here for the deep dive. >>Okay, >>Dave Olanta. I'm with Wiki bond.org and this is Silicon angle's the cube where we extract signal from the noise. We bring you the best guest that we can find. We go into events like ESPN goes into sporting events, we go into tech events, we find the tech athletes and bring to you their knowledge and share with you our community. We're here at Moscone in San Francisco at the AWS summit. We're here with Arielle Kellman who's the head of worldwide marketing for AWS. Arielle, welcome to the cube. Thanks for having me, Dave. Yeah, our pleasure. I really appreciate you guys having us here. Great venue. Uh, let's see. What's the numbers? It looks like you know, many, many thousands, well over 5,000 people here by four or 5,000 people here. We're doing a about a dozen of these around the world, one to 4,000 people to help educate our customers about all the new things we're doing, all the new partners that are available to help them thrive in the AWS cloud. >>It's mind boggling the amount of stuff that you guys are doing. We just heard NG Jesse's keynote, for those of you who saw Andy's keynote at reinvent, a lot of similar themes with some, some new stuff in there, but one of the most impressive, he said, he said, other than security, one of the things that we're most proud of is the pace at which we introduce new services. And he talked about this fly wheel effect. Can you talk about that a little bit? Sure. Well, there's kind of two different things going on. The pace of innovation is we're really trying to be nimble and customer centric and ultimately we're trying to give our customers a complete set of services to run virtually any workload in the cloud. So you see us expanding a broader would additional services. And then as we get feedback we add more and more features. >>Yeah. So we're obviously seeing a big enterprise push. Uh, Andy was, was very, I thought, politically correct. He said, look, there's one model which is to keep charging people as much as you possibly can. And then there's our model, which is we proactively cut prices and we passed that on to customers. Um, and, and he also stressed that that's not something that's not a gimmick. It's not a sort of a onetime thing. Can you talk about that in terms of your philosophy and your DNA? It's just our philosophy. It's actually a lot less dramatic than is often portrayed in the press. Just the way we look at things as we're constantly trying to drive efficiencies out of our operations. And as we lower our cost structure, we have a choice. We can either pocket those savings as extra margin or we can pass those savings along to our customers in the form of lower prices. >>And we feel that the ladder is the approach that customers like and we want to make our customers happy. So this event, uh, we were talking off camera, you said you've been doing these now for about two years. You do re-invent once a year. That's your big conference out in Vegas and it's a very, very large event, very well attended. And you do these regionally and in and around the world, right. Talk about that a little bit. We do about a dozen of these a year. Um, we did, uh, New York a couple of weeks ago, London, Australia and Sydney. I'm going to go to India and Tokyo, really about a dozen cities in the world and it's a little tactic. I'm not going to beat all of them, but you know, the focus is to really, uh, deliver educational content. Uh, we'll do about maybe 12 to 16 technical breakout sessions all for free, uh, for, for customers and people who want to learn about AWS for the first time. >>And the, and the audience here is largely practitioners and partners, right? Can it talk about the makeup a little bit? Sure. It's a pretty diverse set of people. Um, we have a technical executives like CEOs and architects and we have lots of developers and then lots of people from our, our partner ecosystem of integrators wanting to, um, you know, brush up on the latest technologies and skills and a lot of people who just want to learn about the cloud and learn about AWS. I think there are a lot of misconceptions about AWS and I'd like to just tackle some of those with you if I may. So let me just sort of, let's list them off and you can respond. Yeah, we'll let our audience to sort of decide. So the first is that AWS has only tested dev workloads. Can you talk about that a little bit? >>Sure. Um, well test and dev local workloads are very popular. We saw, we covered that in the keynote. Um, and it's often a place where it organizations will start out with AWS, but it is by no means the most popular or most dominant workload. We have a lot of people migrating, uh, enterprise apps to the cloud. Um, if you look at, uh, in New York, uh, in our summit we talked about Bristol Myers Squibb, uh, running all of their, um, clinical trial simulations and reducing the amount of time it takes to run a simulation by 98%. Uh, if people are running Oracle, SharePoint, SAP, pretty much any workload in the cloud. And then another popular use is building brand new applications, uh, for the cloud. You can miss, some people call them cloud native applications. A good example is the Washington post who built an app called the social reader that delivers their content to Facebook and now as more people viewing their content, their than with their print magazines and they just couldn't have done that, uh, on premises. >>So, uh, the other one I want to talk about, we're going to do some serious double clicking on security so we don't have to go crazy on it, but, but there's a sort of common perception that the cloud is not secure. What do you guys say about that? Yeah, so, um, really our number one priority is security. You're looking at a security, operational performance, uh, and then our pace of innovation. But with security, um, what we want to do is to give enterprises everything they need to understand how our security works and to evaluate it and how it meets with their requirements for their projects. So it really all starts with our, our physical security, um, our network security, the access of our people. They're all the similar types of technologies that our customers are familiar with. And then they also tend to look at all the certifications and accreditations, SAS 70 type two SOC one SIS trust. >>I ATAR for our government customers. And then I think it was something a lot of people don't understand is how much work we've put into the security features. It's not just is the cloud secure, but can I interact and integrate, uh, your security functionality with all of my existing systems so we can integrate with people's identity and access systems. You could have a private dedicated connection from your enterprise to AWS with direct connect to, I really encourage anyone who has interest in digging into our security features to go to the security center and our website. It's got tons of information. So I'm putting on the spot. Um, what percent of data centers in the world have security that are, that is as good or better than AWS. It'd be an interesting thing for us to do a survey on. But if you think about security at the infrastructure layer down is what we take care of. >>Now when you build your application, you can build a secure app or non-secure app. So the customer has some responsibility there. But in terms of that cloud infrastructure, um, for a vast majority of our customers, they're getting a pretty substantial upgrade in their security. And here's something to think about is that, um, we run a multitenant service, so we have lots and lots of customers sharing that infrastructure and we get feedback from some of the most security conscious companies in the world and government agencies. So when our customers are giving us a enhancement request, and let's say it is, uh, an oil company like shell or financial services company like NASDAQ, and we implement that improvement because there's always new requirements. We implement that all of our hundreds of thousands of customers get those improvements. So it's very hard for a lot of companies to match that internally, to stay up to speed with all the latest, um, requirements that people need. >>Yeah. Okay. So, uh, and you touched on this as well as the compliance piece of it, but when you think of things like, like HIPAA compliance for example, I think a lot of people don't realize that you guys are a lead in that regard. Can you talk about that a little bit more? Yeah. So, uh, we have a lot of customers running HIPAA compliant, uh, workloads. Um, there's, there's one company or the, the Schumacher group, which does emergency room staffing out of Lafayette, Louisiana. And we, companies like that are going through the process. They have to follow their internal compliance guidelines for implementing a HIPAA compliant plan app. It's actually, it's more about how you implement and manage the application than the infrastructure, which is part of it. But we, we satisfy that for our customers. Let's talk a little bit about SLA. That didn't come up at least today in Andy's keynote, but it didn't reinvent and he made a statement at reinvent. >>He said, we've never lost a piece of business because of SLS. And that caught my attention and I said, okay, interesting. Um, talk about, uh, the criticisms of the SLA. So a lot of people say, wow, SLA, not just of Amazon's cloud, but any public cloud. I mean, SLA is a really a, in essence, a, an indication of the risk that you're able to take and willing to take. What are your customers tell you about SLS? The first thing is we don't hear a lot of questions about SLS from our customers. Some customers, it's very important that we have SLA is for most of our services, but what they're usually judging us on is the operational track record that we provide and doing testing and seeing how we operate and how we perform. Uh, and, uh, we had an analyst from IDC recently do a survey of a bunch of our customers and they found that on average the average app that runs on AWS had 80% less downtime than similar apps that are running on premises. >>So we have a lot of anecdotal evidence to suggest that our customers are seeing a reliability improvement by migrating their apps to AWS. You're saying don't judge us on the paper, judge us on our actual activities in production and in the field. Typically what most of our customers are asking for is they want to dig into the actual operational features and, and a track record. Now the other thing I want to address is the so called, you know, uh, uh, exit tax, right? It's no charge to get my data in there. I keep my data in there. You, you, you charged me for storing it for exercise and compute activity, but it's expensive to get it out. Um, how do you address that criticism? Well, our pricing is different for every service and we really model it around our customers to both really to really satisfy a broad set of use cases. >>So one example I think you may be talking about is I would Amazon glacier archive service, which is one penny per gigabyte per month. And for an archive service, we figured that most people want to keep their data in there for a long period of time so that we want to make it as cheap as possible for people to put it in. And if you actually needed to pull it out, the reason is because you may have had some disaster or you accidentally deleted something and that you are going to be, uh, you're going to be retrieving data on a far less frequent basis. So on an overall basis for most customers it makes sense that we could have done is made the retrieval costs lower and then made the storage costs higher. But the feedback we got from customers is, you know, archiving a majority of customers may never even retrieve that data at all. >>So it ended up being cheaper for a vast majority of our customers. I mean that's the point of glacier. If you put it there, you kind of hope you never have to go back and get it. Um, the other thing I wanted to ask you about is some of the innovations that we've seen lately in the industry, like a red shift, right? The data warehouse, you mentioned glacier. It was interesting. Andy said that glacier is the fastest growing service in terms of customers. Red shift was the fastest growing service, I guess overall at NAWS. So Redshift is an interesting move for you guys. Uh, that whole big data and analytics space. What if you could talk about that a little bit? If you talk to it, executives in the enterprise and even startups now, they have to analyze lots of data. Building a big data warehouse is, is one of the best examples of how much the pain of hardware and software infrastructure gets in the way of people. >>And there's also a gatekeeping aspect to it. If you're working in a big company and you want to run, you have a question and a hypothesis, you want to run queries against terabytes and petabytes of data, you pretty often have to go and ask for permission. Can I borrow some time from the data warehouse? No, no, no, no. You're not as important. Well, what are customers going to go, Hey, I'm going to go load the data, load a petabyte of data, run a bunch of analysis, and shut it down and only pay for a few hours. So it's not just about making a cheaper, it's about making use of technology possible where it was just not possible in feasible and cost prohibited before. Yeah, so that's an important point. I mean, it's not, it's not just about sort of moving workloads to the cloud, you know, the old saying a my mess for less. >>It's about enabling new business processes and new procedures and deeper business integration. Um, can you talk about that a little bit more? Add a little color to that notion of adding value beyond just moving workloads out of, you know, on premise into the cloud to cut costs, cut op ex, but enabling new business capabilities. When you remove the infrastructure burden between your ideas and what you want to do, you enable new things to be possible. I think innovation is a big aspect of this where if you think about if you reduce the cost of failure for technology projects so much that approaches zero, you change the whole risk taking culture in a company and more people can try out new ideas and companies can Greenlight more ideas because if they fail it doesn't cost you that much. You haven't built up all this infrastructure. So if you have more ideas that are, that are cultivated, you end up with more innovation. >>Whereas before people are too afraid to try new things. So I'm a reader of of Jeffrey's a annual letters. I mean I think they're great. They're Warren buffet like in that regard. One of the exact emphasizes, you know this year was the customer focus. You guys are a customer focused organization, not a competitive focused organization. And again, you got to recognize that both models can work, right? Can you talk about that a little bit? Just the church of the culture. Yeah, I mean when, you know, starts out with how we build our products. Anyone who has a new idea for a product, first thing they got to do is write the press release. So what our customers are going to see is it valuable to them. And then we get come get products out quickly and then we iterate with customers. We don't spend five years building the first version of something. >>We get it out quickly. Uh, sort of the, the, the lean startup, if you heard of the minimum viable product approach, get it out there and get feedback from customers. Uh, and iterate. We don't spend a lot of time looking at what our competitors are doing cause they're not the ones that pay our bill. They're not the ones that can hire and fire us. It's the customers. So I'm you've seen this thing come, you know, quite a ways. I mean, you were at Salesforce, right? Um, which I guess started at all in 99. You could sell that, look at that as the modern cloud sort of movement was, wasn't called cloud. And then you guys in 2006 actually announced what we now know is, you know, the cloud, where are we in terms of, you know, the cloud, you know, what ending is it? To use the sports analogy, I don't know what ending is it, but you know, it's an amazing time where there's such a massive amount of momentum of adoption of the cloud from every type of company, every type of government agency. >>But yet still, when you look at the percentage of it spend or you go talk to a large company and you say, even with all these projects, what percentage of your total projects, there's still tremendous growth ahead of us. Yeah. So, um, there's always that conversation about the pie charts. 70% of our, our effort is spent on keeping the lights on. 30% is spent on, on innovation. And I don't know where that number came from but, but I think generally anecdotally it feels about right. Um, talk about that shift. Yeah. Well I mean your customer base, you talk to any CIO, they don't like the idea of having 80% of their staff and budget being focused on keeping the lights on and the infrastructure would they like to do is to really shift the mix of what people are working on within their organization. It's not about getting rid of it, it's about giving it tools so that every ounce of effort they're doing is geared towards delivering things to the business. >>And that, that, that's what gets CIO is excited about the cloud is really shifting that and having a majority of their people building and iterating with their end users and with their customers. So we talked about the competition a little bit. I want to ask you a question in general, general terms, you guys have laid out sort of the playbook and there's a lot more coming. We know that, uh, but you know this industry quite well. You know, it's very competitive. People S people see what leaders are doing and they all sort of go after it. Why do you feel confident that AWS will be able to maintain its lead and Kennedy even extend its lead in why? Well, there's a couple things that we sort of suggest for customers to look at. I think first of all is the track record and experience of when you're looking at a cloud provider, have they been in this business for a long time? >>Do they have a services mentality where they've had customers trust them for their, for applications that really they trust their business on? Um, and then I think secondly, is there a commitment to innovation? Is there a pace of new features and new technologies as requirements change? And I think the other, the other piece that our customers really give us a lot of feedback on is that they can count on us Lauren prices, they can count on a real partnership as we get better at this and we're always learning as we get better and we reduce our cost structure, they're going to get to benefit and lower their costs as well. So I think those are kind of big things. The other thing is, is the customer ecosystem I think is a big part of it where, um, you know, this is technology. Uh, people need advice, they need, uh, best practices. >>They often need help. And I'm in a kind of analogy I make is if I have a problem with my phone, with my iPhone, I can probably close my eyes and throw it, I'm going to hit someone who also has an iPhone. I can ask them for help. Well, if you're a startup in San Francisco or London or if you're an enterprise in New York or Sydney, odds are that your colleagues, if they're doing cloud, they're doing it with AWS and you have a lot of people to help you out. A lot of people to share best practices with. And that's a subtle but important point is as, as industry participants begin to aggregate within your cloud, there's a data angle there, right? Because there's data that potentially those organizations could share if they so choose to a, that is a, that is a value. And as you say, the best practice sharing as well. >>I have two last questions for you. Sure. First is, is what gets you excited in this whole field? I think it's like seeing what customers are doing. I mean, that's the cool thing about, uh, offering cloud infrastructure is that anything is possible. Like we met Ryan, uh, who spoke from atomic fiction. These guys are the world's first digital effects agency that's 100% in the cloud. And to see that they made a movie and all the effects like the Robertson mech, his flight film without owning a single server, um, it's just, it's amazing. And to see what these guys can do, how happy they are to have a group of 30, 40 artists that, um, can say yes when the director says I want it to do differently. I want to add, go from 150 to 300 shots and to see how happy and excited they are. >>I mean that, that's what motivates me. Yeah. Okay. And then my last question, Ariel, is, um, you know, what keeps you up at night? What worries you? Well, I think, you know, the most important thing that we can't forget is to really keep our fingers on the pulse of the customers and what they want, and also helping them to figure out what they want next. Because if we don't keep moving, then we're not going to keep pace with what the customers want to use the cloud for. All right, Ariel Kelman thanks very much. Congratulations on the Mason's progress and we'll be watching and, and really appreciate, again, you having us here. Appreciate your time coming on. Good luck with the rest of the tour. I hope you don't have to do every city. It sounds like you don't, but, uh, but if it sounds like you've enjoyed them, so, uh, congratulations again. Great. All right. This is Dave Milan to keep it right there. This is the cube. We'll be back with our next guest right after this word.