>>live from Denver, Colorado. It's the Q covering com vault. Go 2019. Brought to you by combo. >>Welcome to the Cube. Lisa Martin with student A man we're covering Day one of convo go 19 from Colorado. Stew and I are pleased to welcome to the Cube one of combo longtime customers from AstraZeneca. We have Scott 100 global infrastructure surfaces. Director. Hey, Scott. >>Good afternoon. >>Good afternoon. Welcome to the Cube. AstraZeneca is a name that probably a lot of folks know in the bio medical pharmaceutical space. But for those that don't give us an overview of AstraZeneca. What you guys are what you d'oh! >>Obviously we're we're, ah, bio pharmaceutical company with global presence way be used. The primary care takes off. Medicines be sale throughout the world. So everything from Kearney care to tiu oncology onda also are massive. That diabetes franchise, as well as other core core therapies that are used by our patients, were like, >>All right, So, Scott, maybe bring us inside. What is data mean to your organization? >>And it means loss. Lots of things taxes and cut through our organization from go boat framed in that next molecules to discover them bleeding edge medicines for our patients all the way to have our sales People commercial use data to identify the patients for further rate kid as well and ofcourse, backoffice tree I t enabling functions like each HR and finances. Well, therefore, is this apartment for business >>you've got Global infrastructure service could just lay out a little bit what that entails and how data fits into the picture, what's in your purview and what you have to work with other groups on. >>So my idea looks after architecture, designing governance and for cyber security infrastructure, savage seas, AstraZeneca. So So we be like after within either on premise within their own try our own data centers are in the public load as well. So as you can imagine, their movement on Deron realms and that can environment is pivotal to the coming been successful go forward >>when every time we, you know, you talk about data being the life blood of an organization or the new oil when we're talking about a patient information and the information that could be used to find the next, you know, cure for a particular disease, this it's this is literally life and death data on the ability to have access to it, but also to make sure that it's protected and secure table stakes. Right, so talk to us about when you came on board, he said. Around six years ago, before we went live knowing how critical data is toe AstraZeneca's business, What was the data strategy like a few years ago? >>It was pretty convoluted six years ago when am I fought during the actual Danica over largely exhaust to various companies? So their strategy basically that have one. We don't really have much of a strategy for looking after our deal with five or six different backup products, but then the cinnamon on lane their storage products is now. So over the last 56 years, can stealing that down to one key data storage provider in the APP and also for backup from the store combo. We do still have some leg. It's a very fast environments, but they're being decommissioned. That moved over combo. I speak >>from a what can I t. An initial initiative perspective. A few years ago, six years ago, we didn't have a date, a strategy. What was some of the you know from the top, down from the C suite down, maybe from the board down saying, Hey, guys, we have to get our hands around. I mean, this is before GDP are But in terms of the opportunities that I provided the company, where did that initiative come from? And a new year old come about now. But you guys want a couple of different routes, Talk to us a little bit about that initiative and the initial directions to where you are now. >>Yes, O. R. Xia Old Smalley obviously had a vision for how the country is going to progress. Set sail in his tenure on a massive pile that was understanding with our data waas how it was used on but most importantly have it was protected as well. And so that kind of drove the insertion from likes of HCL Congress and emphasis into looking after our own environment. You can, after our own idea for choosing strategies as well, so that organically company could grow based on best directions for using that there that we could meet from what we had the radio through collaboration with other bio farmers is a game just for the greater good of fame than that. That next medical molecules to help proficient. All right, >>Scott, have you been toe the combo Cho shows before? >>Second thing second time. Tell us a >>little >>bit about you know what brings you to the show? A lot of announcements here. Anything jump out so far? >>Yeah, it is interesting to see some of the new collaborations are Sorry. Party sees it comes I'll be making over the last little well Hedvig acquisition looks looks breaks on the metallic venture that, doomed for public sass is, well, looks like equals x, a n and ammunition and came environments that convo play on. So I think things to very good moves. >>So you're leveraging Public Cloud. How does Khan Vault fit into that? You're to be used babies >>convoked for for M backing up on restoring and our public load environments. Whether we need a B s robotics, start watching in the jury's there with You're in the club zero stack as well. And then we're in the process of bringing on lane production environments and Google Cloud Platform zone. So having that one back up in the store strategies pivotal Isabella's enabling us to move our day off using visibility solution to get calm. Boulders now, which is very powerful, is >>one of the things I noticed when I watched the video that combo has done with you. And they actually shared a quote from you during the keynote before Actually, before everyone walked. It is, you said this constant evolution that come about is delivering was one of the things that that you really like. From a business perspective, Combo has done a lot of evolving in the last nine months with the new leadership. It's too. And you were talking about some of the new technology, some of the new announcements from that evolutionary perspective and what you guys like about it. What are you seeing in terms of them going forward? Are you saying hey, there really listening? They're looking at use cases like ours, learning from it to not only make the technology is better, but to expand their portfolio. >>I mean, for a lot of it's based in the constant evolution of the FBI's that convo used for access and videos need parts. Technology will be backing up of the M two, backing up kubernetes containers and using that in the Secret Service's environment is Val to Tolliver's to ensure that whatever it comes to get from Lourdes cannot feel like several. It's computing environments that don't understand what what they put watch. So we can either reuse it, destroy, are used different manner, so that for us, that's great. Because obviously for our own C A c d pay planes, they're all FBI driven on to be able to use a convert production. The same kind of fashion is >>so, Scott, do you keep up on the quarterly cadence that combo doing and is there anything, uh, kind of either on the road map for things that you're asking for that would make your environment even better? >>And we're kind of used the 90 day cadences for ourselves to ensure that our own strategies are kept in check and we could take advantage off in your aunties are coming not only from convo but for other parts of our the infrastructure really be now for our only storage or a video. Various other providers that be used for insurance are dead as a decibel and used in a proper fashion. I >>want to get into a little bit of the use case, I knew that you had a number of different competing backup solutions in place. Did you start from a data started perspective, like within one division or one part of the company to maybe pilot, because you ended up with a whole bunch of different software solutions in there. Now you standardize on combo, walk us through that process, those decisions and what you're getting by having this now single pane of less >>some of the populated back up in the store sprawl was caused by individual parts of his had been able to do a little thing having little ineighty budget. So give it up. Some parts of business want to use a backup from Veritas or the emcee products that were in play at the time when we source between IBM and HDL chores each pdp for a pre media centers. So I decided another another backup restore productive in the mix. So for us, it just became untenable when we started insourcing, you know, to build a support team support organization to work after that many technologies was pretty difficult hands by way to go for 11 stop strategy. >>You said in the video. That combo had a pretty significantly higher success rate compared to some of the other solutions, so that must have made it a no brainer. >>So our backup critical applications is 99.8% successful to stay on, and that's that. That's what come won't get themselves. So that was a great comfort on the series. Is more and more of our applications move over on the convo platform, then have ah more wrong deeds approached, You know, backup success, but success in the store and say the things as well as Bella's, you know, using the analytics on a more timely fashion again for for drug and manufacturing research. >>So I know that you guys looked at our sorry spoke with a number of combat customers before you made this decision. And now here you are, on the other side of the coin, talking to a lot of combo customers. What advice would you give companies in any industry who in almost 2020 may not have a really robust data strategy? Your recommendations >>should look it over, not just our backup in the store solution. You know, the could base, which has put together for involves very powerful from the beta index. Ease with information going through construe the product to you can use out for things like D. R H A and also immigration off records. Two different defense centers are different parts of public low dreamer, you know, And the new new vision that I have for the analytics is very powerful as well. Forget the name of this tour today that someone that, you know, maybe we've started to use ourselves in a big way. We've got a little science team within my operation, which is made in that they are not coming, that they're more efficient manner. Feed that into our. Praised the architecture so that they could take advantage of what? Worried they got their own confines and makes out with what they need to do for for new discoveries. >>Scott, thank you for joining. Stewing me on the cube today, sharing with us what you're doing at AstraZeneca and looking forward to hearing the next molecule that discovers some great breakthrough. >>Thank you. >>First to Minutemen. I'm Lisa Martin. You're watching the cue from combo go 19

>> live from San Francisco, California. It's the key. You covering the IBM chief Data officer? Someone brought to you by IBM. >> We're back at the IBM CDO conference. Fisherman's Worf Worf in San Francisco. You're watching the Cube, the leader in life tech coverage. My name is David Dante. Glenn Finches. Here's the global leader of Big Data Analytics and IBM, and we're pleased to have Mark Clare. He's the head of data enablement at AstraZeneca. Gentlemen, welcome to the Cube. Thanks for coming on my mark. I'm gonna start with this head of data Data Enablement. That's a title that I've never heard before. And I've heard many thousands of titles in the Cube. What is that all about? >> Well, I think it's the credit goes to some of the executives at AstraZeneca when they recruited me. I've been a cheap date officer. Several the major financial institutions, both in the U. S. And in Europe. Um, AstraZeneca wanted to focus on how we actually enable our business is our science areas in our business is so it's not unlike a traditional CDO role, but we focus a lot more on what the enabling functions or processes would be >> So it sounds like driving business value is really the me and then throw. Sorry. >> I've always looked at this role in three functions value, risk and cost. So I think that in any CDO role, you have to look at all three. I think the you'd slide it if you didn't. This one with the title. Obviously, we're looking at quite a bit at the value we will drive across the the firm on how to leverage our date in a different way. >> I love that because you can quantify all three. All right, Glenn. So you're the host of this event. So awesome. I love that little presentation that you gave. So for those you didn't see it, you gave us pay stubs and then you gave us a website and said, Take a picture of the paste up, uploaded, and then you showed how you're working with your clients. Toe. Actually digitize that and compress all kinds of things. Time to mortgage origination. Time to decision. So explain that a little bit. And what's that? What's the tech behind that? And how are people using it? You know, >> for three decades, we've had this OCR technology where you take a piece of paper, you tell the machine what's on the paper. What longitudinal Enter the coordinates are and you feed it into the hope and pray to God that it isn't in there wrong. The form didn't change anything like that. That's what that's way. We've lived for three decades with cognitive and a I, but I read things like the human eye reads things. And so you put the page in and the machine comes back and says, Hey, is this invoice number? Hey, is this so security number? That's how you train it as compared to saying, Here's what it So we use this cognitive digitization capability to grab data that's locked in documents, and then you bring it back to the process so that you can digitally re imagine the process. Now there's been a lot of use of robotics and things like that. I'm kind of taken existing processes, and I'm making them incrementally. Better write This says look, you now have the data of the process. You can re imagine it. However, in fact, the CEO of our client ADP said, Look, I want you to make me a Netflix, not a blood Urbach Blockbuster, right? So So it's a mind shift right to say we'll use this data will read it with a I will digitally re imagine the process. And it usually cuts like 70 or 80% of the cycle time, 50 to 75% of the cost. I mean, it's it's pretty groundbreaking when you see it. >> So markets ahead of data neighborhood. You hear something like that and you're not. You're not myopically focused on one little use case. You're taking a big picture of you doing strategies and trying to develop a broader business cases for the organization. But when you see an example like that and many examples out there, I'm sure the light bulbs go off. So >> I wrote probably 10 years cases down while >> Glenn was talking about you. You do get tactical, Okay, but but But where do you start when you're trying to solve these problems? >> Well, I look att, Glenn's example, And about five and 1/2 years ago, Glenn was one I went to had gone to a global financial service, firms on obviously having scale across dozens of countries, and I had one simple request. Thio Glenn's team as well as a number of other technology companies. I want cognitive intelligence for on data in Just because the process is we've had done for 20 years just wouldn't scale not not its speed across many different languages and cultures. And I now look five and 1/2 years later, and we have beginning of, I would say technology opportunities. When I asked Glenn that question, he was probably the only one that didn't think I had horns coming out of my head, that I was crazy. I mean, some of the leading technology firms thought I was crazy asking for cognitive data management capabilities, and we are five and 1/2 years later and we're seeing a I applied not just on the front end of analytics, but back in the back end of the data management processes themselves started automate. So So I look, you know, there's a concept now coming out day tops on date offices. You think of what Dev Ops is. It's bringing within our data management processes. It's bringing cognitive capabilities to every process step, And what level of automation can we do? Because the, you know, for typical data science experiment 80 to 90% of that work Estate engineering. If I can automate that, then through a date office process, then I could get to incite much faster, but not in scale it and scale a lot more opportunities and have to manually do it. So I I look at presentations and I think, you know, in every aspect of our business, where we clear could we apply >> what you talk about date engineering? You talk about data scientist spending his or her time just cleaning the wrangling data, All the all the not fun stuff exactly plugging in cables back in the infrastructure date. >> You're seeing horror stories right now. I heard from a major academic institution. A client came to them and their data scientists. They had spent several years building. We're spending 99% of their time trying to cleanse and prep data. They were spend 90% cleansing and prepping, and of the remaining 10% 90% of that fixing it where they fix it wrong and the first time so they had 1% of their job doing their job. So this is a huge opportunity. You can start automating more of that and actually refocusing data science on data >> science. So you've been a chief data officer number of financial institutions. You've got this kind of cool title now, which touches on some of the things a CDO might do and your technical. We got a technical background. So when you look a lot of the what Ginny Rometty calls incumbents, call them incumbent Disruptors two years ago at Ivy and think they've got data that has been hardened, you know, in all these projects and use cases and it's locked and people talk about the silos, part of your role is to figure out Okay, how do we get that data out? Leverage. It put it at the core. Is that is that fair? >> Well, and I'm gonna stay away from the word core cause to make core Kenan for kind of legacy processes of building a single repositories single warehouse, which is very time consuming. So I think I can I leave it where it is, but find a wayto to unify it. >> Not physically, exactly what I say. Corny, but actually the court, that's what we need >> to think about is how to do this logically and cream or of Ah unification approach that has speed and agility with it versus the old physical approaches, which took time. And resource is >> so That's a that's a computer science problem that people have been trying to solve for years. Decentralized, distributed, dark detectors, right? And why is it that we're now able Thio Tap your I think it's >> a perfect storm of a I of Cloud, the cloud native of Io ti, because when you think of I o. T, it's a I ot to be successful fabric that can connect millions of devices or millions of sensors. So you'd be paired those three with the investment big data brought in the last seven or eight years and big data to me. Initially, when I started talking to companies in the Valley 10 years ago, the early days of, um, apparatus, what I saw or companies and I could get almost any of the digital companies in the valley they were not. They were using technology to be more agile. They were finding agile data science. Before we call the data signs the map produce and Hadoop, we're just and after almost not an afterthought. But it was just a mechanism to facilitate agility and speed. And so if you look at how we built out all the way up today and all the convergence of all these new technologies, it's a perfect storm to actually innovate differently. >> Well, what was profound about my producing in the dupe? It was like leave the data where it is and shipped five megabytes a code two upended by the data and that you bring up a good point. We've now, we spent 10 years leveraging that at a much lower cost. And you've got the cloud now for scale. And now machine intelligence comes in that you can apply in the data causes. Bob Pityana once told me, Data's plentiful insights aren't Amen to that. So Okay, so this is really interesting discussion. You guys have known each other for a couple of couple of decades. How do you work together toe to solve problems Where what is that conversation like, Do >> you want to start that? >> So, um, first of all, we've never worked together on solving small problems, not commodity problems. We would usually tackle something that someone would say would not be possible. So normally Mark is a change agent wherever he goes. And so he usually goes to a place that wants to fix something or change something in an abnormally short amount of time for an abnormally small amount of money. Right? So what's strange is that we always find that space together. Mark is very judicious about using us as a service is firm toe help accelerate those things. But then also, we build in a plan to transition us away in transition, in him into full ownership. Right. But we usually work together to jump start one of these wicked, hard, wicked, cool things that nobody else >> was. People hate you. At first. They love you. I would end the one >> institution and on I said, OK, we're going to a four step plan. I'm gonna bring the consultants in day one while we find Thailand internally and recruit talent External. That's kind of phases one and two in parallel. And then we're gonna train our talent as we find them, and and Glenn's team will knowledge transfer, and by face for where, Rayna. And you know, that's a model I've done successfully in several organizations. People can. I hated it first because they're not doing it themselves, but they may not have the experience and the skills, and I think as soon as you show your staff you're willing to invest in them and give them the time and exposure. The conversation changes, but it's always a little awkward. At first, I've run heavy attrition, and some organizations at first build the organizations. But the one instance that Glen was referring to, we came in there and they had a 4 1 1 2 1 12 to 15 year plan and the C I O. Looked at me, he says. I'll give you two years. I'm a bad negotiator. I got three years out of it and I got a business case approved by the CEO a week later. It was a significant size business case in five minutes. I didn't have to go back a second or third time, but we said We're gonna do it in three years. Here's how we're gonna scale an organization. We scaled more than 1000 person organization in three years of talent, but we did it in a planned way and in that particular organization, probably a year and 1/2 in, I had a global map of every data and analytics role I need and I could tell you were in the US they set and with what competitors earning what industry and where in India they set and in what industry And when we needed them. We went out and recruited, but it's time to build that. But you know, in any really period, I've worked because I've done this 20 plus years. The talent changes. The location changes someone, but it's always been a challenge to find him. >> I guess it's good to have a deadline. I guess you did not take the chief data officer role in your current position. Explain that. What's what. What's your point of view on on that role and how it's evolved and how it's maybe being used in ways that don't I >> mean, I think that a CDO, um on during the early days, there wasn't a definition of a matter of fact. Every time I get a recruiter, call me all. We have a great CDO row for first time I first thing I asked him, How would you define what you mean by CDO? Because I've never seen it defined the same way into cos it's just that way But I think that the CDO, regardless of institutions, responsibility end in to make sure there's an Indian framework from strategy execution, including all of the governance and compliance components, and that you have ownership of each piece in the organization. CDO most companies doesn't own all of that, but I think they have a responsibility and too many organizations that hasn't occurred. So you always find gaps and each organization somewhere between risk costs and value, in terms of how how they're, how the how the organization's driving data and in my current role. Like I said, I wanted to focus. We want the focus to really be on how we're enabling, and I may be enabling from a risk and compliance standpoint, Justus greatly as I'm enabling a gross perspective on the business or or cost management and cost reductions. We have been successful in several programs for self funding data programs for multi gears. By finding and costs, I've gone in tow several organizations that it had a decade of merger after merger and Data's afterthought in almost any merger. I mean, there's a Data Silas section session tomorrow. It'd be interesting to sit through that because I've found that data data is the afterthought in a lot of mergers. But yet I knew of one large health care company. They've made data core to all of their acquisitions, and they was one the first places they consolidated. And they grew faster by acquisition than any of their competitors. So I think there's a There's a way to do it correctly. But in most companies you go in, you'll find all kinds of legacy silos on duplication, and those are opportunities to, uh, to find really reduce costs and self fund. All the improvements, all the strategic programs you wanted, >> a number inferring from the Indian in the data roll overlaps or maybe better than gaps and data is that thread between cost risk. And it is >> it is. And I've been lucky in my career. I've report toe CEOs. I reported to see Yellows, and I've reported to CEO, so I've I've kind of reported in three different ways, and each of those executives really looked at it a little bit differently. Value obviously is in a CEO's office, you know, compliance. Maurizio owes office and costs was more in the c i o domain, but you know, we had to build a program looking >> at all three. >> You know, I think this topic, though, that we were just talking about how these rules are evolving. I think it's it's natural, because were about 5 2.0. to 7 years into the evolution of the CDO, it might be time for a CDO Um, and you see Maur CEOs moving away from pure policy and compliance Tomb or value enablement. It's a really hard change, and that's why you're starting to Seymour turnover of some of the studios because people who are really good CEOs at policy and risk and things like that might not be the best enablers, right? So I think it's pretty natural evolution. >> Great discussion, guys. We've got to leave it there, They say. Data is the new oil date is more valuable than oil because you could use data to reduce costs to reduce risk. The same data right toe to drive revenue, and you can't put a gallon of oil in your car and a quart of oil in the car quarter in your house of data. We think it's even more valuable. Gentlemen, thank you so much for coming on the cues. Thanks so much. Lot of fun. Thanks. Keep right, everybody. We'll be back with our next guest. You're watching the Cube from IBM CDO 2019 right back.

(upbeat music) >> Welcome back everyone to Supercloud 2, live here in Palo Alto, our studio, where we're doing a live stage performance and virtually syndicating out around the world. I'm John Furrier with Dave Vellante, my co-host with the The Cube here. We've got Kit Colbert, the CTO of VM. We're doing a keynote on Cloud Chaos, the evolution of SuperCloud Architecture Kit. Great to see you, thanks for coming on. >> Yeah, thanks for having me back. It's great to be here for Supercloud 2. >> And so we're going to dig into it. We're going to do a Q&A. We're going to let you present. You got some slides. I really want to get this out there, it's really compelling story. Do the presentation and then we'll come back and discuss. Take it away. >> Yeah, well thank you. So, we had a great time at the original Supercloud event, since then, been talking to a lot of customers, and started to better formulate some of the thinking that we talked about last time So, let's jump into it. Just a few quick slides to sort of set the tone here. So, if we go to the the next slide, what that shows is the journey that we see customers on today, going from what we call Cloud First into this phase that many customers are stuck in, called Cloud Chaos, and where they want to get to, and this is the term customers actually use, we didn't make this up, we heard it from customers. This notion of Cloud Smart, right? How do they use cloud more effectively, more intelligently? Now, if you walk through this journey, customers start with Cloud First. They usually select a single cloud that they're going to standardize on, and when they do that, they have to build out a whole bunch of functionality around that cloud. Things you can see there on the screen, disaster recovery, security, how do they monitor it or govern it? Like, these are things that are non-negotiable, you've got to figure it out, and typically what they do is, they leverage solutions that are specific for that cloud, and that's fine when you have just one cloud. But if we build out here, what we see is that most customers are using more than just one, they're actually using multiple, not necessarily 10 or however many on the screen, but this is just as an example. And so what happens is, they have to essentially duplicate or replicate that stack they've built for each different cloud, and they do so in a kind of a siloed manner. This results in the Cloud Chaos term that that we talked about before. And this is where most businesses out there are, they're using two, maybe three public clouds. They've got some stuff on-prem and they've also got some stuff out at the edge. This is apps, data, et cetera. So, this is the situation, this is sort of that Cloud Chaos. So, the question is, how do we move from this phase to Cloud Smart? And this is where the architecture comes in. This is why architecture, I think, is so important. It's really about moving away from these single cloud services that just solve a problem for one cloud, to something we call a Cross-Cloud service. Something that can support a set of functionality across all clouds, and that means not just public clouds, but also private clouds, edge, et cetera, and when you evolve that across the board, what you get is this sort of Supercloud. This notion that we're talking about here, where you combine these cross-cloud services in many different categories. You can see some examples there on the screen. This is not meant to be a complete set of things, but just examples of what can be done. So, this is sort of the transition and transformation that we're talking about here, and I think the architecture piece comes in both for the individual cloud services as well as that Supercloud concept of how all those services come together. >> Great presentation., thanks for sharing. If you could pop back to that slide, on the Cloud Chaos one. I just want to get your thoughts on something there. This is like the layout of the stack. So, this slide here that I'm showing on the screen, that you presented, okay, take us through that complexity. This is the one where I wanted though, that looks like a spaghetti code mix. >> Yes. >> So, do you turn this into a Supercloud stack, right? Is that? >> well, I think it's, it's an evolving state that like, let's take one of these examples, like security. So, instead of implementing security individually in different ways, using different technologies, different tooling for each cloud, what you would do is say, "Hey, I want a single security solution that works across all clouds", right? A concrete example of this would be secure software supply chain. This is probably one of the top ones that I hear when I talk to customers. How do I know that the software I'm building is truly what I expect it to be, and not something that some hacker has gotten into, and polluted with malicious code? And what they do is that, typically today, their teams have gone off and created individual secure software supply chain solutions for each cloud. So, now they could say, "Hey, I can take a single implementation and just have different endpoints." It could go to Google, or AWS, or on-prem, or wherever have you, right? So, that's the sort of architectural evolution that we're talking about. >> You know, one of the things we hear, Dave, you've been on theCUBE all the time, and we, when we talk privately with customers who are asking us like, what's, what's going on? They have the same complaint, "I don't want to build a team, a dev team, for that stack." So, if you go back to that slide again, you'll see that, that illustrates the tech stack for the clouds and the clouds at the bottom. So, the number one complaint we hear, and I want to get your reaction to that, "I don't want to have a team to have to work on that. So, I'm going to pick one and then have a hedge secondary one, as a backup." Here, that's one, that's four, five, eight, ten, ten environments. >> Yeah, I got a lot. >> That's going to be the reality, so, what's the technical answer to that? >> Yeah, well first of all, let me just say, this picture is again not totally representative of reality oftentimes, because while that picture shows a solution for every cloud, oftentimes that's not the case. Oftentimes it's a line of business going off, starting to use a new cloud. They might solve one or two things, but usually not security, usually not some of these other things, right? So, I think from a technical standpoint, where you want to get to is, yes, that sort of common service, with a common operational team behind it, that is trained on that, that can work across clouds. And that's really I think the important evolution here, is that you don't need to replicate these operational teams, one for each cloud. You can actually have them more focused across all those clouds. >> Yeah, in fact, we were commenting on the opening today. Dave and I were talking about the benefits of the cloud. It's heterogeneous, which is a good thing, but it's complex. There's skill gaps and skill required, but at the end of the day, self-service of the cloud, and the elastic nature of it makes it the benefit. So, if you try to create too many common services, you lose the value of the cloud. So, what's the trade off, in your mind right now as customers start to look at okay, identity, maybe I'll have one single sign on, that's an obvious one. Other ones? What are the areas people are looking at from a combination, common set of services? Where do they start? What's the choices? What are some of the trade offs? 'Cause you can't do it everything. >> No, it's a great question. So, that's actually a really good point and as I answer your question, before I answer your question, the important point about that, as you saw here, you know, across cloud services or these set of Cross-Cloud services, the things that comprise the Supercloud, at least in my view, the point is not necessarily to completely abstract the underlying cloud. The point is to give a business optionality and choice, in terms of what it wants to abstract, and I think that gets to your question, is how much do you actually want to abstract from the underlying cloud? Now, what I find, is that typically speaking, cloud choice is driven at least from a developer or app team perspective, by the best of breed services. What higher level application type services do you need? A database or AI, you know, ML systems, for your application, and that's going to drive your choice of the cloud. So oftentimes, businesses I talk to, want to allow those services to shine through, but for other things that are not necessarily highly differentiated and yet are absolutely critical to creating a successful application, those are things that you want to standardize. Again, like things like security, the supply chain piece, cost management, like these things you need to, and you know, things like cogs become really, really important when you start operating at scale. So, those are the things in it that I see people wanting to focus on. >> So, there's a majority model. >> Yes. >> All right, and we heard of earlier from Walmart, who's fairly, you know, advanced, but at the same time their supercloud is pretty immature. So, what are you seeing in terms of supercloud momentum, crosscloud momentum? What's the starting point for customers? >> Yeah, so it's interesting, right, on that that three-tiered journey that I talked about, this Cloud Smart notion is, that is adoption of what you might call a supercloud or architecture, and most folks aren't there yet. Even the really advanced ones, even the really large ones, and I think it's because of the fact that, we as an industry are still figuring this out. We as an industry did not realize this sort of Cloud Chaos state could happen, right? We didn't, I think most folks thought they could standardize on one cloud and that'd be it, but as time has shown, that's simply not the case. As much as one might try to do that, that's not where you end up. So, I think there's two, there's two things here. Number one, for folks that are early in to the cloud, and are in this Cloud Chaos phase, we see the path out through standardization of these cross-cloud services through adoption of this sort of supercloud architecture, but the other thing I think is particularly exciting, 'cause I talked to a number of of businesses who are not yet in the Cloud Chaos phase. They're earlier on in the cloud journey, and I think the opportunity there is that they don't have to go through Cloud Chaos. They can actually skip that whole phase if they adopt this supercloud architecture from the beginning, and I think being thoughtful around that is really the key here. >> It's interesting, 'cause we're going to hear from Ionis Pharmaceuticals later, and they, yes there are multiple clouds, but the multiple clouds are largely separate, and so it's a business unit using that. So, they're not in Cloud Chaos, but they're not tapping the advantages that you could get for best of breed across those business units. So, to your point, they have an opportunity to actually build that architecture or take advantage of those cross-cloud services, prior to reaching cloud chaos. >> Well, I, actually, you know, I'd love to hear from them if, 'cause you say they're not in Cloud Chaos, but are they, I mean oftentimes I find that each BU, each line of business may feel like they're fine, in of themselves. >> Yes, exactly right, yes. >> But when you look at it from an overall company perspective, they're like, okay, things are pretty chaotic here. We don't have standardization, I don't, you know, like, again, security compliance, these things, especially in many regulated industries, become huge problems when you're trying to run applications across multiple clouds, but you don't have any of those company-wide standardizations. >> Well, this is a point. So, they have a big deal with AstraZeneca, who's got this huge ecosystem, they want to start sharing data across those ecosystem, and that's when they will, you know, that Cloud Chaos will, you know, come, come to fore, you would think. I want to get your take on something that Bob Muglia said earlier, which is, he kind of said, "Hey Dave, you guys got to tighten up your definition a little bit." So, he said a supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. So, you know, thank you, that was nice and simple. However others in the community, we're going to hear from Dr. Nelu Mihai later, says, no, no, wait a minute, it's got to be an architecture, not a platform. Where do you land on this architecture v. platform thing? >> I look at it as, I dunno if it's, you call it maturity or just kind of a time horizon thing, but for me when I hear the word platform, I typically think of a single vendor. A single vendor provides this platform. That's kind of the beauty of a platform, is that there is a simplicity usually consistency to it. >> They did the architecture. (laughing) >> Yeah, exactly but I mean, well, there's obviously architecture behind it, has to be, but you as a customer don't necessarily need to deal with that. Now, I think one of the opportunities with Supercloud is that it's not going to be, or there is no single vendor that can solve all these problems. It's got to be the industry coming together as a community, inter-operating, working together, and so, that's why, for me, I think about it as an architecture, that there's got to be these sort of, well-defined categories of functionality. There's got to be well-defined interfaces between those categories of functionality to enable modularity, to enable businesses to be able to pick and choose the right sorts of services, and then weave those together into an overall supercloud. >> Okay, so you're not pitching, necessarily the platform, you're saying, hey, we have an architecture that's open. I go back to something that Vittorio said on August 9th, with the first Supercloud, because as well, remember we talked about abstracting, but at the same time giving developers access to those primitives. So he said, and this, I think your answer sort of confirms this. "I want to have my cake eat it too and not gain weight." >> (laughing) Right. Well and I think that's where the platform aspect can eventually come, after we've gotten aligned architecture, you're going to start to naturally see some vendors step up to take on some of the remaining complexity there. So, I do see platforms eventually emerging here, but I think where we have to start as an industry is around aligning, okay, what does this definition mean? What does that architecture look like? How do we enable interoperability? And then we can take the next step. >> Because it depends too, 'cause I would say Snowflake has a platform, and they've just defined the architecture, but we're not talking about infrastructure here, obviously, we're talking about something else. >> Well, I think that the Snowflake talks about, what he talks about, security and data, you're going to start to see the early movement around areas that are very spanning oriented, and I think that's the beginning of the trend and I think there's going to be a lot more, I think on the infrastructure side. And to your point about the platform architecture, that's actually a really good thought exercise because it actually makes you think about what you're designing in the first place, and that's why I want to get your reaction. >> Quote from- >> Well I just have to interrupt since, later on, you're going to hear from near Nir Zuk of Palo Alto Network. He says architecture and security historically, they don't go hand in hand, 'cause it's a big mess. >> It depends if you're whacking the mole or you actually proactively building something. Well Kit, I want to get your reaction from a quote from someone in our community who said about Supercloud, you know, "The Supercloud's great, there are issues around computer science rigors, and customer requirements." So, there's some issues around the science itself as well as not just listen to the customer, 'cause if that's the case, we'd have a better database, a better Oracle, right, so, but there's other, this tech involved, new tech. We need an open architecture with universal data modeling interconnecting among them, connectivity is a part of security, and then, once we get through that gate, figuring out the technical, the data, and the customer requirements, they say "Supercloud should be a loosely coupled platform with open architecture, plug and play, specialized services, ready for optimization, automation that can stand the test of time." What's your reaction to that sentiment? You like it, is that, does that sound good? >> Yeah, no, broadly aligns with my thinking, I think, and what I see from talking with customers as well. I mean, I like the, again, the, you know, listening to customer needs, prioritizing those things, focusing on some of the connective tissue networking, and data and some of these aspects talking about the open architecture, the interoperability, those are all things I think are absolutely critical. And then, yeah, like I think at the end. >> On the computer science side, do you see some science and engineering things that need to be engineered differently? We heard databases are radically going to change and that are inadequate for the new architecture. What are some of the things like that, from a science standpoint? >> Yeah, yeah, yeah. Some of the more academic research type things. >> More tech, or more better tech or is it? >> Yeah, look, absolutely. I mean I think that there's a bunch around, certainly around the data piece, around, you know, there's issues of data gravity, data mobility. How do you want to do that in a way that's performant? There's definitely issues around security as well. Like how do you enable like trust in these environments, there's got to be some sort of hardware rooted trusts, and you know, a whole bunch of various types of aspects there. >> So, a lot of work still be done. >> Yes, I think so. And that's why I look at this as, this is not a one year thing, or you know, it's going to be multi-years, and I think again, it's about all of us in the industry working together to come to an aligned picture of what that looks like. >> So, as the world's moved from private cloud to public cloud and now Cross-cloud services, supercloud, metacloud, whatever you want to call it, how have you sort of changed the way engineering's organized, developers sort of approached the problem? Has it changed and how? >> Yeah, absolutely. So, you know, it's funny, we at VMware, going through the same challenges as our customers and you know, any business, right? We use multiple clouds, we got a big, of course, on-prem footprint. You know, what we're doing is similar to what I see in many other customers, which, you see the evolution of a platform team, and so the platform team is really in charge of trying to develop a lot of these underlying services to allow our lines of business, our product teams, to be able to move as quickly as possible, to focus on the building, while we help with a lot of the operational overheads, right? We maintain security, compliance, all these other things. We also deal with, yeah, just making the developer's life as simple as possible. So, they do need to know some stuff about, you know, each public cloud they're using, those public cloud services, but at the same, time we can abstract a lot of the details they don't need to be in. So, I think this sort of delineation or separation, I should say, between the underlying platform team and the product teams is a very, very common pattern. >> You know, I noticed the four layers you talked about were observability, infrastructure, security and developers, on that slide, the last slide you had at the top, that was kind of the abstraction key areas that you guys at VMware are working? >> Those were just some groupings that we've come up with, but we like to debate them. >> I noticed data's in every one of them. >> Yeah, yep, data is key. >> It's not like, so, back to the data questions that security is called out as a pillar. Observability is just kind of watching everything, but it's all pretty much data driven. Of the four layers that you see, I take that as areas that you can. >> Standardize. >> Consistently rely on to have standard services. >> Yes. >> Which one do you start with? What's the, is there order of operations? >> Well, that's, I mean. >> 'Cause I think infrastructure's number one, but you had observability, you need to know what's going on. >> Yeah, well it really, it's highly dependent. Again, it depends on the business that we talk to and what, I mean, it really goes back to, what are your business priorities, right? And we have some customers who may want to get out of a data center, they want to evacuate the data center, and so what they want is then, consistent infrastructure, so they can just move those applications up to the cloud. They don't want to have to refactor them and we'll do it later, but there's an immediate and sort of urgent problem that they have. Other customers I talk to, you know, security becomes top of mind, or maybe compliance, because they're in a regulated industry. So, those are the sort of services they want to prioritize. So, I would say there is no single right answer, no one size fits all. The point about this architecture is really around the optionality of it, as it allows you as a business to decide what's most important and where you want to prioritize. >> How about the deployment models kit? Do, does a customer have that flexibility from a deployment model standpoint or do I have to, you know, approach it a specific way? Can you address that? >> Yeah, I mean deployment models, you're talking about how they how they consume? >> So, for instance, yeah, running a control plane in the cloud. >> Got it, got it. >> And communicating elsewhere or having a single global instance or instantiating that instance, and? >> So, that's a good point actually, and you know, the white paper that we released back in August, around this sort of concept, the Cross-cloud service. This is some of the stuff we need to figure out as an industry. So, you know when we talk about a Cross-cloud service, we can mean actually any of the things you just talked about. It could be a single instance that runs, let's say in one public cloud, but it supports all of 'em. Or it could be one that's multi-instance and that runs in each of the clouds, and that customers can take dependencies on whichever one, depending on what their use cases are or the, even going further than that, there's a type of Cross-cloud service that could actually be instantiated even in an air gapped or offline environment, and we have many, many businesses, especially heavily regulated ones that have that requirement, so I think, you know. >> Global don't forget global, regions, locales. >> Yeah, there's all sorts of performance latency issues that can be concerned about. So, most services today are the former, there are single sort of instance or set of instances within a single cloud that support multiple clouds, but I think what we're doing and where we're going with, you know, things like what we see with Kubernetes and service meshes and all these things, will better enable folks to hit these different types of cross-cloud service architectures. So, today, you as a customer probably wouldn't have too much choice, but where we're going, you'll see a lot more choice in the future. >> If you had to summarize for folks watching the importance of Supercloud movement, multi-cloud, cross-cloud services, as an industry in flexible, 'cause I'm always riffing on the whole old school network protocol stacks that got disrupted by TCP/IP, that's a little bit dated, we got people on the chat that are like, you know, 20 years old that weren't even born then. So, but this is a, one of those inflection points that's once in a generation inflection point, I'm sure you agree. What scoped the order of magnitude of the change and the opportunity around the marketplace, the business models, the technology, and ultimately benefits the society. >> Yeah. Wow. Getting bigger. >> You have 10 seconds, go. >> I know. Yeah. (laughing) No, look, so I think it is what we're seeing is really the next phase of what you might call cloud, right? This notion of delivering services, the way they've been packaged together, traditionally by the hyperscalers is now being challenged. and what we're seeing is really opening that up to new levels of innovation, and I think that will be huge for businesses because it'll help meet them where they are. Instead of needing to contort the businesses to, you know, make it work with the technology, the technology will support the business and where it's going. Give people more optionality, more flexibility in order to get there, and I think in the end, for us as individuals, it will just make for better experiences, right? You can get better performance, better interactivity, given that devices are so much of what we do, and so much of what we interact with all the time. This sort of flexibility and optionality will fundamentally better for us as individuals in our experiences. >> And we're seeing that with ChatGPT, everyone's talking about, just early days. There'll be more and more of things like that, that are next gen, like obviously like, wow, that's a fall out of your chair moment. >> It'll be the next wave of innovation that's unleashed. >> All right, Kit Colbert, thanks for coming on and sharing and exploring the Supercloud architecture, Cloud Chaos, the Cloud Smart, there's a transition progression happening and it's happening fast. This is the supercloud wave. If you're not on this wave, you'll be driftwood. That's a Pat Gelsinger quote on theCUBE. This is theCUBE Be right back with more Supercloud coverage, here in Palo Alto after this break. (upbeat music) (upbeat music continues)

(upbeat music) >> Welcome back to Supercloud 2. This is Dave Vellante. We're here exploring the intersection of data and analytics in the future of cloud and data. In this segment, we're going to look deeper into the life sciences business with Jesse Cugliotta, who leads the Healthcare and Life Sciences industry practice at Snowflake. And Nicholas Nick Taylor, who's the executive director of Informatics at Ionis Pharmaceuticals. Gentlemen, thanks for coming in theCUBE and participating in the program. Really appreciate it. >> Thank you for having us- >> Thanks for having me. >> You're very welcome, okay, we're go really try to look at data sharing as a use case and try to understand what's happening in the healthcare industry generally and specifically, how Nick thinks about sharing data in a governed fashion whether tapping the capabilities of multiple clouds is advantageous long term or presents more challenges than the effort is worth. And to start, Jesse, you lead this industry practice for Snowflake and it's a challenging and vibrant area. It's one that's hyper-focused on data privacy. So the first question is, you know there was a time when healthcare and other regulated industries wouldn't go near the cloud. What are you seeing today in the industry around cloud adoption and specifically multi-cloud adoption? >> Yeah, for years I've heard that healthcare and life sciences has been cloud diverse, but in spite of all of that if you look at a lot of aspects of this industry today, they've been running in the cloud for over 10 years now. Particularly when you look at CRM technologies or HR or HCM, even clinical technologies like EDC or ETMF. And it's interesting that you mentioned multi-cloud as well because this has always been an underlying reality especially within life sciences. This industry grows through acquisition where companies are looking to boost their future development pipeline either by buying up smaller biotechs, they may have like a late or a mid-stage promising candidate. And what typically happens is the larger pharma could then use their commercial muscle and their regulatory experience to move it to approvals and into the market. And I think the last few decades of cheap capital certainly accelerated that trend over the last couple of years. But this typically means that these new combined institutions may have technologies that are running on multiple clouds or multiple cloud strategies in various different regions to your point. And what we've often found is that they're not planning to standardize everything onto a single cloud provider. They're often looking for technologies that embrace this multi-cloud approach and work seamlessly across them. And I think this is a big reason why we, here at Snowflake, we've seen such strong momentum and growth across this industry because healthcare and life science has actually been one of our fastest growing sectors over the last couple of years. And a big part of that is in fact that we run on not only all three major cloud providers, but individual accounts within each and any one of them, they had the ability to communicate and interoperate with one another, like a globally interconnected database. >> Great, thank you for that setup. And so Nick, tell us more about your role and Ionis Pharma please. >> Sure. So I've been at Ionis for around five years now. You know, when when I joined it was, the IT department was pretty small. There wasn't a lot of warehousing, there wasn't a lot of kind of big data there. We saw an opportunity with Snowflake pretty early on as a provider that would be a lot of benefit for us, you know, 'cause we're small, wanted something that was fairly hands off. You know, I remember the days where you had to get a lot of DBAs in to fine tune your databases, make sure everything was running really, really well. The notion that there's, you know, no indexes to tune, right? There's very few knobs and dials, you can turn on Snowflake. That was appealing that, you know, it just kind of worked. So we found a use case to bring the platform in. We basically used it as a logging replacement as a Splunk kind of replacement with a platform called Elysium Analytics as a way to just get it in the door and give us the opportunity to solve a real world use case, but also to help us start to experiment using Snowflake as a platform. It took us a while to A, get the funding to bring it in, but B, build the momentum behind it. But, you know, as we experimented we added more data in there, we ran a few more experiments, we piloted in few more applications, we really saw the power of the platform and now, we are becoming a commercial organization. And with that comes a lot of major datasets. And so, you know, we really see Snowflake as being a very important part of our ecology going forward to help us build out our infrastructure. >> Okay, and you are running, your group runs on Azure, it's kind of mono cloud, single cloud, but others within Ionis are using other clouds, but you're not currently, you know, collaborating in terms of data sharing. And I wonder if you could talk about how your data needs have evolved over the past decade. I know you came from another highly regulated industry in financial services. So what's changed? You sort of touched on this before, you had these, you know, very specialized individuals who were, you know, DBAs, and, you know, could tune databases and the like, so that's evolved, but how has generally your needs evolved? Just kind of make an observation over the last, you know, five or seven years. What have you seen? >> Well, we, I wasn't in a group that did a lot of warehousing. It was more like online trade capture, but, you know, it was very much on-prem. You know, being in the cloud is very much a dirty word back then. I know that's changed since I've left. But in, you know, we had major, major teams of everyone who could do everything, right. As I mentioned in the pharma organization, there's a lot fewer of us. So the data needs there are very different, right? It's, we have a lot of SaaS applications. One of the difficulties with bringing a lot of SaaS applications on board is obviously data integration. So making sure the data is the same between them. But one of the big problems is joining the data across those SaaS applications. So one of the benefits, one of the things that we use Snowflake for is to basically take data out of these SaaS applications and load them into a warehouse so we can do those joins. So we use technologies like Boomi, we use technologies like Fivetran, like DBT to bring this data all into one place and start to kind of join that basically, allow us to do, run experiments, do analysis, basically take better, find better use for our data that was siloed in the past. You mentioned- >> Yeah. And just to add on to Nick's point there. >> Go ahead. >> That's actually something very common that we're seeing across the industry is because a lot of these SaaS applications that you mentioned, Nick, they're with from vendors that are trying to build their own ecosystem in walled garden. And by definition, many of them do not want to integrate with one another. So from a, you know, from a data platform vendor's perspective, we see this as a huge opportunity to help organizations like Ionis and others kind of deal with the challenges that Nick is speaking about because if the individual platform vendors are never going to make that part of their strategy, we see it as a great way to add additional value to these customers. >> Well, this data sharing thing is interesting. There's a lot of walled gardens out there. Oracle is a walled garden, AWS in many ways is a walled garden. You know, Microsoft has its walled garden. You could argue Snowflake is a walled garden. But the, what we're seeing and the whole reason behind the notion of super-cloud is we're creating an abstraction layer where you actually, in this case for this use case, can share data in a governed manner. Let's forget about the cross-cloud for a moment. I'll come back to that, but I wonder, Nick, if you could talk about how you are sharing data, again, Snowflake sort of, it's, I look at Snowflake like the app store, Apple, we're going to control everything, we're going to guarantee with data clean rooms and governance and the standards that we've created within that platform, we're going to make sure that it's safe for you to share data in this highly regulated industry. Are you doing that today? And take us through, you know, the considerations that you have in that regard. >> So it's kind of early days for us in Snowflake in general, but certainly in data sharing, we have a couple of examples. So data marketplace, you know, that's a great invention. It's, I've been a small IT shop again, right? The fact that we are able to just bring down terabyte size datasets straight into our Snowflake and run analytics directly on that is huge, right? The fact that we don't have to FTP these massive files around run jobs that may break, being able to just have that on tap is huge for us. We've recently been talking to one of our CRO feeds- CRO organizations about getting their data feeds in. Historically, this clinical trial data that comes in on an FTP file, we have to process it, take it through the platforms, put it into the warehouse. But one of the CROs that we talked to recently when we were reinvestigate in what data opportunities they have, they were a Snowflake customer and we are, I think, the first production customer they have, have taken that feed. So they're basically exposing their tables of data that historically came in these FTP files directly into our Snowflake instance now. We haven't taken advantage of that. It only actually flipped the switch about three or four weeks ago. But that's pretty big for us again, right? We don't have to worry about maintaining those jobs that take those files in. We don't have to worry about the jobs that take those and shove them on the warehouse. We now have a feed that's directly there that we can use a tool like DBT to push through directly into our model. And then the third avenue that's came up, actually fairly recently as well was genetics data. So genetics data that's highly, highly regulated. We had to be very careful with that. And we had a conversation with Snowflake about the data white rooms practice, and we see that as a pretty interesting opportunity. We are having one organization run genetic analysis being able to send us those genetic datasets, but then there's another organization that's actually has the in quotes "metadata" around that, so age, ethnicity, location, et cetera. And being able to join those two datasets through some kind of mechanism would be really beneficial to the organization. Being able to build a data white room so we can put that genetic data in a secure place, anonymize it, and then share the amalgamated data back out in a way that's able to be joined to the anonymized metadata, that could be pretty huge for us as well. >> Okay, so this is interesting. So you talk about FTP, which was the common way to share data. And so you basically, it's so, I got it now you take it and do whatever you want with it. Now we're talking, Jesse, about sharing the same copy of live data. How common is that use case in your industry? >> It's become very common over the last couple of years. And I think a big part of it is having the right technology to do it effectively. You know, as Nick mentioned, historically, this was done by people sending files around. And the challenge with that approach, of course, while there are multiple challenges, one, every time you send a file around your, by definition creating a copy of the data because you have to pull it out of your system of record, put it into a file, put it on some server where somebody else picks it up. And by definition at that point you've lost governance. So this creates challenges in general hesitation to doing so. It's not that it hasn't happened, but the other challenge with it is that the data's no longer real time. You know, you're working with a copy of data that was as fresh as at the time at that when that was actually extracted. And that creates limitations in terms of how effective this can be. What we're starting to see now with some of our customers is live sharing of information. And there's two aspects of that that are important. One is that you're not actually physically creating the copy and sending it to someone else, you're actually exposing it from where it exists and allowing another consumer to interact with it from their own account that could be in another region, some are running in another cloud. So this concept of super-cloud or cross-cloud could becoming realized here. But the other important aspect of it is that when that other- when that other entity is querying your data, they're seeing it in a real time state. And this is particularly important when you think about use cases like supply chain planning, where you're leveraging data across various different enterprises. If I'm a manufacturer or if I'm a contract manufacturer and I can see the actual inventory positions of my clients, of my distributors, of the levels of consumption at the pharmacy or the hospital that gives me a lot of indication as to how my demand profile is changing over time versus working with a static picture that may have been from three weeks ago. And this has become incredibly important as supply chains are becoming more constrained and the ability to plan accurately has never been more important. >> Yeah. So the race is on to solve these problems. So it start, we started with, hey, okay, cloud, Dave, we're going to simplify database, we're going to put it in the cloud, give virtually infinite resources, separate compute from storage. Okay, check, we got that. Now we've moved into sort of data clean rooms and governance and you've got an ecosystem that's forming around this to make it safer to share data. And then, you know, nirvana, at least near term nirvana is we're going to build data applications and we're going to be able to share live data and then you start to get into monetization. Do you see, Nick, in the near future where I know you've got relationships with, for instance, big pharma like AstraZeneca, do you see a situation where you start sharing data with them? Is that in the near term? Is that more long term? What are the considerations in that regard? >> I mean, it's something we've been thinking about. We haven't actually addressed that yet. Yeah, I could see situations where, you know, some of these big relationships where we do need to share a lot of data, it would be very nice to be able to just flick a switch and share our data assets across to those organizations. But, you know, that's a ways off for us now. We're mainly looking at bringing data in at the moment. >> One of the things that we've seen in financial services in particular, and Jesse, I'd love to get your thoughts on this, is companies like Goldman or Capital One or Nasdaq taking their stack, their software, their tooling actually putting it on the cloud and facing it to their customers and selling that as a new monetization vector as part of their digital or business transformation. Are you seeing that Jesse at all in healthcare or is it happening today or do you see a day when that happens or is healthier or just too scary to do that? >> No, we're seeing the early stages of this as well. And I think it's for some of the reasons we talked about earlier. You know, it's a much more secure way to work with a colleague if you don't have to copy your data and potentially expose it. And some of the reasons that people have historically copied that data is that they needed to leverage some sort of algorithm or application that a third party was providing. So maybe someone was predicting the ideal location and run a clinical trial for this particular rare disease category where there are only so many patients around the world that may actually be candidates for this disease. So you have to pick the ideal location. Well, sending the dataset to do so, you know, would involve a fairly complicated process similar to what Nick was mentioning earlier. If the company who was providing the logic or the algorithm to determine that location could bring that algorithm to you and you run it against your own data, that's a much more ideal and a much safer and more secure way for this industry to actually start to work with some of these partners and vendors. And that's one of the things that we're looking to enable going into this year is that, you know, the whole concept should be bring the logic to your data versus your data to the logic and the underlying sharing mechanisms that we've spoken about are actually what are powering that today. >> And so thank you for that, Jesse. >> Yes, Dave. >> And so Nick- Go ahead please. >> Yeah, if I could add, yeah, if I could add to that, that's something certainly we've been thinking about. In fact, we'd started talking to Snowflake about that a couple of years ago. We saw the power there again of the platform to be able to say, well, could we, we were thinking in more of a data share, but could we share our data out to say an AI/ML vendor, have them do the analytics and then share the data, the results back to us. Now, you know, there's more powerful mechanisms to do that within the Snowflake ecosystem now, but you know, we probably wouldn't need to have onsite AI/ML people, right? Some of that stuff's very sophisticated, expensive resources, hard to find, you know, it's much better for us to find a company that would be able to build those analytics, maintain those analytics for us. And you know, we saw an opportunity to do that a couple years ago and we're kind of excited about the opportunity there that we can just basically do it with a no op, right? We share the data route, we have the analytics done, we get the result back and it's just fairly seamless. >> I mean, I could have a whole another Cube session on this, guys, but I mean, I just did a a session with Andy Thurai, a Constellation research about how difficult it's been for organization to get ROI because they don't have the expertise in house so they want to either outsource it or rely on vendor R&D companies to inject that AI and machine intelligence directly into applications. My follow-up question to you Nick is, when you think about, 'cause Jesse was talking about, you know, let the data basically stay where it is and you know bring the compute to that data. If that data lives on different clouds, and maybe it's not your group, but maybe it's other parts of Ionis or maybe it's your partners like AstraZeneca, or you know, the AI/ML partners and they're potentially on other clouds or that data is on other clouds. Do you see that, again, coming back to super-cloud, do you see it as an advantage to be able to have a consistent experience across those clouds? Or is that just kind of get in the way and make things more complex? What's your take on that, Nick? >> Well, from the vendors, so from the client side, it's kind of seamless with Snowflake for us. So we know for a fact that one of the datasets we have at the moment, Compile, which is a, the large multi terabyte dataset I was talking about. They're on AWS on the East Coast and we are on Azure on the West Coast. And they had to do a few tweaks in the background to make sure the data was pushed over from, but from my point of view, the data just exists, right? So for me, I think it's hugely beneficial that Snowflake supports this kind of infrastructure, right? We don't have to jump through hoops to like, okay, well, we'll download it here and then re-upload it here. They already have the mechanism in the background to do these multi-cloud shares. So it's not important for us internally at the moment. I could see potentially at some point where we start linking across different groups in the organization that do have maybe Amazon or Google Cloud, but certainly within our providers. We know for a fact that they're on different services at the moment and it just works. >> Yeah, and we learned from Benoit Dageville, who came into the studio on August 9th with first Supercloud in 2022 that Snowflake uses a single global instance across regions and across clouds, yeah, whether or not you can query across you know, big regions, it just depends, right? It depends on latency. You might have to make a copy or maybe do some tweaks in the background. But guys, we got to jump, I really appreciate your time. Really thoughtful discussion on the future of data and cloud, specifically within healthcare and pharma. Thank you for your time. >> Thanks- >> Thanks for having us. >> All right, this is Dave Vellante for theCUBE team and my co-host, John Furrier. Keep it right there for more action at Supercloud 2. (upbeat music)

(upbeat music) (air whooshing) >> Good afternoon, cloud nerds, and welcome back to beautiful Las Vegas, Nevada. We are at AWS re:invent day four. Afternoon of day four here on theCUBE. I'm Savannah Peterson, joined by my fabulous cohost, Paul Gillin. Paul, you look sharp today. How you doing? >> Oh, you're just as fabulous, Savannah. You always look sharp. >> I appreciate that. They pay you enough to keep me buttered up over here at- (Paul laughing) It's wonderful. >> You're holding up well. >> Yeah, thank you. I am excited about our next conversation. Two fabulous gentlemen. Please welcome Sam and Monty, welcome to the show. >> Thank you. >> And it was great. Of the PR 2%, the most interesting man alive. (Paul and Savannah laughing) >> In person. Yeah, yeah. >> In the flesh. Our favorite guests so far. So how's the show been for you guys? >> Sam: It's been phenomenal. >> Just spending a lot of time with customers and partners and AWS. It's been great. It's been great. >> It is great. It's really about the community. It feels good to be back. >> Monty: Eating good food, getting my steps in above goals. >> I feel like the balance is good. We walk enough of these convention centers that you can enjoy the libations and the delicious food that's in Las Vegas and still not go home feeling like a cow. It is awesome. It's a win-win. >> To Sam's point though, meeting with customers, meeting with other technology providers that we may be able to partner with. And most importantly, in my role especially, meeting with all of our AWS key stakeholders in the partnership. So yeah, it's been great. >> Everyone's here. It's just different having a conversation in person. Even like us right now. So just in case folks aren't familiar, tell me about Talend. >> Yeah. Well, Talend is a data integration company. We've been around for a while. We have tons of different ways to get data from point A to point B, lots of different sources, lots of different connectors, and it's all about creating accessibility to that data. And then on top of that, we also have a number of solutions around governance, data health, data quality, data observability, which I think is really taking off. And so that's kind of how we're changing the business here. >> Casual change, data and governance. I don't know if anyone's talking about that at all on the snow floor. >> Been on big topic here. We've had a lot of conversations with the customers about that. >> So governance, what new dynamics has the cloud introduced into data governance? >> Well, I think historically, customers have been able to have their data on-prem. They put it into things like data lakes. And now having the flexibility to be able to bring that data to the clouds, it opens up a lot of doors, but it also opens up a lot of risks. So if you think about the chief data officer role, where you have, okay, I want to be able to bring my data to the users. I want to be able to do that at scale, operationally. But at the same time you have a tension then between the governance and the rules that really restrict the way that you can do that. Very strong tension between those two things. >> It really is a delicate balance. And especially as people are trying to accelerate and streamline their cloud projects, a lot to consider. How do you all help them do that? Monty, let's go to you. >> Yeah, we keep saying data, data, what is it really? It's ones and zeros. In this day and age, everything we see, we touch, we do, we either use data, or we create data, and then that... >> Savannah: We are data quite literally. >> We literally are data. And so then what you end up with is all these disparate data silos and different applications with different data, and how do you bring all that together? And that's where customers really struggle. And what we do is we bring it all together, and we make it actionable for the customer. We make it very simple for them to take the data, use it for the outcomes that they're looking for in their business initiatives. >> Expand on that. What do you mean make it actionable? Do you tag it? Do you organize it in some way? What's different about your approach? >> I mean, it's a really flexible platform. And I think we're part of a broader ecosystem. Even internally, we are a data driven company. Coming into the company in April, I was able to come in and get this realtime view of like, "Hey, here's where our teams are." And it's all in front of me in a Tableau dashboard that's populated from Talend integration, bringing data out of our different systems, different systems like Workday where we're giving offers out to people. And so everything from managing headcount to where our AWS spend is, all of that stuff. >> Now, we've heard a lot of talk about data and in fact the keynote yesterday that was focused mainly on data and getting data out of silos. How do you play with AWS in that role? Because AWS has other data integration partners. >> Sam: For sure. >> What's different about your relationship? Yeah. >> Go ahead. >> Yeah, we've had a strong relationship with AWS for many years now. We've got more than 80 connectors into the different AWS services. So we're not new to the AWS game. We align with the sales teams, we align with the partner teams, and then of course, we align with all the different business units and verticals so that we can enact that co-sell motion together with AWS. >> Sam: Yeah. And I think from our product standpoint, again, just being a hyper flexible platform, being able to put, again, any different type of source of data, to any type of different destination, so things like Redshift, being able to bring data into those cloud data warehouses is really how we do that. And then I think we have between bringing data from A to B, we're also able to do that along a number of different dimensions. Whether that's just like, "Hey, we just need to do this once a day to batch, all the way down to event driven things, streaming and the like. >> That customization must be really valuable for your customers as well. So one of the big themes of the show has been cost reduction. Obviously with the economic times as we're potentially dipping our toes into as well, is just in general, always wanting to increase margins. How do you help customers cut cost? >> Well, it's cost cutting, but it's also speed to market. The faster you can get a product to market, the faster you can help your customers. Let's say healthcare life sciences, pharmaceutical companies, patient outcomes. >> Great and timely example there. >> Patient outcomes, how do they get drugs to market quicker? Well, AstraZeneca leveraged our platform along with AWS. And they even said >> Cool. >> for every dollar that they spend on data initiatives, they get $40 back. That's a billion dollars >> Wow. >> savings by getting a drug to market one month faster. >> Everybody wins. >> How do you accelerate that process? >> Well, by giving them the right data, taking all the massive data that I mentioned, siloed in everywhere, and making it so that the data scientists can take all of this data and make use of it, makes sense of it, and move their drug production along much quicker. >> Yeah. And I think there's other things too like being very flexible in the way that it's deployed. Again, I think like you have this historical story of like, it takes forever for data to get updated, to get put together. >> Savannah: I need it now. And in context. >> And I think where we're coming from is almost more of a developer focus where your jobs are able to be deployed in any way you want. If you want to containerize those, you want to scale them, you need to schedule them that way. We plug into a lot of different ecosystems. I think that's a differentiation as well. >> I want to hang out on this one just for a second 'cause it's such a great customer success story and so powerful. I mean, in VC land, if you can take a dollar and make two, they'll give you a 10x valuation, 40. That is so compelling. I mean, do you think other customers could expect that kind of savings? A billion dollars is nothing to laugh at especially when we're talking about developing a vaccine. Yeah, go for it, Sam. >> It really depends on the use case. I think what we're trying to do is being able to say, "Hey, it's not just about cost cutting, but it's about tailoring the offerings." We have other customers like major fast food vendors. They have mobile apps and when you pull up that mobile app and you're going to do a delivery, they want to be able to have a customized offering. And it's not like mass market, 20% off. It's like, they want to have a very tailored offer to that customer or to that person that's pulling open that app. And so we're able to help them architect and bring that data together so that it's immediately available and reliable to be able to give those promotions. >> We had ARP on the show yesterday. We're talking about 50 million subscribers and how they customize each one of their experiences. We all want it to be about us. We don't want that generic at... Yeah, go for it, Paul. >> Oh, okay. >> Yeah. >> Well, I don't want to break break the rhythm here, but one area where you have differentiated, about two years ago you introduced something called the trust score. >> Sam: Yeah. >> Can you explain what that is and how that has resonated with your customers? >> Yeah, let's talk about this. >> Yeah, the thing about the trust score is, how many times have you gotten a set of data? And you look at it and you say, "Where did you get this data? Something doesn't look right here." And with the trust score, what we're able to do is quantify and value the different attributes of the data. Whether it's how much this is being used. We can profile the data, and we have a trust score that runs over time where you can actually then look at each of these data sets. You can look at aggregates of data sets to then say... If you're the data engineer, you can say, "Oh my, something has gone wrong with this particular dataset." Go in, quickly pull up the data. You can see if some third party integration has polluted your data source. I mean, this happens all the time. And I think if you sort of compare this to the engineering world, you're always looking to solve those problems sooner, earlier in the chain. You don't want your consumer calling you saying, "Hey, I've got a problem with the data, or I've got a problem- >> You don't want them to know there was ever a problem in theory. >> Yeah, the trust score helps those data engineers and those people that are taking care of the data address those problems sooner. >> How much data does somebody need to be able to get to the point where they can have a trust score? If you know what I'm trying to say. How do we train that? >> I mean, it can be all the way from just like a single data source that's getting updated, all the way to very large complex ones. That's where we've introduced this hierarchy of data sets. So it's not just like, "Hey, you've got a billion data sources here and here are the trust scores." But it's like, you can actually architect this to say like, "Okay, well, I have these data sets that belong to finance." And then finance will actually get, "Here's the trust score for these data sets that they rely on." >> What causes datasets to become untrustworthy? >> Yeah. Yeah. I mean, it happens all the time. >> A of different things, right? >> In my history, in the different companies that I've been at, on the product side, we have seen different integrations that maybe somebody changes something. In upstream, some of those integrations can actually be quite brittle. And as a consumer of that data, it's not necessarily your fault, but that data ends up getting put into your production database. All of a sudden your data engineering team is spending two days unwinding those transactions, fixing the data that's in there. And all the while, that bad data that's in your production system, is causing a problem for somebody that is ultimately relying on that. >> Is that usually a governance problem? >> I think governance is probably a separate set of constraints. This is sort of the tension between wanting to get all of the data available to your consumers versus wanting to have the quality around it as well. >> It's tough balance. And I think that it's really interesting. Everybody wants great data, and you could be making decisions that affect people's wellness, quite frankly. >> For sure. >> Very dramatically if you're ill-informed. So that's very exciting. >> To your point, we are all data. So if the data is bad, we're not going to get the outcomes that we want ultimately, >> I know. We certainly want the best outcomes for ourselves. >> We track that data health for its entire life cycle throughout the process. >> That's cool. And that probably increases your confidence in the trust score as well 'cause you're looking at so much data all the time. You got a smart thing going on over here. I like it. I like it a lot. >> We believe in it and so does AWS because they are a strong partner of ours, and so do customers. I think we mentioned we've had some phenomenal customer conversations along with- >> What a success story and case study. I want to dust your shoulders off right now if I wasn't tethered in. That's super impressive. So what's next for you all? >> Yeah, so I think we're going to continue down this path of data health and data governance. Again, I kind of talked about the... you're talking about data health being this differentiator on top of just moving the data around and being really good at that. I think you're also going to have different things around country level or state level governance, literal laws that you need to comply with. And so like- >> Savannah: CCPA- >> I mean, a long list- >> Oodles. Yeah. Yeah, yeah, yeah. >> I think we're going to be doing some interesting things there. We are continuing to proliferate the sources of data that we connect to. We're always looking for the latest and greatest things to put the data into. I think you're going to see some interesting things come out of that too. >> And we continue to grow our relationship with AWS, our already strong relationship. So you can procure Talend products to the AWS marketplace. We just announced Redshift serverless support for Talend. >> All their age. >> Which sounds amazing, but because we've been doing this for so long with AWS, dirty little secret, that was easy for us to do because we're already doing all this stuff. So we made the announcement and everyone was like, "Congratulations." Like, "Thanks." >> Look at you all. Full of the humble brags. I love it. >> Talend has gone through some twists and turns over the last couple of years. Company went private, was purchased by Thoma Bravo about a year and a half ago. At that time, your CEO said that it was a chance to really refocus the company on some core strategic initiatives and move forward. Both of you joined obviously after that happened. But what did you see about sort of the new Talend that attracted you, made you want to come over here? >> For sure. Yeah. I think, when I got a chance to talk to the board and talk to Chris, our chair, we talked about there being the growth thesis behind it. So I think Thoma been a great partner to Talend. I think we're able to do some things internally that would be I think, fairly challenging for companies that are in the public markets right now. I think especially, just a lot of pressure on different prices and the cost capital and all of that. >> Right now. >> That was a really casual way of stating that. But yeah, just a little pressure. >> Little bit of pressure. And who knows? Who knows how long that's going to last, right? But I think we've got a great board in place. They've been very strong strategic partner for us talking about all the different ways that we can grow. I think it's been a good partner for us- >> One of the strengths of Thoma's strategy is synergy between the companies they've acquired. >> Oh, for sure. >> They've acquired about 40 software companies. Are you seeing synergy? You talk to those other companies a lot? >> Yeah, so I have an operating partner. I talk with him on a weekly, sometimes daily basis. If we have questions or like, "Hey, what are you seeing in this space?" We can get plugged in to advisors very quickly. I think it's been a very helpful thing where... otherwise, you're relying on your personal network or things like that. >> This is why Monty was saying it was easy for you guys to go serverless. >> And we keep talking about trust, but in this case, Thoma Bravo really trusts our senior leadership team to make the right decisions that Sam and I are here making as we move forward. It's a great relationship. >> Sam: A good team. >> It sounds like it. All the love. I can feel the love even from you guys talking about it, it's genuine. You're not just getting paid to show this. That's fantastic. >> Are we getting paid for this or... >> Yeah. (Savannah giggling) (Paul laughing) I mean, some folks in the audience are probably going to want your autograph after this, although you get that a lot- >> Pictures are available after- >> Yeah, selfies are 10 bucks. That's how I get my boos budget. So last question for you. We have a challenge here on the theCUBE re:invent. We're looking for your 32nd hot take. Think of it as your thought leadership sizzle reel. Biggest takeaway, key themes from the show or looking forward into 2023? Sam, you're ready to rock, go. >> Yeah, totally. >> I think you're going to continue to hear the tension between being able to bring the data to the masses versus the simplicity and being able to do that in a way that is compliant with all the different laws, and then clean data. It's like a lot of different challenges that arise when you do this at scale. And so I think if you look at the things that AWS is announcing, I think you look at any sort of vendor in the data space are announcing, you see them sort of coming around to that set of ideas. Gives me a lot of confidence in the direction that we're going that we're doing the right stuff and we're meeting customers and prospects and partners, and everybody is like... We kind of get into this conversation and I'll say, "Yeah, that's it. We want to get involved in that." >> You can really feel the momentum. Yeah, it's true. It's great. What about you, Monty? >> I mean, I don't need 30 seconds. I mentioned it. >> Great. >> Between Talend and AWS, we're aligned from the sales teams to the product teams, the partner teams and the alliances. We're just moving forward and growing this relationship. >> I love it. That was perfect. And on that note, Sam, Monty, thank you so much for joining us. >> Yeah, thanks for having us. >> I'm sure your careers are going to continue to be rad at Talend and I can't wait to continue the conversation. >> Sam: Yeah, it's a great team. >> Yeah, clearly. I mean, look at you two. If you're any representation of the culture over there, they're doing something great. (Monty laughing) I thank all of you for tuning in to our nearly... Well, shoot. I think now over 100 interviews at AWS Reinvent in Sin City. We are hanging out here. Paul and I've got a couple more for you. So we hope to see you tuning in with Paul Gillin. I'm Savannah Peterson. You're watching theCUBE, the leader in high tech coverage. (upbeat music)

(upbeat intro music) >> Good afternoon, ladies and gentlemen and welcome back to fabulous Las Vegas, Nevada where we are theCUBE covering AWS re:Invent for the 10th year in a row. John Furrier, you've been here for all 10. How does this one stack up? >> It's feeling great. It's just back into the saddle of more people. Everyone's getting bigger and growing up. The companies that were originally on are getting stronger, bigger. They're doing takeovers in restaurants and still new players are coming in. More startups are coming in and taking care of what I call the (indistinct) on classic, all the primitives. And then you starting to see a lot more ecosystem platforms building on top of AWS. I call that NextGen Cloud, NextGen AWS. It's happening. It's happening right now. >> Best thing about all of these startups is they grow up, they mature, and we stay the same age, John. (John laughing) All right. All right. All right. Very excited to introduce you our next guest, he wears a lot of hats as the CEO, founder, and chairman at Reltio, please welcome Manish. Manish, welcome to the show. How is your show going so far? >> Well, thank you so much. You know, this is amazing. Just the energy, the number of people. You know, I was here last year, just after the pandemic, and I think it's almost double, if not more the number of people this year. >> John: Pushing 50,000. The high water mark was 65,000 in 2019. >> We should be doing like a Price Is Right sort of thing here on the show and figure out. >> Yeah, $1. >> Savannah: Yeah, yeah. (laughing) One guest, 80,000 guests. How many guests are here? Just in case the audience is not familiar, we know you're fast growing, very exciting business. Tell us what Reltio does. >> So, Reltio is a SaaS platform for data unification and we started Reltio in 2011. We have been serving some of the largest customers across industries like life sciences, healthcare, financial services, insurance, high tech, and retail. Those are, you know, some of the areas that we are focused on. The product capabilities are horizontal because we see the same data problem across every industry. Highly fragmented, highly siloed data that is slowing down the business for every organization out there. And that's the problem that we are solving. We are breaking down these silos, you know, one profile or one record, or one customer product supplier information record at a time, and bringing the acceleration of this unified data to every organization. >> This is the show Steam this year, Adam Celeste is going to be on stage talking about data end to end. Okay. Integrating in all aspects of a company. The word data analyst probably goes away pretty shortly. Everyone was going to be using data. This has been, and he talks about horizontal and vertical use cases. We've been saying that in theCUBE, I think it was about seven years ago, we first said we're going to start to see horizontally scalable data not just compute and cloud. This is now primetime conversation. Making that all work with governance is a real hard problem. Understanding the data. Companies have to put this horizontal and vertical capabilities in place together. >> Absolutely. You know, the data problem may be a horizontal problem, but every industry or vertical that you go into adds its own nuance or flavor to it. And that's why, you know, this has to be a combination of the horizontal and vertical. And we at Reltio thought about this for a while, where, you know, every time we enter a conversation, we are talking about patient data or physician data or client data and financial services or policy and customer information and insurance. But every time it's the number of silos that we encounter that is just an increasing number of applications, increasing number of third party data sources, and bringing that together in a manner where you can understand the semantics of it. Because, you know, every record is not created equal. Every piece of information is not created equal. But at the same time, you have to stitch it together in order to create that holistic, you know, the so-called 360 degree view. Because without that, the types of problems that you're trying to solve are not possible. Right? It's not possible to make those breakthroughs. And that's where I think the problem may be horizontal, but the application of the capabilities has to be verticalized. >> John: I'm smiling because, you know, when you're a founder like you are, and Dave, a lot here are at theCUBE, you're often misunderstood before people figure out what you do and why you started the company. And I can imagine, and knowing you and covering your company, that this is not just yesterday you came up with this idea that now everyone's talking about. There was probably moments in your history when you started, you're scratching it, "Hey the future's going to be this horizontal and vertical, especially where machine learning needs to know the data, the linguistics, whatever the data is, it's got to be very particular for the vertical, but you need to expand it." So when did you have the moment where people finally figured out like, what you guys doing is, like, relevant? I mean, now the whole world now sees- >> Savannah: Overnight success 11 years later. >> John: This shows the first time I've heard Amazon and the industry generally agree that horizontally scalable data systems with vertical value, that it's natural. We've been saying it for seven years on theCUBE. You've been doing the startup. >> Yeah. >> As a founder, you were there early. Now people are getting it. What's it like? Tell, take us through. When did you have the moment? When did you tipping point for the world getting it? >> Yeah, and you know, the key thing to remember is that, you know, not only have I been in this space for a long time but the experiences that we have gone through starting in 2011, there was a lot of focus on, you know, even AWS was at that point in time in the infancy stages. >> Yeah. >> And we said that we are going to set up a software as a service capability that runs only on public cloud because we had seen what customers had tried to do behind their firewalls and the types of hurdles that they had run into before. And while the concept was still in its nascent stages, but the directional signals, the fact that number of applications that you see in use today across any organization, that's growing. It used to be a case when in early 2000s, you know, this is early part of my career, where having six different applications across the enterprise landscape was considered complex. But now those same organizations are talking about 400, 500, a thousand different applications that they're using to run their business end to end. So, you know, this direction was clear. The need for digital transformation was becoming clear. And the fact that, you know, cloud was the only vehicle that you could use to solve these types of ad scale problems was also becoming clear. But what wasn't yet mainstream was this notion that, you know, if you're doing digital transformation, you need access to clean, consistent, trusted information. Or if you're doing machine learning or any kind of data analytics, you need similar kinds of trusted information. It wasn't a mainstream concept, but people were struggling with it because, you know, the whole notion of garbage in garbage out was becoming clearer to them as they started running into hurdles. And it's great to see that now, you know, after having gone through the transformation of, yes, we have provided the compute and the storage, but now we really need to unlock the value out of data that goes on this compute and storage. You know, it's great to see that even Amazon or AWS is talking about it. >> Well, as a founder, it's satisfying, and congratulations, we've been covering that. I got to ask, you mention this end to end. I like the example of in the 2006 applications considered complex, now hundreds and thousands of workloads are on an enterprise. Today we're going to hear more end to end data services on AWS and off AWS, hybrid or edge or whatever, that's happens. Now cross, it sounds like it's going to get more complex still. >> I mean... >> John: Right. I mean, that's not easy. >> Savannah: The gentle understatement of the century. I love that. Yes. >> If Adam's message is end to end, it's going to be more complex. How does it get easier? Because the enterprise, you know, the enterprise vendors love solving complexity with more complexity. That's the wrong answer. >> Well, you're absolutely right that things are going to get more complex. But you know, this is where, whether it is Amazon or you know, us, Reltio as a vendor coming in, the goal should always be what are we going to simplify for the customer? Because they are going to end up with a complex landscape on their hands anyway. Right? >> Savannah: Right. >> So that is where, what can be below the surface and simplified for the customers to use versus bringing their focus to the business value that they can get out of it. Unlocking that business value has to be the key aspect that we have to bring to the front. And, you know, that is where, yes, the landscape complexity may grow, but how is the solution making it simpler, easier, faster for you to get value out of the data that you're trying to work with? >> As a mission, that seems very clear and clean cut, but I'm curious, I can imagine there's so many different things that you're prioritizing when you're thinking about how to solve those problems. What is that decision matrix like for you? >> For us, it goes back to the core focus and the core problem that we are in the business of solving which is in a siloed, fragmented landscape, how can we create a single source of truth orientation that your business can depend on? If you're looking for the unified view of the customer, the product, the supplier, the location, the asset, all these are elements that are critical or crucial for you to run your business end to end. And we are there to provide that solution as Reltio to our customers. So, you know, we always, for our decision matrix have to go back to are we simplifying that problem for our customers and how much faster, easier, nimbler can it be, you know, both as a solution and also the time to value that it brings to the equation for the customer. >> Super important, end of the equation. Clearly you are on to something. You are not only a unicorn company, unicorn company being evaluated at over $1 billion latest evaluation, correct me if I'm wrong, is $1.7 billion as of last year. But you are also a centaur, which is seven times more rare than a unicorn, which for the audience maybe not familiar with the mythical creatures that define the Silicon Valley nomenclature in Lexicon. A centaur is a company with a hundred million in annual reoccurring revenue. How does it feel to be able to say that as a CEO or to hear me say that to you? >> Well, as a CEO, it's, you know, something that we have been working towards. the goal that we can deliver value to our customers, help every industry, you know, you just think about the types of products that you touch in a day, whether it's, you know, any healthcare related products that you're looking at. We are working with customers who are solving for the patient record to be unified with our platform. We are working with financial services companies who are helping you simplify how you do banking with them. We are working with retailers who are working in the area of, you know, leisure apparel or athletic goods and they are using our capabilities to simplify how they deliver better experience to you. So as I go across these industries, being able to influence and touch and simplify things overall for the customers that these companies are serving, that's an amazing feeling. And, you know, doing this while we are also making sure that we can build a durable business that has substantial revenue behind it- >> Savannah: Substantial. >> Gives us a lot of legs to stand on and talk about how we can change how the companies should run their entire data stack. >> And you're obviously a very efficient team practicing what you teach. You told me how many employees that you have? >> We have 450 employees across the globe. >> 450 employees and a hundred million in reoccurring revenue. It's pretty strong. It's pretty strong. >> Thank you. >> That's a quarter million in rev per employee. They're doing a pretty good job. That's absolutely fantastic. >> The cloud has been very successful, partnering with the cloud, a lot of leverage for the cloud. >> And that's been a part of our thesis from the very beginning that, you know, the capabilities that we build and bring to life have to be built on public cloud infrastructure. That's something that has been core to our innovation cycle because we look at it as a layer cake of innovation that we sit on and we can continue to drive faster value for our customers. >> John: Okay, so normally we do a bumper sticker. Tell me the bumper sticker for the show. We changed it to kind of modernize it called the Insta Challenge, Instagram challenge. Instagram has reels, short videos. What's the Instagram reel from your perspective? You have to do an Instagram reel right now about why this time in history, this time in for Amazon web services, this point for Reltio. Why is this moment in time important in the computer industry? Because, you know, we've reported, I put a story out, NextGen Clouds here. People are seeing their status go from ISV to ecosystem platforms on top of AWS. Your success has continued to grow. Something's going on. What's the Instagram reel about why this year's so important in the history of the cloud? >> Well, you know, just think about the overall macroeconomic conditions. You know, everybody's trying to think about where the next, you know, the set of growth is going to come from or how we are going to tackle, you know, what we have as challenges in front of us. And at the end of the day, most of the efficiency that came from applying new applications or, you know, buying new products in the application space has delivered its value. The next unlock is going to come from data. And that is the key that we have to think about because the traditional model of going across 500 different applications to run your business is no longer going to be a scalable model to work with. If you really want to move faster with your business, you have to think about how to use data as a strategic asset and think about things differently. And we are talking about delivering experience at the edge, delivering, you know, real time type of engagement with the customers that we work with. And that is where the entire data value proposition starts to deliver a whole new set of options to the customers. And that's something that we all have to think about differently. It's going to require a fundamentally different architecture, innovation, leading with data instead of thinking about the traditional landscape that we have been running with. >> Leading with data and transforming architecture. A couple themes we've had on the show lately already. >> John: Well I think there's been a great, I mean this is a great leadership example of what's going on in the industry. As young people are looking at their careers. I've talked with a lot of folks under 30, they're trying to figure out what's a good career path and they're looking at all this change in front of them. >> That's a great point, John. >> Whether it's a computer science student or someone in healthcare, these industries are being reinvented with data. What's your advice to those young, this up and coming generation that might not take the traditional path traveled 'cause it might not be there. What's your advice for those people making these career decisions? >> I think there are two things that are relevant to every career option out there. Knowledge and awareness of data and how to apply computing techniques to the data is key and relevant. It's the language that we all have to learn and be familiar with. Without that, you know, you'll be missing a key part of your arsenal that you will be required to bring to work but won't have access to if you're not well-versed or familiar with those two areas. So this is lingua franca that we all have to get used to. >> Data and computer technology applied to business or some application or some problem. >> Manish: Applied to business. You know, figuring out how to apply it to deliver business outcomes is the key thing to keep in mind. >> Okay. >> Yeah. Last question for you to wrap us up. It's obviously an exciting, thrilling, vibrant moment here on the show floor, but I'm curious because I can imagine some of your customers, especially given the scale that they're at, I mean we're talking about some Fortune 100s here, how are you delivering value in this uncertain market? I mean, I know you solved this baseline problem but I can imagine there's a little bit of frantic energy within your customer base. >> Manish: Yeah. You know, with data this has been a traditional challenge. Everybody talks about the motherhood and apple pie. If you have better data, you can drive better outcomes. But some of the work that we have been doing is quantifying, measuring those outcomes and translating what the dollar impact of that value is for each one of the customers. And this is where the work that we have done with large, you know, let's say life sciences companies like AstraZeneca or GSK or in financial services with companies like Northwestern Mutual or Fidelity or, you know, common household names like McDonald's where they're delivering their digital transformation with the data capabilities that we are helping build with them. That's the key part that's been, you know, extremely valuable. And that is where in each one of these situations, we are helping them measure what the ROI is at every turn. So being able to go into these discussions with the hard dollar ROI that you can expect out of it is the key thing that we are focused on. >> And that's so mission critical now and at any economic juncture. Just to echo that, I noticed that Forrester did an independent study looking at customers that invested in your MDM solution. 366% ROI and a total net present value of 13 million over three years. So you clearly deliver on what you just promised there with customers and brands that we touch in all of our everyday lives. Manish, thank you so much for being on the show with us today. You and Reltio are clearly crushing it. We can't wait to have you back hopefully for some more exciting updates at next year's AWS re:Invent. John, thanks for- >> Or sooner. >> Yeah, yeah. Or sooner or maybe in the studios or who knows, at one of the other fabulous events we'll all be at. I'm sure you'll be traveling around given the success that the company is seeing. And John, thanks for bringing the young folks into the conversation, was a really nice touch. >> We got skill gaps, we might as well solve that right now. >> Yeah. And I like to think that there are young minds watching theCUBE or at least watching, maybe their parents are- >> We're streaming to Twitch. All the gamers are watching this right now. Stop playing the video games. >> We have the hottest stream on Twitch right now if you're not already ready for it. John Furrier, Manish Sood, thank you so much for being on the show with us. Thank all of you at home or at the office or in outer space or wherever you happen to be tuned in to this fabulous live stream. You are watching theCUBE, the leader in high tech coverage. My name is Savannah Peterson. We're at AWS re:Invent here in Las Vegas where we'll have our head in the clouds all week.

(upbeat music) >> We're here on theCube covering AWS Storage Day 2022. Kevin Miller joins us. He's the vice president and general manager of Amazon S3. Hello, Kevin, good to see you again. >> Hey Dave, it's great to see you as always. >> It seems like just yesterday we were celebrating the 15th anniversary of S3, and of course the launch of the modern public cloud, which started there. You know, when you think back Kevin, over the past year, what are some of the trends that you're seeing and hearing from customers? What do they want to see AWS focus more on? What's the direction that you're setting? >> Yeah, well Dave, really I think there's probably three trends that we're seeing really pop this year. I think one just given the kind of macroeconomic situation right now is cost optimization. That's not a surprise. Everyone's just taking a closer look at what they're using, and where they might be able to pair back. And you know, I think that's a place that obviously S3 has a long history of helping customers save money. Whether it's through our new storage classes, things like our Glacier Instant Retrieval, storage class that we launched to reinvent last year. Or things like our S3 storage lens capability to really dig in and help customers identify where their costs are are being spent. But so certainly every, you know, a lot of customers are focused on that right now, and for obvious reasons. I think the second thing that we're seeing is, just a real focus on simplicity. And it kind of goes hand in hand with cost optimization, because what a lot of customers are looking for is, how do I take the staff that I have, and do more this year. Right, continue to innovate, continue to bring new applications or top line generating revenue applications to the market, but not have to add a lot of extra headcount to do that. And so, what they're looking for is management and simplicity. How do I have all of this IT infrastructure, and not have to have people spending a lot of their time going into kind of routine maintenance and operations. And so that's an area that we're spending a lot of time. We think we have a lot of capability today, but looking at ways that we can continue to simplify, make it easier for customers to manage their infrastructure. Things like our S3 intelligent tiering storage class, which just automatically gives cost savings for data that's not routinely accessed. And so that's a big focus for us this year as well. And then I think the last and probably third thing I would highlight is an emerging theme or it's been a theme, but really continuing to increase in volume, is all around sustainability. And you know, our customers are looking for us to give them the data and the assurances for them, for their own reports and their own understanding of how sustainable is my infrastructure. And so within AWS, of course, you know we're on a path towards operating with 100% renewable energy by 2025. As well as helping the overall Amazon goal of achieving net zero carbon by 2040. So those are some big lofty goals. We've been giving customers greater insights with our carbon footprint tool. And we think that, you know the cloud continues to be just a great place to run and reduce customer's carbon footprint for the similar you know, storage capacity or similar compute capacity. But that's just going to continue to be a trend and a theme that we're looking at ways that we can continue to help customers do more to aggressively drive down their carbon footprint. >> I mean, it makes sense. It's like you're partnering up with the cloud, you know, you did same thing on security, you know, there's that shared responsibility model, same thing now with ESG. And on the macro it's interesting Kevin, this is the first time I can remember where, you know it used to be, if there's a downturn it's cost optimization, you go to simplicity. But at the same time with digital, you know, the rush to digital, people still are thinking about, okay how do I invest in the future? So but let's focus on cost for a moment then we'll come back to sort of the data value. Can you tell us how AWS helps customers save on storage, you know, beyond just the price per terabyte actions that you could take. I mean I love that, you guys should keep doing that. >> Absolutely. >> But what other knobs are you turning? >> Yeah, right and we've had obviously something like 15 cost reductions or price reductions over the years, and we're just going to continue to use that lever where we can, but it's things like the launch of our Glacier Instant Retrieval storage class that we did last year at Reinvent, where that's now you know, 4/10ths of a cent per gigabyte month. For data that customers access pretty infrequently maybe a few times a year, but they can now access that data immediately and just pay a small retrieval fee when they access that data. And so that's an example of a new capability that reduces customer's total cost of ownership, but is not just a straight up price reduction. I mentioned S3 Intelligent-Tiering, that's another case where, you know, when we launch Glacier Instant Retrieval, we integrated that with Intelligent-Tiering as well. So we have the archive instant access tier within Intelligent-Tiering. And so now data that's not accessed for 90 days is just automatically put into AIA and and then results in a reduced storage cost to customers. So again, leaning into this idea that customers are telling us, "Just do, you know what should be done "for my data to help me reduce cost, can you just do it, "and sort of give me the right defaults." And that's what we're trying to do with things like Intelligent-Tiering. We've also, you know, outside of the S3 part of our portfolio, we've been adding similar kinds of capabilities within some of our file services. So things like our, you know elastic file service launched a one zone storage class as well as an intelligent tiering capability to just automatically help customers save money. I think in some cases up to 92% on their their EFS storage costs with this automatic intelligent tiering capability. And then the last thing I would say is that we also are just continuing to help customers in other ways, like I said, our storage lens is a great way for customers to really dig in and figure out. 'Cause you know, often customers will find that they may have, you know, certain data sets that someone's forgotten about or, they're capturing more data than they expected perhaps in a logging application or something that ends up generating a lot more data than they expected. And so storage lens helps them really zoom in very quickly on, you know this is the data, here's how frequently it's being accessed and then they can make decisions about use that data I keep, how long do I keep it? Maybe that's good candidates to move down into one of our very cold storage classes like Glacier Deep Archive, where they they still have the data, but they don't expect to need to actively retrieve it on a regular basis. >> SDL bromide, if you can measure it, you can manage it. So if I can see it, visualize it, that I can take actions. When you think about S3- >> That's right. it's always been great for archival workloads but you made some updates to Glacier that changed the way that we maybe think about archive data. Can you talk about those changes specifically, what it means for how customers should leverage AWS services going forward? >> Yeah, and actually, you know, Glacier's coming up on its 10 year anniversary in August, so we're pretty excited about that. And you know, but there's just been a real increase in the pace of innovation, I think over the last three or four years there. So we launched the Glacier Deep Archive capability in 2019, 2018, I guess it was. And then we launched Glacier Instant Retrieval of course last year. So really what we're seeing is we now have three storage classes that cover are part of the Glacier family. So everything from millisecond retrieval for that data, that needs to be accessed quickly when it is accessed, but isn't being accessed, you know, regularly. So maybe a few times a year. And there's a lot of use cases that we're seeing really quickly emerge for that. Everything from, you know, user generated content like photos and videos, to big broadcaster archives and particularly in media and entertainment segment. Seeing a lot of interest in Glaciers Instant Retrieval because that data is pretty cold on a regular basis. But when they want to access it, they want a huge amount of data, petabytes of data potentially back within seconds, and that's the capability we can provide with Glacier Instant Retrieval. And then on the other end of the spectrum, with Glacier Deep Archive, again we have customers that have huge archives of data that they be looking to have that 3-AZ durability that we provide with Glacier, and make sure that data is protected. But really, you know expect to access it once a year if ever. Now it could be a backup copy of data or secondary or tertiary copy of data, could be data that they just don't have an active use for it. And I think that's one of the things we're starting to see grow a lot, is customers that have shared data sets where they may not need that data right now but they do want to keep it because as they think about, again these like new applications that can drive top line growth, they're finding that they may go back to that data six months or nine months from now and start to really actively use it. So if they want that option value to keep that data so they can use it down the road, Glacier Deep Archive, or Glacier Flexible Retrieval, which is kind of our storage class right in the middle of the road. Those are great options for customers to keep the data, keep it safe and secure, but then have it, you know pretty accessible when they're ready to get it back. >> Got it, thank you for that. So, okay, so customers have choices. I want to get into some of the competitive differentiators. And of course we were talking earlier about cost optimization, which is obviously an important topic given the macro environment you know, but there's more. And so help us understand what's different about AWS in terms of helping customers get value from their data, cost reduction as a component of value, part of the TCO, for sure. But just beyond being a cloud bit bucket, you know just a storage container in the cloud, what are some of the differentiators that you can talk to? >> Yeah, well Dave, I mean, I think that when it comes to value, I think there's tremendous benefits in AWS, well beyond just cost reduction. I think, you know, part of it is S3 now has built, I think, an earned reputation for being resilient, for storing, you know, at massive scale giving customers that confidence that they will be able to scale up. You know, we store more than 200 trillion objects. We regularly peak at over 100 million requests per second. So customers can build on S3 and Glacier with the confidence that we're going to be there to help their applications grow and scale over time. And then I think that in all of the applications both first party and third party, the customers can use, and services that they can use to build modern applications is an incredible benefit. So whether it's all of our serverless offerings, things like Lambda or containers and everything we have to manage that. Or whether it's the deep analytics and machine learning capabilities we have to help really extract, you know value and insight from data in near real time. You know, we're just seeing an incredible number of customers build those kinds of applications where they're processing data and feeding their results right back into their business right away. So I'm just going to briefly mention a couple, like, you know one example is ADP that really helps their customers measure, compare and sort of analyze their workforce. They have a couple petabytes of data, something like 25 billion individual data points and they're just processing that data continuously through their analytics and machine learning applications to then again, give those insights back to their customers. Another good example is AstraZeneca. You know, they are processing petabytes and petabytes of genomic sequencing data. And they have a goal to analyze 2 million genomes over the next four years. And so they're just really scaling up on AWS, both from a pure storage point of view, but more importantly, from all of the compute and analytics capability on top that is really critical to achieving that goal. And then, you know, beyond the first party services we have as I mentioned, it's really our third party, right? The AWS partner network provides customers an incredible range of choice in off the shelf applications that they can quickly provision and make use of the data to drive those business insights. And I think today the APN has something like 100,000 partners over in 150 countries. And we specifically have a storage competency partner where customers can go to get those applications that directly work, you know, on top of their data. And really, like I said, drive some of that insight. So, you know, I think it's that overall benefit of being able to really do a lot more with their data than just have it sit idle. You know, that's where I think we see a lot of customers interested in driving additional value. >> I'm glad you mentioned the ecosystem, and I'm glad you mentioned the storage competency as well. So there are other storage partners that you have, even though you're a head of a big storage division. And then I think there's some other under the cover things too. I've recently wrote, actually have written about this a lot. Things like nitro and rethinking virtualization and how to do, you know offloads. The security that comes, you know fundamentally as part of the platform is, I think architecturally is something that leads the way in the industry for sure. So there's a lot we could unpack, but you've fundamentally changed the storage market over the last 16 years. And again, I've written about this extensively. We used to think about storage in blocks or you got, you know, somebody who's really good in files, there were companies that dominated each space with legacy on-prem storage. You know, when you think about object storage Kevin, it was a niche, right? It was something used for archival, it was known for its simple, get put syntax, great for cheap and deep storage, and S3 changed that. Why do you think that's happened and S3 has evolved, the object has evolved the way it has, and what's the future hold for S3? >> Yeah I mean, you know, Dave, I think that probably the biggest overall trend there is that customers are looking to build cloud native applications. Where as much of that application is managed as they can have. They don't want to have to spend time managing the underlying infrastructure, the compute and storage and everything that goes around it. And so a fully managed service like S3, where there's no provisioning storage capacity, there's, you know we provide the resiliency and the durability that just really resonates with customers. And I think that increasingly, customers are seeing that they want to innovate across the entire range of business. So it's not about a central IT team anymore, it's about engineers that are embedded within lines of business, innovating around what is critical to achieve their business results. So, you know, if they're in a manufacturing segment, how can we pull data from sensors and other instrumentation off of our equipment and then make better decisions about when we need to do predictive maintenance, how quickly we can run our manufacturing line, looking for inefficiencies. And so we've developed around our managed offerings like S3, we've just developed, you know, customers who are investing and executing on plans and you know transformations. That really give them, you know put digital technology directly into the line of business that they're looking for. And I think that trend is just going to continue. People sometimes ask me, well "I mean, 16 years, you know, isn't S3 done?" And I would say, "By no stretcher are we done." We have plenty of feedback from customers on ways that we can continue to simplify, reduce the kinds of things they need to do, when they're looking for example and rolling out new security policies and parameters across their entire organization. So raising the bar there, finding, you know, raising the bar on how they can efficiently manage their storage and reduce costs. So I think we have plenty of innovation ahead of us to continue to help customers provide that fully managed capability. >> Yeah I often say Kevin, the next 10 years ain't going to be like the last in cloud. So I really thank you for coming on theCube and sharing your insights, really appreciate it. >> Absolutely Dave, thanks for having me. >> You're welcome. Okay keep it right there for more coverage of AWS Storage Day 2022 in theCube. (calm bright music)

>>Hello everybody. I am Toshihiko Nishimura from Stanford. University is there to TTT out here, super aging, global OMIM global transportation group about infections, uh, or major point of concerns. In addition, this year, we have the COVID-19 pandemic. As you can see here, while the why the new COVID-19 patients are still increasing, meanwhile, case count per day in the United state, uh, beginning to decrease this pandemic has changed our daily life to digital transformation. Even today, the micro segmentation is being conducted online and doctor and the nurse care, uh, now increase to telemedicine. Likewise, the drug development process is in need of major change paradigm shift, especially in vaccine in drug development for COVID-19 is, should be safe, effective, and faster >>In the >>Anastasia department, which is the biggest department in school of medicine. We have Stanford, a love for drug device development, regulatory science. So cold. Say the DDT RDS chairman is Ron Paul and this love leaderships are long mysel and stable shaper. In the drug development. We have three major pains, one exceedingly long duration that just 20 years huge budget, very low success rate general overview in the drug development. There are Discoverly but clinical clinical stage, as you see here, Tang. Yes. In clinical stage where we sit, say, what are the programs in D D D R S in each stages or mix program? Single cell programs, big data machine learning, deep learning, AI mathematics, statistics programs, humanized animal, the program SNS program engineering program. And we have annual symposium. Today's the, my talk, I do like to explain limitation of my science significance of humanized. My science out of separate out a program. I focused on humanized program. I believe this program is potent game changer for drug development mouse. When we think of animal experiment, many people think of immediately mouse. We have more than 30 kinds of inbred while the type such as chief 57, black KK yarrow, barber C white and so on using QA QC defined. Why did the type mice 18 of them gave him only one intervention using mouse, genomics analyzed, computational genetics. And then we succeeded to pick up fish one single gene in a week. >>We have another category of gene manipulated, mice transgenic, no clout, no Kamal's group. So far registered 40,000 kind as over today. Pretty critical requirement. Wrong FDA PMDA negative three sites are based on arteries. Two kinds of animal models, showing safety efficacy, combination of two animals and motel our mouse and the swine mouse and non-human primate. And so on mouse. Oh, Barry popular. Why? Because mouse are small enough, easy to handle big database we had and cost effective. However, it calls that low success rate. Why >>It, this issue speculation, low success rate came from a gap between preclinical the POC and the POC couldn't stay. Father divided into phase one. Phase two has the city FDA unsolved to our question. Speculation in nature biology using 7,372 new submissions, they found a 68 significant cradle out crazy too, to study approved by the process. And in total 90 per cent Radia in the clinical stages. What we can surmise from this study, FDA confirmed is that the big discrepancy between POC and clinical POC in another ward, any amount of data well, Ms. Representative for human, this nature bio report impacted our work significantly. >>What is a solution for this discrepancy? FDA standards require the people data from two species. One species is usually mice, but if the reported 90% in a preclinical data, then huge discrepancy between pretty critical POC in clinical POC. Our interpretation is data from mice, sometime representative, actually mice, and the humor of different especially immune system and the diva mice liver enzyme are missing, which human Liba has. This is one huge issue to be taught to overcome this problem. We started humanized mice program. What kind of human animals? We created one humanized, immune mice. The other is human eyes, DBA, mice. What is the definition of a humanized mice? They should have human gene or human cells or human tissues or human organs. Well, let me share one preclinical stages. Example of a humanized mouse that is polio receptor mice. This problem led by who was my mentor? Polio virus. Well, polio virus vaccine usually required no human primate to test in 13 years, collaboration with the FDA w H O polio eradication program. Finally FDA well as w H O R Purdue due to the place no human primate test to transgenic PVL. This is three. Our principle led by loss around the botch >>To move before this humanized mouse program, we need two other bonds donut outside your science, as well as the CPN mouse science >>human hormone, like GM CSF, Whoah, GCSF producing or human cytokine. those producing emoji mice are required in the long run. Two maintain human cells in their body under generation here, South the generation here, Dr. already created more than 100 kinds based on Z. The 100 kinds of Noe mice, we succeeded to create the human immune mice led the blood. The cell quite about the cell platelets are beautifully constituted in an mice, human and rebar MAs also succeeded to create using deparent human base. We have AGN diva, humanized mouse, American African human nine-thirty by mice co-case kitchen, humanized mice. These are Hennessy humanized, the immune and rebar model. On the other hand, we created disease rebar human either must to one example, congenital Liba disease, our guidance Schindel on patient model. >>The other model, we have infectious DDS and Waddell council Modell and GVH Modell. And so on creature stage or phase can a human itemize apply. Our objective is any stage. Any phase would be to, to propose. We propose experiment, pose a compound, which showed a huge discrepancy between. If Y you show the huge discrepancy, if Y is lucrative analog and the potent anti hepatitis B candidate in that predict clinical stage, it didn't show any toxicity in mice got dark and no human primate. On the other hand, weighing into clinical stage and crazy to October 15, salvage, five of people died and other 10 the show to very severe condition. >>Is that the reason why Nicole traditional the mice model is that throughout this, another mice Modell did not predict this severe side outcome. Why Zack humanized mouse, the Debar Modell demonstrate itself? Yes. Within few days that chemistry data and the puzzle physiology data phase two and phase the city requires huge number of a human subject. For example, COVID-19 vaccine development by Pfizer, AstraZeneca Moderna today, they are sample size are Southeast thousand vaccine development for COVID-19. She Novak UConn in China books for the us Erica Jones on the Johnson in unite United Kingdom. Well, there are now no box us Osaka Osaka, university hundred Japan. They are already in phase two industry discovery and predict clinical and regulatory stage foster in-app. However, clinical stage is a studious role because that phases required hugely number or the human subject 9,000 to 30,000. Even my conclusion, a humanized mouse model shortens the duration of drug development humanize, and most Isabel, uh, can be increase the success rate of drug development. Thank you for Ron Paul and to Steven YALI pelt at Stanford and and his team and or other colleagues. Thank you for listening.

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Everyone, welcome to this CUBE conversation here in theCUBE studios in Palo Alto. We're here for remote interview. We're continuing with the COVID coverage, the quarantine crew. I'm John Furrier, host of theCUBE. Got a great guest, Christal Bemont. The CEO of Talend, just joined the club in the middle of the pandemic. Christal, thanks for joining us and nice seeing you. >> It's a pleasure to be here. Thank you for having me. Well, I think it's a really great conversation to have a couple of threads that are interesting to me. One is, Talend's... We've been covering for a long time, obviously. Their position in the marketplace, we've been following their trajectory. You're new to the company, but you joined right in the middle of, as COVID was going down. And we're still in this mode and it looks like it's going to be for some time. I'd love to get your thoughts as we're in this mode. First, what attracted you to Talend, your new? And, what's it been like there since you've been there, you can't meet people face to face. So you must be doing a lot of remote interviews, then remote conversations. >> Well, you're right about that, I had a very short window that I could get out on the road. And I'm so grateful that I did because visiting our offices, our customers and our partners is critical to, really surrounding ourselves with amazing people that we have Talend. But you know, I'll just go back to why I joined Talend and it really goes to the customers, our customer stories just captured my attention right away. The way that Talend shows up to drive outcomes for customers that are tangible, that are quantifiable, and that are game changing was something that interested me. And it really is that at the heart of every conversation is data. So it was a simple decision for me to say, those are the types of things I want to be involved in. And so Talend was definitely something that became very attractive. >> It's interesting, we've watched the progression of the big data market and now 10 years in, and the explosion of cloud, obviously, everyone's talking about data as a key ingredient for application development. And you're still seeing kind of the challenges of how do you manage the data. And then how do you put that into action for insights, because now you have these connected experiences. And even more highlighted with the COVID pandemic, you still got to run the business, you still need the data. The workforce is remote. The future of work, work force, workplace, workloads and workflows all have data. This is a real. >> That's right >> Challenge with now the connected experience being the number one problem and making that good, and making that valuable. What's your take on? >> That's right. I couldn't agree more. You know, we talked a lot about digital transformation for years, quite frankly. And I would say, you know, we've been in a digital transformation evolution. And I think what has happened now is COVID is an accelerant and it's a, now it's a digital revolution and at the heart or maybe the cornerstone, if you will, of the any digital transformation is data transformation. You think about digital transformation is about mindset. It's about changing your entire way that you operate as a company. It's not just about systems and technology, that's a really critical part. But everything that fuels the ability to get outcomes out of a digital transformation is data. And so the ability to leverage. Like you said, there's connected data, there's more data than we've ever had. And that's a massive opportunity. But having a lot of data is not always the answer. Sometimes that becomes a big responsibility with regulations, and also something that if not carefully governed, not really something you can leverage properly to run your business. So data is at the heart of all the things going on at this moment. >> It's interesting to, you know, a lot of the main trends outside of kind of the inside the industry discussions around data and the role of data. The consumer side of it, is seeing it with fake news. You're seeing it with the data around COVID. Anyone can make data tell a story. There's always you know, >> Right. causation versus correlation, that discussion. But when you start thinking people being exposed to the data problems, there's an opportunity in there and one of the big things is trust. What data can I trust? What's authentic? And then, how do I make sure that it's not just supporting a story? There's all kinds of things going on around it. It makes it seem like a broader challenge. Trust seems to be at the heart of it. What do you trust? Who's the source? It's just all life now as data infiltrated all of our lives. It's certainly now exposed. >> You couldn't be more right on that one. And you can see it play out, in the media, you can see it play out again. This accelerating set of circumstances that are playing out every single day, as people are staying so closely, watchful of data informing decisions that everyone's making around the world in a lot of different ways. And you've seen a lot of times where there's a question about the quality of the data, the accuracy of the data, who's providing the data. And, that's the environment that Talend, really supports and lives in, even prior to COVID. But it just underscores the importance of not just having a complete set of data. And I would say, even taking it further than just having what we would traditionally call quality of data. And really taking it down to something, you used really important word is, trust. How can you make sure that the data that you're making decisions on is something you can trust, and when it comes to health and well being that's certainly something that you can't afford not to have? And it's an area that is underserved right now that we've spent a lot of time thinking about and how we're starting to show up to provide those solutions to our customers. >> I want to get into the customer conversation. I think there's a lot of use cases I want to unpack with you. But I want to first get your vision on how you guys see the future. What is the vision of Talend? And how do you see it? What's the plan? What's the big story there? >> You know, there's a couple of things. I look at this and say, right now in the industry and in our customers, which we cover all different segments, all different sizes of customers all around the globe. They have a variety of use cases, if you will. A variety of needs, everything from the most simple ingestion to some of the more complex transformation and governance projects that they're running. And first and foremost, we show up uniquely as a platform, a platform that allows people to activate and utilize different parts of our services that we can provide to an entire organization. And that's something that is really important to us. And we also look at how do we make the process in which they're using Talend and the skills that are required, you know, really push the envelope on making those as simple as possible. The ability to get to time to value as quickly as possible is our ultimate goal. And then looking, you know, finally, the third lane is to make sure that we can provide not just, as I said, the completeness of data, but that it's really data that they can boil down to something that has intrinsic and quantifiable trust. Because all the time we spend, all the money that's spent on collecting the data is really only as good as the, ability to say I can emphatically trust it, and I can tell you why. And I can show you the footprint of that data. And that's something really important right now more than ever. >> I was talking to my family, I've four kids, and they're all kind of growing up now. And, we're having these conversations on COVID and the question of AI comes up all the time and AI is very, cool for kids, but they don't really know how to talk about machine learning. So I got to ask you around how you see the machine learning piece come in because data feeds AI, I mean you got, it's a real... And that's how I described my kids, data is the fuel for AI and you got to feed that in there. But it's not that easy. What's your reaction to that? Because I think a lot of companies are saying, I have to automate things, the DevOps world and agility come into the mainstream operations of businesses. And there's a agility piece, there's a value of the data is being recognized. But now I got to put it to practice. What's the playbook? What's your reaction to all that? >> Yeah, I think you're right. I mean, first of all, AI and machine learning have a really important role in the simplification, the ability to move at speed and to, perform functions that quite frankly are going to move us into an entirely new realm of possibility. I still will contend, whether you're feeding that with, anything that you feed data into with data has to be really good quality data. AI machine learning is only as good as the information that you're feeding it with. And so, it is really, really critical that we leverage these technologies to their fullest extent, but that we make sure that we feed it in the right way. So I think it's a really big part of our future. I think it's something that's going to be important. But we have to have the certainty that we're using them in a way that's coming to, a place of the right outcome. And that starts with what you feed it to use to go use to improve the processes. >> Christal, one of the patterns we're seeing is that decision makers and CXOs are looking at the COVID pandemic and saying, okay, I did my thing with triage. Now, I got to reset and get the foundation set again and look at the projects that are going to be important. And I got to figure out the holistic architecture 'cause I need a growth strategy, and I got a reset maybe some of the team members projects and whatnot. What's your view on this? Because now new decisions have to be made, roles that might change as well. So this is going to change, how come he's going to make decisions? What's your reaction to that with the customers? They are trying to figure this out, what's your advice? >> Yeah, that's absolutely right. And this is about re-instrumenting a business, reinventing it in many cases, a great example is Domino's, who is maybe surprisingly, for some a pioneer in, digital transformation that's been a number of years in the making, that really has shown that with being in a state of being able to adapt quickly to circumstances and to be forward looking, how critical it is. And so I think this has been a wake up call for organizations across the globe to say we have have to be on the ready, we have to be able to be instrumented in a way that we can make quick decisions and Domino's case it became, originally the ability to you know, they were the first pizza delivery to try out drones for pizza delivery and, you know, to... And have gaming devices where you can order pizza because that's where their customers read and when COVID hit contact list became a criteria and so you can really see how they are able to separate themselves. You see people being leaders that have been further along in their transformation. So I think what this has done is expose some vulnerabilities, quite frankly. And this is a wake up call for companies around the globe that can no longer afford to be in a state where they can't pivot quickly. And looking backwards is no longer the thing that informs people in a state of something like COVID, because there really aren't examples or patterns to look at. So re-instrumenting the business is really critical, data has to be transformed to perform better for companies. >> It's interesting you bring that, a point about the pivot and the companies resetting and reinventing for that growth strategy is that, you're seeing brand impacts and also financial results are directly related to it. So if you're not ready, this has, it could have a real detrimental impact on the brand value, and ultimately financial results. And this is kind of forcing people to say, it's not just an IT problem. It's a business model change and data is shown now to be the key ingredient, because that's where the agility is going to come from, that's where the value is there. And this is all been talked about in the industry before. But now it's kind of our mainstream. This is now the new reality that my brand opportunity and the financial results, my company are at stake. Can you comment on your thinking around that? Because this is a top line, high order bit, if you will conversation among the top boardrooms. >> Yeah, it is. And I agree with you, many of these conversations have been going on for a while now, right. And I think this just exposes the criticality of what happens when you're not in a state of being able to really reinvent yourself or like I said, re-instrument, and if you're already in that state, how much better off you are. Brands are taking a hit in terms of their ability to show up and it goes beyond just their ability to perform, as a business, but to really show up differently for their customers, support people in a different way. And really make sure that they can respond also from a social perspective, how are they going to help and contribute to what the world is facing. And so, it really is asking companies to really fire on all cylinders, quite frankly. >> I want to give you a thoughts on two thought tracks and they're kind of connected, so bear with me. One is, we've heard a lot from the marketplace that with the pandemic, the reality of the IT teams that collect the data and the business teams that have to make the decisions are changing, obviously with the work at home and all the different dynamics around the re-architecting. And then you have the competitive advantage now which people are pointing to as speed and scale. So you've got your internal kind of organizations that are managing wrangling data, ingesting data, the business teams with the customers, and that's kind of was the slow rolling way it was before. Now you got that changing. And now you got pressure to be faster and more scalable. So scale is a competitive advantage, speeds that competitive advantage. These are important kind of flywheel elements of the new models that people are being successful, what is your reaction to that? >> I couldn't agree more. It is a competitive weapon, quite frankly. It is an operational accelerant. And it is an innovation catalyst. And, you know, time is no one's friend, quite frankly, it's one of those odd things right now where for all of us that are working from home and time has this odd sense of reality to it. But it's... You know, really quite frankly you cannot act fast enough. But what's interesting about enabling companies to act fast, that has to come down to the ability for them to be able to, spend the time in the right places. So for example, when I think about the number one thing that we can do is it takes a lot for organization sometimes to put the information in the hands of the right people at the right time. So that the time that's being spent by an overall company, not just an individual within a company but the entire company. You have to be able to decrease that, so that the time that they're spending is actually on helping drive outcomes. And so some of this and you just struck a chord on in everything I think about is, how quickly we can get the right data in the hands of the right people because, in AstraZeneca's case for example, the difference of being able to do that, their highest cost in their business is clinical trials. Being able to get information you can use and reduce a month of, how fast they can bring those clinical trials to bear is saving them hundreds of millions of dollars. But that right now AstraZeneca is an important player in helping us solve for this. So you think about how important it is to get information to the right people, and time is of critical essence right now. >> Yeah, it's interesting (indistinct) that business model advantage, but also you got a lot of... That's an opportunity not for many, but there's also a lot of, I won't say heavy lifting, but maybe a drag, some might call it compliance. You know, GDPR, whatnot. Balancing that kind of, I won't say drag. I mean, I think it's a drag personally, but I think we have to have those things in place. You want to maintain the compliance, rigidity that's out there, but also have room to innovate. That balance is very difficult. And it's really mostly highlighted in the data bases because that's where the action is around data privacy and those compliance things. But if you got an innovation formula there that you're talking about, and you got compliance, if you get one wrong and right, you got to balance it. What's your take on that? Because that's a huge challenge. It's one of those things that's kind of not talked about much, but pretty much there. >> You're right, indeed it is a complete balance but you can't have one without the other. In highly regulated industries, especially with companies like AstraZeneca. But really, if you think about any company the ironic thing right now is that when you're looking at, even a single report, but certainly across an entire company or line of business, right now you can see that there's quality measures and governance that, we put into play. But the ability to actually, quantifiably say on a single piece of data that you can track, where that data has been, who's touched it? How complete is it? And really kind of put a measurable trust score against it, there's work to be done there. But, with GDPR, with HIPAA, and interestingly enough, we're looking to, kind of challenge some of the norms with COVID that says, we now want to collect data that is formally considered privacy, and maybe something that would be regulated. And now we want to share it for the greater good of, making sure that we can track and trace where people are at that maybe are infected and so forth. And so you're starting to see this interesting conversion of challenging the fact that we've got at least be able to support people in their governance of data, but take that a step further, really. >> Awesome, final question. You had Talend Connect, which is your big kind of confab. What best practices are emerging out of Talend these days for customers? If you had to kind of highlight the top use cases or best practices that customers and your potential customers could leverage right now with data, what are you guys putting out there? What are the key best practices? 'Cause everyone has a new reality sets of knowledge, we talk deeply about it, but what's the best practices? What are you guys offering? >> Well, I think, one of the things that I alluded to before is really making sure that we show up as a strategic business partner. And this is really important to us, you know, there all this these things that we've been talking about, they're heavy lifting for organizations to really look at how they bring the digital revolution to the forefront. There's a lot to consider. And so our part in that is to say, we believe that when you power your business on Talend, and you're able to solve for a number for different problems across platform, then that's really important that we show up in the way that we can meet our customers where they're at, so that's one. Making it simple, you know, really pushing the boundaries on the level of expertise, the specialization, the time to value of making sure that they can leverage. Again, spending their time on the things that are important, which are making sure that they're spending it in quality data and data they trust. And then really making sure that final lane is covered up saying, we want to make sure that data is accessible when you need it, where you need it. Things like IoT and edge devices, this proliferation of data is just becoming immense. And so, taking the data, giving it to people, but in a way that they can have confidence. It's the same thing you just said before, there's a lot to consider. And there's in a way a burden of people not knowing maybe all the data they have and how it's being used. We feel responsibility to make sure that we're part of helping that become easy and identifiable and really taking it to the next step beyond quality, so it's really across all of it just simply putting people in a position to be able to make good decisions and not have to do so much of the heavy lifting. And making sure that they know for a fact that it's something that they've made a good decision around because of the data has been trusted, and they can have the confidence in that. >> Awesome, we think data is added advantage. It's just getting more important then ever as the days go on. So great, great insight. Christal, thank you for that insight. Before we end, take a minute to put the plug in for Talend. What do you up to? You guys are hiring, you looking for folks? What's the business plan? Why you guys winning? What's the hot product? Take a minute to give up a quick update on Talend. >> Sure, we're in a great situation where, this is a point in time at Talend where (indistinct) a great trajectory in front of us, we see speed and scale of our organization that has an opportunity in front of it to really help solve problems for every part of the market, whether it's the, smaller businesses who are certainly in it at a point where they're, having a big impact to the largest organizations. And we feel that there's a set of solutions that we can really work to drive as a partner, to each of those customers to solve for the problems that put them in a position to really be able to re-instrument and to reinvent their business. And when we partner like we have with the companies that I mentioned, Domino's and AstraZeneca, and many others, it comes back to why I join Talend, we have the ability to change the outcome of really separating organizations from the pack and data is the competitive advantage. It is the thing that will put people on a different trajectory. And I'm excited about what we bring to the table and I'm really excited about what's to come and how we'll continue to push the envelope for how we help our customers. >> That's awesome, congratulations. Congrats on the new role of Talend to CEO, Christal Bemont. >> Thank you. >> Big up Talend, data is at the heart of the value proposition. We've been saying that for 10 years now more than ever, it's exposed that the value is there, speed and scales the new table stakes for competitiveness and business models for the applications. Again, great CUBE captures, great insight. Christal thank you for joining me today. I'm John Furrier, host of theCUBE. It's been a CUBE conversation. Thanks for watching. (upbeat music)

>>Live from Denver, Colorado. It's the cube covering comm vault. Go 2019 brought to you by Combolt. >>Hey, good morning. Welcome to the cubes coverage of combo go 19 I'm Lisa Martin and it was stupid man. Hey Sue. Hey Lisa. Are you ready? I was going to ask you. Yes. Are you ready? >>I believe the statement this morning was, we're born ready. >>We are born ready? Yes. That was a big theme this morning. It's the theme of the event here at con Volvo 19 in Colorado and great parody this morning of all these old video clips of all these actors including the Lego movie stars from saying I'm ready. Even SpongeBob. That one got me, so we had a great day. Yesterday's to love some news came out Monday and Tuesdays a lots of great stuff to talk about. We had there a lot of their C level execs and let a new changes a call yesterday. Really got the vibe of, Hey, this is a new Combalt. >>It's interesting Lisa, because one of the things we've been talking about is the 20 years of pedigree that the company has. This Andre Mirchandani said yet they're doing some new items. I was talking to some of the partners in there like how come metallics like a separate brand, don't you worry about brand spread? We knew a thing about having too many brands on the program so it is the history, the experience, the lessons learned, the war chest as they said of all of the things that have gone wrong over the years and I sure know that from my time living on the vendor side is there's no compression algorithm for all the experience you've had and like, Oh we fixed something in that stays in the code as opposed to there's something brand new might need to work through things over time but metallic a separate brand but leveraging the partnerships and the go to market and the experience of Convolt overall. >>So if you want, my quick take is, you know metallic. I definitely, I think coming out of here is the thing we will be talking the most about their SAS plus model. I want to see how that plays in the marketplace. As I probed Rob, when we interviewed him, customers, when you think about SAS, it should just be, I worry about my data and I get up and running and they said they have a very fast up and running less than 15 minutes. That's great. But some of that optionality that they built in, Oh well I can bring this along or I can add this and do this. It's always worried that a wait, do I have to remember my thing? And as it changes down the road, do I have everything set up right? Those are things that we're trying to get away from when we go to a SAS or cloud model. >>And to your point, another theme of the show has been about operational simplification, not just what Combolt is doing internally to simplify their operations, but what they need to deliver to customers. Customers want simplicity rates. Do we, we talk about that at every show regardless of industry, but there is this, this line, and maybe it's blurring, >>like we talked a lot about blurred lines yesterday of too much choice versus simplification. Where's the line there? >> Yeah and a great point Lisa, so one of the items Sandra Mirchandani said yesterday in his keynote was that blurring the line between primary and secondary storage and I probed him on our interview is Convolt going into the primary storage market with Hedvig. Hedvig has got a, you know, a nice offering, strong IP, good engineering team. I think they want to make sure that customers that have bought head vigor want to keep buying Hedvig we'll do it, but it really, I think two years from now when you look back at is that core IP, how does that get baked into the solution? That's why they bought it. That's where it's going to be there. I don't think we're going to be looking two years from now and saying, Oh wow know Convolt they're going up against all the storage star Walton competing a bit gets HCI and everything. >>They have a strong partnership, so I think I got clarity on that for the most part, even though the messaging will will move over time on that, it will move over time on that. >> That's a good point that the song blurred lines kept popping into my head yesterday as we were talking about that. But one of the things that was clear was when we spoke with Rob Kalusi and about metallic, we spoke with Avinash Lakshman about Hedvig Sanjay as well as Don foster. They're already working on the technical integration of of this solutions and we even spoke with their VP of pricing. So from a customer, from a current Hedvig customer perspective, there is focus on that from Combolt's perspective. It's not just about integrating the technologies and obviously that has to be done really well, but it's also about giving customers that consistency and really for combo kind of a new era of transparency with respect to pricing. >>And another thing we talked about some of that transformation of the channel and Mercer row came on board only a couple of days officially on the job. He's helped a number of companies get ready for multicloud and absolutely we've seen that change in the channel over the last five to 10 years. Know back in his days when he was at VM world at VMware there the channel was, Oh my gosh, you know, when Amazon wins we all lose and today we understand it as much more nuance there. The channel that is successful partners with the hyperscale cloud environments, they have practices built around it. The office three 65 and Microsoft practices are an area that Convolt in their partners should be able to do well with and the metallic will tie into as well as of course AWS. The 800 pound gorilla in this space will be there. Combolt plays into that and you know, setting the channel up for that next generation with the SAS, with the software and living in a broader multicloud environment is definitely something to watch you a lot of news about the channel, not just from a leadership standpoint but also so metallic for the mid market >>really delivered exclusively through the channel but also the new initiative that they have. And we talked a little bit about this yesterday about going after and really a big focus with global systems integrators on the largest global enterprises. And when we spoke with their GTM chief of staff yesterday along with Mercer with Carmen, what they're doing, cause I said, you know, channel partners, all the channel partners that they work with work with their competitors. So you have to really deliver differentiation and it can't just be about pricing or marketing messaging goes all the way into getting those feet on the street. And that's another area in which we heard yesterday Combolt making strategic improvements on more feet on the street co-selling with partners, really pulling them deeper into enablement and trainings and to them that's one of the key differentiators that they are delivering to their partners. Yeah >>and Lisa, he, we got to speak to a number, a couple of customers we have more coming on today. It's a little bit telling that you know the average customer you talk to, they have five 10 years of experience there. They are excited about some of the new offerings, but as we've said many times metallic, the new Hedvig we want to talk to the new logos that they're going to get on board. That is something that for the partners has been an incentive. There were new incentives put in place to help capture those new logos because as we know, revenue was actually down in the last fiscal year a bit and Convolt feels that they have turned the corner, they're all ready to go. And one other note I'd like to make, the analogy I used last year is we knew a CEO was canoe CEO search was happening, a lot of things were in motion and it's almost as if you were getting the body ready for an organ transplant and you make sure that the antibodies aren't going to reject it. And in conversation with Sanjay, he was very cognizant of that. His background is dev offs and he was a CIO. We went for it, he was the CEO of puppet. So he's going to make things move even faster. And the pace of change of the last nine months is just the beginning of the change. And for the most part I'm not hearing grumbling underneath the customer seem fully on board. The employees are energized and definitely there was good energy last year, but a raise of the enthusiasm this year. >>Well Stu, first of all, you have just been on fire the last two days comparing their CEO transition to getting a body ready for a transplant. It's probably one of the best things I've heard in a long time. That was awesome. But you're right, we've heard a lot of positivity. Cultural change is incredibly difficult. You talked a minute ago about this as a 20 year old company and as we all have all experience and the industries in which we're in, you know, one of the things that's important is, is messaging that experience and talking about the things that that worked well, but also the things that didn't work well, that they've learned from that message was carried through the keynote this morning. That three customers on stage that we saw before we had to come to the side. And I, I had, my favorite was from Sonic healthcare. Matthew McCabe's coming on in shortly with us and I always appreciate, you know, I think the voice of the customer is the best brand validation that you can get. However, what's even better is a customer talking about when the technologies that they're using fail because it does happen. How are they positioned with the support and the training and the education that is giving them to make those repairs quickly to ensure business continuity and ensure disaster recovery. I think that to me that speaks volumes about the legacy, the 20 years of experience that combo has. >>Yeah, no, Lisa, you're absolutely right. There's certain products out there that we talk about uptime in 100% in this space. You, I believe the stat was about 94% success rate and we had NASA in the keynote yesterday talking about success versus partial success versus failures and Convolt really embraces that and has customers that we'll talk about that because there are times that things will happen and there are things that you need to be able to recover from ransomware. Often it is not a question of if, when it is going to be happened, at least. The other thing I want to get your comment on Jimmy chin who is the director and one of the, the cameraman of the free solo Oscar-winning free solo documentary definitely gave me a little bit of, Oh my gosh, look at some of the Heights and I was nervous just looking at some of this stuff they're doing. I like a little bit of lightweight hiking. I'm not a mountain climber, nothing like that. But he talked about when the camera goes on, there's that added pressure that goes on and it's sitting there. It's like, yeah, you know, we sit here live all day doing that. There's that, that energy to perform. But you know, we all appreciate the everybody watching and understanding that we're all human here and every time, every once in awhile a word or a mistake gets in there, but we keep going summit. Yeah, >>that's life. But also Jimmy chin, phenomenal. I think at 2018 they just won the Oscar just earlier this year for free. Solo. I have to watch that this weekend. But a couple of things that he talked about is that failure is a huge part of preparation. Couldn't agree more. What a simplified statement for somebody that not only has has skied Everest, the climbed Meru, I think they call it the shark fin of India, but what you talked about with what he documented with free solo and all of the thousands of sequences and he talked about that, Alex, I'm forgetting his last name, the guy who closed, who free soloed, El Capitan, all of these different failure scenarios that he rehearsed over and over again in case he encountered any of them, he would immediately be to remedy that situation and get himself back on track. I thought that message to me, failure is a good F-word if you use it properly. You know NASA, you mentioned yesterday and NASA was famous for coining in the 60s failure is not an option and I always say onto that cause I used to work for NASA, but it's a distinct possibility. And so what Jimmy chin shared this morning was electrified, but it also was a great understatement of what Combolt is helping their customers. We have to help you prepare for this. We can't help you prepare for all of it. As you mentioned, ransomware, it's not if but when. >>Well, right and both NASA and when the climbing is understanding where something could go wrong and therefore what the failures scenarios are. So you know rockets today you can't have a failure and by failure they mean look, if the rocket isn't going to work or something goes wrong, we need to make sure we don't have loss of life. That is something that if you look at blue origin and SpaceX that is pre eminent in there is we can't have another challenger disaster. We can't have some of these environments where we have the loss of human life. So that is number one. Some of the other ones, sometimes we know that the unknown happens or things don't go quite right. So being prepared to understand if something goes wrong, how do we recover from that? And that brings us back to the whole data protection and recovery of the environment because the best laid architecture, eventually something will happen and therefore we need to make sure that that data, the lifeblood of the company is able to be recovered and used and that the business can go forward even if some piece of infrastructure or some attack got through. >>There are, and there's inherent risk in every industry, whether you're talking about healthcare data, we talked with AstraZeneca yesterday, you know, genetics, clinical data, or you're talking about a retailer, doesn't matter. There's an inherent risks with every business and one of the most important things that I got out of the NASA talk yesterday, Jimmy Chin's talked today, some of the customers, is that preparation is key. You can't be over prepared. You really can't act fact. He said that you can't be overprepared in his line of work, but I think it applies to the inherent risks that any business has. Managing data. As we talk about Sue all the time, it's the lifeblood. It's the new oil. It is. It has to be available, accessible 24 by seven if it isn't and can't be. Businesses are massive risk in this day and age. Competitive competitors who have maybe better risk fault tolerance scenario in play. >>So that risk that they have to mitigate comes a preparation. We're going to be talking with Sandra Hamilton in just a few minutes about who leads customer success for combo. Really want to dig into the training, the support. We've heard that articulated from customers on stage that I don't wake up in the middle of the night anymore because I have this support from my trusted vendor combo and that is critical to any business staying up. Absolutely. We're going to hear from number of customers. I'm sure they're ready and we are ready for day two. We are ready. See, let's have a great day. Yeah, thanks. All right, so Sue and I will be right back with our first guest on day two of our coverage of comm Volkow for Stu. I'm Lisa Martin. We'll be right back.

>>Live from Denver, Colorado. It's the cube covering com vault go 2019 brought to you by Combolt. >>Welcome to the cube. Lisa Martin with Stu Miniman. We are in Denver, Colorado, specifically Aurora, actually for the fourth annual comm vault. Go. Stu, I'm super excited to be back hosting with you again. Lisa, it's great to be with you our second year doing Convolt go last year. Keith Townsend with here with me and so glad you're here with me because you've got a little bit of background with this company I was with come out 10 years ago. It's scary to think that it's been 10 years that we're at the Gaylord Gaylord Rockies, which is one of their customers, massive conventions that aren't, as you said, the first conference that the Gaylord that probably heard that this, this convention center just opened up a couple of months ago. I think it holds like 1500 people, the 1500 rooms at the hotel and supposedly this is the first large event that they've done and this was planned last year. >>Last year we were in Nashville at the Gaylord the year before. I think they were in DC at the Gaylord and next year I know there'll be at another Gaylord, so definitely putting their customers first. Just like in the keynote this morning they had the state of Colorado opening up the event. We always love to hear a local customer welcoming us and talking about their partnership with the supplier. Absolutely agree with that. The state of Colorado, the statue share the highest number of micro breweries per capita and I don't know about you, I'm not a beer person. I would be super blown away if that, if I was there is the too much choice in beer. It used to be, you know, you'd go in and say, okay, here's the five or 10 beers I like. Now you go in and it's like, all right, there's a hundred new ones. I haven't tried that because they weren't here last time. >>So many beers here, a greater Denver. I've been to Boulder a couple of times. They say if you want to start a microbrewery, there's one that's ready to hand you over a place because they're going out of business. They just churn and go over and everything like that. So yeah, my first time actually hosting an event of the cube here in Colorado. Super excited for that. It's a great locale. And yeah, we're talking about, you know, so Convolt a 20 year old company, a lot of customers, but a lot of new faces. You look, we're going to be talking to the next two days. They run a whole new executive team. We knew this was coming last year. Our final guest in our two days is going to be Al Bunty, who is the CEO, was one of the original 20 years with the company. So we'll, we'll talk about the Baton and some of the changes in some of the things that are, are the same. >>So yeah. Interesting. You mentioned they started things off this morning with the customer at the state of Colorado. I too, like you always love to hear the voice of the customer. And I also really like it when customers talk about the challenges that they had. They talked about the Samsung attack and all of the exposures and vulnerabilities. I love that because that's what happens. We're seeing data protection as a service, the market positive trends in the market. There are a rise in cyber attacks. I love it when customers articulate, yep, nothing is perfect, but here's how working with Combalt we were able to recover quickly from something like that. A lot of big news, you mentioned a lot of new executive leadership. This is Sanjay merchant Donnie's first go. As CEO came in about nine months ago. He's a cube alumni, said we'll get to talk to him later this morning, but he came in after successfully leading puppet through many rounds of millions and venture funding. >>He took puppet worldwide, but he came into a company with declining revenues and one where folks said combat, you've got pressures to find alternative sources of growth. They said three things specifically. One, you need to upgrade your sales force. Two, you need to enhance your marketing, and three, we need to shift gears and expand your market share and there's been a whole bunch of news, not just yesterday, today, but in the last month or so, last few weeks actually where combo is making headway in all three of them. >> At least so right, because you look on paper and you look in the key, the keynote and say we have 20 years of experience. Here's all of the analyst reports that show us as the clear leader in this space. But then you look at it and say, Oh, 2018 to 2019 declining revenues. There are a lot of competitors both as some of the big stalwarts in technology as well as many startups. >>Heck, I'm even seeing the startups now. They're trying to call the last generation of startups that are going after con vault as the legacy. So if you're not fully cloud native microservice sass base architecture, you're the old way. And that's one of the news from Combolt already is they, they've done a couple of what they call Convult ventures. So the first one you were alluding to is they bought Hedvig, which was a software defined storage company. They just bought them back in September. What was their a two 40 $250 million, which was almost half of the cash that Combolt had sitting there. Hedvig company that had been around for a number of years. We're going to have Avinash who's the founder and CEO on the program here. He was on the keynote stage going through the demo. They kind of sat at this interesting line between software defined storage and actually hyperconverged infrastructure because you could in the early days do either storage only or fully converged environments, but massive scale. >>The customer that he talked about was a very large scale deployment. Those large scale deployments are really tough and can be challenging and they're not something that you just deploy everywhere. Unlike the other announcement that Convolt announced is metallic. If you go to metallic.io, they have this new sass based architecture. They built it in months from the ground up from the internal team. Part of me is sitting there saying, okay, wait, if they could do this and you know, six months or nine months or whatever it was, why hadn't they done it before? What has changed what Convolt technology is under there? It's great. It's working, you know, Azure and AWS as well as you can have a local copy in your environment. They call it SAS plus. Um, and we need to understand a little bit more of the technology. So a lot of exciting things. >>Definitely getting awareness, but both metallic and Hedvig they call Convolt ventures. So new areas, areas that they're looking to add some incremental growth. And one of the things Sandra said in his keynote is we want to, you know, rethink primary and secondary storage. So where is Convolt will they start dipping their toe into the primary storage? Does that line blur? We've got HP on the program, you know, NetApp is up on stage with them. They have partnerships. So changing landscape Convolt has long had a strong position in the market, but as things change they want to make sure that they make themselves relevant for the next era. >>Absolutely. And the Hedvig acquisition gives them a pretty significant, a much larger presence in the software defined space. But it also is going to give them a big Tam expansion. We look at metallic as you mentioned, the venture. I want to, I want to break that down. We've got Rob Kelly's, John Colussy, and on a little bit later, what is this Combolt venture, but also giving them, it sounds to me like giving customers in mid market more choice, but one of the things I mentioned that that analysts were saying is, Hey you guys, you gotta, you gotta expand your market share, you really gotta expand marketing. So we're seeing not just the technology announcements with Hedvig for the large scale enterprises of which I think most of their revenue, at least three quarters of combat revenue does come from that large space, metallic for mid market, but also some of the seals, leadership changes that they've made to are really positioning them. New initiatives, new partner initiatives, really focused on the largest global enterprises. We're gonna break some of that down today. So in terms of routes to market, you're seeing a lot of focus on mid-market and enterprise. >>Well, at least 80% of the convulse revenue comes from the partners. So that is hugely important. How does metallic fit in? Will that be as a SAS offering? Will that be direct? Will that go through the channel? Believe it's going to, you know, the channel's going to be able to be enabled. How do all of these pieces go together? One, one note on Hedvig you talk about Tam expansion. Hedwig was not a leader in the market when it comes to where they are. There's a lot of competition there. You know, they were not a, you know, a unicorn that had a road to $1 billion worth of actual revenue there. So they got bought at a very high multiple of what their actual revenue was. And the question was did they just not have the go to market to be able to bring that and maybe Convolt can bring them there where they miss positioned in the market. >>Should they not be really primary storage? Should they go more to secondary storage where partner closely with secondary storage, because I know some of Combolt's competitors did work with Hedvig. I've talked to a number of partners out there that liked Hedvig and was like, Oh it's a nice complimentary offering to what we have, whether they be a hardware or place. So we'll being in Convolt hyper charge that growth. Obviously they've got some smart team, smart team members, have an Ash, came from Amazon and Facebook and his team. But what will this do to accelerate what they're doing? How will there be hit the word but synergies between the two sides of the company. So Sanjay and team really laying out their vision for where they want to take the company and it's challenging to be, we're the trusted, reliable enterprise and we're going to go down to the lower end of the market and we're going to go on all these cool new spaces and everything. So Combalt only has limited resources just like any other company. And how will they maintain and grow their position going forward. >>We are going to hear from a number of their customers do today who been combo customers for 10 plus years. Some of them who have a number of Convolt competitors within, you know, disparate organizations. I love to hear from them, why are you running, you know, comm vault, the backup exec within these different departments. For example. AstraZeneca is one of them. And what makes Combalt in certain departments really ideal. So going to get a good picture of that, but also love to understand from these customers who've been using Combolt for years. Do you see a new combo in 20 in their fiscal year 2020 talked about the leadership changes. As you mentioned, this is a company that's not only 20 years old but at low run. Some stats by you that Sandra Mirchandani shared this morning, they've got 2.8 million. The virtual machines protected, they've got over 700 millions of petabytes. They're protecting in the cloud, 1.6 million servers on and on and on. How is con vault of fiscal year 2020 different and and really poised at this intersection of unified >>in? One of the answers for that that we'll dig into is it's about data. So while con vault does 45 million weekly backup jobs, we used to know backup is something that you just kind of had, but you didn't necessarily use it. Now it's not just having my data and making sure that it's relied on, but how can I leverage that data? It's, you know, data at the core and you know, Sandra said data is the heart of everything they're doing. So coming from puppet, Sanjay knows about dev ops and agile and he's going to bring some of that in. He's brought in a team that's going to infuse some change in the culture and we'll see. I expect Convolt to be moving a little, little faster. They definitely have made a number of changes in the short time that he has already been there and we'll get a little bit of a roadmap as to where we see them going. >>Yeah, there's certainly seems Stu to be moving quickly. You mentioned, you know, Sonjay being nine months metallic. You mentioned also being developed in house in a matter of months, announcing the Hedvig acquisition in September. It closed October 1st there Q2 earnings come out in just a couple of weeks right before Halloween. So it seems like a lot of momentum carrying into the Denver aura area. Is it going to be a trick or a treat? Ooh, I like that as a marketer, I'm jealous that you thought of that and I didn't, but I liked that. We'll go with that all these years on the cube. You gotta you gotta have the snappy comebacks, right? So, Steve, it's gonna be a great day today we are jam packed session interview after interview with combat executives, really dissecting what they're doing, what's new, what's positioning them to really kick the door wide open and really reverse those revenues, taking them positive and really not only meeting the endless expectations, but exceeding them. So I'm looking forward to an action packed two days in Aurora with use to, can't wait. All right, first two minute, man. I'm Lisa Martin. You're watching the cube from comm vault. Go 19 we'll be right back with our first guest.

>> live from San Francisco, California. It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> Welcome back to Fisherman's Fisherman's Wharf in San Francisco. Everybody, my name is David wanted. You're watching the Cube, the leader in live tech coverage, you ought to events. We extract the signal from the noise. We're here. The IBM CDO event. This is the 10th anniversary of this event. Caitlin Hallford is here. She's the director of a I Accelerator and client success at IBM. Caitlin, great to see you again. Wow. 10 years. Amazing. They and Carlo Apple Apple Glace e is here. Who is the program director for data and a I at IBM. Because you again, my friend. Thanks for coming on to Cuba. Lums. Wow, this is 10 years, and I think the Cube is covered. Probably eight of these now. Yeah, kind of. We bounce between San Francisco and Boston to great places for CEOs. Good places to have intimate events, but and you're taking it global. I understand. Congratulations. Congratulations on the promotion. Thank you. Going. Thank you so much. >> So we, as you know well are well, no. We started our chief date officer summits in San Francisco here, and it's gone 2014. So this is our 10th 1 We do two a year. We found we really have a unique cohort of clients. The join us about 100 40 in San Francisco on the spring 140 in Boston in the fall, and we're here celebrating the 10th 10 Summit. >> So, Carlo, talk about your role and then let's get into how you guys, you know, work together. How you hand the baton way we'll get to the client piece. >> So I lead the Data Center League team, which is a group within our product development, working side by side with clients really to understand their needs as well developed, use cases on our platform and tools and make sure we are able to deliver on those. And then we work closely with the CDO team, the global CEO team on best practices, what patterns they're seeing from an architecture perspective. Make sure that our platforms really incorporating that stuff. >> And if I recall the data science that lead team is its presales correct and could >> be posted that it could, it really depends on the client, so it could be prior to them buying software or after they bought the software. If they need the help, we can also come in. >> Okay, so? So it can be a for pay service. Is that correct or Yeah, we can >> before pay. Or sometimes we do it based on just our relation with >> It's kind of a mixed then. Right? Okay, so you're learning the client's learning, so they're obviously good, good customers. And so you want to treat him right >> now? How do you guys work >> together? Maybe Caitlin, you can explain. The two organizations >> were often the early testers, early adopters of some of the capabilities. And so what we'll do is we'll test will literally will prove it out of skill internally using IBM itself as an example. And then, as we build out the capability, work with Carlo and his team to really drive that in a product and drive that into market, and we share a lot of client relationships where CEOs come to us, they're want advice and counsel on best practices across the organization. And they're looking for latest applications to deploy deploy known environments and so we can capture a lot of that feedback in some of the market user testing proved that out. Using IBM is an example and then work with you to really commercialized and bring it to market in the most efficient manner. >> You were talking this morning. You had a picture up of the first CDO event. No Internet, no wife in the basement. I love it. So how is this evolved from a theme standpoint? What do you What are the patterns? Sure. So when >> we started this, it was really a response. Thio primarily financial service is sector regulatory requirements, trying to get data right to meet those regulatory compliance initiatives. Defensive posture certainly weren't driving transformation within their enterprises. And what I've seen is a couple of those core elements are still key for us or data governance and data management. And some of those security access controls are always going to be important. But we're finding his videos more and more, have expanded scope of responsibilities with the enterprise they're looked at as a leader. They're no longer sitting within a c i o function there either appear or, you know, working in partnership with, and they're driving enterprise wide, you know, initiatives for the for their enterprises and organizations, which has been great to see. >> So we all remember when you know how very and declared data science was gonna be the number one job, and it actually kind of has become. I think I saw somewhere, maybe in Glass door was anointed that the top job, which is >> kind of cool to see. So what are you seeing >> with customers, Carlo? You guys, you have these these blueprints, you're now applying them, accelerating different industries. You mentioned health care this morning. >> What are some >> of those industry accelerators And how is that actually coming to fruition? Yes. >> So some of the things we're seeing is speaking of financial clients way go into a lot of them. We do these one on one engagements, we build them from custom. We co create these engineering solutions, our platform, and we're seeing patterns, patterns around different use cases that are coming up over and over again. And the one thing about data science Aye, aye. It's difficult to develop a solution because everybody's date is different. Everybody's business is different. So what we're trying to do is build these. We can't just build a widget that's going to solve the problem, because then you have to force your data into that, and we're seeing that that doesn't really work. So building a platform for these clients. But these accelerators, which are a set of core code source code notebooks, industry models in terms a CZ wells dashboards that allow them to quickly build out these use cases around a turn or segmentation on dhe. You know some other models we can grab the box provide the models, provide the know how with the source code, as well as a way for them to train them, deploy them and operationalize them in an organization. That's kind of what we're doing. >> You prime the pump >> prime minute pump, we call them there right now, we're doing client in eights for wealth management, and we're doing that, ref SS. And they come right on the box of our cloudpack for data platform. You could quickly click and install button, and in there you'll get the sample data files. You get no books. You get industry terms, your governance capability, as well as deployed dashboards and models. >> So talk more about >> cloudpack for data. What's inside of that brought back the >> data is a collection of micro Service's Andi. It includes a lot of things that we bring to market to help customers with their journey things from like data ingestion collection to all the way Thio, eh? I model development from building your models to deploying them to actually infusing them in your business process with bias detection or integration way have a lot of capability. Part >> of it's actually tooling. It's not just sort of so how to Pdf >> dualism entire platform eso. So the platform itself has everything you need an organization to kind of go from an idea to data ingestion and governance and management all the way to model training, development, deployment into integration into your business process. >> Now Caitlin, in the early days of the CDO, saw CDO emerging in healthcare, financialservices and government. And now it's kind of gone mainstream to the point where we had Mark Clare on who's the head of data neighborhood AstraZeneca. And he said, I'm not taking the CDO title, you know, because I'm all about data enablement and CDO. You know, title has sort of evolved. What have you seen? It's got clearly gone mainstream Yep. What are you seeing? In terms of adoption of that, that role and its impact on organizations, >> So couple of transit has been interesting both domestically and internationally as well. So we're seeing a lot of growth outside of the U. S. So we did our first inaugural summit in Tokyo. In Japan, there's a number of day leaders in Japan that are really eager to jump start their transformation initiatives. Also did our first Dubai summit. Middle East and Africa will be in South Africa next month at another studio summit. And what I'm seeing is outside of North America a lot of activity and interest in creating an enabling studio light capability. Data Leader, Like, um, and some of these guys, I think we're gonna leapfrog ahead. I think they're going to just absolutely jump jump ahead and in parallel, those traditional industries, you know, there's a new federal legislation coming down by year end for most federal agencies to appoint a chief data officer. So, you know, Washington, D. C. Is is hopping right now, we're getting a number of agencies requesting advice and counsel on how to set up the office how to be successful I think there's some great opportunity in those traditional industries and also seeing it, you know, outside the U. S. And cross nontraditional, >> you say >> Jump ahead. You mean jump ahead of where maybe some of the U. S. >> Absolute best? Absolutely. And I'm >> seeing a trend where you know, a lot of CEOs they're moving. They're really closer to the line of business, right? They're moving outside of technology, but they have to be technology savvy. They have a team of engineers and data scientists. So there is really an important role in every organization that I'm seeing for every client I go to. It's a little different, but you're right, it's it's definitely up and coming. Role is very important for especially for digital transformation. >> This is so good. I was gonna say one of the ways they are teens really, partner Well, together, I think is weaken source some of these in terms of enabling that you know, acceleration and leap frog. What are those pain points or use cases in traditional data management space? You know, the metadata. So I think you talk with Steven earlier about how we're doing some automated meditate a generation and really using a i t. O instead of manually having to label and tag that we're able to generate about 85% of our labels internally and drive that into existing product. Carlos using. And our clients are saying, Hey, we're spending, you know, hundreds of millions of dollars and we've got teams of massive teams of people manual work. And so we're able to recognize it, adopts something like that, press internally and then work with you guys >> actually think of every detail developer out there that has to go figure out what this date is. If you have a tool which we're trying to cooperate the platform based on the guidance from the CDO Global CEO team, we can automatically create that metadata are likely ingested and provide into platform so that data scientists can start to get value out >> of it quickly. So we heard Martin Schroeder talked about digital trade and public policy, and he said there were three things free flow of data. Unless it doesn't make sense like personal information prevent data localization mandates, yeah, and then protect algorithms and source code, which is an I P protection thing. So I'm interested in how your customers air Reacting to that framework, I presume the protect the algorithms and source code I p. That's near and dear right? They want to make sure that you're not taking models and then giving it to their competitors. >> Absolutely. And we talk about that every time we go in there and we work on projects. What's the I p? You know, how do we manage this? And you know, what we bring to the table with the accelerators is to help them jump start them right, even though that it's kind of our a p we created, but we give it to them and then what they derive from that when they incorporate their data, which is their i p, and create new models, that is then their i. P. So those air complicated questions and every company is a little different on what they're worried about with that, so but many banks, we give them all the I P to make sure that they're comfortable and especially in financial service is but some other spaces. It's very competitive. And then I was worried about it because it's, ah, known space. A lot of the algorithm for youse are all open source. They're known algorithms, so there's not a lot of problem there. >> It's how you apply them. That's >> exactly right how you apply them in that boundary of what >> is P, What's not. It's kind of >> fuzzy, >> and we encourage our clients a lot of times to drive that for >> the >> organisation, for us, internally, GDP, our readiness, it was occurring to the business unit level functional area. So it was, you know, we weren't where we needed to be in terms of achieving compliance. And we have the CEO office took ownership of that across the business and got it where we needed to be. And so we often encourage our clients to take ownership of something like that and use it as an opportunity to differentiate. >> And I talked about the whole time of clients. Their data is impor onto them. Them training models with that data for some new making new decisions is their unique value. Prop In there, I'd be so so we encourage them to make sure they're aware that don't just tore their data in any can, um, service out there model because they could be giving away their intellectual property, and it's important. Didn't understand that. >> So that's a complicated one. Write the piece and the other two seem to be even tougher. And some regards, like the free flow of data. I could see a lot of governments not wanting the free flow of data, but and the client is in the middle. OK, d'oh. Government is gonna adjudicate. What's that conversation like? The example that he gave was, maybe was interpolate. If it's if it's information about baggage claims, you can you can use the Blockchain and crypt it and then only see the data at the other end. So that was actually, I thought, a good example. Why do you want to restrict that flow of data? But if it's personal information, keep it in country. But how is that conversation going with clients? >> Leo. Those can involve depending on the country, right and where you're at in the industry. >> But some Western countries are strict about that. >> Absolutely. And this is why we've created a platform that allows for data virtualization. We use Cooper nannies and technologies under the covers so that you can manage that in different locations. You could manage it across. Ah, hybrid of data centers or hybrid of public cloud vendors. And it allows you to still have one business application, and you can kind of do some of the separation and even separation of data. So there's there's, there's, there's an approach there, you know. But you gotta do a balance. Balance it. You gotta balance between innovation, digital transformation and how much you wanna, you know, govern so governs important. And then, you know. But for some projects, we may want to just quickly prototype. So there's a balance there, too. >> Well, that data virtualization tech is interesting because it gets the other piece, which was prevent data localization mandates. But if there is a mandate and we know that some countries aren't going to relax that mandate, you have, ah, a technical solution for that >> architecture that will support that. And that's a big investment for us right now. And where we're doing a lot of work in that space. Obviously, with red hat, you saw partnership or acquisition. So that's been >> really Yeah, I heard something about that's important. That's that's that's a big part of Chapter two. Yeah, all right. We'll give you the final world Caitlyn on the spring. I guess it's not spring it. Secondly, this summer, right? CDO event? >> No, it's been agreed. First day. So we kicked off. Today. We've got a full set of client panel's tomorrow. We've got some announcements around our meta data that I mentioned. Risk insights is a really cool offering. We'll be talking more about. We also have cognitive support. This is another one. Our clients that I really wanted to help with some of their support back in systems. So a lot of exciting announcements, new thought leadership coming out. It's been a great event and looking forward to the next next day. >> Well, I love the fact >> that you guys have have tied data science into the sea. Sweet roll. You guys have done a great job, I think, better than anybody in terms of of, of really advocating for the chief data officer. And this is a great event because it's piers talking. Appears a lot of private conversations going on. So congratulations on all the success and continued success worldwide. >> Thank you so much. Thank you, Dave. >> You welcome. Keep it right there, everybody. We'll be back with our next guest. Ready for this short break. We have a panel coming up. This is David. Dante. You're >> watching the Cube from IBM CDO right back.

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> Hi everybody, welcome back to theCUBE's coverage of the IBM Chief Data Officer Event. We're here at Fisherman's Wharf in San Francisco at the Centric Hyatt Hotel. This is the 10th anniversary of IBM's Chief Data Officer Summits. In the recent years, anyway, they do one in San Francisco and one in Boston each year, and theCUBE has covered a number of them. I think this is our eighth CDO conference. I'm Dave Vellante, and theCUBE, we like to go out, especially to events like this that are intimate, there's about 140 chief data officers here. We've had the chief data officer from AstraZeneca on, even though he doesn't take that title. We've got a panel coming up later on in the day. And I want to talk about the evolution of that role. The chief data officer emerged out of kind of a wonky, back-office role. It was all about 10, 12 years ago, data quality, master data management, governance, compliance. And as the whole big data meme came into focus and people were realizing that data is the new source of competitive advantage, that data was going to be a source of innovation, what happened was that role emerged, that CDO, chief data officer role, emerged out of the back office and came right to the front and center. And the chief data officer really started to better understand and help companies understand how to monetize the data. Now monetization of data could mean more revenue. It could mean cutting costs. It could mean lowering risk. It could mean, in a hospital situation, saving lives, sort of broad definition of monetization. But it was really understanding how data contributed to value, and then finding ways to operationalize that to speed up time to value, to lower cost, to lower risk. And that required a lot of things. It required new skill sets, new training. It required a partnership with the lines of business. It required new technologies like artificial intelligence, which have just only recently come into a point where it's gone mainstream. Of course, when I started in the business several years ago, AI was the hot topic, but you didn't have the compute power. You didn't have the data, you didn't have the cloud. So we see the new innovation engine, not as Moore's Law, the doubling of transistors every 18 months, doubling of performance. Really no, we see the new innovation cocktail as data as the substrate, applying machine intelligence to that data, and then scaling it with the cloud. And through that cloud model, being able to attract startups and innovation. I come back to the chief data officer here, and IBM Chief Data Officer Summit, that's really where the chief data officer comes in. Now, the role in the organization is fuzzy. If you ask people what's a chief data officer, you'll get 20 different answers. Many answers are focused on compliance, particularly in what emerged, again, in those regulated industries: financial service, healthcare, and government. Those are the first to have chief data officers. But now CDOs have gone mainstream. So what we're seeing here from IBM is the broadening of that role and that definition and those responsibilities. Confusing things is the chief digital officer or the chief analytics officer. Those are roles that have also emerged, so there's a lot of overlap and a lot of fuzziness. To whom should the chief data officer report? Many say it should not be the CIO. Many say they should be peers. Many say the CIO's responsibility is similar to the chief data officer, getting value out of data, although I would argue that's never really been the case. The role of the CIO has largely been to make sure that the technology infrastructure works and that applications are delivered with high availability, with great performance, and are able to be developed in an agile manner. That's sort of a more recent sort of phenomenon that's come forth. And the chief digital officer is really around the company's face. What does that company's brand look like? What does that company's go-to-market look like? What does the customer see? Whereas the chief data officer's really been around the data strategy, what the sort of framework should be around compliance and governance, and, again, monetization. Not that they're responsible for the monetization, but they responsible for setting that framework and then communicating it across the company, accelerating the skill sets and the training of existing staff and complementing with new staff and really driving that framework throughout the organization in partnership with the chief digital officer, the chief analytics officer, and the chief information officer. That's how I see it anyway. Martin Schroeder, the senior vice president of IBM, came on today with Inderpal Bhandari, who is the chief data officer of IBM, the global chief data officer. Martin Schroeder used to be the CFO at IBM. He talked a lot, kind of borrowing from Ginni Rometty's themes in previous conferences, chapter one of digital which he called random acts of digital, and chapter two is how to take this mainstream. IBM makes a big deal out of the fact that it doesn't appropriate your data, particularly your personal data, to sell ads. IBM's obviously in the B2B business, so that's IBM's little back-ended shot at Google and Facebook and Amazon who obviously appropriate our data to sell ads or sell goods. IBM doesn't do that. I'm interested in IBM's opinion on big tech. There's a lot of conversations now. Elizabeth Warren wants to break up big tech. IBM was under the watchful eye of the DOJ 25 years ago, 30 years ago. IBM essentially had a monopoly in the business, and the DOJ wanted to make sure that IBM wasn't using that monopoly to hurt consumers and competitors. Now what IBM did, the DOJ ruled that IBM had to separate its applications business, actually couldn't be in the applications business. Another ruling was that they had to publish the interfaces to IBM mainframes so that competitors could actually build plug-compatible products. That was the world back then. It was all about peripherals plugging into mainframes and sort of applications being developed. So the DOJ took away IBM's power. Fast forward 30 years, now we're hearing Google, Amazon, and Facebook coming under fire from politicians. Should they break up those companies? Now those companies are probably the three leaders in AI. IBM might debate that. I think generally, at theCUBE and SiliconANGLE, we believe that those three companies are leading the charge in AI, along with China Inc: Alibaba, Tencent, Baidu, et cetera, and the Chinese government. So here's the question. What would happen if you broke up big tech? I would surmise that if you break up big tech, those little techs that you break up, Amazon Web Services, WhatsApp, Instagram, those little techs would get bigger. Now, however, the government is implying that it wants to break those up because those entities have access to our data. Google's got access to all the search data. If you start splitting them up, that'll make it harder for them to leverage that data. I would argue those small techs would get bigger, number one. Number two, I would argue if you're worried about China, which clearly you're seeing President Trump is worried about China, placing tariffs on China, playing hardball with China, which is not necessarily a bad thing. In fact, I think it's a good thing because China has been accused, and we all know, of taking IP, stealing IP essentially, and really not putting in those IP protections. So, okay, playing hardball to try to get a quid pro quo on IP protections is a good thing. Not good for trade long term. I'd like to see those trade barriers go away, but if it's a negotiation tactic, okay. I can live with it. However, going after the three AI leaders, Amazon, Facebook, and Google, and trying to take them down or break them up, actually, if you're a nationalist, could be a bad thing. Why would you want to handcuff the AI leaders? Third point is unless they're breaking the law. So I think that should be the decision point. Are those three companies, and others, using monopoly power to thwart competition? I would argue that Microsoft actually did use its monopoly power back in the '80s and '90s, in particular in the '90s, when it put Netscape out of business, it put Lotus out of business, it put WordPerfect out of business, it put Novell out of the business. Now, maybe those are strong words, but in fact, Microsoft's bundling, its pricing practices, caught those companies off guard. Remember, Jim Barksdale, the CEO of Netscape, said we don't need the browser. He was wrong. Microsoft killed Netscape by bundling Internet Explorer into its operating system. So the DOJ stepped in, some would argue too late, and put handcuffs on Microsoft so they couldn't use that monopoly power. And I would argue that you saw from that two things. One, granted, Microsoft was overly focused on Windows. That was kind of their raison d'etre, and they missed a lot of other opportunities. But the DOJ definitely slowed them down, and I think appropriately. And if out of that myopic focus on Windows, and to a certain extent, the Department of Justice and the government, the FTC as well, you saw the emergence of internet companies. Now, Microsoft did a major pivot to the internet. They didn't do a major pivot to the cloud until Satya Nadella came in, and now Microsoft is one of those other big tech companies that is under the watchful eye. But I think Microsoft went through that and perhaps learned its lesson. We'll see what happens with Facebook, Google, and Amazon. Facebook, in particular, seems to be conflicted right now. Should we take down a video that has somewhat fake news implications or is a deep hack? Or should we just dial down? We saw this recently with Facebook. They dialed down the promotion. So you almost see Facebook trying to have its cake and eat it too, which personally, I don't think that's the right approach. I think Facebook either has to say damn the torpedoes. It's open content, we're going to promote it. Or do the right thing and take those videos down, those fake news videos. It can't have it both ways. So Facebook seems to be somewhat conflicted. They are probably under the most scrutiny now, as well as Google, who's being accused, anyway, certainly we've seen this in the EU, of promoting its own ads over its competitors' ads. So people are going to be watching that. And, of course, Amazon just having too much power. Having too much power is not necessarily an indication of abusing monopoly power, but you know the government is watching. So that bears watching. theCUBE is going to be covering that. We'll be here all day, covering the IBM CDO event. I'm Dave Vallente, you're watching theCUBE. #IBMCDO, DM us or Tweet us @theCUBE. I'm @Dvallente, keep it right there. We'll be right back right after this short break. (upbeat music)

>> Announcer: Live from Nashville, Tennessee, it's theCUBE, covering Commvault GO 2018. Brought to you by Commvault. >> Welcome to the Music City. You're watching theCUBE, the worldwide leader in live tech coverage. This is Commvault GO. 20-year-old company, Commvault, the third year of their show, and the first time we have theCUBE here, and the first time we've been in Nashville, Tennessee. I'm Stu Miniman, your host for one day of coverage and joining me to help unlock the Commvault is the CTO advisor, Keith Townsend. >> Good to be back on theCUBE. >> Yeah, Keith, so you've actually been to this show before. It's my first time. I've known Commvault for a long time, but, you know, we talk about companies, they're all going through some kind of digital transformation and Commvault is no exception. I love the energy that I'm seeing at this show. They've got great puns around data. Data is at the center of everything, and really comes to what we see. You know, we know that data is so important. All the tropes out there. It's the new oil, it's the new currency, it is one of the most important things, not only in IT, but in business. So what's your experience been, so far? >> So far great. You know, they did a great job, second go for me. Last year, they had Captain Sully, great inspirational talk. This year they had a comedian, Connell on it, did a fabulous job of fast-paced multimedia sessions, talking about the connection of data, our everyday lives, lives as a technologist. Really high-powered show, a lot of great conversation around data and its applicability. >> Yeah, I did love that. Steve Connell, he is a poet, and some humor, and a lot of geeky things in there, talking about, right, how data fits into all of our lives, and what we do. And then that's one of the reason's why we're here, why the customers are here, and that's what it's about. You look at a company like Commvault. They've got 10s of thousands of customers, and as the big wave's coming in, what is Cloud Mead? I like some of the messages. I know we're going to dig in, both in our analysis, as well as with the guests, how cloud is impacting this, as well as things like the wave of AI. How is that changing the product? How can I access the information? I hear things like ransomware and GDPR, and hacking. It's a dangerous time in technology, whether you're talking social media, or talking in business. So give us a little bit of background, what you're hearing. Keith, you're talking to customers in your day job all the time. How important is data? And things like backup and data recovery, where do they fit in their world? >> Well, you know what? Customers are still learning this journey. I've talked to plenty of customers that have used Commvault, competing products, and a lot of, at the low level, a lot of these guys are still thinking about it as backup, but great, great testimony from one of the larger customers, out there, Merck, who talked about using backup or data protection, as part of their data management strategy, moving workloads from worker mobility, moving workloads from cloud to cloud, location to location. Every customer is dealing with multi-cloud challenges. Stu, we've talked about multi-cloud and the keys to multi-cloud data is absolutely the most important part of getting your multi-cloud strategy, or even cloud strategy, straight. So, I'm looking forward to continuing the conversation I've had out in the field, which is customers challenged with how do I simply identify a data management strategy? To hearing Commvault's message today and throughout the guests that we'll have on, customers, partners, the entire ecosystem, about how Commvault enables multi-cloud through data management. >> Yeah, I was curious what I would see coming in. Would this be, kind of, a hard core, let's get in to the product and understand things like backup and recovery. As you know, backup's important, but recovery is everything. We heard some of the customer stories about how fast they can recover. Those are great stories. How does cloud fit into it? You had the CEO and the COO on stage talking about do you go, when you go to the cloud, do you go simple or do you go smart? And there's some nuance there that you'll want to unpack as to understanding. You know, as we look at cloud, it's not just take the way we were doing things and throw them up there. I mean Keith, they talked about tape and virtual tape. You know, I remember back when, like, the VTLs were first being a thing, I was working at a storage company back then. You know, it was a huge move. Backup, those processes, are really hardened into an environment. What do the admins have to do? What do they have to change in the way they're doing things? Let's look at the news a little bit. So, you know, there was the, Commvault did a good job, I think, of checking all the check boxes. While there was nothing that jumped out at me as, like, wow this is the first time I've heard it, it's what I'm hearing from customers. So, moving to, and as a service portfolio, they've got a full line of appliances, but it's not only hardware. If you'd like to buy the software from them, of course you could do that. Got a number of big partners. We're going to HPE on the program. We're going to have Cisco on the program. NetUP is another big, big partner here. As well as, I think that the product that they're most excited to talk about is Commvault Activate, which is really looking a lot of the governance, which, when you talk in a cloud world, is one of the biggest challenges. By the way, if people in the background hear these cheering, the Commvault employees are really excited, everybody's starting to walk on the show floor. We're in the center of it all, Keith. So, we got a preview yesterday, they actually announced it to the tech field day crew, which you and I sat in with. So, give me your thoughts as to what you saw in the product line. How does that line up with what you're hearing from customers in a competitive nature? >> So, I think I tweeted out yesterday, doing the tech field day session, Commvault does not sleep at the wheel. As you said, Stu, there's nothing amazingly new about what they announced, but a 20-year-old technology company is definitely keeping pace with the innovation that we've seen in the field. Customers want options when it comes to consuming backup and recovery. From a storage layer, they want the storage bricks, they want a hardware solution, they want to consume it via subscription, or perpetual license. They want this cloud-type capability. More importantly, they want, and they talked about it on stage today, this analytics capability. The ability to extract intelligence out of your data. Commvault calls is 4-D indexing. Other vendors just call it, simply, meta-data. But taking advantage of 15, 20 year-old data, to drive innovation in today's society, while keeping compliant with GDPR and other regulations that are coming up, sprouting up as it seems, every other week. >> I did like that terminology that you used. The 4-D innovation, because of course the fourth dimension is time and we're using intelligence. The challenge we have, as we know, is we have so much data and what do we the analytics for? They said we can use the analytics, first of all, compliance. I need to understand that I take care of that. Secondly, what if I want to cull data? What data don't I need anymore? What can I get rid of? There's huge cost savings that I can have there. And lastly, what can I get from analytics? How can I get value out of that information? And more. So, the use of analytics is something I was looking for, obviously want to talk to some of the product people, some of the customers, about what I've heard so far and talking to people. People were excited. I was actually talking to one of the partners of Commvault, they said one of the reasons they partnered deeper and are looking to work with Commvault, is they've got good tech. There's a reason they've been around for 20 years. They're a publicly traded stock. They've been doing well. They have been growing. Revenue wise, I looked, the last three years, I think they're at 700 million, they've been growing in the kind of eight to 9% year over year for the last couple years. Which, as a software company, it's not taking the world by storm, but for, in the infrastructure space, that is good growth. I do have to mention, there was some activist investor activity that came on. We actually we're going to have the CMO, we're going to have the COO on the program. We won't have the CEO, they are in the midst of going through a change there. And, you know, look, say what you will about activist investors. The reason they're getting involved is because they believe that there is more value that can be unlocked in Commvault with some changes and with product line and the things happening that's what we're starting to see here. That's why were excited to dig in and kind of understand. >> Yeah, we can see that even in some of the tech customer's testimonials. The state of Colorado net new customer. This is amazing in an area that we've seen 90 million, 250 million, easily a half a million dollars of investment in the data protection space. Commvault, 20-year-old company, still gaining traction with net new use cases and if I was an activist investor, I'd look at that. I'd look at the overall industry and thinking what can we do to unlock some of the potential of a fairly large customer base? Pretty stable company, but a very, very exciting part of the industry. >> Yeah, and Keith, you brought up meta-data. Meta-data's something that, you know, in the industry we've been talking about for a long time. It's really that intelligence that's going to allow the systems to gather everything. I know, when I get my brand new phone now, I can search my 4,000 photos by location, by date, everything like that. It's auto-recognizing information. The same thing we're getting on the business side. It used be oh okay, let's make sure when you put your photo, your file, in there that you tag it. Come on. Nobody can do this. Nobody's thinking when I'm doing my job, well I really need to think about the meta data 'cause five years from now, I might want to do it. Oh, I can search by person or project or things like that. But it's the intelligence in the system to be able to learn and grow and the more data we have, actually the more that the intelligence can get there. >> And that's critically important for even compliance. Again, culling data. You know, Bill Nye got up on stage and talked about being able to use data, or I'm sorry, AstraZeneca got up on stage and talked about using data that was 15-years-old to rerun through today's algorithms and trials. If you were to cull the wrong data, then they could not have the innovation that they've created by having 15-year-old data. So, the meta data, the ability to go back again, search your repository for key words, content, surface up that data and leverage that data. This is why we say data is the new currency, it's the new oil, it's the most critical. I even heard on stage today, data's the new water. I don't know if I'd go quite that far, you know I like my old-fashioned glass of water, but this is why we hear these terms because companies are reinventing themselves with the data. >> Alright, so Keith, what Dave Allante would point out is water is a limited resource. Data, we can reuse it. We can take a drink of data, we can share it. Data helps complete us. It's the shirts that they have at the show. We've got AstraZeneca, we've got the state of Colorado, we've got other users. The key partners, key executives. We're going to bring you the key data to help you extract the signal from the noise here at Commvault GO. For Keith Townsend, I'm Stu Miniman. Thanks for joining theCUBE. (upbeat music)