>> Narrator: From Sand Hill Road, in the heart of Silicon Valley, it's the CUBE. Presenting, The People First Network, insights from entrepreneurs and tech leaders. >> Hello everyone welcome to this special CUBE conversation, I'm John Furrier, host of theCUBE. We're here at Mayfield Fund, on Sand Hill Road and Menlo Park. As part of Mayfield's People First Network, co-creation with SiliconANGLE and theCUBE and Mayfield. Next guest, Beth Devin, Managing Director of Innovation Network and Emerging Technologies at Citi Ventures. Thanks for coming on. >> Thanks for having me. >> Hey, thanks for coming in. We're here for the Mayfield fiftieth anniversary, where they're featuring luminaries like yourself, and we're talking about conversations around how the world's changing and the opportunities and the challenges can be met, and how you can share some of your best practices. Talk about what your role is at Citi Ventures and what your focus is. >> Sure, sure, and boy howdy, has it been changing. It's hard to keep up with. I've been at Citi Ventures about two years and one of the reasons I joined was to stand up an Emerging Technology practice. Citi Ventures does a lot of work in corporate venture investing. We tend to be strategic investors, for start up companies that are aligned with the strategy of Citi, as well as our client. We serve probably, eighty percent of the Fortune Five Hundred companies in the world. But we also are a really important part of the innovation ecosystem at Citi. Which is looking at how to drive culture change, broaden mindset, and really, enlist our employees to be part of the innovation process. So, we have an internal incubator, we have a Shark Tank-like process we call Discover Ten X. And what I really bring to the table with my team is monitoring, and learning about, and digesting technology that's not quite ready for commercialization but we think it might be disruptive in a good or challenging way for the bank or our clients. We try to educate and provide content that's helpful to our executives, and just the employee body at large. >> I want to get into a LinkedIn post you wrote, called the Tech Whisperer, which I love. >> Thank you. >> You're there to identify new things to help people understand what that is. But that's not what you've done. You've actually implemented technology. So, on the other side of the coin, in your career. Tell us about some of the things you've done in your career, because you've been a practitioner. >> Beth: Yeah. >> and now you're identifying trends and technologies, before you were on the other side of the table. >> That's right, and sometimes I'll tell you, I have that itch. I miss the operator role, sometimes. Yeah, you know, I feel so fortunate I sort of stumbled on computer science early when I was going to school. And, the first, I'd say twenty years of my career, were working in enterprise I.T, which at that time I couldn't even have made that distinction, like why do you have to say enterprise I.T. I was a software developer, and I was then a DBA, and I even did assembler language programing. So way back when, I think I was so fortunate to fall in to software engineering. It's like problem solving, or puzzle making, and you with your own brain and sort of typing can figure out these problems. Then over the years I became more of a manager and a leader, and sort of about a reputation for being somebody you could put on any hard problem and I'd figure a way out. You know tell me where we're trying to go it looks knotty, like not a fun project, and I would tackle that. And then I'd say, I had some experience working in lots of different industries. Which really gave me an appreciation, for you know, at the end of the day, we can all debate the role that technology plays in companies. But industries, whether it's health care or media, or financial services. There's a lot of the same challenges that we have. So I worked at Turner Broadcasting before it was acquired, you know by Time Warner and AOL. And I learned about media. And then I had a fantastic time working at Charles Schwab. That was my first big Financial Services role when it came back to the bay area. I worked at Art.Com, it was a need converse company, the first company I worked at where I was in charge of all the technology. We had no brick and mortar, and if the technology wasn't working, we weren't earning revenue, in fact, not only that, we were really making customers angry. I also had a role at a start up, where I was the third person to join the company, and we had a great CEO who had a vision, but it was on paper. And we hadn't really figured out how to build this. I was very proud to assemble a team, get an office, and have a product launch in a year. >> So you're a builder, you're a doer, an assembler, key coding, hexadecimal cord dumps back in the day. >> Way back when. We didn't even have monitors. I'll tell ya, it was a long time ago. >> Glory days, huh? Back when we didn't have shoes on. You know, technology. But what a change. >> Huge change. >> The variety of backgrounds you have, The LinkedIn, the Charles Schwab, I think was during the growth years. >> And the downturn, so we got both sides. >> Both sides of that coin, but again, the technologies were evolving. >> Yes. >> To serve that kind of high frequency customer base. >> Beth: That's right. >> With databases changing, internet getting faster. >> It has. >> Jeff: More people getting online. >> We were early adopters, I'll tell you. I still will tell people, Charles Schwab is one of the best experiences I have, even though at the end I was part of the layoff process. I was there almost seven years, and I watched, we had crazy times in the internet boom. Going in 98, 99, 2000, I can't even tell you some of the experiences we had. And we weren't a digital native. But we were one of the first companies to put trading online, and to build APIs so our customers could self service, and they could do that all online. We did mobile trading. I remember we had to test our software on like twenty different phone sets. Today, it's actually, so much easier. >> It's only three. Or two. Or one. Depending on how you look at it. >> That's right. We couldn't even test on all the phone sets that were out then. But that was such a great experience, and I still, that Schwab network, is still people I'm in touch with today. And we all sort of sprinkled out to different places. I think, I dunno, there's just something special about that company in terms of what we learned, and what we were able to accomplish. >> You have a fantastic background. Again the waves of innovation you have lived through, been apart of, tackling hard problems, taking it head on. Great ethos, great management discipline. Now more than ever, it seems to be needed, because we're living in an age of massive change. Cause you have the databases are changing, the networks changing, the coding paradigms changing. Dev ops, you've got the role of data. Obviously, mobile clearly is proliferated. And now the business models are evolving. Now you got business model action, technical changes, cultural people changes. All of those theaters are exploding with opportunity, but also challenges. What's your take on that as you look at that world? >> You know, I'm a change junkie, I think. I love when things are changing, when organizations are changing, when companies are coming apart and coming together. So for me, I feel like, I've been again, so fortunate I'm in the perfect place. But, one of the things that I really prided myself on early in my career, is being what I call the bridge, or the, the translator between the different lines of business folks that I work with. Whether it was head of marketing, or somebody in a sales or customer relationship, or service organization, and the technology teams I built and led. And I think I've had a natural curiosity about what makes a business tick, and not so much over indexing on the technology itself. So technology is going to come and go, there's going to be different flavors. But actually, how to really take advantage of that technology, to better engage your customers, which as you said, their needs and their demands are changing, their expectations are so high. They really set the pace now. Who would have though that ten years ago we'd live in an environment where industries and businesses are changing because consumers have sort of set the bar on the way we all want to interact, engage, communicate, buy, pay. So there's this huge impact on organizations, and you know, I have a lot of empathy for large established enterprises that are challenged to make it through this transformation, this change, that somehow, they have to make. And I always try to pay attention on which companies have done it. And I call out Microsoft as an example. I can still remember several years ago, being at a conference. I think it was Jeffrey Moore who was speaking, and he had on one slide... Here's all the companies in technology that have had really large success. Leading up to the internet boom days, there would be a recipe for the four companies that would come together. I think it was Sun, Oracle, and Microsoft. And then he said, and now here's the companies of today. And most young people coming out of college, or getting computer science degrees won't use any of these old technology companies. But Microsoft proved us all wrong, but they did it, focused on people, culture, being willing to say where they screwed up, and where they're not going to focus anymore, and part ways with those parts of their business. And really focus on who are their customers, what are their customer needs. I think there's something to be learned from those changes they made. And I think back to the Tech Whisperer, there's no excuse for an executive today, not to at least understand the fundamentals of technology. So many decisions have to be made around investment, capital, hiring, investment in your people. That without that understanding, you're sort of operating blind. >> And this is the thing that I think I love, and was impressed by that Tech Whisperer article. You know, a play on the Horse Whisperer, the movie. You're kind of whispering in the ears of leaders who won't admit that they're scared. But they're all scared! They're all scared. And so they need to get, maybe it's cognitive dissonance around decision making, or they might not trust their lead. Or they don't know what they're talking about So this certainly is there, I would agree with that. But there's dynamics at play, and I want to get your thoughts on this. I think this plays into the Tech Whisperer. The trend we're seeing is the old days was the engineers are out coding away, hey they're out there coding away, look at them coding away. Now with Cloud they're in the front lines. They're getting closer to the customer, the apps are in charge. They're dictating to the infrastructure what can be done. With data almost every solution can be customized. There's no more general purpose. These are the things we talk about, but this changes the personnel equation. Now you got engineering and product people talking to sales and marketing people, business people. >> And customers. >> They tend not to, they traditionally weren't going well. Now they have to work well, engineers want to work with the customers. This is kind of a new business practice, and now I'm a scared executive. Beth, what do I do? What's your thoughts on that dynamic? >> You know, I'm not sure I would have had insight in that if I hadn't had the oppurtunity to work at this little start up, which we were a digital native. And it was the first time I worked in an environment where we did true extreme programming, pair programming, we had really strong product leads, and engineers. So we didn't have project managers, business analysts, a lot of things that I think enterprise I.T tends to have. Because the folks, historically, at an enterprise, the folks that are specifying the need, the business need, are folks in the lines of business. And they're not product managers, and even product managers, I say in banking for example, they aren't software product managers. And so that change, if you really do want to embrace these new methods and dev ops, and a lot of the automation that's available to engineering and software development organizations today, you really do have to make that change. Otherwise it's just going to be a clumsy version of what you use to do, with a new name on it. The other thing though that I would say, is I don't want to discount for large enterprises is partnerships with start up companies or other tech partners. You don't need to build everything. There's so much great technology out there. You brought up the Cloud. Look at how rich these Cloud stacks are getting. You know, it's not just now, can you provision me some compute, and some storage, and help me connect to the internet. There's some pretty sophisticated capabilities in there around A.I and machine learning, and data management, and analysis. So, I think overtime, we'll see richer and richer Cloud stacks, that enables you know, every company to benefit from the technology and innovation that's going on right now. >> Andy Jassy, the CEO of Amazon Web Search, has always said whenever I've interviewed him, he always talks publicly now about it is, two pizza teams, and automate the undifferentiated heavy lifting. In tech we all know what that is, the boring, mundane, patching, provisioning, ugh. And deploying more creative research. Okay so, I believe that. I'm a big believer of that philosophy. But it opens up the role, the question of the roles of the people. That lonely DBA, that you once were, I did some DBA work myself. System admins, storage administrator, these were roles, network administrator, the sacred God of the network, they ran everything. They're evolving to be much more coding oriented, software driven changes. >> It's a huge change. And you know, one thing that I think is sad, is I run into folks often that are, I'll just say, technology professionals, just say, you know, we're at large. Who are out of work. You know, who sort of hang their head, they're not valued, or maybe there's some ageism involved, or they get marked as, oh that's old school, they're not going to change. So, I really do believe we're at a point, where there's not enough resources out there. And so how we invest in talent that's available today, and help people through this change, not everybody is going to make it. It starts with you, knowing yourself, and how open-minded you are. Are you willing to learn, are you willing to put some effort forth, and sort of figuring out some of these new operating models. Because that's just essential if you want to be part of the future. And I'll tell you, it's hard, and it's exhausting. So I don't say this lightly, I just think. You know about my career, how many changes and twists and turns their have been. Sometimes you're just like, okay I'm ready, I'm ready to just go hiking. (Beth laughs) >> It can be, there's a lot of institutional baggage, associated with the role you had, I've heard that before. Old guard, old school, we don't do that, you're way too old for that, we need more women so lets get women in. So there's like a big dynamic around that. And I want to get your thoughts on it because you mentioned ageism, and also women in tech has also grown. There's a need for that. So there's more opportunities now than ever. I mean you go to the cyber security job boards, there are more jobs for cyber security experts than any. >> Oh, I'll tell you, yesterday, we held an event at our office, in partnership with some different start ups. Because that's one of the things you do when you're in a corporate venture group, and it was all on the future of authentication. So it was really targeted at an audience of information security professionals and chief information security officers. And it was twenty men and one woman. And I thought, wow, you know I'm use to that from having been a CIO that a lot of the infrastructure roles in particular, like as you were saying, the rack and stack, the storage management, the network folks, just tend to be more male dominant, than I think the product managers, designers, even software engineers to some extent. But here you know, how many times can you go online and see how many openings there are for that type of role. So I personally, am not pursuing that type of role, so I don't know what all the steps would need to be, to get educated, to get certified, but boy is there a need. And that needs not going to go away. As more, if everything is digitized and everything is online. Then security is going to be a constant concern and sort of dynamic space. >> Well, we interview a lot of women in tech, great to have you on, you're a great leader. We also interview a lot of people that are older. I totally believe that there's an ageism issue out there. I've seen it first hand, maybe because I'm over fifty. And also women in tech, there's more coming but not enough. The numbers speak for themselves. There's also an opportunity, if you look at the leveling up. I talked to a person who was a network engineer, kind of the same thing as him, hanging his head down. And I said, do you realize that networking paradigm is very similar to how cyber works. So a lot of the old is coming back. So if you look at what was in the computer science programs in the eighties. It was a systems thinking. The systems thinking is coming back. So I see that as a great opportunity. But also the aperture of the field of computer science is changing. So it's not, there are some areas that frankly, women are better than men at in my opinion. In my opinion, might get some crap for that. But the point, I do believe that. And there are different roles. So I think it's not just, there's so much more here. >> Oh, that's what I try to tell people. It's not just coding, right. There's so many different types of roles. And unfortunately I think we don't market ourselves well. So I encourage everyone out there that knows somebody. (Beth laughs) Who's looking-- >> If someone was provisioned Sun micro-systems, or mini computers, or workstations, probably has a systems background that could be a Cloud administrator or a Cloud architect. Same concepts. So I want to get your thoughts on women in tech since you're here. What's your thoughts on the industry, how's it going, things you advise, other folks, men and women, that they could do differently. Any good signs? What's your thoughts in general? >> Yeah so, first of all, I'm just a big advocate for women in general. Young girls, and, young women, just getting into the work force, and always have been. Have to say again, very fortunate early in my career working for companies like a phone company, and Schwab, we had so many amazing female leaders. And I don't even think we had a program, it was just sort of part of the DNA of the company. And it's really only in the last couple of years I really seen we have a big problem. Whether it's reading about some of the cultures of some of the big tech companies, or even spending more time in the valley. I think there's no one answer, it's multifaceted. It's education, it's families, it's you know, each one of us could make a difference in how we hire, sort of checking in what our unintended biases are, I know at Citi right now, there's a huge program around diversity and inclusion. Gender, and otherwise. And one of the ways I think it's going to be impactful. They've set targets that I know are controversial, but it holds people accountable, to make decisions and invest in developing people, and making sure there's a pipeline of talent that can step up into even bigger roles with a more diverse leadership team. It will take time though, it will take time. >> But mind shares are critical. >> It absolutely is. Self-awareness, community awareness, very much so. >> What can men do differently, it's always about women in tech, but what can we, what can men do? >> I think it's a great question. I would say, women can do this too. I hate when I see a group together, and it's all women working on the women issue. Shame on us, for not inviting men into the organization. And then I think it's similar to the Tech Whisperer. Don't be nervous, don't be worried, just step in. Because, you know, men are fathers, men are leaders, men are colleagues. They're brothers, they're uncles. We have to work on this together. >> I had a great guest, and friend, I was interviewing. And she was amazing, and she said, John, it's not diversity and inclusion, it's inclusion and diversity. It's I-N-D not D-I. First of all, I've never heard of it, what's D-N-I? My point exactly. Inclusion is not just the diversity piece, inclusion first is inclusive in general, diversity is different. So people tend to blend them. >> Yes they do. >> Or even forget the inclusion part. >> Final question, since you're a change junkie, which I love that phrase, I'm kind of one myself. Change junkies are always chasing that next wave, and you love waves. Pat Gelsinger at VMWare, wave junkie, always love talking with him. And he's a great wave spotter, he sees them early. There's a big set of waves coming in now, pretty clear. Cloud has done it's thing. It's only going to change and get bigger, hybrid, private, multi Cloud. Data, AI, twenty year cycle coming. What waves are you most excited about? What's out there? What waves are obvious, what waves aren't, that you see? >> Yeah, oh, that's a tough one. Cause we try to track what those waves are. I think one of the things that I'm seeing is that as we all get, and I don't just mean people, I mean things. Everything is connected, and everything has some kind of smarts, some kind of small CPU senser. There's no way that our existing, sort of network, infrastructure and the way we connect and talk can support all of that. So I think we're going to see some kind of discontinuous change, where new models are going to, are going to absolutely be required cause we'll sort of hit the limit of how much traffic can go over the internet, and how many devices can we manage. How much automation can the people and an enterprise sort of oversee and monitor, and secure and protect. That's the thing that I feel like it's a tsunami about to hit us. And it's going to be one of these perfect storms. And luckily, I think there is innovation going on around 5G and edge computing, and different ways to think about securing the enterprise. That will help. But it couldn't come soon enough. >> And model also meaning not just technical business. >> Absolutely. Machine the machine. Like who's identity is on there that's taken an action on your behalf, or the companies behalf. You know, we see that already with RPA, these software robots. Who's making sure that they're doing what they're suppose to do. And they're so easy to create, now you have thousands of them. In my mind, it's just more software to manage. >> And a great contrary to Carl Eschenbach, former VMware CEO now at Sequoia, he's on the board of UIPath, they're on the front page of Forbes today, talking about bots. >> Yes, yes, yes, I've heard them speak. >> This is an issue, like is there a verification. Is there a fake bots coming. If there's fake news, fake bots are probably going to come too. >> Absolutely they will. >> This is a reality. >> And we're putting them in the hands of non-engineers to build these bots. Which there's good and bad, right. >> Regulation and policy are two different things, and they could work together. This is going to be a seminal issue for our industry. Is understanding the societal impact, tech for good. Shaping the technologies. This is what a Tech Whisperer has to do. You have a tough job ahead of you. >> But I love it. >> Jeff: Beth thank you for coming on. >> Thank you for having me. >> I'm Jeff Furrier for the People First Network here at Sand Hill Road at Mayfield as part of theCUBE and SiliconANGLE's co-creation with Mayfield Fund, thans for watching.

>>Welcome to this exclusive discussion. IBM, the next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond. My name is Dave Volante from the Cuban today, we're going to take a deep dive into some of the most important trends impacting the life sciences industry in the next 60 minutes. Yeah, of course. We're going to hear how IBM is utilizing Watson and some really important in life impacting ways, but we'll also bring in real world perspectives from industry and the independent analyst view to better understand how technology and data are changing the nature of precision medicine. Now, the pandemic has created a new reality for everyone, but especially for life sciences companies, one where digital transformation is no longer an option, but a necessity. Now the upside is the events of the past 22 months have presented an accelerated opportunity for innovation technology and real world data are coming together and being applied to support life science, industry trends and improve drug discovery, clinical development, and treatment commercialization throughout the product life cycle cycle. Now I'd like to introduce our esteemed panel. Let me first introduce Lorraine Marshawn, who is general manager of life sciences at IBM Watson health. Lorraine leads the organization dedicated to improving clinical development research, showing greater treatment value in getting treatments to patients faster with differentiated solutions. Welcome Lorraine. Great to see you. >>Dr. Namita LeMay is the research vice-president of IDC, where she leads the life sciences R and D strategy and technology program, which provides research based advisory and consulting services as well as market analysis. The loan to meta thanks for joining us today. And our third panelist is Greg Cunningham. Who's the director of the RWE center of excellence at Eli Lilly and company. Welcome, Greg, you guys are doing some great work. Thanks for being here. Thanks >>Dave. >>Now today's panelists are very passionate about their work. If you'd like to ask them a question, please add it to the chat box located near the bottom of your screen, and we'll do our best to answer them all at the end of the panel. Let's get started. Okay, Greg, and then Lorraine and meta feel free to chime in after one of the game-changers that you're seeing, which are advancing precision medicine. And how do you see this evolving in 2022 and into the next decade? >>I'll give my answer from a life science research perspective. The game changer I see in advancing precision medicine is moving from doing research using kind of a single gene mutation or kind of a single to look at to doing this research using combinations of genes and the potential that this brings is to bring better drug targets forward, but also get the best product to a patient faster. Um, I can give, uh, an example how I see it playing out in the last decade. Non-oncology real-world evidence. We've seen an evolution in precision medicine as we've built out the patient record. Um, as we've done that, uh, the marketplace has evolved rapidly, uh, with, particularly for electronic medical record data and genomic data. And we were pretty happy to get our hands on electronic medical record data in the early days. And then later the genetic test results were combined with this data and we could do research looking at a single mutation leading to better patient outcomes. But I think where we're going to evolve in 2022 and beyond is with genetic testing, growing and oncology, providing us more data about that patient. More genes to look at, uh, researchers can look at groups of genes to analyze, to look at that complex combination of gene mutations. And I think it'll open the door for things like using artificial intelligence to help researchers plow through the complex number of permutations. When you think about all those genes you can look at in combination, right? Lorraine yes. Data and machine intelligence coming together, anything you would add. >>Yeah. Thank you very much. Well, I think that Greg's response really sets us up nicely, particularly when we think about the ability to utilize real-world data in the farm industry across a number of use cases from discovery to development to commercial, and, you know, in particular, I think with real world data and the comments that Greg just made about clinical EMR data linked with genetic or genomic data, a real area of interest in one that, uh, Watson health in particular is focused on the idea of being able to create a data exchange so that we can bring together claims clinical EMR data, genomics data, increasingly wearables and data directly from patients in order to create a digital health record that we like to call an intelligent patient health record that basically gives us the digital equivalent of a real life patient. And these can be used in use cases in randomized controlled clinical trials for synthetic control arms or natural history. They can be used in order to track patients' response to drugs and look at outcomes after they've been on various therapies as, as Greg is speaking to. And so I think that, you know, the promise of data and technology, the AI that we can apply on that is really helping us advance, getting therapies to market faster, with better information, lower sample sizes, and just a much more efficient way to do drug development and to track and monitor outcomes in patients. >>Great. Thank you for that now to meta, when I joined IDC many, many years ago, I really didn't know much about the industry that I was covering, but it's great to see you as a former practitioner now bringing in your views. What do you see as the big game-changers? >>So, um, I would, I would agree with what both Lorraine and Greg said. Um, but one thing that I'd just like to call out is that, you know, everyone's talking about big data, the volume of data is growing. It's growing exponentially actually about, I think 30% of data that exists today is healthcare data. And it's growing at a rate of 36%. That's huge, but then it's not just about the big, it's also about the broad, I think, um, you know, I think great points that, uh, Lorraine and Greg brought out that it's, it's not just specifically genomic data, it's multi omic data. And it's also about things like medical history, social determinants of health, behavioral data. Um, and why, because when you're talking about precision medicine and we know that we moved away from the, the terminology of personalized to position, because you want to talk about disease stratification and you can, it's really about convergence. >>Um, if you look at a recent JAMA paper in 2021, only 1% of EHS actually included genomic data. So you really need to have that ability to look at data holistically and IDC prediction is seeing that investments in AI to fuel in silico, silicone drug discovery will double by 20, 24, but how are you actually going to integrate all the different types of data? Just look at, for example, diabetes, you're on type two diabetes, 40 to 70% of it is genetically inherited and you have over 500 different, uh, genetic low side, which could be involved in playing into causing diabetes. So the earlier strategy, when you are looking at, you know, genetic risk scoring was really single trait. Now it's transitioning to multi rate. And when you say multi trade, you really need to get that integrated view that converging for you to, to be able to drive a precision medicine strategy. So to me, it's a very interesting contrast on one side, you're really trying to make it specific and focused towards an individual. And on the other side, you really have to go wider and bigger as well. >>Uh, great. I mean, the technology is enabling that convergence and the conditions are almost mandating it. Let's talk about some more about data that the data exchange and building an intelligent health record, as it relates to precision medicine, how will the interoperability of real-world data, you know, create that more cohesive picture for the, for the patient maybe Greg, you want to start, or anybody else wants to chime in? >>I think, um, the, the exciting thing from, from my perspective is the potential to gain access to data. You may be weren't aware of an exchange in implies that, uh, some kind of cataloging, so I can see, uh, maybe things that might, I just had no idea and, uh, bringing my own data and maybe linking data. These are concepts that I think are starting to take off in our field, but it, it really opens up those avenues to when you, you were talking about data, the robustness and richness volume isn't, uh, the only thing is Namita said, I think really getting to a rich high-quality data and, and an exchange offers a far bigger, uh, range for all of us to, to use, to get our work done. >>Yeah. And I think, um, just to chime, chime into that, uh, response from Greg, you know, what we hear increasingly, and it's pretty pervasive across the industry right now, because this ability to create an exchange or the intelligent, uh, patient health record, these are new ideas, you know, they're still rather nascent and it always is the operating model. Uh, that, that is the, uh, the difficult challenge here. And certainly that is the case. So we do have data in various silos. Uh, they're in patient claims, they're in electronic medical records, they might be in labs, images, genetic files on your smartphone. And so one of the challenges with this interoperability is being able to tap into these various sources of data, trying to identify quality data, as Greg has said, and the meta is underscoring as well. Uh, we've gotta be able to get to the depth of data that's really meaningful to us, but then we have to have technology that allows us to pull this data together. >>First of all, it has to be de-identified because of security and patient related needs. And then we've gotta be able to link it so that you can create that likeness in terms of the record, it has to be what we call cleaned or curated so that you get the noise and all the missing this out of it, that's a big step. And then it needs to be enriched, which means that the various components that are going to be meaningful, you know, again, are brought together so that you can create that cohort of patients, that individual patient record that now is useful in so many instances across farm, again, from development, all the way through commercial. So the idea of this exchange is to enable that exact process that I just described to have a, a place, a platform where various entities can bring their data in order to have it linked and integrated and cleaned and enriched so that they get something that is a package like a data package that they can actually use. >>And it's easy to plug into their, into their studies or into their use cases. And I think a really important component of this is that it's gotta be a place where various third parties can feel comfortable bringing their data together in order to match it with other third parties. That is a, a real value, uh, that the industry is increasingly saying would be important to them is, is the ability to bring in those third-party data sets and be able to link them and create these, these various data products. So that's really the idea of the data exchange is that you can benefit from accessing data, as Greg mentioned in catalogs that maybe are across these various silos so that you can do the kind of work that you need. And that we take a lot of the hard work out of it. I like to give an example. >>We spoke with one of our clients at one of the large pharma companies. And, uh, I think he expressed it very well. He said, what I'd like to do is have like a complete dataset of lupus. Lupus is an autoimmune condition. And I've just like to have like the quintessential lupus dataset that I can use to run any number of use cases across it. You know, whether it's looking at my phase one trial, whether it's selecting patients and enriching for later stage trials, whether it's understanding patient responses to different therapies as I designed my studies. And so, you know, this idea of adding in therapeutic area indication, specific data sets and being able to create that for the industry in the meta mentioned, being able to do that, for example, in diabetes, that's how pharma clients need to have their needs met is through taking the hard workout, bringing the data together, having it very therapeutically enriched so that they can use it very easily. >>Thank you for that detail and the meta. I mean, you can't do this with humans at scale in technology of all the things that Lorraine was talking about, the enrichment, the provenance, the quality, and of course, it's got to be governed. You've got to protect the privacy privacy humans just can't do all that at massive scale. Can it really tech that's where technology comes in? Doesn't it and automation. >>Absolutely. >>I, couldn't more, I think the biggest, you know, whether you talk about precision medicine or you talk about decentralized trials, I think there's been a lot of hype around these terms, but what is really important to remember is technology is the game changer and bringing all that data together is really going to be the key enabler. So multimodal data integration, looking at things like security or federated learning, or also when you're talking about leveraging AI, you're not talking about things like bias or other aspects around that are, are critical components that need to be addressed. I think the industry is, uh, it's partly, still trying to figure out the right use cases. So it's one part is getting together the data, but also getting together the right data. Um, I think data interoperability is going to be the absolute game changer for enabling this. Uh, but yes, um, absolutely. I can, I can really couldn't agree more with what Lorraine just said, that it's bringing all those different aspects of data together to really drive that precision medicine strategy. >>Excellent. Hey Greg, let's talk about protocols decentralized clinical trials. You know, they're not new to life silences, but, but the adoption of DCTs is of course sped up due to the pandemic we've had to make trade-offs obviously, and the risk is clearly worth it, but you're going to continue to be a primary approach as we enter 2022. What are the opportunities that you see to improve? How DCTs are designed and executed? >>I see a couple opportunities to improve in this area. The first is, uh, back to technology. The infrastructure around clinical trials has, has evolved over the years. Uh, but now you're talking about moving away from kind of site focus to the patient focus. Uh, so with that, you have to build out a new set of tools that would help. So for example, one would be novel trial, recruitment, and screening, you know, how do you, how do you find patients and how do you screen them to see if are they, are they really a fit for, for this protocol? Another example, uh, very important documents that we have to get is, uh, you know, the e-consent that someone's says, yes, I'm, well, I understand this study and I'm willing to do it, have to do that in a more remote way than, than we've done in the past. >>Um, the exciting area, I think, is the use of, uh, eco, uh, E-Pro where we capture data from the patient using apps, devices, sensors. And I think all of these capabilities will bring a new way of, of getting data faster, uh, in, in this kind of model. But the exciting thing from, uh, our perspective at Lily is it's going to bring more data about the patient from the patient, not just from the healthcare provider side, it's going to bring real data from these apps, devices and sensors. The second thing I think is using real-world data to identify patients, to also improve protocols. We run scenarios today, looking at what's the impact. If you change a cut point on a, a lab or a biomarker to see how that would affect, uh, potential enrollment of patients. So it, it definitely the real-world data can be used to, to make decisions, you know, how you improve these protocols. >>But the thing that we've been at the challenge we've been after that this probably offers the biggest is using real-world data to identify patients as we move away from large academic centers that we've used for years as our sites. Um, you can maybe get more patients who are from the rural areas of our countries or not near these large, uh, uh, academic centers. And we think it'll bring a little more diversity to the population, uh, who who's, uh, eligible, but also we have their data, so we can see if they really fit the criteria and the probability they are a fit for the trial is much higher than >>Right. Lorraine. I mean, your clients must be really pushing you to help them improve DCTs what are you seeing in the field? >>Yes, in fact, we just attended the inaugural meeting of the de-central trials research Alliance in, uh, in Boston about two weeks ago where, uh, all of the industry came together, pharma companies, uh, consulting vendors, just everyone who's been in this industry working to help define de-central trials and, um, think through what its potential is. Think through various models in order to enable it, because again, a nascent concept that I think COVID has spurred into action. Um, but it is important to take a look at the definition of DCT. I think there are those entities that describe it as accessing data directly from the patient. I think that is a component of it, but I think it's much broader than that. To me, it's about really looking at workflows and processes of bringing data in from various remote locations and enabling the whole ecosystem to work much more effectively along the data continuum. >>So a DCT is all around being able to make a site more effective, whether it's being able to administer a tele visit or the way that they're getting data into the electronic data captures. So I think we have to take a look at the, the workflows and the operating models for enabling de-central trials and a lot of what we're doing with our own technology. Greg mentioned the idea of electronic consent of being able to do electronic patient reported outcomes, other collection of data directly from the patient wearables tele-health. So these are all data acquisition, methodologies, and technologies that, that we are enabling in order to get the best of the data into the electronic data capture system. So edit can be put together and processed and submitted to the FDA for regulatory use for clinical trial type submission. So we're working on that. I think the other thing that's happening is the ability to be much more flexible and be able to have more cloud-based storage allows you to be much more inter-operable to allow API APIs in order to bring in the various types of data. >>So we're really looking at technology that can make us much more fluid and flexible and accommodating to all the ways that people live and work and manage their health, because we have to reflect that in the way we collect those data types. So that's a lot of what we're, what we're focused on. And in talking with our clients, we spend also a lot of time trying to understand along the, let's say de-central clinical trials continuum, you know, w where are they? And I know Namita is going to talk a little bit about research that they've done in terms of that adoption curve, but because COVID sort of forced us into being able to collect data in more remote fashion in order to allow some of these clinical trials to continue during COVID when a lot of them had to stop. What we want to make sure is that we understand and can codify some of those best practices and that we can help our clients enable that because the worst thing that would happen would be to have made some of that progress in that direction. >>But then when COVID is over to go back to the old ways of doing things and not bring some of those best practices forward, and we actually hear from some of our clients in the pharma industry, that they worry about that as well, because we don't yet have a system for operationalizing a de-central trial. And so we really have to think about the protocol it's designed, the indication, the types of patients, what makes sense to decentralize, what makes sense to still continue to collect data in a more traditional fashion. So we're spending a lot of time advising and consulting with our patients, as well as, I mean, with our clients, as well as CRS, um, on what the best model is in terms of their, their portfolio of studies. And I think that's a really important aspect of trying to accelerate the adoption is making sure that what we're doing is fit for purpose, just because you can use technology doesn't mean you should, it really still does require human beings to think about the problem and solve them in a very practical way. >>Great, thank you for that. Lorraine. I want to pick up on some things that Lorraine was just saying. And then back to what Greg was saying about, uh, uh, DCTs becoming more patient centric, you had a prediction or IDC, did I presume your fingerprints were on it? Uh, that by 20 25, 70 5% of trials will be patient-centric decentralized clinical trials, 90% will be hybrid. So maybe you could help us understand that relationship and what types of innovations are going to be needed to support that evolution of DCT. >>Thanks, Dave. Yeah. Um, you know, sorry, I, I certainly believe that, uh, you know, uh, Lorraine was pointing out of bringing up a very important point. It's about being able to continue what you have learned in over the past two years, I feel this, you know, it was not really a digital revolution. It was an attitude. The revolution that this industry underwent, um, technology existed just as clinical trials exist as drugs exist, but there was a proof of concept that technology works that this model is working. So I think that what, for example, telehealth, um, did for, for healthcare, you know, transition from, from care, anywhere care, anytime, anywhere, and even becoming predictive. That's what the decentralized clinical trials model is doing for clinical trials today. Great points again, that you have to really look at where it's being applied. You just can't randomly apply it across clinical trials. >>And this is where the industry is maturing the complexity. Um, you know, some people think decentralized trials are very simple. You just go and implement these centralized clinical trials, but it's not that simple as it it's being able to define, which are the right technologies for that specific, um, therapeutic area for that specific phase of the study. It's being also a very important point is bringing in the patient's voice into the process. Hey, I had my first telehealth visit sometime last year and I was absolutely thrilled about it. I said, no time wasted. I mean, everything's done in half an hour, but not all patients want that. Some want to consider going back and you, again, need to customize your de-centralized trials model to, to the, to the type of patient population, the demographics that you're dealing with. So there are multiple factors. Um, also stepping back, you know, Lorraine mentioned they're consulting with, uh, with their clients, advising them. >>And I think a lot of, um, a lot of companies are still evolving in their maturity in DCTs though. There's a lot of boys about it. Not everyone is very mature in it. So it's, I think it, one thing everyone's kind of agreeing with is yes, we want to do it, but it's really about how do we go about it? How do we make this a flexible and scalable modern model? How do we integrate the patient's voice into the process? What are the KPIs that we define the key performance indicators that we define? Do we have a playbook to implement this model to make it a scalable model? And, you know, finally, I think what organizations really need to look at is kind of developing a de-centralized mature maturity scoring model, so that I assess where I am today and use that playbook to define, how am I going to move down the line to me reach the next level of maturity. Those were some of my thoughts. Right? >>Excellent. And now remember you, if you have any questions, use the chat box below to submit those questions. We have some questions coming in from the audience. >>At one point to that, I think one common thread between the earlier discussion around precision medicine and around decentralized trials really is data interoperability. It is going to be a big game changer to, to enable both of these pieces. Sorry. Thanks, Dave. >>Yeah. Thank you. Yeah. So again, put your questions in the chat box. I'm actually going to go to one of the questions from the audience. I get some other questions as well, but when you think about all the new data types that are coming in from social media, omics wearables. So the question is with greater access to these new types of data, what trends are you seeing from pharma device as far as developing capabilities to effectively manage and analyze these novel data types? Is there anything that you guys are seeing, um, that you can share in terms of best practice or advice >>I'll offer up? One thing, I think the interoperability isn't quite there today. So, so what's that mean you can take some of those data sources. You mentioned, uh, some Omix data with, uh, some health claims data and it's the, we spend too much time and in our space putting data to gather the behind the scenes, I think the stat is 80% of the time is assembling the data 20% analyzing. And we've had conversations here at Lilly about how do we get to 80% of the time is doing analysis. And it really requires us to think, take a step back and think about when you create a, uh, a health record, you really have to be, have the same plugins so that, you know, data can be put together very easily, like Lorraine mentioned earlier. And that comes back to investing in as an industry and standards so that, you know, you have some of data standard, we all can agree upon. And then those plugs get a lot easier and we can spend our time figuring out how to make, uh, people's lives better with healthcare analysis versus putting data together, which is not a lot of fun behind the scenes. >>Other thoughts on, um, on, on how to take advantage of sort of novel data coming from things like devices in the nose that you guys are seeing. >>I could jump in there on your end. Did you want to go ahead? Okay. So, uh, I mean, I think there's huge value that's being seen, uh, in leveraging those multiple data types. I think one area you're seeing is the growth of prescription digital therapeutics and, um, using those to support, uh, you know, things like behavioral health issues and a lot of other critical conditions it's really taking you again, it is interlinking real-world data cause it's really taking you to the patient's home. Um, and it's, it's, there's a lot of patients in the city out here cause you can really monitor the patient real-time um, without the patient having coming, you know, coming and doing a site visit once in say four weeks or six weeks. So, um, I, and, uh, for example, uh, suicidal behavior and just to take an example, if you can predict well in advance, based on those behavioral parameters, that this is likely to trigger that, uh, the value of it is enormous. Um, again, I think, uh, Greg made a valid point about the industry still trying to deal with resolving the data interoperability issue. And there are so many players that are coming in the industry right now. There are really few that have the maturity and the capability to address these challenges and provide intelligence solutions. >>Yeah. Maybe I'll just, uh, go ahead and, uh, and chime into Nikita's last comment there. I think that's what we're seeing as well. And it's very common, you know, from an innovation standpoint that you have, uh, a nascent industry or a nascent innovation sort of situation that we have right now where it's very fragmented. You have a lot of small players, you have some larger entrenched players that have the capability, um, to help to solve the interoperability challenge, the standards challenge. I mean, I think IBM Watson health is certainly one of the entities that has that ability and is taking a stand in the industry, uh, in order to, to help lead in that way. Others are too. And, uh, but with, with all of the small companies that are trying to find interesting and creative ways to gather that data, it does create a very fragmented, uh, type of environment and ecosystem that we're in. >>And I think as we mature, as we do come forward with the KPIs, the operating models, um, because you know, the devil's in the detail in terms of the operating models, it's really exciting to talk these trends and think about the future state. But as Greg pointed out, if you're spending 80% of your time just under the hood, you know, trying to get the engine, all the spark plugs to line up, um, that's, that's just hard grunt work that has to be done. So I think that's where we need to be focused. And I think bringing all the data in from these disparate tools, you know, that's fine, we need, uh, a platform or the API APIs that can enable that. But I think as we, as we progress, we'll see more consolidation, uh, more standards coming into play, solving the interoperability types of challenges. >>And, um, so I think that's where we should, we should focus on what it's going to take and in three years to really codify this and make it, so it's a, it's a well hum humming machine. And, you know, I do know having also been in pharma that, uh, there's a very pilot oriented approach to this thing, which I think is really healthy. I think large pharma companies tend to place a lot of bets with different programs on different tools and technologies, to some extent to see what's gonna stick and, you know, kind of with an innovation mindset. And I think that's good. I think that's kind of part of the process of figuring out what is going to work and, and helping us when we get to that point of consolidating our model and the technologies going forward. So I think all of the efforts today are definitely driving us to something that feels much more codified in the next three to five years. >>Excellent. We have another question from the audience it's sort of related to the theme of this discussion, given the FDA's recent guidance on using claims and electronic health records, data to support regulatory decision-making what advancements do you think we can expect with regards to regulatory use of real-world data in the coming years? It's kind of a two-parter so maybe you guys can collaborate on this one. What role that, and then what role do you think industry plays in influencing innovation within the regulatory space? >>All right. Well, it looks like you've stumped the panel there. Uh, Dave, >>It's okay to take some time to think about it, right? You want me to repeat it? You guys, >>I, you know, I I'm sure that the group is going to chime into this. I, so the FDA has issued a guidance. Um, it's just, it's, it's exactly that the FDA issues guidances and says that, you know, it's aware and supportive of the fact that we need to be using real-world data. We need to create the interoperability, the standards, the ways to make sure that we can include it in regulatory submissions and the like, um, and, and I sort of think about it akin to the critical path initiative, probably, I don't know, 10 or 12 years ago in pharma, uh, when the FDA also embrace this idea of the critical path and being able to allow more in silico modeling of clinical trial, design and development. And it really took the industry a good 10 years, um, you know, before they were able to actually adopt and apply and take that sort of guidance or openness from the FDA and actually apply it in a way that started to influence the way clinical trials were designed or the in silico modeling. >>So I think the second part of the question is really important because while I think the FDA is saying, yes, we recognize it's important. Uh, we want to be able to encourage and support it. You know, when you look for example, at synthetic control arms, right? The use of real-world data in regulatory submissions over the last five or six years, all of the use cases have been in oncology. I think there've been about maybe somewhere between eight to 10 submissions. And I think only one actually was a successful submission, uh, in all those situations, the real-world data arm of that oncology trial that synthetic control arm was actually rejected by the FDA because of lack of completeness or, you know, equalness in terms of the data. So the FDA is not going to tell us how to do this. So I think the second part of the question, which is what's the role of industry, it's absolutely on industry in order to figure out exactly what we're talking about, how do we figure out the interoperability, how do we apply the standards? >>How do we ensure good quality data? How do we enrich it and create the cohort that is going to be equivalent to the patient in the real world, uh, in the end that would otherwise be in the clinical trial and how do we create something that the FDA can agree with? And we'll certainly we'll want to work with the FDA in order to figure out this model. And I think companies are already doing that, but I think that the onus is going to be on industry in order to figure out how you actually operationalize this and make it real. >>Excellent. Thank you. Um, question on what's the most common misconception that clinical research stakeholders with sites or participants, et cetera might have about DCTs? >>Um, I could jump in there. Right. So, sure. So, um, I think in terms of misconceptions, um, I think the communist misconceptions that sites are going away forever, which I do not think is really happening today. Then the second, second part of it is that, um, I think also the perspective that patients are potentially neglected because they're moving away. So we'll pay when I, when I, what I mean by that neglected, perhaps it was not the appropriate term, but the fact that, uh, will patients will, will, will patient engagement continue, will retention be strong since the patients are not interacting in person with the investigator quite as much. Um, so site retention and patient retention or engagement from both perspectives, I think remains a concern. Um, but actually if you look at, uh, look at, uh, assessments that have been done, I think patients are more than happy. >>Majority of the patients have been really happy about, about the new model. And in fact, sites are, seem to increase, have increased investments in technology by 50% to support this kind of a model. So, and the last thing is that, you know, decentralized trials is a great model and it can be applied to every possible clinical trial. And in another couple of weeks, the whole industry will be implementing only decentralized trials. I think we are far away from that. It's just not something that you would implement across every trial. And we discussed that already. So you have to find the right use cases for that. So I think those were some of the key misconceptions I'd say in the industry right now. Yeah. >>Yeah. And I would add that the misconception I hear the most about is, uh, the, the similar to what Namita said about the sites and healthcare professionals, not being involved to the level that they are today. Uh, when I mentioned earlier in our conversation about being excited about capturing more data, uh, from the patient that was always in context of, in addition to, you know, healthcare professional opinion, because I think both of them bring that enrichment and a broader perspective of that patient experience, whatever disease they're faced with. So I, I think some people think is just an all internet trial with just someone, uh, putting out there their own perspective. And, and it's, it's a combination of both to, to deliver a robust data set. >>Yeah. Maybe I'll just comment on, it reminds me of probably 10 or 15 years ago, maybe even more when, um, really remote monitoring was enabled, right? So you didn't have to have the study coordinator traveled to the investigative site in order to check the temperature of the freezer and make sure that patient records were being completed appropriately because they could have a remote visit and they could, they could send the data in a via electronic data and do the monitoring visit, you know, in real time, just the way we're having this kind of communication here. And there was just so much fear that you were going to replace or supplant the personal relationship between the sites between the study coordinators that you were going to, you know, have to supplant the role of the monitor, which was always a very important role in clinical trials. >>And I think people that really want to do embrace the technology and the advantages that it provided quickly saw that what it allowed was the monitor to do higher value work, you know, instead of going in and checking the temperature on a freezer, when they did have their visit, they were able to sit and have a quality discussion for example, about how patient recruitment was going or what was coming up in terms of the consent. And so it created a much more high touch, high quality type of interaction between the monitor and the investigative site. And I think we should be looking for the same advantages from DCT. We shouldn't fear it. We shouldn't think that it's going to supplant the site or the investigator or the relationship. It's our job to figure out where the technology fits and clinical sciences always got to be high touch combined with high-tech, but the high touch has to lead. And so getting that balance right? And so that's going to happen here as well. We will figure out other high value work, meaningful work for the site staff to do while they let the technology take care of the lower quality work, if you will, or the lower value work, >>That's not an, or it's an, and, and you're talking about the higher value work. And it, it leads me to something that Greg said earlier about the 80, 20, 80% is assembly. 20% is actually doing the analysis and that's not unique to, to, to life sciences, but, but sort of question is it's an organizational question in terms of how we think about data and how we approach data in the future. So Bamyan historically big data in life sciences in any industry really is required highly centralized and specialized teams to do things that the rain was talking about, the enrichment, the provenance, the data quality, the governance, the PR highly hyper specialized teams to do that. And they serve different constituencies. You know, not necessarily with that, with, with context, they're just kind of data people. Um, so they have responsibility for doing all those things. Greg, for instance, within literally, are you seeing a move to, to, to democratize data access? We've talked about data interoperability, part of that state of sharing, um, that kind of breaks that centralized hold, or is that just too far in the future? It's too risky in this industry? >>Uh, it's actually happening now. Uh, it's a great point. We, we try to classify what people can do. And, uh, the example would be you give someone who's less analytically qualified, uh, give them a dashboard, let them interact with the data, let them better understand, uh, what, what we're seeing out in the real world. Uh, there's a middle user, someone who you could give them, they can do some analysis with the tool. And the nice thing with that is you have some guardrails around that and you keep them in their lane, but it allows them to do some of their work without having to go ask those centralized experts that, that you mentioned their precious resources. And that's the third group is those, uh, highly analytical folks that can, can really deliver, uh, just value beyond. But when they're doing all those other things, uh, it really hinders them from doing what we've been talking about is the high value stuff. So we've, we've kind of split into those. We look at people using data in one of those three lanes and it, and it has helped I think, uh, us better not try to make a one fit solution for, for how we deliver data and analytic tools for people. Right. >>Okay. I mean, DCT hot topic with the, the, the audience here. Another question, um, what capabilities do sponsors and CRS need to develop in-house to pivot toward DCT? >>Should I jump in here? Yeah, I mean, um, I think, you know, when, when we speak about DCTs and when I speak with, uh, folks around in the industry, I, it takes me back to the days of risk-based monitoring. When it was first being implemented, it was a huge organizational change from the conventional monitoring models to centralize monitoring and risk-based monitoring, it needs a mental reset. It needs as Lorraine had pointed out a little while ago, restructuring workflows, re redefining processes. And I think that is one big piece. That is, I think the first piece, when, you know, when you're implementing a new model, I think organizational change management is a big piece of it because you are disturbing existing structures, existing methods. So getting that buy-in across the organization towards the new model, seeing what the value add in it. And where do you personally fit into that story? >>How do your workflows change, or how was your role impacted? I think without that this industry will struggle. So I see organizations, I think, first trying to work on that piece to build that in. And then of course, I also want to step back for the second to the, uh, to the point that you brought out about data democratization. And I think Greg Greg gave an excellent point, uh, input about how it's happening in the industry. But I would also say that the data democratization really empowerment of, of, of the stakeholders also includes the sites, the investigators. So what is the level of access to data that you know, that they have now, and is it, uh, as well as patients? So see increasingly more and more companies trying to provide access to patients finally, it's their data. So why shouldn't they have some insights to it, right. So access to patients and, uh, you know, the 80, 20 part of it. Uh, yes, he's absolutely right that, uh, we want to see that flip from, uh, 20%, um, you know, focusing on, on actually integrating the data 80% of analytics, but the real future will be coming in when actually the 20 and 18 has gone. And you actually have analysts the insights out on a silver platter. That's kind of wishful thinking, some of the industries is getting there in small pieces, but yeah, then that's just why I should, why we share >>Great points. >>And I think that we're, we're there in terms that like, I really appreciate the point around democratizing the data and giving the patient access ownership and control over their own data. I mean, you know, we see the health portals that are now available for patients to view their own records, images, and labs, and claims and EMR. We have blockchain technology, which is really critical here in terms of the patient, being able to pull all of their own data together, you know, in the blockchain and immutable record that they can own and control if they want to use that to transact clinical trial types of opportunities based on their data, they can, or other real world scenarios. But if they want to just manage their own data because they're traveling and if they're in a risky health situation, they've got their own record of their health, their health history, uh, which can avoid, you know, medical errors occurring. So, you know, even going beyond life sciences, I think this idea of democratizing data is just good for health. It's just good for people. And we definitely have the technology that can make it a reality. Now >>You're here. We have just about 10 minutes left and now of course, now all the questions are rolling in like crazy from the crowd. Would it be curious to know if there would be any comments from the panel on cost comparison analysis between traditional clinical trials in DCTs and how could the outcome effect the implementation of DCTs any sort of high-level framework you can share? >>I would say these are still early days to, to drive that analysis because I think many companies are, um, are still in the early stages of implementation. They've done a couple of trials. The other part of it that's important to keep in mind is, um, is for organizations it's, they're at a stage of, uh, of being on the learning curve. So when you're, you're calculating the cost efficiencies, if ideally you should have had two stakeholders involved, you could have potentially 20 stakeholders involved because everyone's trying to learn the process and see how it's going to be implemented. So, um, I don't think, and the third part of it, I think is organizations are still defining their KPIs. How do you measure it? What do you measure? So, um, and even still plugging in the pieces of technology that they need to fit in, who are they partnering with? >>What are the pieces of technology they're implementing? So I don't think there is a clear cut as answered at this stage. I think as you scale this model, the efficiencies will be seen. It's like any new technology or any new solution that's implemented in the first stages. It's always a little more complex and in fact sometimes costs extra. But as, as you start scaling it, as you establish your workflows, as you streamline it, the cost efficiencies will start becoming evident. That's why the industry is moving there. And I think that's how it turned out on the long run. >>Yeah. Just make it maybe out a comment. If you don't mind, the clinical trials are, have traditionally been costed are budgeted is on a per patient basis. And so, you know, based on the difficulty of the therapeutic area to recruit a rare oncology or neuromuscular disease, there's an average that it costs in order to find that patient and then execute the various procedures throughout the clinical trial on that patient. And so the difficulty of reaching the patient and then the complexity of the trial has led to what we might call a per patient stipend, which is just the metric that we use to sort of figure out what the average cost of a trial will be. So I think to point, we're going to have to see where the ability to adjust workflows, get to patients faster, collect data more easily in order to make the burden on the site, less onerous. I think once we start to see that work eases up because of technology, then I think we'll start to see those cost equations change. But I think right now the system isn't designed in order to really measure the economic benefit of de-central models. And I think we're going to have to sort of figure out what that looks like as we go along and since it's patient oriented right now, we'll have to say, well, you know, how does that work, ease up? And to those costs actually come down and then >>Just scale, it's going to be more, more clear as the media was saying, next question from the audiences, it's kind of a best fit question. You all have touched on this, but let me just ask it is what examples in which, in which phases suit DCT in its current form, be it fully DCT or hybrid models, none of our horses for courses question. >>Well, I think it's kind of, uh, it's, it's it's has its efficiencies, obviously on the later phases, then the absolute early phase trials, those are not the ideal models for DCTs I would say so. And again, the logic is also the fact that, you know, when you're, you're going into the later phase trials, the volume of number of patients is increasing considerably to the point that Lorraine brought up about access to the patients about patient selection. The fact, I think what one should look at is really the advantages that it brings in, in terms of, you know, patient access in terms of patient diversity, which is a big piece that, um, the cities are enabling. So, um, if you, if, if you, if you look at the spectrum of, of these advantages and, and just to step back for a moment, if you, if you're looking at costs, like you're looking at things like remote site monitoring, um, is, is a big, big plus, right? >>I mean, uh, site monitoring alone accounts for around a third of the trial costs. So there are so many pieces that fall in together. The challenge actually that comes when you're in defining DCTs and there are, as Rick pointed out multiple definitions of DCTs that are existing, uh, you know, in the industry right now, whether you're talking of what Detroit is doing, or you're talking about acro or Citi or others. But the point is it's a continuum, it's a continuum of different pieces that have been woven together. And so how do you decide which pieces you're plugging in and how does that impact the total cost or the solution that you're implementing? >>Great, thank you. Last question we have in the audience, excuse me. What changes have you seen? Are there others that you can share from the FDA EU APAC, regulators and supporting DCTs precision medicine for approval processes, anything you guys would highlight that we should be aware of? >>Um, I could quickly just add that. I think, um, I'm just publishing a report on de-centralized clinical trials should be published shortly, uh, perspective on that. But I would say that right now, um, there, there was a, in the FDA agenda, there was a plan for a decentralized clinical trials guidance, as far as I'm aware, one has not yet been published. There have been significant guidances that have been published both by email and by, uh, the FDA that, um, you know, around the implementation of clinical trials during the COVID pandemic, which incorporate various technology pieces, which support the DCD model. Um, but I, and again, I think one of the reasons why it's not easy to publish a well-defined guidance on that is because there are so many moving pieces in it. I think it's the Danish, uh, regulatory agency, which has per se published a guidance and revised it as well on decentralized clinical trials. >>Right. Okay. Uh, we're pretty much out of time, but I, I wonder Lorraine, if you could give us some, some final thoughts and bring us home things that we should be watching or how you see the future. >>Well, I think first of all, let me, let me thank the panel. Uh, we really appreciate Greg from Lily and the meta from IDC bringing their perspectives to this conversation. And, uh, I hope that the audience has enjoyed the, uh, the discussion that we've had around the future state of real world data as, as well as DCT. And I think, you know, some of the themes that we've talked about, number one, I think we have a vision and I think we have the right strategies in terms of the future promise of real-world data in any number of different applications. We certainly have talked about the promise of DCT to be more efficient, to get us closer to the patient. I think that what we have to focus on is how we come together as an industry to really work through these very vexing operational issues, because those are always the things that hang us up and whether it's clinical research or whether it's later stage, uh, applications of data. >>We, the healthcare system is still very fragmented, particularly in the us. Um, it's still very, state-based, uh, you know, different states can have different kinds of, uh, of, of cultures and geographic, uh, delineations. And so I think that, you know, figuring out a way that we can sort of harmonize and bring all of the data together, bring some of the models together. I think that's what you need to look to us to do both industry consulting organizations, such as IBM Watson health. And we are, you know, through DTRA and, and other, uh, consortia and different bodies. I think we're all identifying what the challenges are in terms of making this a reality and working systematically on those. >>It's always a pleasure to work with such great panelists. Thank you, Lorraine Marshawn, Dr. Namita LeMay, and Greg Cunningham really appreciate your participation today and your insights. The next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond has been brought to you by IBM in the cube. You're a global leader in high tech coverage. And while this discussion has concluded, the conversation continues. So please take a moment to answer a few questions about today's panel on behalf of the entire IBM life sciences team and the cube decks for your time and your feedback. And we'll see you next time.

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Much of the discussion around Intel's current challenges, is focused on manufacturing issues and it's ongoing market share skirmish with AMD. Of course, that's very understandable. But the core issue Intel faces is that it has lost the volume game forever. And in Silicon volume is king. As such incoming CEO Pat Gelsinger faces some difficult decisions. I mean, on the one hand he could take some logical steps to shore up the company's execution, maybe outsource a portion of its manufacturing. Make some incremental changes that would unquestionably please Wall Street and probably drive shareholder value when combined with the usual stock buybacks and dividends. On the other hand, Gelsinger could make much more dramatic moves shedding it's vertically integrated heritage and transforming Intel into a leading designer of chips for the emerging multi-trillion dollar markets that are highly fragmented and generally referred to as the edge. We believe Intel has no choice. It must create a deep partnership in our view with a semiconductor manufacturer with aspirations to manufacture on US soil and focus Intel's resources on design. Hello, everyone. And welcome to this week's Wikibon's Cube Insights powered by ETR. In this breaking analysis will put forth our prognosis for what Intel's future looks like and lay out what we think the company needs to do not only to maintain its relevance but to regain the position it once held as perhaps the most revered company in tech. Let's start by looking at some of the fundamental factors that we've been tracking and that have shaped and are shaping Intel and our thinking around Intel today. First, it's really important to point out that new CEO Gelsinger is walking into a really difficult situation. Intel's ascendancy and its dominance it was created by PC volumes. And its development of an ecosystem that the company created around the x86 instruction set. In semiconductors volume is everything. The player with the highest volumes has the lowest manufacturing costs. And the math around learning curves is very clear and it's compelling. It's based on Wright's law named after Theodore Wright T.P Wright. He was an aeronautical engineer and he discovered that for every cumulative doubling of units manufactured, costs are going to fall by a constant percentage. Now in semiconductor way for manufacturing that cost is roughly around 22% declines. And when you consider the economics of manufacturing a next generation technology, for example going from ten nanometers to seven nanometers this becomes huge. Because the cost of making seven nanometer tech for example is much higher relative to 10 nanometers. But if you can fit more circuits on a chip your wafer costs can drop by 30% or even more. Now this learning curve benefit is why volume is so important. If the time it takes to double volume is elongated then the learning curve benefit they get elongated as well and it become less competitive from a cost standpoint. And that's exactly what is happening to Intel. You see x86 PC volumes, they peaked in 2011 and that marked the beginning of the end of Intel's dominance from manufacturing and cost standpoint. You know, ironically HDD hard disk drive volumes peaked around the same time and you're seeing a similar fundamental shift in that market relative to flash. Now because Intel has a vertically integrated model it's designers are limited by the constraints in the manufacturing process. What used to be Intel's ace in the hole its process manufacturing has become a hindrance, frustrating Intel's chip designers and really seeding advantage to a number of competitors including AMD, ARM and Nvidia. Now, during this time we've seen high profile innovators adapting alternative processors companies like Apple which chose its own design based on ARM for the M1. Tesla is a fascinating case study where Intel was really not in the running. AWS probably Intel's largest customer is developing its own chips. You know through Intel, a little bone at the recent reinvent it announced its use of Intel's Habana chips in a practically the same sentence that talked about how it was developing a similar chip that would provide even better price performance. And just last month it was reported that Microsoft Intel's monopoly partner in the PC era was developing its own ARM-based chips for the surface PCs and for its servers. Intel's Zenith was marked by those peak PC volumes that we talked about. Now to stress this point this chart shows x86 PC volumes over time. That red highlighted area shows the peak years. Now, volumes actually grew in 2020 in part due to COVID which is not really reflected in this chart but the volume game was lost for Intel. When it has been widely reported that in 2005 Steve Jobs approached Intel as it was replacing IBM microprocessors with with Intel processors for the Mac and asked Intel to develop the chip for the iPhone Intel passed and the die was cast. Now to the earlier point, PC markets are actually quite good if you're Dell. Here's some ETR data that shows Dell's laptop net score. Net score is a measure of spending momentum for 2020 and into 2021. Dell's client business has been very good and profitable and frankly, it's been a pleasant surprise. You know, PCs they're doing well. And as you can see in this chart, Dell has momentum. There's approximately 275 million maybe as high as 300 million PC units shipped worldwide in 2020, you know up double digits by some estimates. However, ARM chip units shipped exceeded 20 billion units last year worldwide. And it's not apples to apples. You know, we're comparing x86 based PCs to ARM chips. So this excludes x86 servers, but the way for volume for ARM dwarfs that of x86 probably by a factor of 10 times. Back to Wright's law, how long is it going to take Intel to double wafer volumes? It's not going to happen. And trust me, Pat Gelsinger understands this dynamic probably better than anyone in the world and certainly better than I do. And as you look out to the future, the story for Intel and it's vertically integrated approach it's even tougher. This chart shows Wikibon's 2020 forecast for ARM based compared to x86 based PCs. It also includes some other devices but as you can see what happens by the end of the decade is ARM really starts to eat in to x86. As we've seen with the M1 at Apple, ARM is competing in PCs in much better position for these emerging devices that support things like video and virtual reality systems. And we think even will start to eat into the enterprise. So again, the volume game is over for Intel, period. They're never going to win it back. Well, you might ask what about revenue? Intel still dominates in the data center right? Well, yes. And that is much higher revenue per unit but we still believe that revenue from ARM-based systems are going to surpass that of x86 by the end of the decade. Arm compute revenue is shown in the orange area in this chart with x86 in the blue. This means to us that Intel's last mot is going to be its position in the data center. It has to protect that at all costs. Now the market knows this. It knows something's wrong with Intel. And you can see that is reflected in the valuations of semiconductor companies. This chart compares the trailing 12 month revenue in the market valuations for Intel, Nvidia, AMD and Qualcomm. And you can see at a trailing 12 month multiple revenue with 3 X compared to about 22 X for Nvidia about 10 X for AMT and Qualcomm, Intel is lagging behind in the street's view. And Intel, as you can see here, it's now considered a cheap stock by many, you know. Here's a graph that shows the performance over the past 12 months compared to the NASDAQ which you can see that major divergence. NASDAQ has been powered part by COVID and all the new tech and the work from home. The stock reacted very well to the appointment of Gelsinger. That's no surprise. The question people are asking is what's next for Intel? How will Pat turn the company's fortunes around? How long is it going to take? What moves can he and should he make? How will they be received by the market? And internally, very importantly, within Intel's culture. These are big chewy questions and people are split on what should be done. I've heard everything from Pat should just clean up the execution issues. It's no.. This is, you know, very workable and not make any major strategic moves all the way to Intel should do a hybrid outsourced model to Intel should aggressively move out of manufacturing. Let me read some things from Barron's and some other media. Intel has fallen behind rivals and the rest of tech Intel is replacing Bob Swan. Investors are cheering the move. Intel would likely turn to Taiwan semiconductor for chips. Here's who benefits most. So let's take a look at some of the opinions that are inside these articles. So, first one I'm going to pull out Intel has indicated a willingness to try new things and investors expect the company to announce a hybrid manufacturing approach in January. Now, if you take a look at that and you quote a CEO Swan, he says, what has changed is that we have much more flexibility in our designs. And with that type of design we have the ability to move things in and move things out. And that gives us a little more flexibility about what we will make and what we might take from the outside. So let's unpack that a little bit. The new Intel, we know is a highly vertically integrated workflow from design to manufacturing production. But to me, the designers are the artists and the flexibility you would think would come from outsourcing manufacturer to give designers more flexibility to take advantage of say seven nanometer or five nanometer process technologies versus having to wait for Intel to catch up. It used to be that Intel's process was the industry's best and it could supercharge a design or even mask certain design challenges so that Intel could maintain its edge but that's no longer the case. Here's a sentiment from an analyst, Daniel Donnelly. Donnelly is at Citi. It says he's confident. Donnelly is confident that Intel's decision to outsource more of its production won't result in the company divesting its entire manufacturing segment. And he cited three reasons. One, it would take roughly three years to bring a chip to market. And two, Intel would have to share IP. And three, it would hurt Intel's profit margins. He said it would negatively impact gross margins by 10 points and would cause a 25% decline in EPS. Now I don't know about this. I would... To that I would say one, Intel needs to reduce its current cycle time, to go from design to production from let's say three to four years where it is today. It's got to get it under you know, at least at two years maybe even less. Second, I would say is what good is intellectual property if it's not helping you win in the market? And three, I think profitability is nuance. So here's another take from a UBS analyst. His name is Timothy Arcuri. And he says, quote, We see but no option but for Intel to aggressively pursue an outsourcing strategy. He wrote that Intel could be 80% outsourced by 2026. And just by going to 50% outsourcing, he said would save the company $4 billion annually in CapEx and 25% would drop to free cashflow. So look, maybe Gelsinger has to sacrifice some gross margin in EPS for the time being. Reduce the cost of goods sold by outsourcing manufacturing lower its CapEx and fund innovation in design with free cash flow. Here's our take, Pat Gelsinger needs to look in the mirror and ask what would Andy Grove do? You know, Grove's quote that only the paranoid survive its famous less well-known are the words that proceeded that quote. Success breeds complacency and complacency breeds failure. Intel in our view is headed on a path to a long drawn out failure if it doesn't act aggressively. It simply can't compete on cost as an integrated manufacturer because it doesn't have the volume. So what will Pat Gelsinger do? You know, we've probably done 30 Cube interviews with Pat and I just don't think he's taking the job to make some incremental changes to Intel to get the stock price back up. Why would that excite Pat Gelsinger? Trends, markets, people, society, he's a dot connector and he loves Intel deeply. And he's a legend at the company. Here's what we strongly believe. We think Intel has to do a deal with TSM or maybe Samsung perhaps some kind of joint venture or other innovative structure that both protects its IP and secures its future. You know, both of these manufacturers would love to have a stronger US presence. In markets where Intel has many manufacturing facilities they may even be willing to take a loss to get this started and deeply partner with Intel for some period of time This would allow Intel to better compete on a cost basis with AMD. It would protect its core data center revenue and allow it to fight the fight in PCs with better cost structures. Maybe even gain some share that could count for, you know another $10 billion to the top line. Intel should focus on reducing its cycle times and unleashing its designers to create new solutions. Let a manufacturing partner who has the learning curve advantages enable Intel designers to innovate and extend ecosystems into new markets. Autonomous vehicles, factory floor use cases, military security, distributed cloud the coming telco explosion with 5G, AI inferencing at the edge. Bite the bullet, give up on yesterday's playbook and reinvent Intel for the next 50 years. That's what we'd like to see. And that's what we think Gelsinger will conclude when he channels his mentor. What do you think? Please comment on my LinkedIn posts. You can DM me at dvellante or email me at david.vellante@siliconangle.com. I publish weekly on wikibon.com and siliconangle.com. These episodes remember are also available as podcasts for your listening pleasure. Just search Breaking Analysis podcast. Many thanks to my friend and colleague David Floyer who contributed to this episode and that has done great work in the last better part of the last decade and has really thought through some of the cost factors that we talked about today. Also don't forget to check out etr.plus for all the survey action. Thanks for watching this episode of the Cube Insights powered by ETR. Be well. And we'll see you next time. (upbeat music)

(upbeat music) >> Welcome to this special Cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE, we have two special guests, Karim Toubba, CEO of Kenna Security, and Caroline Japic, CMO, Kenna Security. Great to see you guys, thanks for coming on, appreciate you taking the time, appreciate it. >> Thanks for having us. >> So RSA is coming up, big show, security's at the top of the list of all companies. You guys have a very interesting company. Risk based vulnerability management is like the core secret sauce, but there's a lot going on. Take a minute to talk about your company. What do you guys do? Why do you exist? >> Yeah, sure. Thanks for having us. Some, the security landscape as you very well know, pretty crowded space, a lot of different vendors, a lot of technologies that enterprises and organisations have to deal with. What we do has a lot of complexity behind it, but in an app practicality for enterprises is actually quite simple. They have many, many data sources that are finding problems for them, mapping to their attack surface, what are misconfigurations? Where are there vulnerabilities in your network or your host, where there vulnerabilities in your applications, we taking all of that data, specifically from 48 different data sources, we map it to what attackers are doing in the wild, run it through a lens of risk, and then enable the collaboration between I.T. and security, on what to focus on at the tip of the spear with a high degree of fidelity and efficacy so that they know that they can't fix everything, but prioritize the things that matter and are going to move the meter the most. >> So you guys have emerged as one of those kind of new models, the new guard of security, it's interesting, it's been around for 10 years, but yet a lot's changed in 10 years but a lot of evolving. Risk based vulnerability management is the buzzword, R-B- >> V-M >> Okay, really comes from the founder of the company. Why is this becoming an important theme? Because you got endpoints, you got all kinds of predictive stuff with data, you got surface area is growing, but what specifically about this approach makes it unique and popular? >> Yeah, I think what's happening is if you, to really answer that question, you have to look at two different ends of the spectrum in terms of the business, the security side and the IT DevOps and application development side. And at the core of that is what was largely traditional tension. If you think about security teams, operations teams, incident response teams, and if you sit down with them and understand what they do on a day to day basis, beyond the incident response and reaction side, they have a myriad of tools and technologies that discover problems, typically millions of issues. Then you go to the IT side, and the application and DevOps side, and they care about building the next application, making sure the systems are up and running. And what happens is they, we've gotten to the point where they can't possibly fix everything security is asking them to fix, and that's created a lot of tension, people have woken up, started to realize that that tension has to give way to collaboration. And the only way you can do that is enable security to detect all the problems, but then very quickly focus and prioritize on the things that matter, and then go to IT and then tell them specifically what to fix so that they have a high degree of precision and understanding, that the needle will be moved relative to what they're asking them to do. >> So is it the timing of the marketplace and the evolution of the business where it used to be IT that handled it, and now security has gotten broader in its scope, that there's now too many cooks in the kitchen, so to speak? >> Yeah, it's gotten broader in its scope, and there's also been a realization that if you think about the security problem statement, they find all the problems, but if you if you peel back the layers, you quickly realize, they own very little the remediation path. Who fixes-- >> John: They being IT? >> They being security. >> John: Okay. >> Yeah, so it's actually quite fascinating. If you think about who fixes a vulnerability on an operating system like Windows or Linux, it's the IT team. If you think about who fixes or upgrades a Java library or rewrites an application it's DevOps or the application developers, but security's finding all the problems. So they're realizing, as they deploy more tools, find more issues, and increase the amount of data, they've got to get very precise and really enable an entirely new way of collaborating with IT so that they can get them to focus on the things that matter the most. >> Karim, I want to dig into some of the complexity, but first want to get the Caroline on the brand, and the marketing challenge because it's almost an easy job in the sense, because there's a lot of security problems out there to solve, but it's also hard on the other side, is that, where's the differentiation? There's so many vendors out, there's a lot of noise. How are you looking at the marketplace? Because you guys are emerging in with nice, lift on the value proposition, you won some recent awards. How do you view the marketplace? RSA is going to be packed with vendors, it's going to be wall to wall, we get put in the corner, we are going to have small space for theCUBE, but there's a lot there and customers are being bombarded. How are you marketing the value proposition? >> You are right. There's so much noise out there, but we are very clear and precise on the value we bring to our customers, we also let our customers tell the story. So whether it's HSBC, or SunTrust, or Levi, we work with them very closely with those CSOs, with their head of IT to understand their challenges, and then to bring those stories to life so we can help other companies because our biggest challenge is that people just don't know that there's a better solution to this problem. This problem's been around a long time, it's getting worse every day, we're reading about the vulnerabilities that are happening on a regular basis, and we're here to let people know we can fix it, and we can do it in a pretty quick and painless way. >> You had mentioned before we came on camera that when you you're getting known, as the brand gets out there, but when you're in the deals, you win. Could you guys share some commentary on why that's the case? Why are you winning? >> Yeah, by the way, just to piggyback off that a little bit, there is a really interesting paradigm happening within the security space, if you look at the latest publications, I don't know, there are 1400 of us all buzzing around with the same words? I think what Caroline and the team have done an exceptional job on, particularly in relative to the positioning is, we don't want to scare people into looking at Kenna. We want to be more ethereal than that and make them understand that we're ushering in a new way away from tension to an era of collaboration with IT, DevOps and application teams. That's very different than telling somebody in your messaging, Hey, did you hear the latest attack that happened at XYZ? >> Yeah. >> That sort of fear and marketing through FUD, is creating a lot of challenges for organizations, and candidly, is making CISOs and other people in security close the door. >> I've definitely heard that, do you think that's happening a lot? >> I think that's happening a lot. I think we're sort of, I like to think that Caroline and the team are sort of at the forefront of leading that initiative, and you can, and we're doing it in every way possible to really sort of tell a much more positive story about how security can be smarter and spin in a positive light, and in fact, the technology is enabling that, so it's consistent. >> We live in dark times. Unfortunately, a lot of people like, if it bleeds, it leads, and that's a really kind of bad way to look at it. But back to your point about tension and collaborations, I think that's an interesting thread. There's a ton of tension out there, that's real, from the CISO's perspective. Because there's too many teams, I mean, you got, Blue Team, Red Team, IT, governance, compliance, full stack developers, app. So you have now too many teams, too many tools that have been bought and it's like, people have all these platforms, they're drowning in this. How do you guys solve that problem? >> Yeah, it's back to that point of collaboration, and what we've really found that's been interesting in solving that problem, because what we're doing if you step back, is, we're bringing in all these data sources, and where that tension comes in, if you unpack it a little bit, is from different people coming in with different data sources. So IT comes to the table about what to fix, with their own point of view, security comes with their own point of view, application teams come with their own point of view, governance and compliance comes with their point of view. What we do is we come in and even though we're technology, we're really aligning people in process. We're saying, "Look, we're going to to amass all that data, "we're going to very quickly use machine learning "and a bunch of algorithms to sift through "millions of pieces of data "and divine what actually matters." It's empirical, it's evidence based, and we align all the organizations around that filter through risks so that there's agreement on how to measure that, what to prioritize, what to action and what the results look like. And when it turns out that when you get a bunch of people across an organization, to get aligned around data that they all agree with as the source of truth, it gets much easier to get them to really focus on the things that ultimately matter. >> It's a single version of the truth, right? It's a single version that they all can work from. Security isn't telling IT, "This should be your priority today," when they say, "You don't know what my priorities are," is actually the data that's telling them what their priorities are by role, and that's really important and really gets past all the, the friction and the fighting in between the teams. >> Yeah, that's great point, back to my other question when I get back to you Caroline, is what is the success formula look like for you guys? Why are you winning? What are the feedback you're hearing from your customers? Because at the end of the day, references are important, but also, success is a tell sign. So what's the reasons behind the success? >> Yeah, I'll let Karim talk about being face to face with customers, because he does that all the time. But what we're saying is that, the customers are resonating with the story that we're telling, they understand they have the problem we're laying out in a very simple way for, to be able to solve their solution, and that's working. We've redone our positioning, our messaging, we've trained our sales team, people understand the value we can bring, and that's what we're communicating, and that's what's working. >> Karim, please add on that, I want to get more into this. >> Yeah, and on the customer side, what we see and I'll give you a pretty classic example for us with a very large bank that's a customer of ours. We actually started on the security side, right? We sold to their deputy CISO to deploy, and then eventually, they doubled down and then deployed globally across 64 countries. And that happened sponsored by the CIO. Now we're a security company, so you ask the question, well, why did that get driven in that structure? And why did that deal go down ultimately in that way? And what was the real value? The value to the security person was clear, I want to aggregate 10 to 12 different data sources, I want to prioritize, I want to collaborate with IT. The value to the CIO was the CIO happens to own all the application developers and all the IT people and the security people on a global basis. And so what they wanted to do, is they wanted to understand what the risk was for each of the lines of businesses they had within organization so that they can hold the business users accountable to paying a small tax for security, not just developing the next billion dollar high net worth application, which is extremely important to those businesses, but at the same time, ensuring that they're secure. And so that leverage when you start with security, and then branch out in other organizations, especially in large, multinational organizations, is really where the the real value comes into the platform. >> So if I hear you correctly, you come in for security, okay, we can get rid of the noise, help you out, check, win, and then the rest of the organization doesn't have security teams per se, >> Karim: Correct. >> Needs security to be built in from day one. >> Karim: Correct. >> You're providing a cross connect of value to the other teams? >> That's right. >> It's almost like, security is code, if you will. >> Karim: That's right. And nowhere is that more evident in our utilization statistics. So we're a SaaS platform, so of course we, like many other SaaS companies do a bunch of analytics on utilization of our customers, more often than not, in our large scale enterprises, we actually have more IT and non security users logging into Kenna, in a self service model, because they're the ones, back to the point you made earlier, that are actually driving the remediation path. >> Take us through how that works. So say I'm interested, okay, you sold me on it, great, I need the pain relief on the security side, I need the enablement and empowerment on the collaboration side, what do I do? Do I just plug my databases into you? Is it API driven? Are you on Amazon? Are you on Azure? What's cloud? What am I dealing with? Take me through the engagement. >> Yeah, so we're 100% cloud based platform. Multi cloud, so we can deploy in AWS, we can deploy in Google et cetera. And then what we do is we effectively through a bunch of API's called connectors that are transparent to the customers, we enable them to bring in their data. So this is everything from traditional scanning data like Qualys, Rapid7, Tenable, more, newer data like CrowdStrike, Tanium, DaaS SaaS, software composition analysis tools, WhiteHat, Veracode, Black Duck, Sonatype, you name it. The list goes on, specifically, there's about 48 of them. All of that is basically helps us understand what the totality of the attack surface is. That's very useful for security because they're using multiple tools. We then overlay what we call exploit and tell, this is the data that tells us about what attackers are doing in the wild. Specifically, we have 5 billion pieces of data that tell us about what vulnerabilities are being popped, what's the rate of change, what malware are they being embedded in? That use, that information is used through machine learning to help us prioritize and risk score each of the findings we get from the customer tools. And then where it pivots over to IT, is we then allow them to take all of that data and that metadata and asset criticality into what we call risk meters. So they're basically aligned with where, how IT operates. So for example, if you own all the Linux infrastructure in the cloud, you log in, you'll only see the risk across the infrastructure you own. Whereas if Caroline owns all the endpoint real estate across Windows, she logs in and understands what her risk is across Windows. And then we of course, integrate in the ticketing systems to drive the remediation and report up to executives and then over to security, about what the workflow you-- >> So you guys really focusing not so much on the security knock or the sock, it's more on indexing, if you will, for lack of a better description, the surface area, >> Karim: Correct. >> And getting that prepared from a visibility standpoint to acquire the data. >> Karim: That's right. >> And then leveraging that across-- >> Across the organizations, yeah. >> Did I get that, right? >> It's exactly right. And if you ask, if you again, double click deeper on that, what's fascinating to watch, so we have a an annual, or bi annual report that we do called prioritization or prediction, or P2P. And this is all of our customer data completely anonymized in a warehouse, and then we run a bunch of reports, and lot of the analytics we ran initially were around security. Now we're starting to pivot in IT. If you look at our latest report, one of the most interesting things I found in my time here is that the average large scale enterprise has actually no more than 10% remediation capacity, right? So what does that tell you? That tells you that 90% of the problems are going to go unsolved, which pinpoints why it's even more important to have specific prioritization on the things that matter. >> They solve the right 10%. >> At the right time too, >> At the right time. >> 10% capacity, operating capacity, assuming some automation that might take care of some of the low hanging fruit >> Exactly. >> Through DevOps or automation. You can focus on those 10% at the right time, which by the way, if you use that capacity for the wrong problems at the wrong time, it's wasted capacity. >> Karim: That's right. >> That's what you guys are trying to get at, right? >> Karim: That's exactly right, work smarter, not harder. >> So Kenna security, what's the vision? What's the next step? Why should someone care about working with you guys? Why is it important to engage you guys? What's the big deal? Is it the risk based vulnerability, kind of origination invention, which is the core or the DNA, or is it something bigger? What's the vision? What's the why? Yeah, well look for us, we started, our company was actually founded by a gentleman by the name Ed Bellis, who's the ex chief security officer at Orbitz, and he founded the company out of a need. We started very early in the traditional pure vulnerability space. This was like calling Classic Qualys, Rapid7, Tenable. We then expanded into the application world. So this is starting to take in, moving up stack if you will full stack, as the environment moves to cloud, as the environment moves to containers, as the environment moves to configuration management as the environment moves to a much more ephemeral state, that will drive an entirely new set of data sources that will drive an entirely different new set of priorities all aligned with the same model of risk. So our view of the future is that we are the platform that enables the organization to understand the totality of the attack surface, that enables collaboration across all the groups that deal with technology within enterprises, and allows them to really prioritize and understand risk in a way that not only fosters the collaboration, but gives you that return on investment that candidly ultimately CIOs are looking for. >> Caroline the story from a marketing perspective, what's the story you're trying to tell? >> We started this space, our founder Ed Bellis is the father of risk based vulnerability management and he loves it when I say that, but it's 100% true. We are continuing down this path, I mean, there are so many companies that have this problem that don't know that there's a better way to solve it. And so for now, our mission is to make sure that we're educating those people, they understand what's possible to do today, and then continuing from there, so. >> Well, I really appreciate you guys coming in and introducing and sharing more about Kenna Security, we've been seeing successes. I'm going to ask you about what you guys think about RSA, I'd love to get both you guys to weigh in. But before we get to the RSA kind of what's coming, take a quick minute to plug the company. What do you guys looking to do? You hiring? You just got some funding? Give the quick pitches. >> Yeah, sure, we did. We just closed $48 million series D round. We had all of our investors and a new investor, Sorenson Ventures come in. We also had two strategic investors, Citi and HSBC, because we do quite well, that very good validation. And we're also quite prominent in the financial services vertical, it helps that. And so for us, it's really about scaling, right? Scaling people, scaling the technology, scaling capabilities-- >> John: Across the board. >> Across the board. >> Engineering, obviously. >> Engineering, sales, geographies, it's really about getting the word out there and then being able to follow that up with the feed on the street that matter. >> We're definitely hiring, but we're also growing through OEMs. So we have a relationship with VMware, they're embedding us into their app defense products, and so if you buy app defense from VMware, you are buying Kenna whether you know it or not. >> So you're going to be an ingredient in other products. >> That's right. >> And or direct or indirect, probably some channel ecosystem opportunities? >> That's right. >> So we're growing on the technology partner OEM front, definitely interested in talking to companies that are interested on that front. >> We should do a whole segment on my fascination with what I call tier two or tier 1B clouds, specialty clouds, security clouds. So maybe do that another time. Okay, final question for you guys. RSA is coming this year 2020, and then a series of other events. Cloud Security has been a hot topic since re:Inforce last year was launched, we were there, kicking off theCUBE in security. What do you guys expect this year at RSA? What do you think the big themes are going to be? The hype? The meat on the bone? What's the real deal? What's the hype? What do you guys think is going to happen? >> Karim: I'll let you start. >> Yeah, I can tell you our theme is the right fight club. Because we are focused on the right fight that you need to have every day inside your enterprise. It's not focused on all the vulnerabilities that are hitting you because they're hundreds of thousands of them, millions of them, and there's going to be more every single day, it's about fighting the right fight. So if you come by our booth, you'll see that, it's going to be very exciting-- >> And of course, don't talk about the Fight Club vulnerabilities. (Karim laughs) >> You know the rules of the fight club. >> The first rule is to talk to Kenna about the right fight club. That is the first rule. >> That's cool. >> Yeah, I mean, it's interesting. Every, as you very well know, every year when people walk away from RSA, there's a few blogs that are written about what was the theme this year, I suspect this year's in security specifically, is going to be about AI driven security. We've been starting to see that for a while, it started to bleed into last year's event. I think for us in particular, we have a very particular point of view, and our book point of view is that doesn't matter if it's ML, if it's AI, or what type of algorithms you're running, the question is, what's the value? What is the value when you have 1400 people all screaming to get in the door of an organization? Everybody really has to begin to answer that question fundamentally. And I think the people that have that position in the market are the people that are going to be able to stand out. It's interesting, as always the hype with AI, but it's interesting, I was just trying to figure out when the term there is no perimeter was kind of first coined in theCUBE, I'm thinking probably about five years ago, it really became a narrative and then more recently, with the cloud, the perimeter is dead. Edge is out there. >> Karim: Right. >> So this is, what's the gestation period of real scalable security post perimeter is dead. It's interesting, is it years, is it seems to be hitting this year. It seems to be the point where, okay, I tried everything, now I've got to be data driven or figure out a way to map the surface area. >> That's right. >> End to end. Well, thanks to Kenna Security coming in, a solution for figuring out the vulnerabilities with a real invention. We're going to be covering security at RSA with Kenna Security and others. Thanks for watching, this is theCUBE. (upbeat music)

>> Announcer: Live from Las Vegas, it's the Cube. Covering VMworld 2017. Brought to you by VMware and its ecosystem partners. >> Welcome back to the Cube. Our continuing coverage of Vmworld 2017 continues. I'm Lisa Martin with my co-host Stu Miniman. We're excited to be joined next by Jason Brown a Cube alumni consultant and product marketing for Dell EMC ScaleIO. Welcome back to the Cube Jason. >> Thank you for having me. >> Good to have you here, so day two of the event, lot's of announcements, lots of buzz. Talk to us about ScaleIO. What's the current state of the business. >> Well, it's actually really exciting right now. We're doing really well. We're seeing great customer adoption. We're seeing massive petabytes of ScaleIO deployed in data centers, and were here at the show really to talk to you about customers for ScaleIO for Vmware. 'Cause everyone here's above E10, obviously, they're doing awesome. We love it. They're doing great. But there's some differences and similarities between the two products that people get confused about, so we're here at the show really trying to help, you know, ease confusion, talk about how it's like peanut butter and jelly, right? Some people like peanut butter, some people like jelly but most people like 'em both, so we're just trying to help people out and understand when to choose which and sometimes it's both. >> Alright, Jason, I've got a history watching ScaleIO since before the acquisition, you know, service providers that usually kind of fit their model a little bit more than VSAN, so when I think scale, I tend to think ScaleIO. I interviewed ADP yesterday. Big customer, rolling out like 30,000 nodes of compute with VSAN. So, scales >> Yeah >> not only one piece of it. Maybe, help us kind of understand some of the, you know, of course there's going to be places that overlap, but what is the, you know, kind of ideal ScaleIO customer, what are they looking for, and how's that differ from the VSAN? >> Sure, so in particular if you're looking at ScaleIO for VMware, there's a few things you need to understand. First and foremost, with ScaleIO we're talking about consolidating resources across the data center. So we're talking data center grade software to find storage which can run in a hyperconverged model or not. And that's really key differentiating, 'cause if you look at these enterprises, especially, these large enterprises that built an IT organization of past 20 years, right? And so when you introduce HCI to them, you're transforming the architecture of the data center but also the IT operating environment. And that's scary for a lot of people who have spent millions of dollars having a server team, a network team, and the storage team. So one of the key things for ScaleIO in a VMware environment is, if you want to transform the architecture to software defined, but preserve that IT operating model, this two layer deployment, we call it, you can do that with ScaleIO. But on the flip side you can also do a more modern architecture with hyperconverged as well. So you can get the best of both worlds. So whether today you're ready to go all the way with the service providers, they'll go hyperconverged, you know out of the gate, but enterprises usually start more traditional and then move to that hyperconverged and ScaleIO provides that pathway to get there. >> Yeah, bring us inside those customers a little. 'Cause I've talked to a couple of very large customers of ScaleIO actually, did a case study at Citi and Citi told me, internally, we're just not ready to go fully hyperconverged. >> Jason: Exactly. >> So they kept that. They're massive scale. Talked to a large global hospitality company that, once again, looked more at kind of the storage usage of what they're doing so, I mean hyperconverged VSAN seems to be having, you know they've got 10,000 customers, they're all in that-- model. >> Exactly. >> So, what is it that gets a customer ready for that? What kind of pushes or pulls them towards being ready for, you know, embracing? >> Well, I think it's understanding your business goals and your desired outcomes. So with something like ScaleIO you're looking at simplicity in the data centers. So you're looking for scale, you know, not tens of nodes where traditional, I hear this said that traditional VSAN deployment is eight to 16 nodes, 'cause they're you know, VMware's everywhere, right? There's a lot of ROBO, SMB, VDI, use scales right there, and that's not really where ScaleIO plays. ScaleIO is about data center, so Tier 1 application, databases, data analytics. It's looking at things like containers and microservices, Splunk, NoSQL. Applications like that. So when you look at those types of applications and workloads, you have to understand that your scale will probably go from tens to hundreds of nodes. Your performance may go from a million IOPS to tens of millions of IOPS. You may need six nines availability 'cause again, you're running in the data center. Customers are replacing their SAN arrays with ScaleIO. So you need all that enterprise class, data center grade functionality with the scale performance and flexibility, the key thing is flexibility as well, if you want to run multiple workloads on a cluster, you need to be able to support VMware, Hyper-V, KVM, Linux, Windows, so and ScaleIO enables all of those things. And therefore, that's why when you look at your business goals, your business ops and what your data center looks like, you need to understand that functionality. Then you decide okay, is it going to be VSAN or ScaleIO or is it going to be both, 'cause I have both of those use cases there. >> So you talked about VSAN and ScaleIO, peanut butter and jelly. Michael Dell on main stage with Pat Gelsinger said VMware and Dell EMC are like peanut butter and chocolate. Both, all good flavors, in my opinion. I'd love to hear an example though, of where, like to your point, before I asked the question. We just had the CTO of Dell EMC storage, speaking with Stu and I a few minutes ago and one year post-combination, and he said customers are starting to understand now the value of Dell EMC-- >> Yes. >> Together. So with that, you know, a year later and customers now understanding the value proposition of this company that now also owns VMware, how much easier is the conversation, you know, away from VSAN verses ScaleIO? I'd love to understand where are you seeing where they both, those peanut butter and jelly sandwiches play together. What are some of the maybe industries or key use cases where a customer would need ScaleIO and VSAN? >> Sure. So if you think about financial services, Citi as Stu mentioned, one of the larger ones there, definitely plays there, in healthcare there's a few large big partner network companies that have come together to be successful there. Telco, Verizon, Comcast, right? Not only just private Cloud but public Clous as well, so when you look at your data center, you got to look at the whole thing. So, for your VDI, your ROBO, your SMB and maybe for a few of your enterprise applications that only need you know, 50,000 in an IOPS performance for your VMs then VSAN is going to be great there, but then you look to the other side of your data center and you've got something like SAP, you know HANA, I think any other, in fact, ORACLE, etc or you're looking to build a private cloud of hundreds of nodes, well that's where ScaleIO is going to sit. Over in that corner, you know? So, it really is understanding what your workloads are and where they play. You know, it's important to know too that for ScaleIO our primary use cases are array consolidation, so you've got silos of arrays in your data center and you want to stop managing silos of arrays, and you want to bring everything together into a single resource, a single cluster, boom, ScaleIO. You want to build the cloud environment whether you're a service provider building a public cloud like Swisscom for example, who built a public cloud based off of ScaleIO, or a private cloud like CitiGroup for example. It's pretty much a private cloud; mix of array consolidation as well. And then something like a gaming company that we've worked with where they are doing this next generation DevLogs containers, microservices, well ScaleIO's great for that too, 'cause it has the flexibility to start small and grow and support the various things that they need to be able to deploy their applications 32% faster. So you know, it really encompasses the whole data center. >> Yeah, a bunch of interesting points that I want to unpack a little bit there. Specifically, you're talking about all the new applications and the new technologies that people are doing. One of the challenges most people have, you know, the stack we've been using, I think, for my entire IT career is, you know, we spend what, somewhere between 70 and 90% of our time keeping the lights on. >> Jason: Yes. >> And the wave of kind of software-defined, you know, all of these type things, supposed to be, we need to simplify our environment, you know and, therefore I can take those resources and reallocate them, retrain them, put them on cool new things. What are you seeing from the customers, you know, just organizationally from what happens to the storage people as well as how do they take advantage of some of these tougher things like application modernization? >> Good question. Good question. So, you know it depends on the company right? There are, like you said, there are some customers that want to keep them separated and that's perfectly fine you know, there are tools that you can use with ScaleIO so that you can manage the storage independently of the compute. But then you've got things like our tight integration with vSphere, where the VMadmin can manage the storage as well. So, it depends on the preferences as well as the maturity of the organization and the skillset of the folks that are managing it as well. If you can have a storage admin become more agile and be able to manage the compute and the VMs as well then perfect. They become more generalists, right? We've talked about how these specialties becomes more generalists in these types of HCI and NextGen environments. So if they have that skillset then perfect and both ScaleIO and VSAN can enable that. And then if you're looking at app modernization, you know what do you need from an infrastructure storage perspective to achieve that, and how can you enable your application developers access that storage even faster? And that's really was ScaleIO does with the whole automation points behind everything. With, be able to add resources on the fly, remove resources on the fly, reallocate on the fly. So being able to be flexible for what they need when they all of a sudden are ramping up a new application is really critical. >> Yeah. I guess, I'm wondering if you have any specific examples. One of the critiques if you talk about, you know, storage, admins, fast is not something that usually, you think of. Flash is fast and everything like that but, how do we keep up with the pace change, how do I move things? How does ScaleIO help change that equation? Even just specifically for storage? >> Well I think that in order to be able to keep up with that change, right, it's about, as you said, simplifying their job and making it easier. So, if you've got the tools and the, just the functionality in the product itself, to be able to help them learn faster, be able to press a button as opposed to being able to allocate an array group and (murmurs) things that have an architecture, that makes that be able to achieve that as well, that's really how you do it. You know I haven't talked to any storage admins lately, unfortunately. So I can't give you a specific example, but that's really what we see at kind of the one on one level. >> And from a buyer's train of perspective, so much has changed and shifted towards this C-Suite. When we look at things like data protection, we, you know, some announcements about that yesterday, storage, and you said you haven't spoken with storage admins in a while. There's a lot of data that show that data protection storage isn't an IT problem, it's a business problem. So how has the conversation now with Dell EMC with respect to whether it's ScaleIO or whatnot, shifted upstream if you will, talking to more senior executives, rather than the storage guys and gals that are managing specific pieces? Tell us about that-- >> Sure. >> Conversation and maybe cultural shift. >> Well when you talk to any C level executive, what's the top of mind, right? Security, saving, cost savings, budget, right? So when we're talking to executives, where they talk about data center transformation, how software defines storage and enables that both at the architectural level and at the IT level, but also about how we can make their business easier to run and how it can save them money. so if you're able to get all this great flexibility and scalability and all this you know, performance, but then be able to preserve the features that you need, like compression and snapshots and being able to connect to your data protections suites as well? So if you can tell them all that and say hey and you know what, we have customers saving 50% five year TCO by doing that, without needing to do data migration or tech refreshers anymore. They're like alright, sign me up. Because you have to understand too, when you talk to them, they don't need to go buy an array the next day, and spend a couple million dollars they maybe be will be able to utilize in the future or not. They can start very small. Three nodes, four nodes, and have this pay as you go licensing so they love that as well because it grows on their terms. Not on our terms, on their terms. And that's really important for you know people that in those C level suites that are trying to maximize the efficiency of the business. >> Alright, Jason, one thing's when customers buy into a solution like this, it's more of a platform discussion these days and of course one of the things they're looking for is where are you taking me down the road? So it's great, here's what I can do today, one of the things I love this whole wave of it, is, you know, upgrades and migrations were like, you know, the four letter words for anybody in storage. >> Dirty words. >> And I said, you know, when we have a pool of resources and I can kind of add and remove nodes it was like, oh my God, that was, we conservatively estimated like five years ago that 30% of the overall TCO was based on that alone and. Wow. Scrap that. Last time you're ever going to need to, you know, migrate once you get on this platform. But, I want you to talk to us a little bit about, you know a little bit, kind of the vision and roadmap. What are >> Sure. >> You talking to customers about. >> Absolutely. So, you know with a product like this, it's constantly evolving and innovating so when we talk to customers about what's in the future, well you have to first be thinking about data services. Data services are always very important and with ScaleIO, you know, admittedly, we're a little short on some data services because we more focus on scalability and performance and making sure that we have a six nines architecture. So, the first and biggest thing that's coming very soon, if you were at Dell EMC with ScaleIO is compression. So being able to, you know for your block storage workloads, being able to maximize the efficiency of your storage even more with some in line compression? Very important. So we're doing that. We're also enhancing our snapshot's functionality so that, you know when you talk snapshots and SDS, you know, you compare it to an enterprise array, probably not up to snuff. Well what we're doing now with our snapshot keeping in relation to ScaleIO is we're actually going to have them be better or even much better than something you'd find in like an all flash array. You know, where you can have you know, thousands of snapshots in a v-tree and things like that. But it also goes to hardware as well. 'Cause there's always hardware, right? And with the innovation within Dell EMC with Dell PowerEdge servers with our friends in CPSD, we're able to innovate a lot faster with ScaleIO and SDS. So, 14G was announced. Well ScaleIO's going to be one of the first products within Dell EMC through our ScaleIO Ready Node to support mV dims and MVME. So as you know we support MVME today, one of the few software device storage platforms out there today that supports it, in a roll your own server model. With the Ready Node 14G coming out later this year, with the ScaleIO Ready Node, immediately out of the gate mVdim and MVME technology in a ScaleIO Dell EMC hardware product, 'cause it's already you know its Dell PowerEdge servers and ScaleIO software. And then helping our management keep our management keep (murmurs) as well so, introducing VVols for our VMware customers, being able to provide something called AMS which is our automated management services for the Ready Node so that you can deploy, configure, manage, upgrade, not only the storage software but the firmware as well as the EXS hypervisor all in a single button, in all a single interface, so we're doing that as well. So it's all about, you know, taking advantage of NextGeneration functionality from the hardware perspective, simplifying the management, then introducing critical features and functionality that our customers have been asking for. >> Just to make sure I'm 100% on this, things like the data services, that's software, so everybody that's got it today, will be able to upgrade it. Obviously the next generation of hardware always helps along the way, but you know, you manage those a little bit separate even though you want to handle both of those vectors. >> Yes, exactly. So when you upgrade to ScaleIO.next when it comes out you'll get that feature functionality. Now there's a few things you need to understand, right? You should have Mvdims and some type of flash media to support it. >> Stu: Sure. >> Because you're trying to maximize scalability and performance while providing these features, there's some dependencies there. But yeah, out of the gate, those features will be available. That's why it's called software-defined storage. It's all in the software, all this world of goodness is. >> Okay so take me upstream. Lot of new features, functionality coming out; what are the new business benefits if I'm the CEO of Swisscom, that I'm going to be able to achieve from that? >> Well I think definitely increased performance. Definitely increased efficiency of your storage with things like compression and snapshots. Now, if you're able to compress that data, get more out of your system-- >> But what kind of like, in terms of TCL. How am I going to be able to reduce. >> Oh, well. >> What are the factors of-- (grunts loudly) >> You know, we haven't run the numbers yet, but you know, the fact that we already can achieve 50% TCO, it can only get better from there when we're introducing these types of features, where you're maximizing efficiency, so, we expect it to bump up a bit. We're hoping we can work with you guys to get some good numbers that come out of it. >> Excellent. So continued strengthening of those-- business outcomes is, >> Yeah, that's it. You know, making sure, >> what you're talking about. >> Makings sure that the customers that want to move to software-defined storage in their data center, are able to achieve that in the most seamless way, and be able to reap the benefits. >> Fantastic. Well Jason, thanks so much for sharing your insights what's happening, um, peanut butter and jelly. Makes me hungry. I think it's time for lunch. >> It is lunch time, yeah. >> We thank you so much for coming back-- on the Cube. >> Thanks for having me. I really appreciate it. >> And for my co-host Stu Miniman, I'm Lisa Martin you are watching the Cube live, day two of our continuing coverage from VMworld 2017. Stick around. We'll be right back after a short break. (electronic music)

>> Voiceover: Live from New York, it's The Cube, covering Big Data New York City 2016. Brought to you by headline sponsors, Cisco, IBM, Nvidia, and our ecosystem sponsors. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to New York City, everybody. Nenshad Bardoliwalla is here, he's the co-founder and chief product officer at Paxata, a company that, three years ago, I want to say three years ago, came out of stealth on The Cube. >> October 27, 2013. >> Right, and we were at the Warwick Hotel across the street from the Hilton. Yeah, Prakash came on The Cube and came out of stealth. Welcome back. >> Thank you very much. >> Great to see you guys. Taking the world by storm. >> Great to be here, and of course, Prakash sends his apologies. He couldn't be here so he sent his stunt double. (Dave and George laugh) >> Great, so give us the update. What's the latest? >> So there are a lot of great things going on in our space. The thing that we announced here at the show is what we're calling Paxata Connect, OK? We are moving just in the same way that we created the self-service data preparation category, and now there are 50 companies that claim they do self-service data prep. We are moving the industry to the next phase of what we are calling our business information platform. Paxata Connect is one of the first major milestones in getting to that vision of the business information platform. What Paxata Connect allows our customers to do is, number one, to have visual, completely declarative, point-and-click browsing access to a variety of different data sources in the enterprise. For example, we support, we are the only company that we know of that supports connecting to multiple, simultaneous, different Hadoop distributions in one system. So a Paxata customer can connect to MapR, they can connect to Hortonworks, they can connect to Cloudera, and they can federate across all of them, which is a very powerful aspect of the system. >> And part of this involves, when you say declarative, it means you don't have to write a program to retrieve the data. >> Exactly right. Exactly right. >> Is this going into HTFS, into Hive, or? >> Yes it is. In fact, so Hadoop is one part of, this multi-source Hadoop capability is one part of Paxata Connect. The second is, as we've moved into this information platform world, our customers are telling us they want read-write access to more than just Hadoop. Hadoop is obviously a very important part, but we're actually supporting no-sequel data sources like Cloudant, Mongo DB, we're supporting read and write, we're supporting, for the first time, relational databases, we already supported read, but now we actually support write to relational databases. So Paxata is really becoming kind of this fabric, a business-centric information fabric, that allows people to move data from anywhere to any destination, and transform it, profile it, explore it along the way. >> Excellent. Let's get into some of the use cases. >> Yeah, tell us where the banks are. The sense at the conference is that everyone sort of got their data lakes to some extent up and running. Now where are they pushing to go next? >> Sure, that's an excellent question. So we have really focused on the enterprise segment, as you know. So the customers that are working with Paxata from an industry perspective, banking is, of course, a very important one, we were really proud to share the stage yesterday with both Citi and Standard Chartered Bank, two of our flagship banking customers. But Paxata is also heavily used in the United States government, in the intelligence community, I won't say any more about that. It's used heavily in retail and consumer products, it's used heavily in the high-tech space, it's used heavily by data service providers, that is, companies whose entire business is based on data. But to answer your question specifically, what's happening in the data lake world is that a lot of folks, the early adopters, have jumped onto the data lake bandwagon. So they're pouring terabytes and petabytes of data into the data lake. And then the next question the business asks is, OK, now what? Where's the data, right? One of the simplest use cases, but actually one that's very pervasive for our customers, is they say, "Look, we don't even know, "our business people, they don't even know "what's in Hadoop right now." And by the way, I will also say that the data lake is not just Hadoop, but Amazon S3 is also serving as a data lake. The capabilities inside Microsoft's cloud are also serving as a data lake. Even the notion of a data lake is becoming this sort of polymorphic distributed thing. So what they do is, they want to be able to get what we like to say is first eyes on data. We let people with Paxata, especially with the release of Connect, to just point and click their way and to actually explore the data in all of the native systems before they even bring it in to something like Paxata. So they can actually sneak preview thousands of database tables or thousands of compressed data sets inside of Amazon S3, or thousands of data sets inside of Hadoop, and now the business people for the first time can point and click and actually see what is in the data lake in the first place. So step number one is, we have taken the approach so far in the industry of, there have been a lot of IT-driven use cases that have motivated people to go to the data lake approach. But now, we obviously want to show, all of our companies want to show business value, so tools and platforms like Paxata that sit on top of the data lake, that can federate across multiple data lakes and provide business-centric access to that information is the first significant use case pattern we're seeing. >> Just a clarification, could there be two roles where one is for slightly more technical business user exposes views summarizing, so that the ultimate end user doesn't have to see the thousands of tables? >> Absolutely, that's a great question. So when you look at self-service, if somebody wants to roll out a self-service strategy, there are multiple roles in an organization that actually need to intersect with self-service. There is a pattern in organizations where people say, "We want our people to get access to all the data." Of course it's governed, they have to have the right passwords and SSO and all that, but they're the companies who say, yes, the users really need to be able to see all of the data across these different tables. But there's a different role, who also uses Paxata extensively, who are the curators, right? These are the people who say, look, I'm going to provision the raw data, provide the views, provide even some normalization or transformation, and then land that data back into another layer, as people call the data relay, they go from layer zero to layer one to layer two, they're different directory structures, but the point is, there's a natural processing frame that they're going through with their data, and then from the curated data that's created by the data stewards, then the analysts can go pick it up. >> One of the other big challenges that our research is showing, that chief data officers express, is that they get this data in the data lake. So they've got the data sources, you're providing access to it, the other piece is they want to trust that data. There's obviously a governance piece, but then there's a data quality piece, maybe you could talk about that? >> Absolutely. So use case number one is about access. The second reason that people are not so -- So, why are people doing data prep in the first place? They are trying to make information-driven decisions that actually help move their business forward. So if you look at researchers from firms like Forrester, they'll say there are two reasons that slow down the latency of going from raw data to decision. Number one is access to data. That's the use case we just talked about. Number two is the trustworthiness of data. Our approach is very different on that. Once people actually can find the data that they're looking for, the big paradigm shift in the self-service world is that, instead of trying to process data based on transforming the metadata attributes, like I'm going to draw on a work flow diagram, bring in this table, aggregate with this operator, then split it this way, filter it, which is the classic ETL paradigm. The, I don't want to say profound, but maybe the very obvious thing we did was to say, "What if people could actually look at the data in the first place --" >> And sort of program it by example? >> We can tell, that's right. Because our eyes can tell us, our brains help us to say, we can immediately look at a data set, right? You look at an age column, let's say. There are values in the age column of 150 years. Maybe 20 years from now there may be someone who, on Earth, lives to 150 years. But pretty much -- >> Highly unlikely. >> The customers at the banks you work with are not 150 years old, right? So just being able to look at the data, to get to the point that you're asking, quality is about data being fit for a specific purpose. In order for data to be fit for a specific purpose, the person who needs the data needs to make the decision about what is quality data. Both of you may have access to the same transactional data, raw data, that the IT team has landed in the Hadoop cluster. But now you pull it up for one use case, you pull it up for another use case, and because your needs are different, what constitutes quality to you and where you want to make the investment is going to be very different. So by putting the power of that capability into the hands of the person who actually knows what they want, that is how we are actually able to change the paradigm and really compress the latency from "Here's my raw data" to "Here's the decision I want to make on that data." >> Let me ask, it sounds like, having put all of the self-service capabilities together, you've democratized access to this data. Now, what happens in terms of governance, or more importantly, just trust, when the pipeline, you know, has to go beyond where you're working on it, to some of the analytics or some of the basic ingest? To say, "I know this data came from here "and it's going there." >> That's right, how do we verify the fidelity of these data sources? It's a fantastic question. So, in my career, having worked in BI for a couple of decades, I know I look much younger but it actually has been a couple of decades. Remember, the camera adds about 15 pounds, for those of you watching at home. (Dave and George laugh) >> George: But you've lost already. >> Thank you very much. >> So you've lost net 30. (Nenshad laughs) >> Or maybe I'm back to where I'm supposed to be. What I've seen as the two models of governance in the enterprise when it comes to analytics and information management, right? There's model one, which is, we're going to build an enterprise data warehouse, we're going to know all the possible questions people are going to ask in advance, we're going to preprogram the ETL routines, we're going to put something like a MicroStrategy or BusinessObjects, an enterprise-reporting factory tool. Then you spend 10 million dollars on that project, the users come in and for the first time they use the system, and they say, "Oh, I kind of want to change this, this way. "I want to add this calculation." It takes them about five minutes to determine that they can't do it for whatever reason, and what is the first feature they look for in the product in order to move forward? Download to Excel, right? So you invested 15 million dollars to build a download to Excel capability which they already had before. So if you lock things down too much, the point is, the end users will go around you. They've been doing it for 30 years and they'll keep doing it. Then we have model two. Model two is, Excel spreadsheet. Excel Hell, or spreadmarts. There are lots of words for these things. You have a version of the data, you have a version of the data, I have a version of the data. We all started from the same transactional data, yet you're the head of sales, so suddenly your forecast looks really rosy. You're the head of finance, you really don't like what the forecast looks like. And I'm the product guy, so why am I even looking at the forecast in the first place, but somehow I got access to the data, right? These are the two polarities of the enterprise that we've worked with for the last 30 years. We wanted to find sort of a middle path, which is to say, let's give people the freedom and flexibility to be able to do the transformations they need to. If they want to add a column, let them add a column. If they want to change a calculation, let them add a a calculation. But, every single step in the process must be recorded. It must be versioned, it must be auditable. It must be governed in that way. So why the large banks and the intelligence community and the large enterprise customers are attracted to Paxata is because they have the ability to have perfect retraceability for every decision that they make. I can actually sit next to you and say, "This is why the data looks like this. "This is how this value, which started at one million, "became 1.5 million." That covers the Paxata part. But then the answer to the question you asked is, how do you even extend that to a broader ecosystem? I think that's really about some of the metadata interchange initiatives that a lot of the vendors in the Hadoop space, but also in the traditional enterprise space, have had for the last many years. If you look at something like Apache Atlas or Cloudera Navigator, they are systems designed to collect, aggregate, and connect these different metadata steps so you can see in an end-to-end flow, this is the raw data that got ingested into Hadoop. These are the transformations that the end user did in Paxata in order to make it ready for analytics. This is how it's getting consumed in something like Zoom Data, and you actually have the entire life cycle of data now actually manifested as a software asset. >> So those not, in other words, those are not just managing within the perimeter of Hadoop. They are managers of managers. >> That's right, that's right. Because the data is coming from anywhere, and it's going to anywhere. And then you can add another dimension of complexity which is, it's not just one Hadoop cluster. It's 10 Hadoop clusters. And those 10 Hadoop clusters, three of them are in Amazon. Four of them are in Microsoft. Three of them are in Google Cloud platform. How do you know what people are doing with data then? >> How is this all presented to the user? What does the user see? >> Great question. The trick to all of this, of self service, first you have to know very clearly, who is the person you are trying to serve? What are their technical skills and capabilities, and how can you get them productive as fast as possible? When we created this category, our key notion was that we were going to go after analysts. Now, that is a very generic term, right? Because we are all, in some sense, analysts in our day-to-day lives. But in Paxata, a business analyst, in an enterprise organizational context, is somebody that has the ability to use Microsoft Excel, they have to have that skill or they won't be successful with today's Paxata. They have to know what a VLOOKUP is, because a VLOOKUP is a way to actually pull data from a second data source into one. We would all know that as a join or a lookup. And the third thing is, they have to know what a pivot table is and know how a pivot table works. Because the key insight we had is that, of the hundreds of millions of analysts, people who use Excel on a day-to-day basis, a lot of their work is data prep. But Excel, being an amazing generic tool, is actually quite bad for doing data prep. So the person we target, when I go to a customer and they say, "Are we a good candidate to use Paxata?" and we're talking to the actual person who's going to use the software, I say, "Do you know what a VLOOKUP is, yes or no? "Do you know what a pivot table is, yes or no?" If they have that skill, when they come into Paxata, we designed Paxata to be very attractive to those people. So it's completely point-and-click. It's completely visual. It's completely interactive. There's no scripting inside that whole process, because do you think the average Microsoft Excel analyst wants to script, or they want to use a proprietary wrangling language? I'm sorry, but analysts don't want to wrangle. Data scientists, the 1% of the 1%, maybe they like to wrangle, but you don't have that with the broader analyst community, and that is a much larger market opportunity that we have targeted. >> Well, very large, I mean, a lot of people are familiar with those concepts in Excel, and if they're not, they're relatively easy to learn. >> Nenshad: That's right. Excellent. All right, Nenshad, we have to leave it there. Thanks very much for coming on The Cube, appreciate it. >> Thank you very much for having me. >> Congratulations for all the success. >> Thank you. >> All right, keep it right there, everybody. We'll be back with our next guest. This is The Cube, we're live from New York City at Big Data NYC. We'll be right back. (electronic music)

>>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.