>>Alteryx is a company with a long history that goes all the way back to the late 1990s. Now the one consistent theme over 20 plus years has been that Ultrix has always been a data company early in the big data and Hadoop cycle. It saw the need to combine and prep different data types so that organizations could analyze data and take action Altrix and similar companies played a critical role in helping companies become data-driven. The problem was the decade of big data, brought a lot of complexities and required immense skills just to get the technology to work as advertised this in turn limited, the pace of adoption and the number of companies that could really lean in and take advantage of the cloud began to change all that and set the foundation for today's theme to Zuora of digital transformation. We hear that phrase a ton digital transformation. >>People used to think it was a buzzword, but of course we learned from the pandemic that if you're not a digital business, you're out of business and a key tenant of digital transformation is democratizing data, meaning enabling, not just hypo hyper specialized experts, but anyone business users to put data to work. Now back to Ultrix, the company has embarked on a major transformation of its own. Over the past couple of years, brought in new management, they've changed the way in which it engaged with customers with the new subscription model and it's topgraded its talent pool. 2021 was even more significant because of two acquisitions that Altrix made hyper Ana and trifecta. Why are these acquisitions important? Well, traditionally Altryx sold to business analysts that were part of the data pipeline. These were fairly technical people who had certain skills and were trained in things like writing Python code with hyper Ana Altryx has added a new persona, the business user, anyone in the business who wanted to gain insights from data and, or let's say use AI without having to be a deep technical expert. >>And then Trifacta a company started in the early days of big data by cube alum, Joe Hellerstein and his colleagues at Berkeley. They knocked down the data engineering persona, and this gives Altryx a complimentary extension into it where things like governance and security are paramount. So as we enter 2022, the post isolation economy is here and we do so with a digital foundation built on the confluence of cloud native technologies, data democratization and machine intelligence or AI, if you prefer. And Altryx is entering that new era with an expanded portfolio, new go-to market vectors, a recurring revenue business model, and a brand new outlook on how to solve customer problems and scale a company. My name is Dave Vellante with the cube and I'll be your host today. And the next hour, we're going to explore the opportunities in this new data market. And we have three segments where we dig into these trends and themes. First we'll talk to Jay Henderson, vice president of product management at Ultrix about cloud acceleration and simplifying complex data operations. Then we'll bring in Suresh Vetol who's the chief product officer at Altrix and Adam Wilson, the CEO of Trifacta, which of course is now part of Altrix. And finally, we'll hear about how Altryx is partnering with snowflake and the ecosystem and how they're integrating with data platforms like snowflake and what this means for customers. And we may have a few surprises sprinkled in as well into the conversation let's get started. >>We're kicking off the program with our first segment. Jay Henderson is the vice president of product management Altryx and we're going to talk about the trends and data, where we came from, how we got here, where we're going. We get some launch news. Well, Jay, welcome to the cube. >>Great to be here, really excited to share some of the things we're working on. >>Yeah. Thank you. So look, you have a deep product background, product management, product marketing, you've done strategy work. You've been around software and data, your entire career, and we're seeing the collision of software data cloud machine intelligence. Let's start with the customer and maybe we can work back from there. So if you're an analytics or data executive in an organization, w J what's your north star, where are you trying to take your company from a data and analytics point of view? >>Yeah, I mean, you know, look, I think all organizations are really struggling to get insights out of their data. I think one of the things that we see is you've got digital exhaust, creating large volumes of data storage is really cheap, so it doesn't cost them much to keep it. And that results in a situation where the organization's, you know, drowning in data, but somehow still starving for insights. And so I think, uh, you know, when I talk to customers, they're really excited to figure out how they can put analytics in the hands of every single person in their organization, and really start to democratize the analytics, um, and, you know, let the, the business users and the whole organization get value out of all that data they have. >>And we're going to dig into that throughout this program data, I like to say is plentiful insights, not always so much. Tell us about your launch today, Jay, and thinking about the trends that you just highlighted, the direction that your customers want to go and the problems that you're solving, what role does the cloud play in? What is what you're launching? How does that fit in? >>Yeah, we're, we're really excited today. We're launching the Altryx analytics cloud. That's really a portfolio of cloud-based solutions that have all been built from the ground up to be cloud native, um, and to take advantage of things like based access. So that it's really easy to give anyone access, including folks on a Mac. Um, it, you know, it also lets you take advantage of elastic compute so that you can do, you know, in database processing and cloud native, um, solutions that are gonna scale to solve the most complex problems. So we've got a portfolio of solutions, things like designer cloud, which is our flagship designer product in a browser and on the cloud, but we've got ultra to machine learning, which helps up-skill regular old analysts with advanced machine learning capabilities. We've got auto insights, which brings a business users into the fold and automatically unearths insights using AI and machine learning. And we've got our latest edition, which is Trifacta that helps data engineers do data pipelining and really, um, you know, create a lot of the underlying data sets that are used in some of this, uh, downstream analytics. >>Let's dig into some of those roles if we could a little bit, I mean, you've traditionally Altryx has served the business analysts and that's what designer cloud is fit for, I believe. And you've explained, you know, kind of the scope, sorry, you've expanded that scope into the, to the business user with hyper Anna. And we're in a moment we're going to talk to Adam Wilson and Suresh, uh, about Trifacta and that recent acquisition takes you, as you said, into the data engineering space in it. But in thinking about the business analyst role, what's unique about designer cloud cloud, and how does it help these individuals? >>Yeah, I mean, you know, really, I go back to some of the feedback we've had from our customers, which is, um, you know, they oftentimes have dozens or hundreds of seats of our designer desktop product, you know, really, as they look to take the next step, they're trying to figure out how do I give access to that? Those types of analytics to thousands of people within the organization and designer cloud is, is really great for that. You've got the browser-based interface. So if folks are on a Mac, they can really easily just pop, open the browser and get access to all of those, uh, prep and blend capabilities to a lot of the analysis we're doing. Um, it's a great way to scale up access to the analytics and then start to put it in the hands of really anyone in the organization, not just those highly skilled power users. >>Okay, great. So now then you add in the hyper Anna acquisition. So now you're targeting the business user Trifacta comes into the mix that deeper it angle that we talked about, how does this all fit together? How should we be thinking about the new Altryx portfolio? >>Yeah, I mean, I think it's pretty exciting. Um, you know, when you think about democratizing analytics and providing access to all these different groups of people, um, you've not been able to do it through one platform before. Um, you know, it's not going to be one interface that meets the, of all these different groups within the organization. You really do need purpose built specialized capabilities for each group. And finally, today with the announcement of the alternates analytics cloud, we brought together all of those different capabilities, all of those different interfaces into a single in the end application. So really finally delivering on the promise of providing analytics to all, >>How much of this you've been able to share with your customers and maybe your partners. I mean, I know OD is fairly new, but if you've been able to get any feedback from them, what are they saying about it? >>Uh, I mean, it's, it's pretty amazing. Um, we ran a early access, limited availability program that led us put a lot of this technology in the hands of over 600 customers, um, over the last few months. So we have gotten a lot of feedback. I tell you, um, it's been overwhelmingly positive. I think organizations are really excited to unlock the insights that have been hidden in all this data. They've got, they're excited to be able to use analytics in every decision that they're making so that the decisions they have or more informed and produce better business outcomes. Um, and, and this idea that they're going to move from, you know, dozens to hundreds or thousands of people who have access to these kinds of capabilities, I think has been a really exciting thing that is going to accelerate the transformation that these customers are on. >>Yeah, those are good. Good, good numbers for, for preview mode. Let's, let's talk a little bit about vision. So it's democratizing data is the ultimate goal, which frankly has been elusive for most organizations over time. How's your cloud going to address the challenges of putting data to work across the entire enterprise? >>Yeah, I mean, I tend to think about the future and some of the investments we're making in our products and our roadmap across four big themes, you know, in the, and these are really kind of enduring themes that you're going to see us making investments in over the next few years, the first is having cloud centricity. You know, the data gravity has been moving to the cloud. We need to be able to provide access, to be able to ingest and manipulate that data, to be able to write back to it, to provide cloud solution. So the first one is really around cloud centricity. The second is around big data fluency. Once you have all of the data, you need to be able to manipulate it in a performant manner. So having the elastic cloud infrastructure and in database processing is so important, the third is around making AI a strategic advantage. >>So, uh, you know, getting everyone involved and accessing AI and machine learning to unlock those insights, getting it out of the hands of the small group of data scientists, putting it in the hands of analysts and business users. Um, and then the fourth thing is really providing access across the entire organization. You know, it and data engineers, uh, as well as business owners and analysts. So, um, cloud centricity, big data fluency, um, AI is a strategic advantage and, uh, personas across the organization are really the four big themes you're going to see us, uh, working on over the next few months and, uh, coming coming year. >>That's good. Thank you for that. So, so on a related question, how do you see the data organizations evolving? I mean, traditionally you've had, you know, monolithic organizations, uh, very specialized or I might even say hyper specialized roles and, and your, your mission of course is the customer. You, you, you, you and your customers, they want to democratize the data. And so it seems logical that domain leaders are going to take more responsibility for data, life cycles, data ownerships, low code becomes more important. And perhaps this kind of challenges, the historically highly centralized and really specialized roles that I just talked about. How do you see that evolving and, and, and what role will Altryx play? >>Yeah. Um, you know, I think we'll see sort of a more federated systems start to emerge. Those centralized groups are going to continue to exist. Um, but they're going to start to empower, you know, in a much more de-centralized way, the people who are closer to the business problems and have better business understanding. I think that's going to let the centralized highly skilled teams work on, uh, problems that are of higher value to the organization. The kinds of problems where one or 2% lift in the model results in millions of dollars a day for the business. And then by pushing some of the analytics out to, uh, closer to the edge and closer to the business, you'll be able to apply those analytics in every single decision. So I think you're going to see, you know, both the decentralized and centralized models start to work in harmony and a little bit more about almost a federated sort of a way. And I think, you know, the exciting thing for us at Altryx is, you know, we want to facilitate that. We want to give analytic capabilities and solutions to both groups and types of people. We want to help them collaborate better, um, and drive business outcomes with the analytics they're using. >>Yeah. I mean, I think my take on another one, if you could comment is to me, the technology should be an operational detail and it has been the, the, the dog that wags the tail, or maybe the other way around, you mentioned digital exhaust before. I mean, essentially it's digital exhaust coming out of operationals systems that then somehow, eventually end up in the hand of the domain users. And I wonder if increasingly we're going to see those domain users, users, those, those line of business experts get more access. That's your goal. And then even go beyond analytics, start to build data products that could be monetized, and that maybe it's going to take a decade to play out, but that is sort of a new era of data. Do you see it that way? >>Absolutely. We're actually making big investments in our products and capabilities to be able to create analytic applications and to enable somebody who's an analyst or business user to create an application on top of the data and analytics layers that they have, um, really to help democratize the analytics, to help prepackage some of the analytics that can drive more insights. So I think that's definitely a trend we're going to see more. >>Yeah. And to your point, if you can federate the governance and automate that, then that can happen. I mean, that's a key part of it, obviously. So, all right, Jay, we have to leave it there up next. We take a deep dive into the Altryx recent acquisition of Trifacta with Adam Wilson who led Trifacta for more than seven years. It's the recipe. Tyler is the chief product officer at Altryx to explain the rationale behind the acquisition and how it's going to impact customers. Keep it right there. You're watching the cube. You're a leader in enterprise tech coverage. >>It's go time, get ready to accelerate your data analytics journey with a unified cloud native platform. That's accessible for everyone on the go from home to office and everywhere in between effortless analytics to help you go from ideas to outcomes and no time. It's your time to shine. It's Altryx analytics cloud time. >>Okay. We're here with. Who's the chief product officer at Altryx and Adam Wilson, the CEO of Trifacta. Now of course, part of Altryx just closed this quarter. Gentlemen. Welcome. >>Great to be here. >>Okay. So let me start with you. In my opening remarks, I talked about Altrix is traditional position serving business analysts and how the hyper Anna acquisition brought you deeper into the business user space. What does Trifacta bring to your portfolio? Why'd you buy the company? >>Yeah. Thank you. Thank you for the question. Um, you know, we see, uh, we see a massive opportunity of helping, um, brands, um, democratize the use of analytics across their business. Um, every knowledge worker, every individual in the company should have access to analytics. It's no longer optional, um, as they navigate their businesses with that in mind, you know, we know designer and are the products that Altrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyper Rana now kind of renamed, um, Altrix auto. We even speak with the business owner and the line of business owner. Who's looking for insights that aren't real in traditional dashboards and so on. Um, but we see this opportunity of really helping the data engineering teams and it organizations, um, to also make better use of analytics. Um, and that's where the drive factor comes in for us. Um, drive factor has the best data engineering cloud in the planet. Um, they have an established track record of working across multiple cloud platforms and helping data engineers, um, do better data pipelining and work better with, uh, this massive kind of cloud transformation that's happening in every business. Um, and so fact made so much sense for us. >>Yeah. Thank you for that. I mean, you, look, you could have built it yourself would have taken, you know, who knows how long, you know, but, uh, so definitely a great time to market move, Adam. I wonder if we could dig into Trifacta some more, I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well, uh, on the cube coming at the problem of taking data from raw refined to an experience point of view. And Joe in the early days, talked about flipping the model and starting with data visualization, something Jeff, her was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you may be in some others changed that model with ELL and then T explain how Trifacta really changed the data engineering game. >>Yeah, that's exactly right. Uh, David, it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research, uh, at Berkeley and Stanford that really birth Trifacta was, you know, why is it that the people who know the data best can't do the work? You know, why is this become the exclusive purview of the highly technical? And, you know, can we rethink this and make this a user experience, problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those. So, so a broader set of, of users can, um, can really see for themselves and help themselves. And, and I think that, um, there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data-driven and then the whole time they're saying, well, then give me the data, you know, in the shape that I could use it with the right level of quality and I'm happy to be, but don't tell me to be more data-driven and then, and, and not empower me, um, to, to get in there and to actually start to work with the data in meaningful ways. >>And so, um, that was really, you know, what, you know, the origin story of the company and I think is, as we, um, saw over the course of the last 5, 6, 7 years that, um, you know, uh, real, uh, excitement to embrace this idea of, of trying to think about data engineering differently, trying to democratize the, the ETL process and to also leverage all these exciting new, uh, engines and platforms that are out there that allow for processing, you know, ever more diverse data sets, ever larger data sets and new and interesting ways. And that's where a lot of the push-down or the ELT approaches that, you know, I think it could really won the day. Um, and that, and that for us was a hallmark of the solution from the very beginning. >>Yeah, this is a huge point that you're making is, is first of all, there's a large business, it's probably about a hundred billion dollar Tam. Uh, and the, the point you're making, because we've looked, we've contextualized most of our operational systems, but the big data pipeline is hasn't gotten there. But, and maybe we could talk about that a little bit because democratizing data is Nirvana, but it's been historically very difficult. You've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altryx as you bring these puzzle pieces together, how is this going to impact your customers who would like to take that one? >>Yeah, maybe, maybe I'll take a crack at it. And Adam will, um, add on, um, you know, there hasn't been a single platform for analytics, automation in the enterprise, right? People have relied on, uh, different products, um, to solve kind of, uh, smaller problems, um, across this analytics, automation, data transformation domain. Um, and, um, I think uniquely Alcon's has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics, for AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. Um, and so by bringing drive factor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where it's applied and so multiple personas. Um, and we just talked about the data engineers. They are really a growing stakeholder in this transformation of data and analytics. >>Yeah, good. Maybe we can stay on this for a minute cause you, you you're right. You bring it together. Now at least three personas the business analyst, the end user slash business user. And now the data engineer, which is really out of an it role in a lot of companies, and you've used this term, the data engineering cloud, what is that? How is it going to integrate in with, or support these other personas? And, and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? >>Yeah, no, that's great. Uh, yeah, I think for us, we really looked at this and said, you know, we want to build an open and interactive cloud platform for data engineers, you know, to collaboratively profile pipeline, um, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that, what I would describe as collaborative curation of the data together, so that you're starting to see, um, uh, you know, increasing returns to scale as this, uh, as this rolls out. I just think that is an incredibly powerful combination and, and frankly, something that the market is not crack the code on yet. And so, um, I think when we, when I sat down with Suresh and with mark and the team at Ultrix, that was really part of the, the, the big idea, the big vision that was painted and got us really energized about the acquisition and about the potential of the combination. >>And you're really, you're obviously writing the cloud and the cloud native wave. Um, and, but specifically we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, Snowflake's doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right. It's, it's simplified get there fast, don't necessarily have to go through the central organization to share data. Uh, and, and, and, but it's really all about simplification, right? Isn't that really what the democratization comes down to. >>Yeah. It's simplification and collaboration. Right. I don't want to, I want to kind of just what Adam said resonates with me deeply. Um, analytics is one of those, um, massive disciplines inside an enterprise that's really had the weakest of tools. Um, and we just have interfaces to collaborate with, and I think truly this was all drinks and a superpower was helping the analysts get more out of their data, get more out of the analytics, like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, um, understanding data models better, I think, um, uh, curating those insights. I boring Adam's phrase again. Um, I think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. >>So how should we think about designer cloud, which is from Altrix it's really been the on-prem and the server desktop offering. And of course Trifacta is with cloud cloud data warehouses. Right. Uh, how, how should we think about those two products? Yeah, >>I think, I think you should think about them. And, uh, um, as, as very complimentary right designer cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, the really appeals to the business analysts, um, and gets a lot of the things that they do well, we've also built it with interoperability in mind, right. So if you started building your workflows in designer desktop, you want to share that with design and cloud, we want to make it super easy for you to do that. Um, and I think over time now we're only a week into, um, this Alliance with, um, with, um, Trifacta, um, I think we have to get deeper inside to think about what does the data engineer really need? What's the business analysts really need and how to design a cloud, and Trifacta really support both of those requirements, uh, while kind of continue to build on the trifecta on the amazing Trifacta cloud platform. >>You know, >>I think we're just going to say, I think that's one of the things that, um, you know, creates a lot of, uh, opportunity as we go forward, because ultimately, you know, Trifacta took a platform, uh, first mentality to everything that we built. So thinking about openness and extensibility and, um, and how over time people could build things on top of factor that are a variety of analytic tool chain, or analytic applications. And so, uh, when you think about, um, Ultrix now starting to, uh, to move some of its capabilities or to provide additional capabilities, uh, in the cloud, um, you know, Trifacta becomes a platform that can accelerate, you know, all of that work and create, uh, uh, a cohesive set of, of cloud-based services that, um, share a common platform. And that maintains independence because both companies, um, have been, uh, you know, fiercely independent, uh, and, and really giving people choice. >>Um, so making sure that whether you're, uh, you know, picking one cloud platform and other, whether you're running things on the desktop, uh, whether you're running in hybrid environments, that, um, no matter what your decision, um, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse transform shape structure, that data, and ultimately to deliver, uh, the analytics that you need. And so I think in that sense, um, uh, you know, this, this again is another reason why the combination, you know, fits so well together, giving people, um, the choice. Um, and as they, as they think about their analytics strategy and their platform strategy going forward, >>Yeah. I make a chuckle, but one of the reasons I always liked Altrix is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the organization said, wow, this big data stuff has taken off, but we need security. We need governance. And it's interesting because you've got, you know, ETL has been complex, whereas the visualization tools, they really, you know, really weren't great at governance and security. It took some time there. So that's not, not their heritage. You're bringing those worlds together. And I'm interested, you guys just had your sales kickoff, you know, what was their reaction like? Uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Um, thanks for asking about our sales kickoff. So we met for the first time and you've got a two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was a, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we added a Trifacta to, um, the, the potty such as the tour, um, and getting all of our sales teams and product organizations, um, to meet in person in one location. I thought that was very powerful for other the company. Uh, but then I tell you, um, um, the reception for Trifacta was beyond anything I could have imagined. Uh, we were working out him and I will, when he's so hot on, on the deal and the core hypotheses and so on. And then you step back and you're going to share the vision with the field organization, and it blows you away, the energy that it creates among our sellers out of partners. >>And I'm sure Madam will and his team were mocked, um, every single day, uh, with questions and opportunities to bring them in. But Adam, maybe you should share. Yeah, no, it was, uh, it was through the roof. I mean, uh, uh, the, uh, the amount of energy, the, uh, certainly how welcoming everybody was, uh, uh, you know, just, I think the story makes so much sense together. I think culturally, the company is, are very aligned. Um, and, uh, it was a real, uh, real capstone moment, uh, to be able to complete the acquisition and to, and to close and announced, you know, at the kickoff event. And, um, I think, you know, for us, when we really thought about it, you know, when we ended, the story that we told was just, you have this opportunity to really cater to what the end users care about, which is a lot about interactivity and self-service, and at the same time. >>And that's, and that's a lot of the goodness that, um, that Altryx is, has brought, you know, through, you know, you know, years and years of, of building a very vibrant community of, you know, thousands, hundreds of thousands of users. And on the other side, you know, Trifacta bringing in this data engineering focus, that's really about, uh, the governance things that you mentioned and the openness, um, that, that it cares deeply about. And all of a sudden, now you have a chance to put that together into a complete story where the data engineering cloud and analytics, automation, you know, coming together. And, um, and I just think, you know, the lights went on, um, you know, for people instantaneously and, you know, this is a story that, um, that I think the market is really hungry for. And certainly the reception we got from, uh, from the broader team at kickoff was, uh, was a great indication. >>Well, I think the story hangs together really well, you know, one of the better ones I've seen in, in this space, um, and, and you guys coming off a really, really strong quarter. So congratulations on that jets. We have to leave it there. I really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the cube you're leader in enterprise tech coverage. >>This is your data housed neatly insecurely in the snowflake data cloud. And all of it has potential the potential to solve complex business problems, deliver personalized financial offerings, protect supply chains from disruption, cut costs, forecast, grow and innovate. All you need to do is put your data in the hands of the right people and give it an opportunity. Luckily for you. That's the easy part because snowflake works with Alteryx and Alteryx turns data into breakthroughs with just a click. Your organization can automate analytics with drag and drop building blocks, easily access snowflake data with both sequel and no SQL options, share insights, powered by Alteryx data science and push processing to snowflake for lightning, fast performance, you get answers you can put to work in your teams, get repeatable processes they can share in that's exciting because not only is your data no longer sitting around in silos, it's also mobilized for the next opportunity. Turn your data into a breakthrough Alteryx and snowflake >>Okay. We're back here in the queue, focusing on the business promise of the cloud democratizing data, making it accessible and enabling everyone to get value from analytics, insights, and data. We're now moving into the eco systems segment the power of many versus the resources of one. And we're pleased to welcome. Barb Hills camp was the senior vice president partners and alliances at Ultrix and a special guest Terek do week head of technology alliances at snowflake folks. Welcome. Good to see you. >>Thank you. Thanks for having me. Good to see >>Dave. Great to see you guys. So cloud migration, it's one of the hottest topics. It's the top one of the top initiatives of senior technology leaders. We have survey data with our partner ETR it's number two behind security, and just ahead of analytics. So we're hovering around all the hot topics here. Barb, what are you seeing with respect to customer, you know, cloud migration momentum, and how does the Ultrix partner strategy fit? >>Yeah, sure. Partners are central company's strategy. They always have been. We recognize that our partners have deep customer relationships. And when you connect that with their domain expertise, they're really helping customers on their cloud and business transformation journey. We've been helping customers achieve their desired outcomes with our partner community for quite some time. And our partner base has been growing an average of 30% year over year, that partner community and strategy now addresses several kinds of partners, spanning solution providers to global SIS and technology partners, such as snowflake and together, we help our customers realize the business promise of their journey to the cloud. Snowflake provides a scalable storage system altereds provides the business user friendly front end. So for example, it departments depend on snowflake to consolidate data across systems into one data cloud with Altryx business users can easily unlock that data in snowflake solving real business outcomes. Our GSI and solution provider partners are instrumental in providing that end to end benefit of a modern analytic stack in the cloud providing platform, guidance, deployment, support, and other professional services. >>Great. Let's get a little bit more into the relationship between Altrix and S in snowflake, the partnership, maybe a little bit about the history, you know, what are the critical aspects that we should really focus on? Barb? Maybe you could start an Interra kindly way in as well. >>Yeah, so the relationship started in 2020 and all shirts made a big bag deep with snowflake co-innovating and optimizing cloud use cases together. We are supporting customers who are looking for that modern analytic stack to replace an old one or to implement their first analytic strategy. And our joint customers want to self-serve with data-driven analytics, leveraging all the benefits of the cloud, scalability, accessibility, governance, and optimizing their costs. Um, Altrix proudly achieved. Snowflake's highest elite tier in their partner program last year. And to do that, we completed a rigorous third party testing process, which also helped us make some recommended improvements to our joint stack. We wanted customers to have confidence. They would benefit from high quality and performance in their investment with us then to help customers get the most value out of the destroyed solution. We developed two great assets. One is the officer starter kit for snowflake, and we coauthored a joint best practices guide. >>The starter kit contains documentation, business workflows, and videos, helping customers to get going more easily with an altered since snowflake solution. And the best practices guide is more of a technical document, bringing together experiences and guidance on how Altryx and snowflake can be deployed together. Internally. We also built a full enablement catalog resources, right? We wanted to provide our account executives more about the value of the snowflake relationship. How do we engage and some best practices. And now we have hundreds of joint customers such as Juniper and Sainsbury who are actively using our joint solution, solving big business problems much faster. >>Cool. Kara, can you give us your perspective on the partnership? >>Yeah, definitely. Dave, so as Barb mentioned, we've got this standing very successful partnership going back years with hundreds of happy joint customers. And when I look at the beginning, Altrix has helped pioneer the concept of self-service analytics, especially with use cases that we worked on with for, for data prep for BI users like Tableau and as Altryx has evolved to now becoming from data prep to now becoming a full end to end data science platform. It's really opened up a lot more opportunities for our partnership. Altryx has invested heavily over the last two years in areas of deep integration for customers to fully be able to expand their investment, both technologies. And those investments include things like in database pushed down, right? So customers can, can leverage that elastic platform, that being the snowflake data cloud, uh, with Alteryx orchestrating the end to end machine learning workflows Alteryx also invested heavily in snow park, a feature we released last year around this concept of data programmability. So all users were regardless of their business analysts, regardless of their data, scientists can use their tools of choice in order to consume and get at data. And now with Altryx cloud, we think it's going to open up even more opportunities. It's going to be a big year for the partnership. >>Yeah. So, you know, Terike, we we've covered snowflake pretty extensively and you initially solve what I used to call the, I still call the snake swallowing the basketball problem and cloud data warehouse changed all that because you had virtually infinite resources, but so that's obviously one of the problems that you guys solved early on, but what are some of the common challenges or patterns or trends that you see with snowflake customers and where does Altryx come in? >>Sure. Dave there's there's handful, um, that I can come up with today, the big challenges or trends for us, and Altrix really helps us across all of them. Um, there are three particular ones I'm going to talk about the first one being self-service analytics. If we think about it, every organization is trying to democratize data. Every organization wants to empower all their users, business users, um, you know, the, the technology users, but the business users, right? I think every organization has realized that if everyone has access to data and everyone can do something with data, it's going to make them competitively, give them a competitive advantage with Altrix is something we share that vision of putting that power in the hands of everyday users, regardless of the skillsets. So, um, with self-service analytics, with Ultrix designer they've they started out with self-service analytics as the forefront, and we're just scratching the surface. >>I think there was an analyst, um, report that shows that less than 20% of organizations are truly getting self-service analytics to their end users. Now, with Altryx going to Ultrix cloud, we think that's going to be a huge opportunity for us. Um, and then that opens up the second challenge, which is machine learning and AI, every organization is trying to get predictive analytics into every application that they have in order to be competitive in order to be competitive. Um, and with Altryx creating this platform so they can cater to both the everyday business user, the quote unquote, citizen data scientists, and making a code friendly for data scientists to be able to get at their notebooks and all the different tools that they want to use. Um, they fully integrated in our snow park platform, which I talked about before, so that now we get an end to end solution caring to all, all lines of business. >>And then finally this concept of data marketplaces, right? We, we created snowflake from the ground up to be able to solve the data sharing problem, the big data problem, the data sharing problem. And Altryx um, if we look at mobilizing your data, getting access to third-party datasets, to enrich with your own data sets, to enrich with, um, with your suppliers and with your partners, data sets, that's what all customers are trying to do in order to get a more comprehensive 360 view, um, within their, their data applications. And so with Altryx alterations, we're working on third-party data sets and marketplaces for quite some time. Now we're working on how do we integrate what Altrix is providing with the snowflake data marketplace so that we can enrich these workflows, these great, great workflows that Altrix writing provides. Now we can add third party data into that workflow. So that opens up a ton of opportunities, Dave. So those are three I see, uh, easily that we're going to be able to solve a lot of customer challenges with. >>So thank you for that. Terrick so let's stay on cloud a little bit. I mean, Altrix is undergoing a major transformation, big focus on the cloud. How does this cloud launch impact the partnership Terike from snowflakes perspective and then Barb, maybe, please add some color. >>Yeah, sure. Dave snowflake started as a cloud data platform. We saw our founders really saw the challenges that customers are having with becoming data-driven. And the biggest challenge was the complexity of having imagine infrastructure to even be able to do it, to get applications off the ground. And so we created something to be cloud-native. We created to be a SAS managed service. So now that that Altrix is moving to the same model, right? A cloud platform, a SAS managed service, we're just, we're just removing more of the friction. So we're going to be able to start to package these end to end solutions that are SAS based that are fully managed. So customers can, can go faster and they don't have to worry about all of the underlying complexities of, of, of stitching things together. Right? So, um, so that's, what's exciting from my viewpoint >>And I'll follow up. So as you said, we're investing heavily in the cloud a year ago, we had two pre desktop products, and today we have four cloud products with cloud. We can provide our users with more flexibility. We want to make it easier for the users to leverage their snowflake data in the Alteryx platform, whether they're using our beloved on-premise solution or the new cloud products were committed to that continued investment in the cloud, enabling our joint partner solutions to meet customer requirements, wherever they store their data. And we're working with snowflake, we're doing just that. So as customers look for a modern analytic stack, they expect that data to be easily accessible, right within a fast, secure and scalable platform. And the launch of our cloud strategy is a huge leap forward in making Altrix more widely accessible to all users in all types of roles, our GSI and our solution provider partners have asked for these cloud capabilities at scale, and they're excited to better support our customers, cloud and analytic >>Are. How about you go to market strategy? How would you describe your joint go to market strategy with snowflake? >>Sure. It's simple. We've got to work backwards from our customer's challenges, right? Driving transformation to solve problems, gain efficiencies, or help them save money. So whether it's with snowflake or other GSI, other partner types, we've outlined a joint journey together from recruit solution development, activation enablement, and then strengthening our go to market strategies to optimize our results together. We launched an updated partner program and within that framework, we've created new benefits for our partners around opportunity registration, new role based enablement and training, basically extending everything we do internally for our own go-to-market teams to our partners. We're offering partner, marketing resources and funding to reach new customers together. And as a matter of fact, we recently launched a fantastic video with snowflake. I love this video that very simply describes the path to insights starting with your snowflake data. Right? We do joint customer webinars. We're working on joint hands-on labs and have a wonderful landing page with a lot of assets for our customers. Once we have an interested customer, we engage our respective account managers, collaborating through discovery questions, proof of concepts really showcasing the desired outcome. And when you combine that with our partners technology or domain expertise, it's quite powerful, >>Dark. How do you see it? You'll go to market strategy. >>Yeah. Dave we've. Um, so we initially started selling, we initially sold snowflake as technology, right? Uh, looking at positioning the diff the architectural differentiators and the scale and concurrency. And we noticed as we got up into the larger enterprise customers, we're starting to see how do they solve their business problems using the technology, as well as them coming to us and saying, look, we want to also know how do you, how do you continue to map back to the specific prescriptive business problems we're having? And so we shifted to an industry focus last year, and this is an area where Altrix has been mature for probably since their inception selling to the line of business, right? Having prescriptive use cases that are particular to an industry like financial services, like retail, like healthcare and life sciences. And so, um, Barb talked about these, these starter kits where it's prescriptive, you've got a demo and, um, a way that customers can get off the ground and running, right? >>Cause we want to be able to shrink that time to market, the time to value that customers can watch these applications. And we want to be able to, to tell them specifically how we can map back to their business initiatives. So I see a huge opportunity to align on these industry solutions. As BARR mentioned, we're already doing that where we've released a few around financial services working in healthcare and retail as well. So that is going to be a way for us to allow customers to go even faster and start to map two lines of business with Alteryx. >>Great. Thanks Derek. Bob, what can we expect if we're observing this relationship? What should we look for in the coming year? >>A lot specifically with snowflake, we'll continue to invest in the partnership. Uh, we're co innovators in this journey, including snow park extensibility efforts, which Derek will tell you more about shortly. We're also launching these great news strategic solution blueprints, and extending that at no charge to our partners with snowflake, we're already collaborating with their retail and CPG team for industry blueprints. We're working with their data marketplace team to highlight solutions, working with that data in their marketplace. More broadly, as I mentioned, we're relaunching the ultra partner program designed to really better support the unique partner types in our global ecosystem, introducing new benefits so that with every partner, achievement or investment with ultra score, providing our partners with earlier access to benefits, um, I could talk about our program for 30 minutes. I know we don't have time. The key message here Alteryx is investing in our partner community across the business, recognizing the incredible value that they bring to our customers every day. >>Tarik will give you the last word. What should we be looking for from, >>Yeah, thanks. Thanks, Dave. As BARR mentioned, Altrix has been the forefront of innovating with us. They've been integrating into, uh, making sure again, that customers get the full investment out of snowflake things like in database push down that I talked about before that extensibility is really what we're excited about. Um, the ability for Ultrix to plug into this extensibility framework that we call snow park and to be able to extend out, um, ways that the end users can consume snowflake through, through sequel, which has traditionally been the way that you consume snowflake as well as Java and Scala, not Python. So we're excited about those, those capabilities. And then we're also excited about the ability to plug into the data marketplace to provide third party data sets, right there probably day sets in, in financial services, third party, data sets and retail. So now customers can build their data applications from end to end using ultrasound snowflake when the comprehensive 360 view of their customers, of their partners, of even their employees. Right? I think it's exciting to see what we're going to be able to do together with these upcoming innovations. Great >>Barb Tara, thanks so much for coming on the program, got to leave it right there in a moment, I'll be back with some closing thoughts in a summary, don't go away. >>1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops make that 2.3. The sector times out the wazoo, whites are much of this velocity's pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into insights, they turn to Altryx Qualtrics analytics, automation, >>Okay, let's summarize and wrap up the session. We can pretty much agree the data is plentiful, but organizations continue to struggle to get maximum value out of their data investments. The ROI has been elusive. There are many reasons for that complexity data, trust silos, lack of talent and the like, but the opportunity to transform data operations and drive tangible value is immense collaboration across various roles. And disciplines is part of the answer as is democratizing data. This means putting data in the hands of those domain experts that are closest to the customer and really understand where the opportunity exists and how to best address them. We heard from Jay Henderson that we have all this data exhaust and cheap storage. It allows us to keep it for a long time. It's true, but as he pointed out that doesn't solve the fundamental problem. Data is spewing out from our operational systems, but much of it lacks business context for the data teams chartered with analyzing that data. >>So we heard about the trend toward low code development and federating data access. The reason this is important is because the business lines have the context and the more responsibility they take for data, the more quickly and effectively organizations are going to be able to put data to work. We also talked about the harmonization between centralized teams and enabling decentralized data flows. I mean, after all data by its very nature is distributed. And importantly, as we heard from Adam Wilson and Suresh Vittol to support this model, you have to have strong governance and service the needs of it and engineering teams. And that's where the trifecta acquisition fits into the equation. Finally, we heard about a key partnership between Altrix and snowflake and how the migration to cloud data warehouses is evolving into a global data cloud. This enables data sharing across teams and ecosystems and vertical markets at massive scale all while maintaining the governance required to protect the organizations and individuals alike. >>This is a new and emerging business model that is very exciting and points the way to the next generation of data innovation in the coming decade. We're decentralized domain teams get more facile access to data. Self-service take more responsibility for quality value and data innovation. While at the same time, the governance security and privacy edicts of an organization are centralized in programmatically enforced throughout an enterprise and an external ecosystem. This is Dave Volante. All these videos are available on demand@theqm.net altrix.com. Thanks for watching accelerating automated analytics in the cloud made possible by Altryx. And thanks for watching the queue, your leader in enterprise tech coverage. We'll see you next time.

(upbeat music) >> Welcome back to theCUBE's coverage of HPE GreenLake announcements. We're seeing the transition of Hewlett Packard Enterprise as a company, yes they're going all in for as a service, but we're also seeing a transition from a hardware company to what I look at increasingly as a data management company. We're going to talk today to Vishal Lall who's GreenLake cloud services solutions at HPE and Matt Maccaux who's a global field CTO, Ezmeral Software at HPE. Gents welcome back to theCube. Good to see you again. >> Thank you for having us here. >> Thanks Dave. >> So Vishal let's start with you. What are the big mega trends that you're seeing in data? When you talk to customers, when you talk to partners, what are they telling you? What's your optic say? >> Yeah, I mean, I would say the first thing is data is getting even more important. It's not that data hasn't been important for enterprises, but as you look at the last, I would say 24 to 36 months has become really important, right? And it's become important because customers look at data and they're trying to stitch data together across different sources, whether it's marketing data, it's supply chain data, it's financial data. And they're looking at that as a source of competitive advantage. So, customers were able to make sense out of the data, enterprises that are able to make sense out of that data, really do have a competitive advantage, right? And they actually get better business outcomes. So that's really important, right? If you start looking at, where we are from an analytics perspective, I would argue we are in maybe the third generation of data analytics. Kind of the first one was in the 80's and 90's with data warehousing kind of EDW. A lot of companies still have that, but think of Teradata, right? The second generation more in the 2000's was around data lakes, right? And that was all about Hadoop and others, and really the difference between the first and the second generation was the first generation was more around structured data, right? Second became more about unstructured data, but you really couldn't run transactions on that data. And I would say, now we are entering this third generation, which is about data lake houses, right? Customers what they want really is, or enterprises, what they want really is they want structured data. They want unstructured data altogether. They want to run transactions on them, right? They want to use the data to mine it for machine learning purposes, right? Use it for SQL as well as non-SQL, right? And that's kind of where we are today. So, that's really what we are hearing from our customers in terms of at least the top trends. And that's how we are thinking about our strategy in context of those trends. >> So lake house use that term. It's an increasing popular term. It connotes, "Okay, I've got the best of data warehouse "and I've got the best of data lake. "I'm going to try to simplify the data warehouse. "And I'm going to try to clean up the data swamp "if you will." Matt, so, talk a little bit more about what you guys are doing specifically and what that means for your customers. >> Well, what we think is important is that there has to be a hybrid solution, that organizations are going to build their analytics. They're going to deploy algorithms, where the data either is being produced or where it's going to be stored. And that could be anywhere. That could be in the trunk of a vehicle. It could be in a public cloud or in many cases, it's on-premises in the data center. And where organizations struggle is they feel like they have to make a choice and a trade-off going from one to the other. And so what HPE is offering is a way to unify the experiences of these different applications, workloads, and algorithms, while connecting them together through a fabric so that the experience is tied together with consistent, security policies, not having to refactor your applications and deploying tools like Delta lake to ensure that the organization that needs to build a data product in one cloud or deploy another data product in the trunk of an automobile can do so. >> So, Vishal I wonder if we could talk about some of the patterns that you're seeing with customers as you go to deploy solutions. Are there other industry patterns? Are there any sort of things you can share that you're discerning? >> Yeah, no, absolutely. As we kind of hear back from our customers across industries, I think the problem sets are very similar, right? Whether you look at healthcare customers. You look at telco customers, you look at consumer goods, financial services, they're all quite similar. I mean, what are they looking for? They're looking for making sense, making business value from the data, breaking down the silos that I think Matt spoke about just now, right? How do I stitch intelligence across my data silos to get more business intelligence out of it. They're looking for openness. I think the problem that's happened is over time, people have realized that they are locked in with certain vendors or certain technologies. So, they're looking for openness and choice. So that's an important one that we've at least heard back from our customers. The other one is just being able to run machine learning on algorithms on the data. I think that's another important one for them as well. And I think the last one I would say is, TCO is important as customers over the last few years have realized going to public cloud is starting to become quite expensive, to run really large workloads on public cloud, especially as they want to egress data. So, cost performance, trade offs are starting to become really important and starting to enter into the conversation now. So, I would say those are some of the key things and themes that we are hearing from customers cutting across industries. >> And you talked to Matt about basically being able to essentially leave the data where it belongs, bring the compute to data. We talk about that all the time. And so that has to include on-prem, it's got to include the cloud. And I'm kind of curious on the edge, where you see that 'cause that's... Is that an eventual piece? Is that something that's actually moving in parallel? There's lot of fuzziness as an observer in the edge. >> I think the edge is driving the most interesting use cases. The challenge up until recently has been, well, I think it's always been connectivity, right? Whether we have poor connection, little connection or no connection, being able to asynchronously deploy machine learning jobs into some sort of remote location. Whether it's a very tiny edge or it's a very large edge, like a factory floor, the challenge as Vishal mentioned is that if we're going to deploy machine learning, we need some sort of consistency of runtime to be able to execute those machine learning models. Yes, we need consistent access to data, but consistent access in terms of runtime is so important. And I think Hadoop got us started down this path, the ability to very efficiently and cost-effectively run large data jobs against large data sets. And it attempted to work into the source ecosystem, but because of the monolithic deployment, the tightly coupling of the compute and the data, it never achieved that cloud native vision. And so what as role in HPE through GreenLake services is delivering with open source-based Kubernetes, open source Apache Spark, open source Delta lake libraries, those same cloud native services that you can develop on your workstation, deploy in your data center in the same way you deploy through automation out at the edge. And I think that is what's so critical about what we're going to see over the next couple of years. The edge is driving these use cases, but it's consistency to build and deploy those machine learning models and connect it consistently with data that's what's going to drive organizations to success. >> So you're saying you're able to decouple, to compute from the storage. >> Absolutely. You wouldn't have a cloud if you didn't decouple compute from storage. And I think this is sort of the demise of Hadoop was forcing that coupling. We have high-speed networks now. Whether I'm in a cloud or in my data center, even at the edge, I have high-performance networks, I can now do distributed computing and separate compute from storage. And so if I want to, I can have high-performance compute for my really data intensive applications and I can have cost-effective storage where I need to. And by separating that off, I can now innovate at the pace of those individual tools in that opensource ecosystem. >> So, can I stay on this for a second 'cause you certainly saw Snowflake popularize that, they were kind of early on. I don't know if they're the first, but they certainly one of the most successful. And you saw Amazon Redshift copied it. And Redshift was kind of a bolt on. What essentially they did is they teared off. You could never turn off the compute. You still had to pay for a little bit compute, that's kind of interesting. Snowflakes at the t-shirt sizes, so there's trade offs there. There's a lot of ways to skin the cat. How did you guys skin the cat? >> What we believe we're doing is we're taking the best of those worlds. Through GreenLake cloud services, the ability to pay for and provision on demand the computational services you need. So, if someone needs to spin up a Delta lake job to execute a machine learning model, you spin up that. We're of course spinning that up behind the scenes. The job executes, it spins down, and you only pay for what you need. And we've got reserve capacity there. So you, of course, just like you would in the public cloud. But more importantly, being able to then extend that through a fabric across clouds and edge locations, so that if a customer wants to deploy in some public cloud service, like we know we're going to, again, we're giving that consistency across that, and exposing it through an S3 API. >> So, Vishal at the end of the day, I mean, I love to talk about the plumbing and the tech, but the customer doesn't care, right? They want the lowest cost. They want the fastest outcome. They want the greatest value. My question is, how are you seeing data organizations evolve to sort of accommodate this third era of this next generation? >> Yeah. I mean, the way at least, kind of look at, from a customer perspective, what they're trying to do is first of all, I think Matt addressed it somewhat. They're looking at a consistent experience across the different groups of people within the company that do something to data, right? It could be a SQL users. People who's just writing a SQL code. It could be people who are writing machine learning models and running them. It could be people who are writing code in Spark. Right now they are, you know the experience is completely disjointed across them, across the three types of users or more. And so that's one thing that they trying to do, is just try to get that consistency. We spoke about performance. I mean the disjointedness between compute and storage does provide the agility, because there customers are looking for elasticity. How can I have an elastic environment? So, that's kind of the other thing they're looking at. And performance and DCU, I think a big deal now. So, I think that that's definitely on a customer's mind. So, as enterprises are looking at their data journey, those are the at least the attributes that they are trying to hit as they organize themselves to make the most out of the data. >> Matt, you and I have talked about this sort of trend to the decentralized future. We're sort of hitting on that. And whether it's in a first gen data warehouse, second gen data lake, data hub, bucket, whatever, that essentially should ideally stay where it is, wherever it should be from a performance standpoint, from a governance standpoint and a cost perspective, and just be a node on this, I like the term data mesh, but be a node on that, and essentially allow the business owners, those with domain context to you've mentioned data products before to actually build data products, maybe air quotes, but a data product is something that can be monetized. Maybe it cuts costs. Maybe it adds value in other ways. How do you see HPE fitting into that long-term vision which we know is going to take some time to play out? >> I think what's important for organizations to realize is that they don't have to go to the public cloud to get that experience they're looking for. Many organizations are still reluctant to push all of their data, their critical data, that is going to be the next way to monetize business into the public cloud. And so what HPE is doing is bringing the cloud to them. Bringing that cloud from the infrastructure, the virtualization, the containerization, and most importantly, those cloud native services. So, they can do that development rapidly, test it, using those open source tools and frameworks we spoke about. And if that model ends up being deployed on a factory floor, on some common X86 infrastructure, that's okay, because the lingua franca is Kubernetes. And as Vishal mentioned, Apache Spark, these are the common tools and frameworks. And so I want organizations to think about this unified analytics experience, where they don't have to trade off security for cost, efficiency for reliability. HPE through GreenLake cloud services is delivering all of that where they need to do it. >> And what about the speed to quality trade-off? Have you seen that pop up in customer conversations, and how are organizations dealing with that? >> Like I said, it depends on what you mean by speed. Do you mean a computational speed? >> No, accelerating the time to insights, if you will. We've got to go faster, faster, agile to the data. And it's like, "Whoa, move fast break things. "Whoa, whoa. "What about data quality and governance and, right?" They seem to be at odds. >> Yeah, well, because the processes are fundamentally broken. You've got a developer who maybe is able to spin up an instance in the public cloud to do their development, but then to actually do model training, they bring it back on-premises, but they're waiting for a data engineer to get them the data available. And then the tools to be provisioned, which is some esoteric stack. And then runtime is somewhere else. The entire process is broken. So again, by using consistent frameworks and tools, and bringing that computation to where the data is, and sort of blowing this construct of pipelines out of the water, I think is what is going to drive that success in the future. A lot of organizations are not there yet, but that's I think aspirationally where they want to be. >> Yeah, I think you're right. I think that is potentially an answer as to how you, not incrementally, but revolutionized sort of the data business. Last question, is talking about GreenLake, how this all fits in. Why GreenLake? Why do you guys feel as though it's differentiable in the market place? >> So, I mean, something that you asked earlier as well, time to value, right? I think that's a very important attribute and kind of a design factor as we look at GreenLake. If you look at GreenLake overall, kind of what does it stand for? It stands for experience. How do we make sure that we have the right experience for the users, right? We spoke about it in context of data. How do we have a similar experience for different users of data, but just broadly across an enterprise? So, it's all about experience. How do you automate it, right? How do you automate the workloads? How do you provision fast? How do you give folks a cloud... An experience that they have been used to in the public cloud, on using an Apple iPhone? So it's all about experience, I think that's number one. Number two is about choice and openness. I mean, as we look at GreenLake is not a proprietary platform. We are very, very clear that the design, one of the important design principles is about choice and openness. And that's the reason we are, you hear us talk about Kubernetes, about Apaches Spark, about Delta lake et cetera, et cetera, right? We're using kind of those open source models where customers have a choice. If they don't want to be on GreenLake, they can go to public cloud tomorrow. Or they can run in our Holos if they want to do it that way or in their Holos, if they want to do it. So they should have the choice. Third is about performance. I mean, what we've done is it's not just about the software, but we as a company know how to configure infrastructure for that workload. And that's an important part of it. I mean if you think about the machine learning workloads, we have the right Nvidia chips that accelerate those transactions. So, that's kind of the last, the third one, and the last one, I think, as I spoke about earlier is cost. We are very focused on TCO, but from a customer perspective, we want to make sure that we are giving a value proposition, which is just not about experience and performance and openness, but also about costs. So if you think about GreenLake, that's kind of the value proposition that we bring to our customers across those four dimensions. >> Guys, great conversation. Thanks so much, really appreciate your time and insights. >> Matt: Thanks for having us here, David. >> All right, you're welcome. And thank you for watching everybody. Keep it right there for more great content from HPE GreenLake announcements. You're watching theCUBE. (upbeat music)

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

>> Good morning, everyone. My name is Sudip Datta. I head up product management for Infrastructure Management and Analytics at CA Technologies. Today I am going to talk about the key pillars for modern analytics and monitoring for hybrid cloud. So before we get started, let's set the context. Let's take a stock of where we are today. Today in terms of digital business, software is driving business. Software is the backbone, is the driving force for most of the business services. Whether you are a financial institution or a hospitality service or a health care service or even a restaurant service pizza, you are front-ended by software. And therefore the user experience is of paramount importance. Just to give you some factoids. Eighty-three percent of U.S. consumers say that the brand that, the frontal software portal is more important than the product itself. And the companies are reciprocating by putting a lot of emphasis on user experience, as you see in the second factoid. The third factoid, it's even more interesting that 53% of the users of a mobile app actually abandon the app if the app doesn't load within a specified time. So we all understand now the importance of user experience in today's business. So what's happening to the infrastructure underneath that's hosting these applications? The infrastructure itself is evolving, right? How? First of all, as we all know there is a huge movement, a huge shift towards cloud. Customers are adopting cloud for reasons of economy, agility and efficiency. And whether you are running on cloud or on prem, the architecture itself is getting more and more dynamic. On the server side we hear about server-less computing. More and more enterprises are adopting containers, could be Dockers or other containers. And on the networking side we see an adoption of software-defined networking. The logical overlay on top of the physical underlay is abstracting the network. While we see a huge shift, a movement towards cloud, it is also true that customers are also retaining some of their assets on prem, and that's why we talk about hybrid cloud. Hybrid cloud is a reality, and it's going to be a reality for the foreseeable future. Take for example a bank that has its systems of engagement on public cloud, and systems of records on prem deeply nested within their DNC. So the transaction, the end-to-end transaction has to traverse multiple clouds. Similarly we talk to customers who run their production tier one application on prem, while tier two and tier three desktop applications run on public cloud. So that's the reality. Multi-cloud dynamic environment is a reality of today. While that's a reality, they pose a serious challenge for IT operations. What are the challenges? Because of multiple clouds, because of assets spanning multiple data centers, multiple clouds, there are blind spots getting created. IT ops is often blindsided on things that are happening on the other side of the firewall. And as a result what's happening is they're late to react, and often they react to problems much later than their customers find it, and that's an embarrassment. The other thing that's happening is because of the dynamic nature of the cloud, things are ephemeral, things are dynamic, things come and go, assets come and go, IT ops is often in the business of keeping pace with these changes. They are reacting to these changes. They are trying to keep pace with these changes, and silo'd tools are not the way to go. They are trying to keep up with these changes, but they are failing in doing so. And as a result we see poor user experience, low productivity, capacity problems and delayed time to market. Now what's the solution? What is the solution to all these problems? So what we are recommending is a four-pronged solution, what we represent as four pillars. The first pillar is about dynamic policy-based configuration and discovery. The second one is unification of the monitoring and analytics. The third one is contextual intelligence, and the fourth one is integration and collaboration. Let's go through them one by one. First of all, in terms of dynamic policy-based configuration, why is it important? I was talking to a VP of IT last week, and he commented that the time to deploy the monitoring for an application is longer than the time to deploy the application itself, and that's a shame. That's a real shame because in today's world application needs to be monitored straight out of the box. This is compounded by the fact that once you deploy the application, the application today is dynamic, as I said, the cloud assets are dynamic. The topology changes, and monitoring tools need to keep pace with that changing topology. So we need automated discovery. We need API driven discovery, and we need policy-based monitoring for large scale standardization. And last but not the least, the policies need to be based on dynamic baselines. The age, the era of static thresholds is long over because static thresholds lead to false alerts, resulting in higher opics for IT, and IT personnel absolutely, absolutely want to move away from it. Unified monitoring and analytics. This morning I stumbled upon a Lincoln white paper which said 20 tools you need for your hybrid monitoring, and I was absolutely dumbfounded. Twenty tools? I mean, that's a conversation non-starter. So how do we rationalize the tools, minimize the silos, and bring them under single pane of glass, or at least minimal panes for glass for monitoring? So IT admins can have a coherent view of servers, storage, network and applications through a single pane of glass? And why is that important? It's important because it results in lesser blame game. Because of silo'd tools what happens is admins are often fighting with each other, blaming each other. Server admins think that it's a storage problem. The storage admin thinks it's a database problem, and they are pointing to each other, right? So the tools, the management tools should be a point of collaboration, not a point of contention. Talking about blame game, one area that often gets ignored is the area of fault management and monitoring. Why is it important? And I will give a specific example. Let's say you have 100 VMs, and all those VMs become unreachable as a result of router being down. The root cause of the problem therefore are not the VMs, but the router. So instead of generating 101 alarms, the management tool needs to be smart enough to generate one single alarm. And that's why fault management and root cause analysis is of paramount importance. It suppresses unnecessary noise and results in lesser blaming. Contextual intelligence. Now when we talk about the cloud administrator, the cloud admin, the cloud admin in the past were living in the cocoon of their hybrid infrastructure. They were managing the hybrid infrastructure, but in today's world to have an end-to-end visibility of the digital chain, they need to integrate with application performance management tools, APM, as well as what lies underneath, which is the network, so that they have an end-to-end visibility of what's happening in the whole digital chain. But that's not all. They also need what we call is the context of the application. I will give you a specific example. For example, if the server runs out of memory when a lot of end users log into the system, or run out of capacity when a particular marketing promotion is running, then the context really is the business that leads to a saturation in IT. So what you need is to capture all the data, whether they come from logs, whether they come from alarms, capacity events as well as business events, into a single analytics platform and perform analytics on top of it. And then augment it with machine learning and pattern recognition capabilities so that it will not only perform root cause analysis for what happened in the past, but you're also able to anticipate, predict and prevent future problems. The fourth pillar is collaboration and integration. IT ops in today's world doesn't and shouldn't run in a silo. IT ops need to interact with dev ops. Within dev ops developers need to interact with QA. Storage admins need to collaborate with server admins, database admins and various other admins. So the tools need to encourage and provide a platform for collaboration. Similarly IT tools, IT management tools should not run standalone. They need to integrate with other tools. For example, if you want monitoring straight out of the box, the monitoring needs to integrate with provisioning processes. The monitoring downstream needs to integrate with ticketing systems. So integration with other tools, whether third party or custom developed, whatever it is, it's very, very important. Having said that, having laid what the solution should be, what the prescription should be, how is CA Technologies gearing up for it? In CA we have the industry's most comprehensive, the richest portfolio of infrastructure management tools, which is capable of managing all forms of infrastructure, traditional, private cloud, public cloud. Just to give you an example, in private cloud we support the traditional VMs as well as hyper converged infrastructure like Nutanix. We support Docker and other forms of containers. In public cloud we support the monitoring of infrastructure as a service, platform as a service, software as a service. We support all the popular clouds, AWS, Azure, Office 365 on Azure, as well as Salesforce.com. In terms of network, out net ops tools manage the latest and greatest SDN and SD-WAN, the VMware SDN, the open stack SDN, in terms of SD-WAN Cisco, Viptella. If you are a hybrid cloud customer, then you are no longer blindsided on things that are happening on the cloud side because we integrate with tools like Ixia. And once we monitor all these tools, we provide value on top of it. First of all, we monitor not only performance, but also packet, flow, all the net ops attributes. Then on top of that we provide predictive insights and learning. And because of our presence in the application performance management space, we integrate with APM to provide application to infrastructure correlation. Finally our monitoring is integrally linked with our operational intelligence platform. So in CA we have an operational intelligence platform built around CA Jarvis technology, which is based on open source technology, Elastic Logstash and Kibana, supplemented by Hadoop and Spark. And what we are doing is we are ingesting data from our monitoring tools into this data lake to provide value added insights and intelligence. When we talk about big data we talk about the three Vs, the variety, the volume and the velocity of data. But there is a fourth V that we often ignore. That's the veracity of the data, the truthfulness of data. CA being a leader in monitoring space, we have been in the business of collecting and monitoring data for ages, and what we are doing is we are ingesting these data into the platform and provided value added analytics on top of it. If you can read the slide, it's also an open framework we have the APIs from for ingesting data from third-party sources as well. For example, if you have your business data, your business sentiment data, and if you want to correlate that with IT metrics, how your IT is keeping up with your business cycles, you can do that as well. Now some of the applications that we are building, and this product is in beta as you see, are correlation between the various events, IT events and business events, network events and server events. Contextual log analytics. The operative word is contextual. There are a plethora of tools in the market that perform log analytics, but log analytics in the context of a problem when you really need it is of paramount importance. Predictive capacity analytics. Again, capacity analytics is not only about trending, right? It's about what if analysis. What will happen to your infrastructure? Or can your infrastructure sustain the pressure if your business grows by 2X, for example? That kind of what if analysis we should be able to do. And finally machine learning, we are working on it. Out of box machine learning algorithm to make sure that problems are not only corrected after the fact, but we can predict problems. We can prevent the problems in future. So for those who may be listening to this might be wondering where do we start? If you are already a CA customer, you are familiar with CA tools, but if you're not, what's the starting point? So I would recommend the starting point is CA Unified Infrastructure Manager, which is the market leading tool for hybrid cloud management. And it's not a hollow claim that we are making, right? It has been testified, it has been blessed by customers and analysts alike. And you can see it was voted the cloud monitoring software of the year 2016 by a third party. And here are some of the customer experiences. NMSP, they were able to achieve 15% productivity improvement as a result of adopting UIM. A healthcare provider, their meantime to repair, MTTR, went down by 40% as a result of UIM. And a telecom provider, they had a faster adoption to cloud as a result of UIM, the reason being UIM gave them for the first time a single pane of glass to manage their on prem and cloud environments, which has been a detriment for them for adopting cloud. And once they were able to achieve that, they were able to switch onto cloud much, much faster. Finally, the infrastructure management capabilities that I talked about is now being delivered as a turnkey solution, as a SAS solution, which we call digital experience insights. And I strongly, strongly encourage you to try UIM via CA digital experience insights, and here is the URL. You can go and sign up for the trial. With that, thank you.

>> Okay welcome back everyone to our special live presentation for cloud and IT analytics for the hybrid cloud. I'm John Furrier, your host. We just had an interview with Peter Burris, keynote presenter. Our second one-on-one conversation is with our second keynote, Sudip Datta is the Vice President of Product Manager for CA Technologies. Sudip, great to see you. Great keynote. >> Good to see you. Thank you. >> A lot of information on your keynote so folks can check it out online and on demand, but I wanted to ask you, you mentioned evolving infrastructure, so it's the first thing that you kind of set the table with. What do you mean by that? >> Sure. So first of all, as I mentioned in my keynote, the infrastructure today is intimately connected with business operations and the user experience, right? So how is the infrastructure evolving and catering to this ongoing demand of app economy? Before we get there, let's define what infrastructure means to CA, right? Infrastructure is servers, storage, network. Could be running on prem, could be running on public cloud, right? So let's look at what's happening on each layer, right. In the server layer, we are seeing bi-directional, somewhat antithetical movement, right? One on the consolidation side of things and the other on expansion to multiple clouds, right? On the consolidation side of things, of course there are re-amps and now we see more and more containers getting adopted like I was looking at a survey. The container growth between 2016 and 2017 is more than 40%. So we are also hearing about serverless compute, stateless compute, and so on and so forth. So that's on the server side of things, right? Storage, we are hearing about object storage. Network is getting more and more abstracted with software defined networking, right? Another survey portrayed that between 2014 and 2020, the SDN market is anticipated to grow at a CAGR of 53% and that's huge. Huge. So the infrastructure is evolving, getting more dynamic, getting more abstract, right? And therefore there are challenges to monitoring and management. >> And you're seeing growth in Kubernetes just to throw a cherry on top of that conversation because that's orchestrating the apps which require programmable infrastructure. >> Absolutely. >> I want to just make a comment, I was just talking with Peter Burris and I want to highlight one of your pieces of your keynote that you mentioned that there's four pillars of modern analytics and monitoring and Peter and I were talking about the digital business requirement for a modern infrastructure and I was kind of teasing it out, I want to see where he wanted to go with it, I kind of put him on the spot, but I was saying hey, data's been a department, analytics has kind of been a department, but now it's kind of holistic. He kind of slapped me around, said "no no, it's still going to be a department." Although technically right, I was trying to say there's a bigger picture. >> Sudip: Sure. >> This is kind of a mindset shift. People are are re-imagining their analytics as a strategic asset just like data's becoming a strategic asset. My question is, if you don't monitor it, how do you even understand it? So you need these four pillars, and they are policy based configuration that's dynamic, unified monitoring, contextual intelligence, and collaboration integration. With the trend of the true private cloud report, you're starting to see the shift in labor from non-differentiated to differentiated. And those kind of four pillars as kind of a breeding ground for innovation. Are they connecting, do you see that connecting into this new IT role? >> Absolutely. As you rightly pointed out, the non-differentiated labor is being replaced by automation, by machine learning, by scripts, whatever it is. It's whole-scale automation. So that itself lends to the fact that there is a different shade of labor which is the value-added labor. So how does labor create value? And that's related to the four pillars that we talked about. How to manage these dynamic environments and glean data out of these environments to provide valuable insights and intelligence. We talked about contextual intelligence. So when it comes to contextual intelligence, IT can be intimately involved with the business to provide the IT context to the business or the business context to the IT and vice versa and add value to the business. Giving a specific example... In prior times, IT used to be reactive. When business runs out of, runs a camping, they run out of capacity and they say we need to add servers and they're rolling a server and so on and so forth. Now, of course the automation side of server provisioning has been taken care of. There are a lot of APIs out there, there are Amazon cloud formations and all that, but you still need a policy that is going to proactively detect, perform a what-if analysis that if there is a 2x ramp in business, there is going to be corresponding pressure on infrastructure and act proactively. That way, I can get to be the friend of business. It's not really acting after the fact, but acting proactively. >> I was talking with Umar Kahn, one of your colleagues yesterday. We talked about cars. I love Teslas 'cause it's a great example of innovation and you got old cars and you got Teslas. Really we're seeing kind of a move in IT where modern looks like the Tesla of IT where things are just different but work much better. So I got to ask you a question, Tesla's a great cool car, there's a lot of hype and buzz around it, but it's still got to drive, right? It's still got to be great. So you mentioned faults, fault detection and machine learning in your presentation, but IT ops still needs to run. And you got IOT Edge that Peter pointed out that needs to be figured out. So you got to figure out these new things and you got to run stuff, so you need the fault detection with the machine but you still got to be cool. Like the Tesla of IT. How are you guys becoming the Tesla of IT? >> Absolutely. I will touch upon a few points. First of all, as I mentioned right at the beginning, that data is important but we focused on the three Vs of data, which is velocity, volume and variety. But there is also the veracity of data and CA has been in the business of monitoring, capturing this data from various systems. From mobiles to mainframes, right? For the last few decades, right? So we have the true data, we are collecting the data, and now we are building a data analytics platform on which the data will be ingested and we will give insights. So that's going to be a big differentiator. The other is, we have all the tools from application management to infrastructure management tools, net ops tools, and we are connecting all of them to cover the entire digital chain. The reason is important, and I will highlight only one particular aspect of it. Network, the most neglected compliment in the infrastructure-- >> And the most important. Everyone complains about the network the most. >> Most important. Even when a kid plays a video game, it's an app. Most of us tend to forget that it's an app and the most important element in that app is the network. And we are in the business of network management so we are not only server and storage and app, we are also tying network management into this overall analytics platform. And within network management, it's tacit management, flow management. These are all important things because today's world, if your network betrays on you, then your user experience-- >> So I got to ask you, the products are in the company. And this is kind of important because most people who think about monitoring analytics would have kind of a different view based on what they're instrumenting. You're kind of talking about network and apps. You're kind of looking at the big picture. Are you tying that together? >> Absolutely. >> Can you explain how? >> Absolutely. So CA has been a market leader in application performance management and in network management and in server and cloud management. So we are tying all this together, the whole digital chain, as I said we are ingesting all the data into an open standard space, open source-based analytics platform, and we are collating the data so you can see what are the networks elements that preceded before a server got choked? Or before the application became inaccessible? We can tie it all together, all the units together, and perform assisted create and root cause analysis. >> Well I wanted to put you on the spot today because we are live, so I got to ask you as the VP of Product Management, what's your favorite product? Do you have a favorite child? (laughing) >> I mean, all of them are my favorite. >> There it is! Of course you can't pick a favorite, everyone's watching. >> Yeah, so yeah. >> As a parent you can't pick a favorite child. They're all good in their own way, right? >> They're all kind of horses for courses. Really, they do fabulous things. At the same time, we don't want the proliferation of tools. We are trying to rationalize tools like the net ops, the cloud ops and application performance management and tie them all together into our analytics platform. You can say like the analytics is my favorite word today because that's the new kid on the block but as I said, all of them are very very important. >> Well I always say, whoever could be the Tesla for IT is going to win it all. So with that, serious question, as VP of Product Management, do want to ask a serious question around that. What's your North Star? When you talk to your product teams, they're specking out products, they're talking to customers, and the engineers are building it out. What is the North Star? What is the ethos of CA these days? 'Cause you guys are pushing the envelope while maintaining that install base of customers. What is the North Star? What is the ethos? What's the guiding principles for CA Technologies? >> Absolutely. Customers, customers and customers, right? And the reason being, and I will give you... Of course, the user experience matters, but there is also an empirical reason. We are a market leader in the MSP space, for example. MSP and and just the space, and not only do we care about our customers but the customers of our customers as well. MSPs like ONE-NET, and Bespin Global, that you see is monitoring tools for managing their customers. So our allegiance goes all the way to our customers and their customers. So that's a guiding principle. But at the same time, we try to innovate beyond what our customers have been asking for. That's where the intuitive integration between application performance management, infrastructure management, network management, comes. And we want to be absolutely a leader in this end-to-end management. >> We talk with our WebOn team all the time and Peter and I talk about with Dave Alante all the time about how important IT operations are going to be right now because all the market research shows, Peter mentioned it, private cloud, true private cloud, hybrid cloud, massive growth area. Lot of opportunities for ops to really deliver value because the dev-ops momentum, because of the things like containers and Kubernetes, the programmable infrastructure has to be there. So I got to ask you the question, from a customer standpoint, and folks watching. What's the most important thing that your customers need to know when they start to re-think the architecture and ultimately make that 10 to 20 year investment in this new modern IT operations with CA? >> Sure. The first thing is, and I will re-visit the four pillars, right? That the dynamic, discovery, policy-based management is very very important because discovery, a lot of times we neglect discovery because it's always there. But the thing is, that's the starting point. That's the cradle where the overall monitoring takes birth. So that's the first point. The second is bring everything into, if not a single but minimal panes of glass. Maybe net ops has a tool and cloud ops has a tool and of course you have a tool for applications performance management. So those are the building blocks of monitoring. And then, overlay it with contextual intelligence and analytics. As I said, we are ingesting all the data, not only from CA tools, but using open APIs from other tools into our analytics framework and provide contextual intelligence. And last but not the least, collaboration and integration. We are integrating with frameworks such as Slack to provide collaboration between dev ops and IT ops, between storage admins and server admins, and so on and so forth, right? So those are the building blocks. So if you are thinking about what you are going to do in 10 years timeframe, first of all, hybrid cloud is a reality. So for managing the overall, entire spectrum of hybrid cloud, you need a tool that's unified, that can do dynamic policy-based management, that can provide intelligence, and that can encourage collaboration. >> Sudip, thank you so much for sharing this one-on-one conversation. For the folks watching, there's a great slide that outlines that operational intelligence. It was a beautiful eye candy, it's like an architecture slide, I was geekin' out on it. Check it out on the keynote on the on demand. Sudip, thank you so much for sharing your insight here on the future of modern analytics and monitoring strategies. This is a special presentation. One-on-one drill-down with keynote presenter Sudip Datta who is the Vice President of Product Management, part of the cloud and IT analytics digital business. We'll be right back with more one-on-one interviews after this short break.

>> Welcome to our Palo Alto studio, all of you coming in over the airwaves. It's a wonderful opportunity today to talk about something very important with Computer Associates or, CA Tech, as they're now known. And I want to highlight one point about the slide title, the title they chose for the day, we chose for the day, Cloud and Hybrid IT Analytics for Digital Business. One of the most interesting things that you're going to hear about today is that it's going to keep coming back to business challenges and business problems. At the end of the day that's what the focus needs to be on. While we certainly do want to do more with the technology we have and drive greater effectiveness and utilization out of the technology that we use in our digital business, increasingly the ability to tie technology decisions to business outcomes is possible and all IT professionals must make that effort, as well as all IT vendors, if the community is going to be successful. Now what I'm going to talk about specifically is how cloud monitoring plays inside this drive to increase the effectiveness of business through digital technologies. And to do that, I'm going to talk about a few things. The first thing I'm going to talk about is what is a digital business and how does it impact strategic technology capabilities? Now the reason why this is so important is because there's an enormous amount of conversation in the industry about digital businesses, multi-channel for digital businesses, customer experience for digital businesses, some other attribute. And while those are all examples or potential benefits of digital business, at its core digital business is something else. We want to articulate what that is because it informs all decisions that we're going to make about a lot of different things. The second thing I'm going to talk about is this notion of advanced analytics and how advanced analytics are crucial to not only achieving the outcomes of digital business but also to sustain the effort in the transformation process. And as you might expect, if we're going to use analytics to improve our effectiveness, then we have to be in a position to gather the data that we need from the variety of resources necessary to succeed with a digital business strategy. Those are the three things I'm going to talk about but let's start with this first one. What is digital business and how does it impact technology capabilities? Now to do that, I want to show you something that we're quite proud of here at Wikibon SiliconANGLE because we're a research firm and a company that's dedicated to helping communities make better decision. The power of digital community is clear. It's a very, very important resource, overall, inside any business. And what we do is we have a tool that we call CrowdChat. And the purpose of CrowdChat is to bring together members of the community and surface the best insights they have about their undertakings. Now I'm not using this to just pitch what CrowdChat is, I really want to talk through how this is a representation of the power of digital community. I want to point you to a few things in this slide. First off, note that it's, very importantly, this was from a CrowdChat that we did on 31 January 2017 but the thing to note here is a couple of things. Now let's see if I can click through them here. Well the first thing to note is that it reached 3.4 million people linked to the technology decision making. Think about that. Wikibon SiliconANGLE is not a huge company. We're a very focused company that strongly emphasizes the role that technology can play in helping to make decisions and improve business outcomes. But this CrowdChat reached 3.4 million decision makers as part of our ongoing effort. And it clearly is an indication, ultimately, that today customers, in fact, are at the center of what goes on within digital business decision making. So customers are at the centers of these crucial market information flows. Now this is going to be something we come back to over and over and over. It used to be that folks who sold stuff were the primary centers of what happened with the information flows of the industry. But through social media, tools like CrowdChat and others, today customers are in a much better position overall to establish their voices and share their insights about what works and what doesn't work. In many respects, that is the core focus of digital business. So that leads us to this question of what is digital business. Now I am a fan of Peter Drucker. It's hard to argue with Peter Drucker and it's one of the reasons I start with Peter Drucker is because people don't typically argue with me when I start there. And Peter Drucker famously said many years ago that the purpose of a business is to create and keep a customer. Now you can go on about what about shareholder value, what about employees, and those are all true things. There's no question that that's also important. But the fundamental keeps coming back that if you don't have customers and you don't provide a great experience for those customers, you're not going to have a business. So what's the difference between digital business and business? The biggest difference between digital business and business and in fact how we properly define the concept of digital business is that digital businesses apply data to create and keep customers. That's the basis of digital business. It's how do you use your data assets to differentiate your business and especially to provide a superior experience, a superior value proposition, and superior outcomes for your customers. That is the core of digital business. If you're using data to differentiate how you engage customers, how you provide that experience for customers, and how you improve their outcomes, then you are more digital business than you were yesterday. If you use more data, you are more digital business than your competition. So this is a way of properly thinking about the role of digital business. And to summarize it slightly differently, what we strongly believe is that what decision makers have to do over the course of the next number of years is find ways to put their data to work. That is the fundamental goal of an IT professional today. And increasing, increasingly the goal of many business professionals. Find ways to apply data so that you can increase the work the firm does for customers. That's kind of the simple thread we're trying to pull here. Data, put to work, superior customer experience. Now at the centerpiece of this simple prescriptive is an enormous amount of complexity. A lot of decisions that have to be made because most businesses are not organized around their data. Most businesses don't institutionalize the way they engage customers or perform their work based on what their data assets can provide. Most businesses are built around the hardware, at least if you're an IT person, they're built around the hardware assets or maybe even the application assets. But increasingly it's become incumbent on CIOs and IT leaders to recognize that the central value of the business, at least that they work with, is the data and how that data performs work for the business. So that leads to the second question. Given the enormity of data in the future of digital business, we have to ask the question, "Well what role "is advanced analytics playing to keep us on track "as we thing about, ultimately, driving forward "for a digital business?" Now we draw this picture out to customers to try to explain the things that they'll have to do to become an increasingly digital business. And it starts with this idea that a digital business transformation requires investment in new capabilities, new business capabilities that foster the role that digital assets can play within the business that simplify making decisions about where to put people and how to institutionalize work and ultimately help sustain the value of the data within the business over time. And a way to think about it is that any digital business has to establish the capabilities to better capture data create catalysts from data. Now what do we mean by that? We mean basically that data is a catalyst for action. Data can actually be the source of value if you're a media company, for example. But in most businesses data is a catalyst, the next best action, a better prediction of superior forecast, a faster and simpler, and less expensive report for compliance purposes. Data is a catalyst. So we capture it and we translate it into a catalyst that then can actually guide action. That's the simple set of capabilities that we have to deploy here. Capturing data, turning it into the catalysts that then have consequential impacts in front of customers, provides superior experience and better business. Now if we try to map those prescriptions for business capabilities onto industry buzzwords, here's what we end with. Capture Data, well that's the centerpiece of what the industrial internet of things is about, or the internet of things is about, if we're talking mainly about small devices in a consumer world. Capturing data is essential and IIoT is going to be crucial to that effort as well as mobile computing and other types of things. We like to talk about it sometimes is the internet of things and people. Big data and analytics should be properly thought of as helping businesses turn those streams of information into models and insights that can lead to action. So that's what the whole purpose of what big data analytics is all about. It's not to just capture more data and store more data, it's about using that data that comes from a lot of different locations and turning it into catalysts, sources of value within the business. And the final one is branded customer experience. At the end of the day, what we're talking about is how we're going to use digital technology to better engage our customers, better engage our partners, better engage our markets, and better engage our employees. And increasingly, as customers demonstrate a preference for greater utilization of digital technology in their lives, the whole notion of a branded experience is going to be tied back to how well we provide these essential digital capabilities to our customers in our markets. So analytics plays an incredibly important role here because we've always been pretty good at capturing data and we've always, we're getting better I guess I should say, at utilizing insights from that data that could be gleaned on an episodic basis and turning that into some insight for a customer. Usually really smart people in sales or marketing or manufacturing or product management play that role. But what we're talking about is operationalizing, turning data into value for customers on a continuous ongoing basis. And Analytics is crucial for that and analytics also is crucial to ensure that we could stay on track as we effect these transformations and transitions. Now I want to draw your attention, obviously, to an important piece as we go forward here. And that is this notion how do we capture that data so that it is appropriately prepped and set up so that we can create value from analytics. And that's going to be the basis of the third point that I'm going to talk about. Why is hybrid cloud monitoring emerging as a crucial transformation tool? Now monitoring has been around for a long time. We've been monitoring individual assets to ensure we get greater efficiency and utilization. CA's been a master of that for 30, 35 years. Increasingly though, we need to think about how systems come together in a lot of different ways to increase what we call the plasticity of the infrastructure. The ability of the infrastructure to not only scale but to reconfigure itself in response to the crucial new work that digital businesses have to perform. So how's that going to play out? It's become very popular within the industry to talk about how data is going to move to the cloud. And that's certainly going to happen. There's going to be a lot of data that ends up in the cloud. But as we think about the realities of moving data, data is not just an ephemeral thing. Data has real physical characteristics, real legal implications. And ultimately intellectual property is increasingly rendered in the form of data. And so we have to be very careful how we think about data being moved across the enterprise into any number of different locations. It's one of the most strategic decisions that a board of directors is going to make. How do we handle and take care of our data assets? Now I want to focus just on one element of that. Hopefully provide a simple proof point to make this argument. And that is, if we looked at how data is generated, for example, in an Edge setting. Say we looked at the cost of moving data from a wind farm. A relatively small straightforward wind farm with a number of different sensors. What does it cost to move that data to the cloud? And that's provided here. If we think about the real costs of data, the cost of moving data from an Edge situation, even in a relatively simple example, back to the cloud can be dramatic. Hundreds of thousands of dollars. Limitations based on latencies, concerns about traversing borders that have legal jurisdictions, and obviously also, as I said, the intellectual property realities. But the bottom line here is that it shows that it's going to be much cheaper to process the data in place, process the data close to where the action needs to be taken, than to move it all to the cloud. And we think that's going to become a regular feature of how we think about setting up infrastructure in business in the future. Increasingly, it's not going to be about moving data to the cloud only, we're going to have additional options about moving cloud and cloud services to the data. Increasingly this is going to be the tact that businesses are going to take. It's find ways to move that sense of control, that notion of quality of service, and that flexibility in how we provision infrastructure so that the cloud experience comes to where the event needs to take place. That going forward will be the centerpiece of a lot of technology decision making. It doesn't mean we're not going to move data to the cloud it just means that we're going to be smart about when we do it, how we do it, and understanding when it makes more sense to move the cloud or the cloud set of services closer to the event so that we can process it in place. Now this is a really crucial concern because it suggests there's going to be a greater distribution of data and not a greater centralization of data. And you can probably see where I'm going with this. Greater distribution of data ultimately means that there's going to be a lot more things that require that we have to have visibility into their performance, visibility into how they work. If it was all going to be in one place then we could let someone else actually handle a lot of those questions about what's going on, how is it working. But as our businesses become more digital and our data assets become more central to how we provide customer experience, it means that the resources that we use to generate value out of those assets have to be managed and monitored appropriately. Now we have done a lot of work around this and what our research pretty strongly shows is that over the next 10 years, we're going to see three things happen. First off, we're going to see a lot of investment in public cloud options both in the form of SaaS as well as infrastructure as a service. So that will continue. There's no question that we're going to see some of the big public cloud suppliers become more important. But our expectation also, is we will see significant net new investment in what we call true private cloud. The idea of moving those cloud services on premise so that we can support local events that need high quality data and that kind of capability. The second thing I want to point out here is that while we do expect to see significant net new efficiencies and how we run all these resources, if we look at the cost of labor over the course of operational labor over the course of the next decade, we do expect to see the cost go down about around 7%. So we will see greater productivity in the world of IT labor. But it's not going to crash like many people predict. And one of the reasons it's not going to crash is because of the incredible net new reports of digital assets. But the third thing to note here is that we are not going to see the type of massive dumping of traditional infrastructure that many people predict. There's too many assets, too much value already in place in a lot of systems, and instead what we're going to see is a blending of all of these different capabilities in a rational way so that the business can achieve the digital outcomes that it seeks. The challenge, though, over the course of the next decade, however, is going to be to find ways, while we're going to have all these different resources, be a feature of our technology plan, be a feature of how we run our business. Historically we've tended to think about these in silos and the monitoring challenge that we put in place was to better generate efficiencies out of an individual asset. Well as we go forward, increasingly we need to think about how not one resource works, but how all these resources work. It's time for business to think about the internet not as something that's external, but as the basis for their computing. The internet is a computer. How we slice it up for our business is a statement about how we're going to build a set of distributive capabilities but weave them together so that we have a set of resources that can, in fact, reflect the business needs and support business requirements. And monitoring becomes crucial to that because as we move forward the goal needs to be to be able to enfranchise, federate a lot of these distributive resources into a working coherent statement of how computing serves our business. And that's going to require an approach that is much more focused on how things come together and how things can be bought into a coherent whole as opposed just the efficiency of any single tool or any single device. That's where digital business has to go, how can we bring all of these resources together into a coherent whole that supports our business needs. And that is the goal of the next generation of monitoring is to make that possible. Okay, so as we think about what we've talked about we basically made a couple of points here. The first when we talked about what is digital business, the first point that I made is data is the digital business asset. That's what we're trying to do here is use data to improve the effectiveness of the outcomes that we seek for customers. Digital business elevates IT but forces real and material changes. The second point that I made is how are advanced analytics helping. Well analytics turns business, or turns data into business catalysts that ultimately guide and shape customer experience. Crucial point. And the last point that I want to make is when we think about cloud monitoring remember that if we move forward in the digital world, as you make choices, your brand fails when your infrastructure fails. So as a consequence for those of you who are in the midst of thinking about the future role that monitoring is going to play in your world, choose your suppliers carefully. It's not about having a tool for a device, it's about thinking about how all of this can be, how monitoring can bring a lot of different resources into a coherent picture to ensure that your business is able to process, compute, store, and effect dramatic improvements to customer experience across the entire infrastructure asset. And the last thought that I'll leave you with is that CA Tech has been one of the companies of the vanguard of thinking about how this is going to work over the next decade in the industry.

>> Hey, welcome back everyone. We're here live in the Palo Alto Cube studios for our special digital live event sponsored by CA Technologies. I'm here with Peter Burris, Head of Research Wikibon.com, General Manager of Research for SiliconANGLE Media. Peter, you gave the Keynote this morning along with Sudip Datta talking about analytics. Interesting connection. Dave has been around for a while but now it's more instrumental. CA's had analytics, and monitoring for a while, now it's more instrumental. That seems to be the theme we're seeing here with the research that you're representing and your insight around digital business. Some of the leading research on the topic. Your thoughts on how they connect, what should users know about the connection between data and business, CA analytics and data? >> I think two things, John, first off as I kind of mentioned number one is that more devices are going to be more instrumental to the flow of, to the information flow to the data flows are going to create business value, and that's going to increase the need for greater visibility into how each of these things work together individually, but increasingly it's not just about having individual devices or individual things up and running or having visibility into them. You have to understand how they end up interacting with each other and so the whole modern anthropology becomes more important. We need to start finding ways of improving the capability of monitoring while at the same time simplifying it is the only way that we're going to achieve the goal of these increasingly complex infrastructures that nonetheless consistently deliver the business value that the business requires and customers expect. >> It's been interesting, monitoring has been around for awhile, you can monitor this, you can monitor that, you can kind of bring it all together in a database, but as we move to the cloud and you're seeing internet or things as you pointed out, there's a real connection here and the point that I wanted to talk about is, you mentioned the internet as a computer. Okay, which involves, system software kind of thinking, Let's tease that out. I want to unpack that concept because if the internet now is the platform that everyone will be basing and reimagining their business around, how do companies need to figure this out because this is on everyone's mind because it might miss the fact that it costs a hell of a lot of cash just to move stuff from the edge to the cloud or even just architectural strategies. What's that importance of the internet as a computer? >> Well, the notion of internet scale computing has been around for quite sometime. And the folks who take that kind of systems approach to things, may of them are sitting within 50 miles of where we sit right here. In fact, most of them. So, Google looks at the internet as a computer, that it can process. Facebook sees things the same way. So, if you look at some of these big companies that are actually thinking about internet scale computing, any service, any data, anytime, anywhere, then that thinking has started to permeate, certainly Silicon Valley. And in my conversations with CIO's, they increasingly want to think the same way. What is it, what, how do I have to think about my business relative to all of the available resources that are out there so I can have my company think about gaining access to a service wherever it might be. Gaining access to data that would be relevant to my company, wherever it might be. Appropriately moving the data, minimizing the amount of data that I have to move. Moving the events to the data when necessary. So, the, this is, in many respects the architectural question in IT today. How do we think about the way we weave together all these possible resources, possible combinations into something that sustains, sustainably delivers business value in a coherent manageable, predictable way? >> It's interesting, you and I have both seen many waves of innovation going back to the mini computer mainframe days and there used to be departments called data processing and this would be departments that handle analytics and monitoring. But now we're in a new era, a modern era where everything can be instrumented which elevates the notion of a department into a holistic perspective. You brought this up in your talk during the Keynote where it said data has to permeate throughout the organization whether it's IOT edge or wherever, so how do companies move from that department mindset, oh, the department handles the data warehouse or analytics, to a much more strategic, intelligent system? >> Well, that's an interesting question, John. I think it's one of the biggest things a business, you're going to have to think about. On the one hand, our expectations, we will continue to see a department. And the reason why that is, but not in a way that's historically been thought about, one of the reasons why that is, is because the entire business is going to share claims against the capabilities of technology. Marketing's going to lay a claim to it. Sales is going to lay claim to it. Manufacturing and finance are going to lay claims to it. And those claims have to be arbitrated. They have to be negotiated. So there will be a department, a group that's responsible for ensuring that the fundamental plant, the fundamental capabilities of the business are high quality and up and running and sustained. Having said that, the way that that is manifest is going to be much faster, much more local, much more in response to customer needs which often will break down functional type barriers. And so it's going to be this interesting combination of, on the one hand for efficiency and effectiveness standpoint, we're going to sustain that notion of a group that delivers while at the same time, everybody in the business is going to be participating more clearly in establishing the outcomes and how technology achieves those outcomes. It's very dynamic world and we haven't figured out how it's all going to come together. >> Well, we're seeing some trends, now you're seeing the marketing departments and these other departments taking some of that core competence that used to be kind of outsourced to the IT departments so analytics are moving in and data science and so you're seeing the early signs of that. I think modern analytics that CA was talking about was interesting, but I want to get your thoughts on the data value piece cause this is another billion dollar question or gazillion dollar question. Where is the value in the data? And from your research in the impact of digital business, where's the value come from? And how should companies think about extracting that value? >> Well, the value, first off, when we talk about the value of data we perhaps take a little license with the concept. And by that I mean, software to a computer scientist is data. It happens to be the absolutely most structured data you can possibly have. It is data that is so tightly structured that it can actually execute. So we bring software in under that rubric of the value of data. That's one way. The data is the basis for software and how we think about the business actually having consequential actions that are differentiated, increasing the digital world. One of the most important things, ultimately, about data is that unlike virtually every other asset that I can think about, money, labor, materials, all of those different types of assets are dominated by the economics of scarcity. You and I are sitting here having a conversation. I'm not running around and walking my dog right now. I can only do one thing with my time. I may have in my mind, thinking, but I can't create value at the same moment that I'm talking to you. I mean, we can create value here, I guess. Same thing if you have a machine and the machine is applied to pull a wire of a certain diameter, it's not pulling a wire of a different diameter. So these are all assets or sources that are dominated by scarcity. Data's different because the characteristics of data, the things that make data so unique and so interesting is that the same data can be applied to a lot of things at the same time. So we're talking about an asses that can actually amplify business value if it's appropriately utilized. And I think this is one of the, on the one hand, one of the reasons why data is often regarded, it's disposable, is because, oh I can just copy it or I can just do this with it or I can do that with it. It just goes away, it's ephemeral. But on the other hand, why leading businesses and a lot of these digital native companies, but increasing the other companies are now recognizing that with data as an asset, that kind of a thinking, you can apply the same data to a lot of different pursuits at the same time and quite frankly, that's what our customers want to see. Our customers want to see their requests, their needs be matched to capabilities, but also be used to build better products in the future, be used to ensure that the quality of the services that they're getting is high. That their needs are being met, their needs are being responded to. So they want to see data being applied to all these different uses. It's an absolutely essential feature in the future of digital business. >> And you've got to monitor in order to understand it. And for the folks watching, Peter had a great description in his Keynote, go check that video out around the elements of the digital business, how it's all working together. I'll let you go look at that. My final question for you is, you mention in your Keynote, the Wikibon private, true private cloud report. One of the things that's interesting in that graph, again on the Keynote he did present the slide, it's also on Wikibon.com if you're a member of the research subscription. It shows that actually the on premise assets are super valuable and that there's going to be a decline in labor, non differentiated labor or operational labor over the next six, seven years, around 1.6 billion dollars, but it shifts. And I think this was your point. Can you just explain in a little deeper way, the importance of that statistic because what it shows is, yes, automations coming. Whether it's analytics or machine learning and what not, but the value's shifting. Can you talk about that? >> Yeah, the very nature of the work that's performed within what we today call IT operations is shifting. It always has been. So when I was running around inside an IT organization, I remember some of the most frenetic activity that I saw was tape jockeys. We don't have too many tape jockeys in the world anymore, we still have tape, but we don't have a lot of tape jockeys anymore. So the first thing it suggests is that the very nature of the IT work that's going to be performed is going to change over the next few years. It's going to change largely in response to the fact that as folks recognize the value of the data and acknowledge that the placement of data to the event is going to be crucial to achieving that event within the envelope of time that that event requires. That ultimately the slow motion of dev op, which is still a maturing, changing, not broadly adopted set of concepts will start to change the nature of the work that we perform within that shared IT organization we were talking about a second ago. But the second thing it says is that we are going to be called upon to do a lot more work within an IT organization. A digital business is utilizing technology to perform a multitude of activities and that's just going to explode over the course of the next dozen years. So we have this combination of the works going to change, the amount of work that has, that's going to be performed by this group is going to expand dramatically, which means ultimately the only way out of this is the tooling is going to improve. So we expect to see significant advances in the productivity of an individual within an IT organization to support, sustain a digital business. And that's why we start to see some of the down tick in the cost of labor within IT. It's more important, more works going to be performed, but it's pretty clear that the industries now focus on improving that tooling and simplifying the way that that tooling works together. >> And having intelligence. >> Having intelligence, but also simplifying how it works together so it becomes more coherent. That's where we're going to need to improve these new levels of productivity. >> Real quick to end this segment, quickly talk about how CA connects to this because you know, they have modern analytics, they have modern monitoring strategies, the four pillars that you talked about. How do they connect into your research that you're talking about? >> Well I think one of the biggest things that a CIO is going to have to understand over the course of the next few years and we talked about a couple of them is, that this new architecture is not fully baked yet. We don't know what the new computing model is going to look like exactly. You know, not every business is Google. So Google's got a vision of it. Amazon's got a vision of it. But not every business is of those guys. So a lot of work on what is that new computing model? A second thing is this notion of ultimately where is or how is an IT organization going to deliver value? And it's clear that you're not going to deliver value by optimizing a single resource. You're going to deliver value by looking at all of these resources holistically and understand the inner connections and the interplay of these resources and how they achieve the business outcomes. So when I think about CA, I think of two things. First off, it is a company that has been at the vanguard of understanding how IT operations has worked, is working, and will likely continue to work as it evolves. And that's an important thing for a technology company that's serving IT operations to have. The second thing is, CA's core message, CA's tech core message now is evolving from just best of breed to how these things are going to come together. So the notion of modern moddering is to improve the visibility into everything as a holistic whole going back to that notion of, it's not just one device, it's how all devices holistically come together and the moddering fabric that we put in place has to focus on that and not just the productivity of any one piece. >> It's like an early day's test lick, it only gets better as they have that headroom to grow. Peter Burris head of research at Wikibon.com here, for one-on-one conversations, part of the cloud and modern analytics for digital business. Be back with more one-on-one conversations after this short break.

>>from around the globe. It's the queue with digital coverage of AWS public sector online brought to you by Amazon Web services. >>Everyone welcome back to the Cube's virtual coverage of AWS Public sector summit online. That's the virtual conference. Public Sector Summit is the big get together for Teresa Carlson and her team and Amazon Web services from the public sector, which includes all the government agencies as well as education state governments here in United States and also abroad for other governments and countries. So we're gonna do an analysis of Teresa's keynote and also summarize the event as well. I'm John Furrow, your host of the Cube. I'm joined with my co host of the Cube, Dave Volante Stew Minimum. We're gonna wrap this up and analyze the keynote summit a really awkward, weird situation going on with the Summit because of the virtual nature of it. This event really prides itself. Stew and Dave. We've all done this event. It's one of our favorites. It's a really good face to face environment, but this time is virtual. And so with the covert 19 that's the backdrop to all this. >>Yeah, so I mean, a couple of things, John. I think first of all, A Z, you've pointed out many times. The future has just been pulled forward. I think the second thing is with this whole work from home in this remote thing obviously was talking about how the cloud is a tailwind. But let's face it. I mean, everybody's business was affected in some way. I think the cloud ultimately gets a tail wind out of this, but but But I think the third thing is security. Public sector is always heavily focused on security, and the security model has really changed overnight to what we've been talking about for years that the moat that we've built the perimeter is no longer where organizations need to be spending money. It's really to secure remote locations. And that literally happened overnight. So things like a security cloud become much, much more important. And obviously endpoint security and other other things that we've talked about in the Cube now for last 100 days. >>Well, Steve, I want to get your thoughts cause you know, we all love space. Do we always want to go the best space events that they're gonna be virtual this year as well? Um, But the big news out of the keynote, which was really surprising to me, is Amazon's continued double down on their efforts around space, cyber security, public and within the public sector. And they're announcing here, and the big news is a new space business segment. So they announced an aerospace group to serve those customers because space to becoming a very important observation component to a lot of the stuff we've seen with ground station we've seen at reinvent public sector. These new kinds of services are coming out. It's the best, the cloud. It's the best of data, and it's the best of these new use cases. What's your thoughts? >>Yeah, interesting. John, of course. You know, the federal government has put together Space Forces, the newest arm of the military. It's really even though something it is a punchline. There's even a Netflix show that I believe got the trademark board because they registered for it first. But we've seen Amazon pushing into space. Not only there technology being used. I had the pleasure of attending the Amazon re Marcia last year, which brought together Jeff Bezos's blue origin as well as Amazon AWS in that ecosystem. So AWS has had a number of services, like ground Station that that that are being used to help the cloud technology extend to what's happening base. So it makes a lot of sense for for the govcloud to extend to that type of environment aside you mentioned at this show. One of the things we love always is. You know, there's some great practitioner stories, and I think so many over the years that we've been doing this show and we still got some of them. Theresa had some really good guests in her keynote, talking about transformation and actually, one of the ones that she mentioned but didn't have in the keynote was one that I got to interview. I was the CTO for the state of West Virginia. If you talk about one of those government services that is getting, you know, heavy usage, it's unemployment. So they had to go from Oh my gosh, we normally had people in, you know, physical answering. The phone call centers to wait. I need to have a cloud based contact center. And they literally did that, you know, over the weekend, spun it up and pulled people from other organizations to just say, Hey, you're working from home You know you can't do your normal job Well, we can train your own, we can get it to you securely And that's the kind of thing that the cloud was really built for >>and this new aerospace division day this really highlights a lot of not just the the coolness of space, but on Earth. The benefits of there and one of Amazon's ethos is to do the heavy lifting, Andy Jassy told us on the Cube. You know, it could be more cost effective to use satellites and leverage more of that space perimeter to push down and look at observation. Cal Poly is doing some really interesting work around space. Amazon's worked with NASA Jet Propulsion Labs. They have a lot of partnerships in aerospace and space, and as it all comes together because this is now an augmentation and the cost benefits are there, this is going to create more agility because you don't have to do all that provisioning to get this going spawned. All kinds of new creativity, both an academic and commercial, your thoughts >>Well, you know, I remember the first cloud first came out people talked a lot about while I can do things that I was never able to do before, you know, The New York Times pdf example comes to mind, but but I think what a lot of people forget is you know the point to a while. A lot of these mission critical applications Oracle databases aren't moving to the cloud. But this example that you're giving and aerospace and ground station. It's all about being able to do new things that you weren't able to do before and deliver them as a service. And so, to me, it shows a great example of tam expansion, and it also shows things that you never could do before. It's not just taking traditional enterprise APs and sticking them in the cloud. Yeah, that happens. But is re imagining what you can do with computing with this massive distributed network. And you know, I O. T. Is clearly coming into into play here. I would consider this a kind of I o t like, you know, application. And so I think there are many, many more to come. But this is a great example of something that you could really never even conceive in enterprise Tech before >>you, Dave the line on that you talked about i o t talk a lot about edge computing. Well, if you talk about going into space, that's a new frontier of the edge that we need to talk about >>the world. Glad it's round. So technically no edge if you're in space so again not to get nuance here and nerdy. But okay, let's get into the event. I want to hold on the analysis of the keynote because I think this really society impact public service, public sector, things to talk about. But let's do a quick review of kind of what's happened. We'll get to the event. But let's just review the guests that we interviewed on the Cube because we have the cube virtual. We're here in our studios. You guys were in yours. We get the quarantine cruise. We're still doing our job to get the stories out there. We talked to Teresa Carlson, Shannon Kellogg, Ken Eisner, Sandy Carter, Dr Papa Casey Coleman from Salesforce, Dr Shell Gentleman from the Paragon Institute, which is doing the fairground islands of researcher on space and weather data. Um, Joshua Spence math you can use with the Alliance for Digital Innovation Around some of this new innovation, we leave the Children's National Research Institute. So a lot of great guests on the cube dot net Check it out, guys. I had trouble getting into the event that using this in Toronto platform and it was just so hard to navigate. They've been doing it before. Um, there's some key notes on there. I thought that was a disappointment for me. I couldn't get to some of the sessions I wanted to, um, but overall, I thought the content was strong. Um, the online platform just kind of wasn't there for me. What's your reaction? >>Well, I mean, it's like a Z. That's the state of the art today. And so it's essentially a webinar like platforms, and that's what everybody's saying. A lot of people are frustrated with it. I know I as a user. Activity clicks to find stuff, but it is what it is. But I think the industry is can do better. >>Yeah, and just to comment. I'll make on it, John. One of things I always love about the Amazon show. It's not just what AWS is doing, But, you know, you walk the hallways and you walk the actual So in the virtual world, I walk the expo floor and its okay, Here's a couple of presentations links in an email address if you want to follow up, I felt even the A previous AWS online at a little bit more there. And I'm sure Amazon's listening, talking to all their partners and building out more there cause that's definitely a huge opportunity to enable both networking as well. As you know, having the ecosystem be able to participate more fully in the event >>and full disclosure. We're building our own platform. We have the platforms. We care about this guys. I think that on these virtual events that the discovery is critical having the available to find the sessions, find the people so it feels more like an event. I think you know, we hope that these solutions can get better. We're gonna try and do our best. Um, so, um well, keep plugging away, guys. I want to get your thoughts. They have you been doing a lot of breaking analysis on this do and your interviews as well in the technology side around the impact of Covert 19 with Teresa Carlson and her keynote. Her number one message that I heard was Covad 19 Crisis has caused a imperative for all agencies to move faster, and Amazon is kind of I won't say put things to the side because they got their business at scale. Have really been honing in on having deliverables for crisis solutions. Solving the problems and getting out to Steve mentioned the call centers is one of the key interviews. This is that they're job. They have to do this cove. It impacts the public services of the public sector that she's that they service. So what's your reaction? Because we've been covering on the commercial side. What's your thoughts of Teresa and Amazon's story today? >>Yeah, well, she said, You know, the agencies started making cloud migrations that they're at record pace that they'd never seen before. Having said that, you know it's hard, but Amazon doesn't break out its its revenue in public sector. But in the data, I look at the breaking analysis CTR data. I mean, it definitely suggests a couple of things. Things one is I mean, everybody in the enterprise was affected in some way by Kobe is they said before, it wouldn't surprise me if there wasn't a little bit of a pause and aws public sector business and then it's picking up again now, as we sort of exit this isolation economy. I think the second thing I would say is that AWS Public sector, based on the data that I see, is significantly outpacing the growth of AWS. Overall number one number two. It's also keeping pace with the growth of Microsoft Azure. Now we know that AWS, on balance is much bigger than Microsoft Azure and Infrastructures of Service. But we also know that Microsoft Azure is growing faster. That doesn't seem to be the case in public sector. It seems like the public sector business is is really right there from in terms of growth. So it really is a shining star inside of AWS. >>Still, speed is a startup game, and agility has been a dev ops ethos. You couldn't see more obvious example in public sector where speed is critical. What's your reaction to your interviews and your conversations and your observations? A keynote? >>Yeah, I mean something We've all been saying in the technology industry is Just imagine if this had happened under 15 years ago, where we would be So where in a couple of the interviews you mentioned, I've talked to some of the non profits and researchers working on covert 19. So the cloud really has been in the spotlight. Can I react? Bask scale. Can I share information fast while still maintaining the proper regulations that are needed in the security so that, you know, the cloud has been reacting fast when you talk about the financial resource is, it's really nice to see Amazon in some of these instances has been donating compute occasional resource is and the like, so that you know, critical universities that are looking at this when researchers get what they need and not have to worry about budgets, other agencies, if you talk about contact centers, are often they will get emergency funding where they have a way to be able to get that to scale, since they weren't necessarily planning for these expenses. So you know what we've been seeing is that Cloud really has had the stress test with everything that's been going on here, and it's reacting, so it's good to see that you know, the promise of cloud is meeting that scale for the most part, Amazon doing a really good job here and you know, their customers just, you know, feel The partnership with Amazon is what I've heard loud and clear. >>Well, Dave, one of these I want to get your reaction on because Amazon you can almost see what's going on with them. They don't want to do their own horn because they're the winners on the pandemic. They are doing financially well, their services. All the things that they do scale their their their position, too. Take advantage. Business wise of of the remote workers and the customers and agencies. They don't have the problems at scale that the customers have. So a lot of things going on here. These applications that have been in the i t world of public sector are old, outdated, antiquated, certainly summer modernize more than others. But clearly 80% of them need to be modernized. So when a pandemic hits like this, it becomes critical infrastructure. Because look at the look of the things unemployment checks, massive amount of filings going on. You got critical service from education remote workforces. >>these are >>all exposed. It's not just critical. Infrastructure is plumbing. It's The applications are critical. Legit problems need to be solved now. This is forcing an institutional mindset that's been there for years of, like, slow two. Gotta move fast. I mean, this is really your thoughts. >>Yeah. And well, well, with liquidity that the Fed put into the into the market, people had, You know, it's interesting when you look at, say, for instance, take a traditional infrastructure provider like an HP era Dell. Very clearly, their on Prem business deteriorated in the last 100 days. But you know HP Q and, well, HBO, you had some some supply chain problem. But Dell big uptick in this laptop business like Amazon doesn't have that problem. In fact, CEOs have told me I couldn't get a server into my data center was too much of a hassle to get too much time. It didn't have the people. So I just spun up instances on AWS at the same time. You know, Amazon's VD I business who has workspaces business, you know, no doubt, you know, saw an uptick from this. So it's got that broad portfolio, and I think you know, people ask. Okay, what remains permanent? Uh, and I just don't see this This productivity boom that we're now finally getting from work from home pivoting back Teoh, go into the office and it calls into question Stu, when If nobody is in the corporate office, you know the VP ends, you know, the Internet becomes the new private network. >>It's to start ups moving fast. The change has been in the past two months has been, like, two years. Huge challenges. >>Yeah, John, it's an interesting point. So, you know, when cloud first started, it was about developers. It was about smaller companies that the ones that were born in the cloud on The real opportunity we've been seeing in the last few months is, you know, large organizations. You talk about public sector, there's non profits. There's government agencies. They're not the ones that you necessarily think of as moving fast. A David just pointing out Also, many of these changes that we're putting into place are going to be with us for a while. So not only remote work, but you talk about telehealth and telemedicine. These type of things, you know, have been on our doorstep for many years, but this has been a forcing function toe. Have it be there. And while we will likely go back to kind of a hybrid world, I think we have accelerated what's going on. So you know, there is the silver lining in what's going on because, you know, Number one, we're not through this pandemic. And number two, you know, there's nothing saying that we might have another pandemic in the future. So if the technology can enable us to be more flexible, more distributed a xai I've heard online. People talk a lot. It's no longer work from home but really work from anywhere. So that's a promise we've had for a long time. And in every technology and vertical. There's a little bit of a reimagining on cloud, absolutely an enabler for thinking differently. >>John, I wonder if I could comment on that and maybe ask you a question. That's okay. I know your host. You don't mind. So, first of all, I think if you think about a framework for coming back, it's too said, You know, we're still not out of this thing yet, but if you look at three things how digital is an organization. How what's the feasibility of them actually doing physical distancing? And how essential is that business from a digital standpoint you have cloud. How digital are you? The government obviously, is a critical business. And so I think, you know, AWS, public Sector and other firms like that are in pretty good shape. And then there's just a lot of businesses that aren't essential that aren't digital, and those are gonna really, you know, see a deterioration. But you've been you've been interviewing a lot of people, John, in this event you've been watching for years. What's your take on AWS Public sector? >>Well, I'll give an answer that also wants to do away because he and I both talk to some of the guests and interview them. Had some conversations in the community is prep. But my take away looking at Amazon over the past, say, five or six years, um, a massive acceleration we saw coming in that match the commercial market on the enterprise side. So this almost blending of it's not just public sector anymore. It looks a lot like commercial cause, the the needs and the services and the APS have to be more agile. So you saw the same kind of questions in the same kind of crazy. It wasn't just a separate division or a separate industry sector. It has the same patterns as commercial. But I think to me my big takeaways, that Theresa Carlson hit this early on with Amazon, and that is they can do a lot of the heavy lifting things like fed ramp, which can cost a $1,000,000 for a company to go through. You going with Amazon? You onboard them? You're instantly. There's a fast track for you. It's less expensive, significantly less expensive. And next thing you know, you're selling to the government. If you're a start up or commercial business, that's a gold mine. I'm going with Amazon every time. Um, and the >>other >>thing is, is that the government has shifted. So now you have Covad 19 impact. That puts a huge premium on people who are already been setting up for digital transformation and or have been doing it. So those agencies and those stakeholders will be doing very, very well. And you know that Congress has got trillions of dollars day. We've covered this on the Cube. How much of that coverage is actually going for modernization of I T systems? Nothing. And, you know, one of things. Amazon saying. And rightfully so. Shannon Kellogg was pointing out. Congress needs to put some money aside for their own agencies because the citizens us, the taxpayers, we got to get the services. You got veterans, you've got unemployment. You've got these critical services that need to be turned on quicker. There's no money for that. So huge blind spot on the whole recovery bill. And then finally, I think that there's a huge entrepreneurial thinking that's going to be a public private partnership. Cal Poly, Other NASA JPL You're starting to see new applications, and this came out of my interviews on some of the ones I talked to. They're thinking differently, the doing things that have never been done before. And they're doing it in a clever, innovative way, and they're reinventing and delivering new things that are better. So everything's about okay. Modernize the old and make it better, and then think about something new and completely different and make it game changing. So to me, those were dynamics that are going on than seeing emerge, and it's coming out of the interviews. Loud and clear. Oh, my God, I never would have thought about that. You can only do that with Cloud Computing. A super computer in the Cloud Analytics at scale, Ocean Data from sale Drone using satellite over the top observation data. Oh, my God. Brilliant. Never possible before. So these are the new things that put the old guard in the Beltway bandits that check because they can't make up the old excuses. So I think Amazon and Microsoft, more than anyone else, can drive change fast. So whoever gets there first, well, we'll take most of the shares. So it's a huge shift and it's happening very fast more than ever before this year with Covert 19 and again, that's the the analysis. And Amazon is just trying to like, Okay, don't talk about us is we don't want to like we're over overtaking the world because outside and then look opportunistic. But the reality is we have the best solution. So >>what? They complain they don't want to be perceived as ambulance station. But to your point, the new work loads and new applications and the traditional enterprise folks they want to pay the cow path is really what they want to dio. And we're just now seeing a whole new set of applications and workloads emerging. What about the team you guys have been interviewing? A lot of people we've interviewed tons of people at AWS reinvent over the years. We know about Andy Jassy at all. You know, his his lieutenants, about the team in public sector. How do they compare, you know, relative to what we know about AWS and maybe even some of the competition. Where do you Where do you grade them? >>I give Amazon and, um, much stronger grade than Microsoft. Microsoft still has an old DNA. Um, you got something to tell them is bring some fresh brand there. I see the Jedi competition a lot of mud slinging there, and I think Microsoft clearly got in fear solution. So the whole stall tactic has worked, and we pointed out two years ago the number one goal of Jet I was for Amazon not to win. And Microsoft looks like they're gonna catch up, and we'll probably get that contract. And I don't think you're probably gonna win that out, right? I don't think Amazon is gonna win that back. We'll see. But still doesn't matter. Is gonna go multi cloud anyway. Um, Teresa Carlson has always had the right vision. The team is exceptional. Um, they're superb experience and their ecosystem partners Air second and NASA GPL Cal Poly. The list goes on and on, and they're attracting new talent. So you look at the benchmark new talent and unlimited capability again, they're providing the kinds of services. So if we wanted to sell the Cube virtual platform Dave, say the government to do do events, we did get fed ramp. We get all this approval process because Amazon customer, you can just skate right in and move up faster versus the slog of these certifications that everyone knows in every venture capitalists are. Investor knows it takes a lot of time. So to me, the team is awesome. I think that the best in the industry and they've got to balance the policy. I think that's gonna be a real big challenge. And it's complex with Amazon, you know, they own the post. You got the political climate and they're winning, right? They're doing well. And so they have an incentive to to be in there and shape policy. And I think the digital natives we are here. And I think it's a silent revolution going on where the young generation is like, Look at government served me better. And how can I get involved? So I think you're going to see new APS coming. We're gonna see a really, you know, integration of new blood coming into the public sector, young talent and new applications that might take >>you mentioned the political climate, of course. Pre Cove. It'll you heard this? All that we call it the Tech lash, right, The backlash into big tech. You wonder if that is going to now subside somewhat, but still is the point You're making it. Where would we be without without technology generally and big tech stepping up? Of course, now that you know who knows, right, Biden looks like he's, you know, in the catbird seat. But there's a lot of time left talking about Liz more on being the Treasury secretary. You know what she'll do? The big tech, but But nonetheless I think I think really it is time to look at big tech and look at the Tech for good, and you give them some points for that. Still, what do you think? >>Yeah, first of all, Dave, you know, in general, it felt like that tech lash has gone down a little bit when I look online. Facebook, of course, is still front and center about what they're doing and how they're reacting to the current state of what's happening around the country. Amazon, on the other hand, you know, a done mentioned, you know, they're absolutely winning in this, but there hasn't been, you know, too much push back if you talk culturally. There's a big difference between Amazon and AWS. There are some concerns around what Amazon is doing in their distribution facilities and the like. And, you know, there's been lots of spotlights set on that, um, but overall, there are questions. Should AWS and Amazon that they split. There's an interesting debate on that, Dave, you and I have had many conversations about that over the past couple of years, and it feels like it is coming more to a head on. And if it happens from a regulation standpoint, or would Amazon do it for business reason because, you know, one of Microsoft and Google's biggest attacks are, well, you don't want to put your infrastructure on AWS because Amazon, the parent company, is going to go after your business. I do want to pull in just one thread that John you and Dave were both talking about while today you know, Amazon's doing a good job of not trying todo ambulance case. What is different today than it was 10 or 20 years ago. It used to be that I t would do something and they didn't want to talk to their peers because that was their differentiation. But Amazon has done a good job of explaining that you don't want to have that undifferentiated heavy lifting. So now when an agency or a company find something that they really like from Amazon talking all their peers about it because they're like, Oh, you're using this Have you tried plugging in this other service or use this other piece of the ecosystem? So there is that flywheel effect from the cloud from customers. And of course, we've talked a lot about the flywheel of data, and one of the big takeaways from this show has been the ability for cloud to help unlock and get beyond those information silos for things like over 19 and beyond. >>Hey, John, if the government makes a ws spin out or Amazon spin out AWS, does that mean Microsoft and Google have to spin out their cloud businesses to? And, uh, you think that you think the Chinese government make Alibaba spin out its cloud business? >>Well, you know the thing about the Chinese and Facebook, I compare them together because this is where the tech lash problem comes in. The Chinese stolen local property, United States. That's well documented use as competitive advantage. Facebook stole all the notional property out of the humans in the world and broke democracy, Right? So the difference between those bad tech actors, um, is an Amazon and others is 11 enabling technology and one isn't Facebook really doesn't really enable anything. If you think about it, enables hate. It enables some friends to talk some emotional reactions, but the real societal benefit of historically if you look at society, things that we're enabling do well in free free societies. Closed systems don't work. So you got the country of China who's orchestrating all their actors to be state driven, have a competitive advantage that's subsidised. United States will never do that. I think it's a shame to break up any of the tech companies. So I'm against the tech lash breakup. I think we should get behind our American companies and do it in an open, transparent way. Think Amazon's clearly doing that? I think that's why Amazon's quiet is because they're not taking advantage of the system that do things faster and cheaper gets that's there. Ethos thinks benefits the consumer with If you think about it that way, and some will debate that, but in general Amazon's and enabling technology with cloud. So the benefits of the cloud for them to enable our far greater than the people taking advantage of it. So if I'm on agency trying to deliver unemployment checks, I'm benefiting the citizens at scale. Amazon takes a small portion of that fee, so when you have enabling technologies, that's how to me, The right capitalism model works Silicon Valley In the tech companies, they don't think this way. They think for profit, go big or go home and this has been an institutional thing with tech companies. They would have a policy team, and that's all they did. They didn't really do anything t impact society because it wasn't that big. Now, with networked economies, you're looking at something completely different to connected system. You can't handle dissidents differently is it's complex? The point is, the diverse team Facebook and Amazon is one's an enabling technology. AWS Facebook is just a walled garden portal. So you know, I mean, some tech is good, some text bad, and a lot of people just don't know the difference what we do. I would say that Amazon is not evil Amazon Web services particular because they enable people to do things. And I think the benefits far outweigh the criticisms. So >>anybody use AWS. Anybody can go in there and swipe the credit card and spin up compute storage AI database so they could sell the problems. >>The problems, whether it's covert problems on solving the unemployment checks going out, are serving veterans or getting people getting delivering services. Some entrepreneurs develop an app for that, right? So you know there's benefits, right? So this you know, there's not not Amazon saying Do it this way. They're saying, Here's this resource, do something creative and build something solve a problem. And that was the key message of the keynote. >>People get concerned about absolute power, you know, it's understandable. But if you know you start abusing absolute power, really, I've always believed the government should come in, >>but >>you know, the evidence of that is is pretty few and far between, so we'll see how this thing plays out. I mean, it's a very interesting dynamic. I point about why should. I don't understand why AWS, you know, gets all the microscopic discussion. But I've never heard anybody say that Microsoft should spend on Azure. I've never heard that. >>Well, the big secret is Azure is actually one of Amazon's biggest customers. That's another breaking analysis look into that we'll keep on making noted that Dave's do Thanks for coming to do great interviews. Love your conversations. Final words to I'll give you What's the big thing you took away from your conversations with your guests for this cube? Virtual coverage of public sector virtual summit >>so biggest take away from the users is being able to react to, you know, just ridiculously fast. You know it. Talk about something where you know I get a quote on Thursday on Friday and make a decision, and on Monday, on up and running this unparalleled that I wouldn't be able to do before. And if you talk about the response things like over nine, I mean enabling technology to be able to cut across organizations across countries and across domains. John, as you pointed out, that public private dynamic helping to make sure that you can react and get things done >>Awesome. We'll leave it there. Stew. Dave. Thanks for spending time to analyze the keynote. Also summarize the event. This is a does public sector virtual summit online Couldn't be face to face. Of course. We bring the Cube virtual coverage as well as content and our platform for people to consume. Go the cube dot net check it out and keep engaging. Hit us up on Twitter if any questions hit us up. Thanks for watching. >>Yeah, yeah, yeah, yeah, yeah, yeah

>> Narrator: Live from Las Vegas, it's The Cube, covering AWS re:Invent 2017. Presented by AWS, Intel, and our ecosystem of partners. >> Bob: It's actually a little quieter here. >> Hey, welcome back to AWS re:Invent 2017. I am Lisa Martin. We're all very chatty. You can hear a lot of chatty folks behind us. This is day two of our continuing coverage. 42,000 people here, amazing. I'm Lisa Martin with my co-host Keith Townsend, and we're very excited to be joined by a Cube alumni Bob Muglia, CEO and President of Snowflake. >> Thank you. >> Lisa: Welcome back. >> Thank you, good to be back. >> And Kelly Mungary, the Director of Enterprise Data and Analytics from Lionsgate. A great use case from Snowflake. Thanks so much guys for joining us. So one of the hot things going on today at the event is your announcement Bob with AWS and Snowpipe. What is Snowpipe? How do customers get started with it? >> Great, well thanks. We're excited about Snowpipe. Snowpipe is a way of ingesting data into Snowflake in a streaming, continuous way. You simply can drop new data that's coming in into S3 and we'll ingest it for you automatically. Makes that super, super simple. Brings the data in continuously into your data warehouse, ensuring that you're always up to date and your analysts are getting the latest insights and the latest data. >> So, when you guys were founded, about five years ago, as the marketing says on your website, a complete data warehouse built for the Cloud. What was the opportunity back then? What did you see that was missing, and how has Snowflake evolved to really be a leader in this space? >> So you know, if you go back five years this was a time frame where no SQL was the big rage, and everybody was talking about how SQL was passe and it's something that you're not see in the future. Our founders had a different view, they had been working on true relational databases for almost 20 years, and they recognized the power of SQL and relational technology but they also saw that customers were experiencing significant limits with existing technology, and those limits really restricted what people could do. They saw in the Cloud and what Amazon had done the ability to build a all new database that takes advantage of the full elasticity and power of the Cloud to deliver whatever set of analytics capabilities that the business requires. However much data you want, however many queries simultaneously. Snowflake takes what you love about a relational database and removes all the limits, and allows you to operate in a very different way. And our founders had that vision five years ago, and really successfully executed on it. The product has worked beyond our dreams, and our customers, our response from our customers is what we get so excited about. >> So, the saying is "Data is the new oil". However, just as oil is really hard to drill for and find, finding the data to service up, to even put in a data lake to analyze has been a challenge. How did you guys go about identifying what data should even be streamed to Snowpipe? >> Well, yeah, that's a great question. I mean, in entertainment today, we're experiencing probably like in pretty much every type of business. A data explosion. We have, you know, streaming is big now. We have subscription data coming in, billing data, social media data, and on and on. And the thing is, it's not coming in a normal, regular format. It's coming in what we call a semi-structured, structured, json, xml. So, up until Snowflake came onto the scene with a truly Cloud based SAAS solution for data warehousing pretty much everyone was struggling to wrangle in all these data sets. Snowpipe is a great example of one of the avenues of bringing in these multiple data sets, merging them real time, and getting the analytics out to your business in an agile way that has never been seen before. >> So, can you talk a little bit about that experience? Kinda that day one up, you were taking these separate data sources, whether it's ERP solution, data from original content, merging that together and then being able to analyze that. What was that day one experience like? >> Well, you know, I gotta tell you, it evolves around a word, that word is "Yes", okay? And data architects and executives and leaders within pretty much every company are used to saying, "We'll get to that" and "We'll put it on the road map", "We could do that six months out", "Three months out". So what happened when I implemented Snowflake was I was just walking into meetings and going, "Yes". "You got it". "No worries, let's do it". >> Lisa: It liberated. >> Well, it's changes, it's not only liberating, it changes the individual's opportunities, the team's opportunities, the company's opportunities, and ultimately, revenue. So, I think it's just an amazing new way of approaching data warehousing. >> So Bob, can you talk a little bit about the partnership with AWS, and the power to bring that type of capability to customers? Data lakes are really hard to do that type of thing run a query against to get instant answers. Talk about the partnership with AWS to bring that type of capability. >> Well Amazon's been a fantastic partner of ours, and we really enjoy working with Amazon. We wind up working together with them to solve customer problems. Which is what I think is so fantastic. And with Snowflake, on top of Amazon, you can do what Kelly's saying. You can say yes, because all of a sudden you can now bring all of your data together in one place. Technology has limited, it's technology that has caused data to be in disparate silos. People don't want their data all scattered all over the place. It's all in these different places because limits to technology force people to do that. With the Cloud, and with what Amazon has done and with a product like Snowflake, you can bring all of that data together, and the thing that's interesting, where Kelly is going, is it can change the culture of a company, and the way people work. All of a sudden, data is not power. Data is available to everyone, and it's democratizing. Every person can work with data and help to bring the business forward. And it can really change the dynamics about the way people work. >> And Kelly, you just spoke at the multi-city Cloud Analytics Tour that Snowflake just did. You spoke in Santa Monica, one of my favorite places. You talked about a data driven culture. And we hear data driven in so many different conversations, but how did you actually go about facilitating a data driven culture. Who are some of the early adopters, and what business problems have you been able to solve by saying yes? >> Well, I can speak entertainment in general. I think that it's all about technology it's about talent, and it's about teaching. And with technology being the core of that. If we go back five years, six years, seven years, it was really hard to walk into a room, have an idea, a concept, around social media, around streaming data, around billing, around accounting. And to have an agile approach that you could bring together within a week or so forth. So what's happening is, now that we've implemented Snowflake on AWS and some of the other what I call dream tools on top of that. The dream stack, which includes Snowflake. It's more about integrating with the business. Now we can speak the same language with them. Now we can walk into a room and they're glad to see me now. And at the end of the day, it's new, it's all new. So, this is something that I say sometimes, in kidding, but it's actually true. It's as if Snowflake had a time traveler on staff that went forward in the future ten years to determine how things should be done in the big data space, and then came back and developed it. And that's how futuristic they are, but proven at the same time. And that allows us to cultivate that data driven culture within entertainment, because we have tools and we have the agile approach that the business is looking for. >> So, Kelly, I'm really interested, and I love the concept of making data available to everyone. That's been a theme of this conference from the keynote this morning, which is putting tools in builder's hands, and allowing builders to do what they do. >> Kelly: That's right. >> And we're always surprised at what users come back with. What's one of the biggest surprises from the use cases, now that you've enabled your users. >> Well, I'm gonna give you one that's based on AWS and Snowflake. A catch phrase you hear a lot of is "Data center of excellence", and a lot of us are trying to build out these data centers of excellence, but it's a little bit of an oxymoron to the fact that a data center of excellence is really about enabling your business and finding champions within marketing, within sales, within accounting, and giving them the ability to have self-service business intelligence, self-service data warehousing. The kinds of things that, again, we go back five, six years ago, you couldn't even have that conversation. I'll tell you today, I can walk into a room, and say, "Okay, who here is interested in learning "about data warehousing?". And there'll be somebody, "Okay, great". Within an hour, I'll have you being dangerous in terms of setting up, standing up, configuring and loading a data warehouse. That's unheard of, and it's all due to Snowflake and their new technology. >> I'd love to understand Bob, from your perspective. First of all, it sounds like you have a crystal ball according to Kelly, which is awesome. But second of all, collaboration, we talked about that earlier. Andy Jassy is very well known and very vocal about visiting customers every week. And I love their bottom, their backwards approach to, before building a product, to try to say, "What problem can we solve?". They're actually working with customers first. What are their requirements? Tell me a little bit Bob about the collaboration that Snowflake has with Lionsgate, or other customers. How are they helping to influence your crystal ball? >> You know what, this is where I think what Amazon has done, and Andy has done a fantastic job. There's so much to learn from them, and the customer centricity that Amazon has always had is something that we have really focused to bring into Snowflake, and really build deeply into our culture. I've sort of said many, many times, Snowflake is a value space company. Our values are important to us, they're prominent in our website. Our first value is we put our customer's first. What I'm most proud of is, every customer who has focused on deploying Snowflake, has successfully deployed Snowflake, and we learn from them. We engage with them. We partner with them. All of our customers are our partners. Kelly and Lionsgate are examples of customers that we learn from every day, and it's such a rewarding thing to hear what they want to do. You look at Snowpipe and what Snowpipe is, that came from customers, we learned that from customers. You look at so many features, so many details. It's iterative learning with customers. And what's interesting about that, it's listening to customers, but it's also understanding what they do. One of the things that's interesting about Snowflake is is that as a company we run Snowflake on Snowflake. All of our data is in Snowflake. All of our sales data, our financial data, our marketing data, our product support data, our engineering data. Every time a user runs a query, that query is logged in Snowflake and intrinsics about it are logged. So what's interesting is because it's all in one place, and it's all accessible, we can answer essentially any question, about what's been done. And then, driving the culture to do that is an important thing. One of the things I do find interesting is, even at Snowflake, even at this data centered company, even where everything is all centralized, I still find sometimes people don't reference it. And I'm constantly reinforcing that your intuition, you know, you're really smart, you're really intuitive, but you could be wrong. And if you can answer the question based on what's happened, what your customers are doing, because it's in the data, and you can get that answer quickly, it's a totally different world. And that's what you can do when you have a tool with the power of what Snowflake can deliver, is you could answer effectively any business question in just a matter of minutes, and that's transformative, it's transformative to the way people work, and that, to me, that's about what it means to build a data driven culture. Is to reinforce that the answer is inside what customers are doing. And so often, that is encapsulated in the data. >> Wow, your energy is incredible. We thank you so much Bob and Kelly for coming on and sharing your story. And I think a lot of our viewers are gonna learn some great lessons from both of you on collaboration on transformations. So thanks so much for stopping by. >> Yeah. >> Thank you so much, we really enjoyed it. Thanks a lot. >> Likewise, great to meet you. >> Thanks Kelly. >> Thank you. >> For my co-host Keith Townsend, and for Kelly and Bob, I am Lisa Martin. You've been watching The Cube, live on day two, continuing coverage at AWS re:Invent 2017. Stick around, we have great more guests coming up. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2017. Presented by: AWS, Intel and our ecosystem of partners. (lively electronic music) >> Welcome back everyone, this is theCUBE special exclusive coverage of AWS re:Invent 2017. CUBE's our flagship program, we go out to the events and we extract the signal noise. I'm John Furrier your co-host. With me today is Justin Moore, an analyst. We have two sets here in Las Vegas. Our next guest is Sumeet Singh, Vice President of Cloud Analytics with Juniper Networks, formerly of AppFormix, which was bought about a year ago. CUBE alumni back. New team, Juniper, welcome back. Last time we chatted with you you were entrepreneurial. >> Yeah. >> Taking names, kicking ass, now you're-- >> Bought out Juniper Networks, yeah. >> You bought out Juniper Net, what's going on? >> So we've essentially been building, building more and more and it's actually been a totally awesome experience. So, Last year when we spoke, we were essentially looking at a whole lot of private cloud deployment. Looking at OpenStack, looking at (mumbles), looking at VMware, and since, what we've now started really expanding into is, of course, the multi-cloud and hybrid cloud scenario. And looking at how to secure these clouds on prem, in the cloud, multi-cloud, as well as bring rich analytics into real-time operations insight as to what's going on in all of these environments. And how to optimize them. >> Yeah, that whole multi-cloud hybrid cloud thing is really exploded in the last 12 months. I'm hearing from customers a lot more that they are pursuing a multi-cloud strategy, but it seems that there's just this proliferation of things that you've now got to monitor and secure. So, how are you helping customers to do that? >> So, I mean, you're going to start with the basics. Right? So, the first thing that we got to realize is there are, of course, those companies that are born in the cloud. But then, there's a whole bunch of others who have for long run their own data centers and run their application stacks on prem, who are now looking to migrate to the public cloud and build all that multi-cloud scenario. In that situation, I would say, you need a little bit of hand-holding. You need to understand how your application's running on prem, which ones can be moved to the cloud, how can they be moved to the cloud, you want to ensure that those policies that you were implementing on prem you'll be able to implement those same policies in the public cloud, as well. The monitoring really starts on prem. All of those policies that operation starts on prem, and then you take them and you build them and you >> I'll get your take on, we'll have to get your take too, Justin, on something that's going on that I see clear visibility on. Infrastructure operations, data center cloud, get your house in order, networks, migration, hybrid cloud, multi-cloud, and then all that stuff. Then you've got this developer tsunami going on, a renaissance of real new development, new kinds of development, multiple databases using in app, IoT, so, the software development methodologies are changing for developers. That's obvious. What's the impact to the infrastructure guys, because you're starting to see Lambda and Server List as a way for saying complete infrastructure is code. How does that change the notion of, what the hell the data center is? Because you could argue that's just an edge now. So, what's the software, what are some of the software practices you see that are notable? >> The ones truly amazing, like in all these things that you're saying, is that you no longer need to use one approach to build anything. Any product that we put out, or any service that you put out, now uses a combination of all of these things. It could be Lambda, it could be IoT, it could be a wholesale application that's office started using (mumbles), that's spanning that multi-cloud environment. So, it's the beauty of all of this is the power of choice. We have so much more choice available to us. Right? But then, when choices, with choice comes that explosion and that complexity. It's >> Complexity is key, but speed is also there. You see. So, the question is, at what point does the cream rise to the top, and the people that are slow get run over. >> We're just seeing another evolution in obstruction, really, it's like, we don't write an assembly code anymore. We're writing directly to the hardware. We added in high-level programming languages, and now, in terms of the infrastructure, developers don't care about infrastructure as much as people talk about dev ops, and the thing like dev ops is a thing, developers don't want to deal with the infrastructure. They want to deal with code, cuz that's where they live. And the infrastructure folks, well, a lot of them are actually becoming developers now. So, they're learning how to use tools like, using development tools to actually get their job done. Which is where we're seeing infrastructure is going. So, there's a lot more ob abstraction into pure software, so you don't have to worry about the underlying obstructions, at least, not very much. >> All right, Sumeet, question to you now on that is, that requires the network guys, Juniper, you're part of that, and all the analytics to think differently about what you're instrumenting. To do what he said, to make it free, you gotta enable a lot of policy, a lotta data analytics, take us through what's the current state of the art there. >> So, the current state of the art, is essentially, if you talk about Juniper products, we have our family of SRX products, where you can have on prem firewalls, as well as virtual firewalls in the cloud, and using these tools, you can have consistent policies on prem and in the cloud. You can free up transit VPCs. Then there are the obligations in the multi cloud world, and do all kinds of fancy things. But where we also going with our solutions is to make them much more simpler to consume. It's truly all about simplicity, right? Because now you have all this choice, and you can have Lambda, and you can have all these new ways to bring up your applications. What becomes key is that the policies that you wanna implement become automatic. Right? And the way to do that is, the way we are doing that is, essentially, doing this auto-learning of your environment. Right? Automatically understanding, Automation, right? But, not, automation in two parts, as in automatically detect what's going on, but then automatically apply the policies as well, no matter where the workload is and where it's scaling, we automatically apply the policies to it. >> So, it's a lot of investment in this mart of underly-- Making something simple is actually quite complex to do. So, you need to understand what are the right things to automate, and what are the few things where you actually wanna give humans that choice, without it becoming overwhelming, so that, okay, I have to choose between one of 800 different ways of doing this. That's just not something that humans cope well with, whereas machines are actually really good at that. >> And that's the value here. We want to hide all the complexity under the hood. You know, use those advanced logarithms, use, you know, where they be on prem or in the cloud, but running all the analysis, implementing all the right policies for you, right? And new, new workload comes up it should automatically get the policy, right? And we are now able to do that both in the private data center, as well as in the public cloud, and bridge those policies together for you, automatically. >> The common theme we're seeing in cloud, we had a guest on from Thorn, where they automate, essentially, police officers writing down notes in a notebook to fully spotting with machine learning and all this great stuff, to find missing and exploited children, manual sucks, basically. Manual's slow-- >> The workload's too dominant now for you to think about manual. >> I want real-time. So most organizations, what's going on there? How do you guys help there, what's the progress? >> Oh. So, this is actually a great question, by the way, so, and this is part of the reason why we like, as a company, as a start up, maybe, we're like, doing all this cool stuff, and, you know, not really thinking about all the, hey, this is slowing me down. The reason why we went to Juniper, if you look at the history of Juniper, and the product portfolio, and the stock at Juniper, when it comes to automation, when it comes to things like ABI, when it comes to things like policy, they've always kind of like led the pack in that networking space, and now this is the opportunity to take that that wealth of knowledge, and scale it out, and take it to a little bit broader multi-cloud, hybrid cloud space. But, that's truly where it is, and even if you, kind of like go down low level to the devices, all Juniper devices are able to stream real-time elements. We are able to do ML in real-time, even on the physical devices, right? Similar for all virtual devices, and now, with our Formix, we even bring in the performance and operations inside, from the running infrastructure, whether it's on prem and in the cloud, not just networking, but the compute, the databases, your applications, your clusters, all of that, to build for you this end-to-end view, right? Not just the networks, your servers, Vms, workloads, the underlying network, the connectivity, all of it. >> How does that, because the developers, they live in application land, and again, they don't really care about that infrastructure, but as it turns out, sometimes it's quite useful to know which particular network devices, or what the infrastructure is that underpins things, like where you sometimes need to be able to drop into assembly code to really optimize things, so are you making that information about the infrastructure visible to developers in a way that they like to, to know and consume? >> Absolutely, so, one key thing about, you know, our product portfolio, and how we are releasing our services, essentially, we've wrapped everything around, you know, these role-based access interfaces. Where both the operators are able to get their views, they're able to construct views that the developers are able to see, and then both can implement their own policies, right? If, let's say, there's some infrastructure that's down, or is unhealthy, then having that global topology view helps you in real-time totally, and in real-time informs you what the impact of that outage is. Like who are the developers who would be impacted, what are their obligations? And, you know, we can bring that insight, and consume it to run the automation. So, if, let's just say, some infrastructure's unhealthy, can you read off the graph? >> Sumeet, talk about what you guys are doing here. How's Amazon, big learning conference, but it's a massive show, 45,000 people here, across multiple hotels. A lot of sessions. What do you guys talk about? What's the big cloud piece for you guys? >> For us, really, first, it's just visibility, right? We have a product portfolio that gives you visibility. Like, both for your physical infrastructure, and your virtual structure. Then, the next thing is, of course, You know, yeah, you have the visibility. But then, at our scale, no human can consume all that information. It's too slow. It's too slow. So, you've gotta have the machine-learning built in. So, it's promoting that visibility into insights in real time, and then, it's about how do you secure your workloads? So, consuming all of that insight to implement all of the policies, implement all of the automation, to ensure that everything is running as you want it to. >> What's your Juniper message to the developers here? Is there a new face to Juniper, a new vibe? You mentioned Juniper's always had great products, like, you move packets around at lightning speeds, you know, wire speeds, all that great stuff. How do you, what's new? What's it mean for me as a developer, what is Juniper, how's it make my life easier? >> What's new is that now it's easier for developers to consume our products. Our products are now available in the Amazon marketplace, right? Our visibility products, our machine-learning products, our security products, right? You can just click, install, and start using them. That's new for Juniper, right? I mean, traditionally you would think of-- >> You probably get Juniper goodness just by treating it like a library. >> That's it. You can just download, not even download, right? You're -- >> It's server-less. It's router-less. It's device-less. >> There you go. You can just start consuming them. And then, if you do have that knowledge of how do you use those devices on prem, then you can apply that knowledge in the cloud, and then use them all. >> Must be computing back in, what, like 20 years ago. I mean, is it just like a grid now. >> Oh, yeah, pretty much, yeah. >> It's a fabric. >> It's the same, if you already know how to use it one place, you know how to use it everywhere. >> Yeah, but, I mean, it's, really, the value of the cloud is making it even simpler, right? Running all of that automation, like we talked about Lambda, like even within our products family, we can, we use Lambda to constantly see what's changing, and that's how we process lots of our internal transactions, as well. >> Sumeet, congratulations on your acquisition and your entrepreneurial journey, and now you're at Juniper. Looking forward to keeping in touch. Sumeet Singh, Vice President of Cloud Analytics, and now at Juniper Networks, formerly AppFormix, CUBE alumni, thanks for coming on and sharing your commentary. I'm John Furrier, and Justin Moore, here on theCUBE, main stage in Las Vegas at AMS re:Invent We'll be back with more after this short break. (lively electronic music)

>> Announcer: Live from New York, it's the CUBE. Covering the IBM Machine Learning Launch Event. Brought to you by IBM. Now here are your hosts Dave Vellante and Stu Miniman. >> Welcome back to New York City everybody the is The CUBE the leader in live tech coverage. We're here at the IBM Machine Learning Launch Event, bringing machine learning to the Z platform. Steve Astorino is here, he's the VP for Development for the IBM Private Cloud Analytics Platform. Steve, good to see you, thanks for coming on. >> Hi how are you? >> Good thanks, how you doing? >> Good, good. >> Down from Toronto. So this is your baby. >> It is >> This product right? >> It is. So you developed this thing in the labs and now you point it at platforms. So talk about, sort of, what's new here today specifically. >> So today we're launching and announcing our machine learning, our IBM machine learning product. It's really a new solution that allows, obviously, machine learning to be automated and for data scientists and line of business, business analysts to work together and create models to be able to apply machine learning, do predictions and build new business models in the end. To provide better services for their customers. >> So how is it different than what we knew as Watson machine learning? Is it the same product pointed at Z or is it different? >> It's a great question. So Watson is our cloud solution, it's our cloud brand, so we're building something on private cloud for the private cloud customers and enterprises. Same product built for private cloud as opposed to public cloud. Think of it more as a branding and Watson is sort of a bigger solution set in the cloud. >> So it's your product, your baby, what's so great about it? How does it compare with what else is in the marketplace? Why should we get excited about this product? >> Actually, a bunch of things. It's great for many angles, what we're trying to do, obviously it's based on open source, it's an open platform just like what we've been talking about with the other products that we've been launching over the last six months to a year. It's based on Spark, you know we're bringing in all the open source technology, to your fingertips. As well as we're integrating with IBM's top-notch research and capabilities that we're driving in-house, integrating them together and being able to provide one experience to be able to do machine learning. That's at a very high level, also if you think about it there's three things that we're calling out, there's freedom, basically being able to choose what tools you want to use, what environments you want to use, what language you want to use, whether it's Python, Scala, R, right there's productivity. So we really enable and make it simple to be productive and build these machine learning models and then an application developer can leverage and use within their application. The other one is trust. IBM is very well known for its enterprise level capabilities, whether it's governance, whether its trust of the data, how to manage the data, but also more importantly, we're creating something called The Feedback Loop which allows the models to stay current and the data scientists, the administrators, know when these models, for example, is degrading. To make sure it's giving you the right outcome. >> OK, so you mention it's built on Spark. When I think about the efforts to build a data pipeline I think I've got to ingest the data, I've got to explore, I've got to process it and clean it up and then I've got to ultimately serve whomever, the business. >> Right, Right. >> What pieces of that does Spark unify and simplify? >> So we leveraged Spark to able to, obviously for the analytics. When you're building a model you one, have your choice of tooling that you want to use, whether it's programmatic or not. That's one of the value propositions we're bringing forward. But then we create these models, we train them, we evaluate them, we leverage Spark for that. Then obviously, we're trying to bring the models where the data is. So one of the key value proposition is we operationalize these models very simply and quickly. Just at a click of a button you can say hey deploy this model now and we deploy it right on where the data is in this case we're launching it on mainframe first. So Spark on the mainframe, we're deploying the model there and you can score the model directly in Spark on the mainframe. That's a huge value add, get better performance. >> Right, okay, just in terms of differentiates from the competition, you're the only company I think, providing machine learning on Z, so. >> Definitely, definitely. >> That's pretty easy, but in terms of the capabilities that you have, how are you different from the competition? When you talk to clients and they say well what about this vendor or that vendor, how do you respond? >> So let me talk about one of the research technologies that we're launching as part of this called CADS, Cognitive Assistant for Data Scientists. This is a feature where essentially, it takes the complexity out of building a model where you tell it, or you give it the algorithms you want to work with and the CADS assistant basically returns which one is the best which one performs the best. Now, all of a sudden you have the best model to use without having to go and spend, potentially weeks, on figuring out which one that is. So that's a huge value proposition. >> So automating the choice of the algorithm, an algorithm to choose the algorithm. what have you found in terms of it's level of accuracy in terms of the best fit? >> Actually it works really well. And in fact we have a live demo that we'll be doing today, where it shows CADS coming back with a 90% accurate model in terms of the data that we're feeding it and outcome it will give you in terms of what model to use. It works really well. >> Choosing an algorithm is not like choosing a programming language right, this bias if I like Scala or R or whatever, Java, Python okay fine, I've got skill sets associated with that. Algorithm choice is one that's more scientific, I guess? >> It is more scientific, it's based on the algorithm, the statistical algorithm and the selection of the algorithm or the model itself is a huge deal because that's where you're going to drive your business. If you're offering a new service that's where you're providing that solution from, so it has to be the right algorithm the right model so that you can build that more efficiently. >> What are you seeing as the big barriers to customer adopting machine learning? >> I think everybody, I mean it's the hottest thing around right now, everybody wants machine learning it's great, it's a huge buzz. The hardest thing is they know they want it, but don't really know how to apply it into their own environment, or they think they don't have the right skills. So, that actually one of the things that we're going after, to be able to enable them to do that. We're for example working on building different industry-based examples to showcase here's how you would use it in your environment. So last year when we did the Watson data platform we did a retail example, now today we're doing a finance example, a churn example with customers potentially churning and leaving a bank. So we're looking at all those different scenarios, and then also we're creating hubs, locations we're launching today also, announcing today, actually Dinesh will be doing that. There is a hub in Silicon Valley where it would allow customers to come in and work with us and we help them figure out how they can leverage machine learning. It is a great way to interact with our customers and be able to do that. >> So Steve nirvana is, and you gave that example, the retail example in September, when you launched Watson Data Platform, the nirvana in this world is you can use data, and maybe put in an offer, or save a patients life or effect an outcome in real time. So the retail example was just that. If I recall, you were making an offer real-time it was very fast, live demo it wasn't just a fakey. The example on churn, is the outcome is to effect that customer's decisions so that they don't leave? Is that? >> Yes, pretty much, Essentially what we are looking at is , we're using live data, we're using social media data bringing in Twitter sentiment about a particular individual for example, and try to predict if this customer, if this user is happy with the service that they are getting or not. So for example, people will go and socialize, oh I went to this bank and I hated this experience, or they really got me upset or whatever. Bringing that data from Twitter, so open data and merging it with the bank's data, banks have a lot of data they can leverage and monetize. And then making an assessment using machine learning to predict is this customer going to leave me or not? What probability do they have that they are going to leave me or not based on the machine learning model. The example or scenario we are using now, if we think they are going to leave us, we're going to make special offers to them. It's a way to enhance your service for those customers. So that they don't leave you. >> So operationalizing that would be a call center has some kind on dashboard that says red, green, yellow, boom heres an offer that you should make, and that's done in near real time. In fact, real time is before you lose the customer. That's as good a definition as anything else. >> But it's actually real-time, and when we call it the scoring of the data, so as the data transaction is coming in, you can actually make that assessment in real time, it's called in-transaction scoring where you can make that right on the fly and be able to determine is this customer at risk or not. And then be able to make smarter decisions to that service you are providing on whether you want to offer something better. >> So is the primary use case for this those streams those areas I'm getting you know, whether it be, you mentioned Twitter data, maybe IoT, you're getting can we point machine learning at just archives of data and things written historically or is it mostly the streams? >> It's both of course and machine learning is based on historical data right and that's hot the models are built. The more accurate or more data you have on historical data, the more accurate that you picked the right model and you'll get the better predictition of what's going to happen next time. So it's exactly, it's both. >> How are you helping customers with that initial fit? My understanding is how big of a data set do you need, Do I have enough to really model where I have, how do you help customers work through that? >> So my opinion is obvious to a certain extent, the more data you have as your sample set, the more accurate your model is going to be. So if we have one that's too small, your prediction is going to be inaccurate. It really depends on the scenario, it depends on how many features or the fields you have you're looking at within your dataset. It depends on many things, and it's variable depending on the scenario, but in general you want to have a good chunk of historical data that you can build expertise on right. >> So you've worked on both the Watson Services in the public cloud and now this private cloud, is there any differentiation or do you see significant use case different between those two or is it just kind of where the data lives and we're going to do similar activities there. >> So it is similar. At the end of the day, we're trying to provide similar products on both public cloud and private cloud. But for this specific case, we're launching it on mainframe that's a different angle at this. But we know that's where the biggest banks, the insurance companies, the biggest retailers in the world are, and that's where the biggest transactions are running and we really want to help them leverage machine learning and get their services to the next level. I think it's going to be a huge differentiator for them. >> Steve, you gave an example before of Twitter sentiment data. How would that fit in to this announcement. So I've got this ML on Z and I what API into the twitter data? How does that sort of all get adjusted and consolidated? >> So we allow hooks to be able to access data from different sources, bring in data. That is part of the ingest process. Then once you have that data there into data frames into the machine learning product, now you're feeding into a statistical algorithm to figure out what the best prediction is going to be, and the best model's going to be. >> I have a slide that you guys are sharing on the data scientist workflow. It starts with ingestion, selection, preparation, generation, transform, model. It's a complex set of tasks, and typically historically, at least in the last fIve or six years, different tools to de each of those. And not just different tools, multiples of different tools. That you had to cobble together. If I understand it correctly the Watson Data Platform was designed to really consolidate that and simplify that, provide collaboration tools for different personas, so my question is this. Because you were involved in that product as well. And I was excited about it when I saw it, I talked to people about it, sometimes I hear the criticism of well IBM just took a bunch of legacy products threw them together, threw and abstraction layer on top and is now going to wrap a bunch of services around it. Is that true? >> Absolutely not. Actually, you may have heard a while back IBM had made a big shift into design first design methodology. So we started with the Watson Data Platform, the Data Science Experience, they started with design first approach. We looked at this, we said what do we want the experience to be, for which persona do we want to target. Then we understood what we wanted the experience to be and then we leverage IBM analytics portfolio to be able to feed in and provide and integrate those services together to fit into that experience. So, its not a dumping ground for, I'll take this product, it's part of Watson Data Platform, not at all the case. It was the design first, and then integrate for that experience. >> OK, but there are some so-called legacy products in there, but you're saying you picked the ones that were relevant and then was there additional design done? >> There was a lot of work involved to take them from a traditional product, to be able to componentize, create a micro service architecture, I mean the whole works to be able to redesign it and fit into this new experience. >> So microservices architecture, runs on cloud, I think it only runs on cloud today right? >> Correct, correct. >> OK, maybe roadmap without getting too specific. What should we be paying attention to in the future? >> Right now we're doing our first release. Definitely we want to target any platform behind the firewall. So we don't have specific dates, but now we started with machine learning on a mainframe and we want to be able to target the other platforms behind the firewall and the private cloud environment. Definitely we should be looking at that. Our goal is to make, I talked about the feedback loop a little bit, so that is essentially once you deploy the model we actually look at that model you could schedule in a valuation, automatically, within the machine learning product. To be able to say, this model is still good enough. And if it's not we automatically flag it, and we look at the retraining process and redeployment process to make sure you always have the most up to date model. So this is truly machine learning where it requires very little to no intervention from a human. We're going to continue down that path and continue that automation in providing those capabilities so there's a bigger roadmap, there's a lot of things we're looking at. >> We've sort of looked at our big data analyst George Gilbert has talked about you had batch and you had interactive, not the sort of emergent workload is this continuous, streaming data. How do you see the adoption. First of all, is it a valid assertion? That there is a new class of workload, and then how do you see that adoption occurring? Is it going to be a dominant force over the next 10 years? >> Yeah, I think so. Like I said there is a huge buzz around machine learning in general and artificial intelligence, deep learning, all of these terms you hear about. I think as users and customers get more comfortable with understanding how they're going to leverage this in their enterprise. This real-time streaming of data and being able to do analytics on the fly and machine learning on the fly. It's a big deal and it will really helps them be more competitive in their own space with the services we're providing. >> OK Steve, thanks very much for coming on The CUBE. We'll give you the last word. The event, very intimate event a lot of customers coming in very shortly here in just a couple of hours. Give us the bumper sticker. >> All of that's very exciting, we're very excited, this is a big deal for us, that's why whenever IBM does a signature moment it's a big deal for us and we got something cool to talk about, we're very excited about that. Lot's of clients coming so there's an entire session this afternoon, which will be live streamed as well. So it's great, I think we have a differentiating product and we're already getting that feedback from our customers. >> Well congratulations, I love the cadence that you're on. We saw some announcements in September, we're here in February, I expect we're going to see more innovation coming out of your labs in Toronto, and cross IBM so thank you very much for coming on The CUBE. >> Thank you. >> You're welcome OK keep it right there everybody, we'll be back with our next guest right after this short break. This is The CUBE we're live from New York City. (energetic music)