(bright upbeat music) >> Okay, we're back with theCUBE's continuous coverage of HPE's latest GreenLake announcement, the continuous cadence that we're seeing here. You know, when you're trying to figure out how to optimize workloads, it's getting more and more complex. Data-driven workloads are coming in to the scene, and so how do you know, with confidence, how to configure your systems, keep your costs down, and get the best performance and value for that? So we're going to talk about that. With me are Chris Shin, who is the founder of CloudPhysics and the senior director of HPE CloudPhysics, and Sandeep Singh, who's the vice-president of Storage Marketing. Gents, great to see you. Welcome. >> Dave, it's a pleasure to be here. >> So let's talk about the problem first, Sandeep, if we could. what are you guys trying to solve? What are you hearing from customers when they talk to you about their workloads and optimizing their workloads? >> Yeah, Dave, that's a great question. Overall, what customers are asking for is just to simplify their world. They want to be able to go faster. A lot of business is asking IT, let's go faster. One of the things that cloud got right is that overall cloud operational experience, that's bringing agility to organizations. We've been on this journey of bringing this cloud operational agility to customers for their data states, especially with HPE GreenLake Edge-to-Cloud platform. >> Dave: Right. >> And we're doing that with, you know, powering that with data-driven intelligence. Across the board, we've been transforming that operational support experience with HPE InfoSight. And what's incredibly exciting is now we're talking about how we can transform that experience in that upfront IT procurement portion of the process. You asked me what are customers asking about in terms of how to optimize those workloads. And when you think about when customers are purchasing infrastructure to support their app workloads, today it's still in the dark ages. They're operating on heuristics, or a gut feel. The data-driven insights are just missing. And with this incredible complexity across the full stack, how do you figure out where should I be placing my apps, whether on Prim or in the public cloud, and/or what's the right size infrastructure built upon what's actually being consumed in terms of resource utilization across the board. That's where we see a tremendous opportunity to continue to transform the experience for customers now with data-driven insights for smarter IT decisions. >> You know, Chris, Sandeep's right. It's like, it's like tribal knowledge. Well, Kenny would know how to do that, but Kenny doesn't work here anymore. So you've announced CloudPhysics. Tell us more about what that is, what impact it's going to have for customers. >> Sure. So just as Sandeep said, basically the problem that exists in IT today is you've got a bunch of customers that are getting overwhelmed with more and more options to solve their business problems. They're looking at cloud options, they're looking at new technologies, they're looking at new sub-technologies and the level at which people are competing for infrastructure sales is down at the very, very, you know, splitting hairs level in terms of features. And they don't know how much of these they need to acquire. Then on the other side, you've got partners and vendors who are trying to package up solutions and products to serve these people's needs. And while the IT industry has, for decades, done a good job of automating problems out of other technology spaces, hasn't done a good job of automating their own problems in terms of what does this customer need? How do I best service them? So you've got an unsatisfied customer and an inadequately equipped partner. CloudPhysics brings those two together in a common data platform, so that both those customers and their partners can look at the same set of data that came out of their data center and pick the solutions that will solve their problems most efficiently. >> So talk more about the partner angle, because it sounds like, you know, if they don't have a Kenny, they really need some help, and it's got to be repeatable. It's got to be consistent. So how have partners reacting to this? >> Very, very strongly. Over the course of the four or five years that that CloudPhysics has been doing this in market, we've had thousands and thousands of VARs, SIs and others, as well as many of the biggest technology providers in the market today, use CloudPhysics to help speed up the sales process, but also create better and more satisfied customers. >> So you guys made... Oh, go ahead, please. >> Well, I was just going to chime into that. When you think about partners that with HPE CloudPhysics, where it supports heterogeneous data center environments, partners all of a sudden get this opportunity to be much more strategic to their customers. They're operating on real world insights that are specific to that customer's environment. So now they can really have a tailored conversation as well as offer tailored solutions designed specifically for the areas, you know, where help is needed. >> Well, I think it builds an affinity with the customer as well, because if the partners that trust advisor, if you give a customer some advice and it's kind of the wrong advice, "Hey, we got to go back and reconfigure that workload. We won't charge you that much for it". You're now paying twice. Like when an accountant makes a mistake on your tax return, you got to pay for that again. But so, you guys acquired CloudPhysics in February of this year. What can you tell us about what's transpired since then? How many engagements that you've done? What kind of metrics can you share? >> Yeah. Chris, do you want to weigh in for that? >> Sure, sure. The start of it really has been to create a bunch of customized analytics on the CloudPhysics platform to target specific sales motions that are relevant to HPE partners. So what do I mean by that? You'll remember that in May, we announced the Alletra Series 6,000 and 9,000. In tandem with that, CloudPhysics released a new set of analytics that help someone who's interested in those technologies figure out what model might be best for them and how much firepower they would need from one or the other of those solutions. Similarly, we have a bunch solutions and a market strength in the HCI world, hyper converged, and that's both SimpliVity and dHCI. And we've set up some analytics that specifically help someone who's interested in that form factor to accelerate, and again, pick the right solutions that will serve their exact applications needs. >> When you talk to customers, are they able to give you a sense as to the cost impacts? I mean, even if it's subjective, "Hey, we think we, you know, we save 10% versus the way we used to do it", or more or less. I mean, just even gut feel metrics. >> So I'll start that one, Sandeep. So there's sort of two ways to look at it. One thing is, because we know everything that's currently running in the data center - we discovered that - we have a pretty good cost of what it is costing them today to run their workloads. So anything that we compare that to, whether it's a transition to public cloud or a transition to a hosted VMware solution, or a set of new infrastructure, we can compare their current costs to the specific solutions that are available to them. But on the more practical side of things, oftentimes customers know intuitively this is a set of servers I bought four years ago, or this is an old array that I know is loose. It's not keeping up anymore. So they typically have some fairly specific places to start, which gives that partner a quick win, solving a specific customer problem. And then it can often boil out into the rest of the data center, and continual optimization can occur. >> How unique is this? I mean, is it, you know, can you give us a little glimpse of the secret sauce behind it? Is this kind of table stakes for the industry? >> Yeah. I mean, look, it's unique in the sense that CloudPhysics brings along over 200 metrics across the spectrum of virtual machines and guest OSs, as well as the overall CPU and RAM utilization, overall infrastructure analysis, and built in cloud simulators. So what customers are able to do is basically, in real time, be able to: A - be aware of exactly what their environment looks like; B - be able to simulate if they were going to move and give an application workload to the cloud; C - they're able to just right-size the underlying infrastructure across the board. Chris? >> Well, I was going to say, yeah, along the same lines, there have been similar technology approaches to different problems. Most notably in the current HPE portfolio, InfoSight. Best in class, data lake driven, very highly analytical machine learning, geared predominantly toward an optimization model, right? CloudPhysics is earlier in the talk track with the customer. We're going to analyze your environment where HPE may not even have a footprint today. And then we're going to give you ideas of what products might help you based on very similar techniques, but approaching a very different problem. >> So you've got data, you've got experience, you know what best practice looks like. You get a sense as to the envelope as to what's achievable, right? And that is just going to get better and better and better over time. One of the things that that I've said, and we've said on theCUBE, is that the definition of cloud is changing. It's expanding, it's not just public cloud anymore. It's a remote set of services, it's coming on Prim, there's a hybrid connection. We're going across clouds, we're going out to the edge. So can CloudPhysics help with that complexity? >> Yeah, absolutely. So we have a set of analytics in the cloud world that range from we're going to price your on-premise IT. We also have the ability to simulate a transition, a set of workloads to AWS, Azure, or Google Cloud. We also have the ability to translate to VMware based solutions on many of those public clouds. And we're increasingly spreading our umbrella over GreenLake as well, and showing the optimization opportunities for a GreenLake solution when contrasted with some of those other clouds. So there's not a lot of... >> So it's not static. >> It's not static at all. And Dave, you were mentioning earlier in terms such as proven. CloudPhysics now has operated on trillions of data points over millions of virtual machines across thousands of overall data assessments. So there's a lot of proven learnings through that as well as actual optimizations that customers have benefited from. >> Yes. I mean, there's benchmarks, but it's more than that because benchmarks tend to be static, okay. We consider rules of thumb. We're living in an age with a lot more data, a lot more machine intelligence. And so this is organic, it'll evolve. >> Sandeep: Absolutely. >> And the partners who work with their customers on a regular basis over at CloudPhysics, and then build up a history over time of what's changing in their data center can even provide better service. They can look back over a year, if we've been collecting, and they can see what the operating system landscape has changed, how different workloads have lost popularity, how other ones have gained. And they really can become a much better solution provider to that customer the longer CloudPhysics is used. >> Yeah, it gives your partners a competitive advantage, it's a much stickier model because the customer is going to trust your partner more if they get it right. So we're not going to change horses in the middle of the street. We're going to go back to the partner that set us up, and they keep getting better and better and better each time, we've got a good cadence going. All right. Sandeep, bring us home. What's your sort of summary? How should we think about this going forward? >> Well, I'll bring us right back to the way I started is, and to end, we're looking at how we continue to deliver best in class cloud operational experience for customers across the board with HPE GreenLake. And earlier this year, we unveiled this cloud operation experience for data, and for customers, that experience starts with a cloud consult where they can essentially discover services, consume services, that overall operational and support experience is transformed with HPE InfoSight. And now we're transforming this experience where any organization out there that's looking to get data-driven insights into what should they do next? Where should they place their workloads? How to right-size the infrastructure? And in the process, be able to transform how they are working and collaborating with their partners. They're able to do that now with HPE CloudPhysics, bringing these data driven insights for smarter IT decision-making. >> I like this a lot, because a lot of the cloud is trial and error. And when you try and you make a mistake, you're paying each time. So this is a great innovation to really help clients focus on the things that matter, you know, helping them apply technology to solve their business problems. Guys, thanks so much for coming to theCUBE. Appreciate it. >> Dave, always a pleasure. >> Thanks very much for having us. >> And keep it right there. We got more content from HPE's GreenLake announcements. Look for the cadence. One of the hallmarks of cloud is the cadence of announcements. We're seeing HPE on a regular basis, push out new innovations. Keep it right there for more. (bright upbeat music begins) (bright upbeat music ends)

>> Okay, we continue now with the theme of turning ideas into insights so ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only. And a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real-time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal we heard, or at least semi normal as we begin to better understand and forecast demand and supply imbalances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processings, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz, who's a Cube alum and VP and principal analyst at Forrester, and doin' some groundbreaking work in this area. And Cindy Maike who is the vice president of industry solutions and value management at Cloudera. Welcome to both of you. >> Welcome, thank you. >> Thanks Dave. >> All right Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >> It's really about democratization. If you can't make your data accessible, it's not usable. Nobody's able to understand what's happening in the business and they don't understand what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships with their customers due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and peck around within your ecosystem to find what it is that's important. >> Great thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >> Yeah, there's quite a few. And especially as we look across all the industries that were actually working with customers in. A few that stand out in top of mind for me is one is IQVIA. And what they're doing with real-world evidence and bringing together data across the entire healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making it accessible by both internally, as well as for the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, they're are a European car manufacturer and how do they make sure that they have efficient and effective processes when it comes to fixing equipment and so forth. And then also there's an Indonesian based telecommunications company, Techsomel, who's bringing together over the last five years, all their data about their customers and how do they enhance a customer experience, how do they make information accessible, especially in these pandemic and post pandemic times. Just getting better insights into what customers need and when do they need it? >> Cindy, platform is another core principle. How should we be thinking about data platforms in this day and age? Where do things like hybrid fit in? What's Cloudera's point of view here? >> Platforms are truly an enabler. And data needs to be accessible in many different fashions, and also what's right for the business. When I want it in a cost and efficient and effective manner. So, data resides everywhere, data is developed and it's brought together. So you need to be able to balance both real time, our batch, historical information. It all depends upon what your analytical workloads are and what types of analytical methods you're going to use to drive those business insights. So putting in placing data, landing it, making it accessible, analyzing it, needs to be done in any accessible platform, whether it be a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing being the most successful. >> Great, thank you. Michelle let's move on a little bit and talk about practices and processes, the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >> Yeah, it's a really great question 'cause it's pretty complex when you have to start to connect your data to your business. The first thing to really gravitate towards is what are you trying to do. And what Cindy was describing with those customer examples is that they're all based off of business goals, off of very specific use cases. That helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in near time or real time, or later on, as you're doing your strategic planning. What that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, "Well, can I also measure the outcomes from those processes and using data and using insight? Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my analytic capabilities that are allowing me to be effective in those environments?" But everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it? But coming in more from that business perspective, to then start to be data driven, recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions. And ultimately getting to the point of being insight driven, where you're able to both describe what you want your business to be with your data, using analytics to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize and you can innovate. Because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >> I like how you said near time or real time, because it is a spectrum. And at one end of the spectrum, autonomous vehicles. You've got to make a decision in real time but near real-time, or real-time, it's in the eyes of the beholder if you will. It might be before you lose the customer or before the market changes. So it's really defined on a case by case basis. I wonder Michelle, if you could talk about in working with a number of organizations I see folks, they sometimes get twisted up in understanding the dependencies that technology generally, and the technologies around data specifically can sometimes have on critical business processes. Can you maybe give some guidance as to where customers should start? Where can we find some of the quick wins and high returns? >> It comes first down to how does your business operate? So you're going yo take a look at the business processes and value stream itself. And if you can understand how people, and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process? Or are you collecting information, asking for information that is going to help satisfy a downstream process step or a downstream decision? So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize do you need that real time, near real time, or do you want to start creating greater consistency by bringing all of those signals together in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process, and the decision points, and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >> Got it. Let's, bring Cindy back into the conversation here. Cindy, we often talk about people, process, and technology and the roles they play in creating a data strategy that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? >> Yeah. And that's kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but the fuel behind the process and how do you actually become insight-driven. And you look at the capabilities that you're needing to enable from that business process, that insight process. Your extended ecosystem on how do I make that happen? Partners and picking the right partner is important because a partner is one that actually helps you implement what your decisions are. So looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data within your process that if you need to do it in a real-time fashion, that they can actually meet those needs of the business. And enabling on those process activities. So the ecosystem looking at how you look at your vendors, and fundamentally they need to be that trusted partner. Do they bring those same principles of value, of being insight driven? So they have to have those core values themselves in order to help you as a business person enable those capabilities. >> So Cindy I'm cool with fuel, but it's like super fuel when you talk about data. 'Cause it's not scarce, right? You're never going to run out. (Dave chuckling) So Michelle, let's talk about leadership. Who leads? What does so-called leadership look like in an organization that's insight driven? >> So I think the really interesting thing that is starting to evolve as late is that organizations, enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be. Data driving into the insight or the raw data itself has the ability to set in motion what's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, your CRO coming back and saying, I need better data. I need information that's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come. Not just one month, two months, three months, or a year from now, but in a week or tomorrow. And so that is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity. You have your chief data officer that is shaping the experiences with data and with insight and reconciling what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities. And either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data-driven, but ultimately to be insight driven, you're seeing way more participation and leadership at that C-suite level and just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >> Great, thank you. Let's wrap, and I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. A lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a maturity model. I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on an insight driven organization, Cindy what do you see as the major characteristics that define the differences between sort of the early beginners sort of fat middle, if you will, and then the more advanced constituents? >> Yeah, I'm going to build upon what Michelle was talking about is data as an asset. And I think also being data aware and trying to actually become insight driven. Companies can also have data, and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, you're not going to be insight-driven. So you've got to move beyond that data awareness, where you're looking at data just from an operational reporting. But data's fundamentally driving the decisions that you make as a business. You're using data in real time. You're leveraging data to actually help you make and drive those decisions. So when we use the term you're data-driven, you can't just use the term tongue-in-cheek. It actually means that I'm using the recent, the relevant, and the accuracy of data to actually make the decisions for me, because we're all advancing upon, we're talking about artificial intelligence and so forth being able to do that. If you're just data aware, I would not be embracing on leveraging artificial intelligence. Because that means I probably haven't embedded data into my processes. Yes, data could very well still be a liability in your organization, so how do you actually make it an asset? >> Yeah I think data aware it's like cable ready. (Dave chuckling) So Michelle, maybe you could add to what Cindy just said and maybe add as well any advice that you have around creating and defining a data strategy. >> So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? Bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing it and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset, it has value. But you may not necessarily know what that value is yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action, for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away the gap between when you see it, know it, and then get the most value and really exploit what that is at the time when it's right, so in the moment. We talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. So are we just introducing it as data-driven organizations where we could see spreadsheets and PowerPoint presentations and lots of mapping to help make longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder if I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight, there is none, it's all coming together for the best outcomes. >> Right, it's like people are acting on perfect or near perfect information. Or machines are doing so with a high degree of confidence. Great advice and insights, and thank you both for sharing your thoughts with our audience today, it was great to have you. >> Thank you. >> Thank you. >> Okay, now we're going to go into our industry deep dives. There are six industry breakouts. Financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments. Now each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session of choice. Or for more information, click on the agenda page and take a look to see which session is the best fit for you and then dive in. Join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community, and enjoy the rest of the day. (upbeat music)

>>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got a particular expertise in, in, in data and finance and insurance. I mean, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We, we talk more about digital, you know, or, or, or data-driven when you think about sort of where we've come from and where we're going, what are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital transformation journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third-party real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on, on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That data. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? >>Absolutely. I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of they having multiple, uh, distributors, what did they have in stock? So there are millions of data points that you need to drill down, down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their businesses and >>The ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting in? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Mick Halston about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict a, they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, w what do you see in that regard? >>Yeah, I think it's, I mean, we're definitely not at a point where when I talk to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? Where you can get machines to solve general knowledge problems, where they can solve one problem, and then a distinctly different problem, right? That's still many years away, but narrow AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So, for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience and pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer, and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this address actually, you know, a business that's a restaurant with indoor dining, does it have a bar is an outdoor dining, and it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do, even with narrow AI that can really drive top line of business results. >>Yeah. I like that term narrow AI because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. >>I mean, I think for most right, most fortune 500 companies, they can't just their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're half they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to, to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh, on-premise and public cloud as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought about? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. Then the salespeople, they know the CRM data and, you know, logistics folks. There they're very much in tune with ERP. I almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. >>I mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience. And that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really, >>I think data as a product is a very powerful concept. And I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data, and that's not necessarily what you mean. You mean thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea of I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my, my data architecture is, is that kind of thinking starting to really hit the marketplace. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware, and is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we, you know, collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies are doing >>Great examples of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss. Exactly. And it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight as to yeah. So, >>Um, I I'm in the executive sponsor for, um, the Accenture cloud era partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud errors, the right data platform for that. So, um, >>That'd be Cloudera ushered in the modern big data era. We, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, >>Absolutely. Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role apply. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Thank you.

>>Yeah. Okay. Now we're going to dig deeper into HP es moral and try to better understand how it's going to impact customers. And with me to do that are Robert Christensen is the vice president strategy in the office of the C, T. O. And Kumar Srikanth is the chief technology officer and head of software both, of course, with Hewlett Packard Enterprise. Gentlemen, welcome to the program. Thanks for coming on. >>Good seeing you. Thanks for having us. >>Always. Great. Great to see you guys. So, Esmeralda, kind of a interesting name. Catchy name. But tomorrow, what exactly is H P E s bureau? >>Yeah. It's indeed a catchy name. Our branding team done a fantastic job. I believe it's actually a derivation from Esmeralda. The Spanish for Emerald Berlin. Supposed to have some very mystical powers. Um, and they derived as moral from there, and we all actually, initially that we heard it was interesting. Um, so as well was our effort to take all the software, the platform tools that HB has and provide these modern operating platform to the customers and put it under one brand. It has a modern container platform. It has a persistent stories distribute the date of February. It has been foresight, as many of our customers similar, So it's the think of it as a container platform offering for modernization of the civilization of the customers. >>Yeah, it's an interesting to talk about platform, so it's not a lot of times people think product, but you're positioning it as a platform, so it has a broader implications. >>That's very true. So as the customers are thinking of this civilization, modernization containers and microservices, as you know there has become, has become the stable whole. So it's actually a container orchestration platform. It offers open source proven. It is as well as the persistence always bolted to >>so by the way, s moral, I think emerald in Spain, I think in the culture it also has immunity powers as well. So immunity >>from >>lock in and all those other terrible diseases. Maybe it helps us with covid to rob Robert. When you talk to customers, what problems do you probe for that that is immoral. Can can do a good job solving. >>Yeah, they That's a really great question because a lot of times they don't even know what it is that they're trying to solve for, other than just a very narrow use case. But the idea here is to give them a platform by which they can bridge both the public and private environment for what to do an application development specifically in the data side. So when they're looking to bring Container Ization, which originally got started on the public cloud and has moved its way, I should say, become popular in the public cloud and has moved its way on premises. Now Esmeralda really opens the door to three fundamental things. But how do I maintain an open architecture like you're referring to some low or oh, no lock in of my applications And there were two. How do I gain a data fabric or data consistency of accessing the data so I don't have to rewrite those applications when I do move them around and then, lastly, where everybody is heading down, the real value is in the AI ML initiatives that companies are are really bringing that value of their data and locking the data at where the data is being generated and stored. And so the is moral platform is those multiple pieces that I was talking about stacked together to deliver those solutions for the client. >>So come on, what's the How does it work? What's the sort of I p or the secret sauce behind it all? What makes HP different? >>Continuing our team of medical force around, uh, it's a moral platform for optimizing the data Indians who were close. I think I would say there are three unique characteristics of this platform. Number one is actually provides you both an ability to run stable and stateless were close under the same platform, and number two is as we were thinking about. Unlike analogues, covenant is open source. It actually produce you all open source government as well as an orchestration behind you. So you can actually you can provide this hybrid, um, thing that drivers was talking about. And then actually we built the work flows into it. For example, we're actually announced along with Esmeralda MLS, but on their customers can actually do the work flow management. Our own specifically did the work force. So the magic is if you want to see the secrets of is all the efforts that have been gone into some of the I p acquisitions that HBs the more years we should be. Blue Data bar in the nimble emphasize, all these pieces are coming together and providing a modern digitalization platform for the customers. >>So these pieces, they all have a little bit of a machine intelligence in them. Yeah, People used to think of a I as the sort of separate thing, having the same thing with containers, right? But now it's getting embedded in into the stack. What? What is the role of machine intelligence or machine learning in Edinburgh? >>I would take a step back and say, You know this very well. They're the customer's data amount of data that is being generated, and 95% or 98% of data is machine generated, and it has a serious amount of gravity, and it is sitting at the edge, and we were the only the only one that edge to the cloud data fabric that's built. So the number one is that we are bringing computer or a cloud to the data. They're taking the data to the cloud like if you go, it's a cloud like experience that provides the customer. Yeah, is not much value to us if we don't harness the data. So I said this in one of the blood. Of course, we have gone from collecting the data era to the finding insights into the data so that people have used all sorts of analysis that we are to find data is the new oil to the air and the data. And then now you're applications have to be modernized. And nobody wants to write an obligation in a non microservices fashion because you want to build the modernization. So if you bring these three things, I want to have a data. Gravity have lots of data. I had to build an area applications and I want to have an idea those three things I think we bring together to the customs. >>So, Robert, let's stay on customers from it. I mean, you know, I want to understand the business impact, the business case. I mean, why should all the you know, the cloud developers have all the fun? You mentioned that you're bridging the cloud and on Prem, uh, they talk about when you talk to customers and what they are seeing is the business impact. What's the real drivers for them. >>That's a great question because at the end of the day I think the reason survey that was that cost and performance is still the number one requirement for the real close. Second is agility, the speed of which they want to move. And so those two are the top of mind every time. But the thing we find in as moral, which is so impactful, is that nobody brings together the silicon, the hardware, the platform and all that stacked together work and combined, like as moral does with the platforms that we have and specifically, you know, when we start getting 90 92 93% utilization out of ai ml workloads on very expensive hardware, it really, really is a competitive advantage over a public cloud offering which does not offer those kind of services. And the cost models are so significantly different. So we do that by collapsing the stack. We take out as much intellectual property, give me, um, as much software pieces that are necessary. So we are closest to the silicon closest to the applications bring into the hardware itself, meaning that we can inter leave the applications, meaning that you can get to true multi tendency on a particular platform that allows you to deliver a cost optimized solution. So when you talk about the money side, absolutely. There's just nothing out there and then on the second side, which is agility. Um, one of the things that we know is today is that applications need to be built in pipelines. Right? This is something that has been established now for quite some time now. That's really making its way on premises. And what Kumar was talking about was, how do we modernize? How do we do that? Well, there's going to be something that you want to break into Microservices and containers. There's something you don't now the ones that they're going to do that they're gonna get that speed and motion etcetera out of the gate. And they can put that on premises, which is relatively new these days to the on premises world. So we think both will be the advantage. >>Okay, I want to unpack that a little bit. So the cost is clearly really 90 plus percent utilization. I mean, come on. You know, even even a pre virtualization. We know what it was like even with virtualization, you never really got that high. I mean, people would talk about it, but are you really able to sustain that in real world workloads? >>Yeah, I think when you I think when you when you make your exchangeable currency into small pieces, you can insert them into many areas. And we have one customer was running 18 containers on a single server and each of those containers, as you know, early days of data. You actually modernized what we consider we won containers of micro B. Um, so if you actually build these microservices and you have all anti affinity rules and you have rationing formulas all correctly, you can pack being part of these things extremely violent. We have seen this again. It's not a guarantee. It all depends on your application and your I mean, as an engineer, we want to always understand how this can be that sport. But it is a very modern utilization of the platform with the data and once you know where the data is, and then it becomes very easy to match those >>now. The other piece of the value proposition that I heard Robert is it's basically an integrated stack, so I don't have to cobble together a bunch of open source components. It's there. There's legal implications. There's obviously performance implications that I would imagine that resonates is particularly with the enterprise buyer, because they have the time to do all this integration. >>That's a very good point. So there is an interesting, uh, interesting question that enterprise they want to have an open source, so there is no lock in. But they also need help to implement and deploy and manage it because they don't have expertise. And we all know that Katie has actually brought that AP the past layer standardization. So what we have done is we've given the open source and you write to the covenant is happy, but at the same time orchestration, persistent stories, the data fabric, the ai algorithms, all of them are bolted into it. And on the top of that, it's available both as a licensed software and run on Prem. And the same software runs on the Green Lake so you can actually pay as you go and you don't we run it for them in in a collar or or in their own data center. >>Oh, good. I was one of my latter questions, so I can get this as a service paid by the drink. Essentially, I don't have to install a bunch of stuff on Prem and pay >>a perpetual license container at the service and the service in the last Discover. And now it's gone production. So both MLRS is available. You can run it on friends on the top of Admiral Container platform or you can run inside of the Green Bay. >>Robert, are there any specific use case patterns that you see emerging amongst customers? >>Yeah, absolutely. So there's a couple of them. So we have a really nice relationship that we see with any of the Splunk operators that were out there today. Right? So Splunk containerized their operator. That operator is the number one operator, for example, for Splunk, um, in the i t operation side or notifications as well as on the security operation side. So we found that that runs highly effective on top of his moral on top of our platforms that we just talked about what, uh, Kumar just talked about, but I want to also give a little bit of backgrounds to that same operator platform. The way that the Admiral platform has done is that we've been able to make highly active, active with a check availability at 95 nines for that same spark operator on premises on the kubernetes open source, which is, as far as I'm concerned. Very, very high end computer science work. You understand how difficult that is? Uh, that's number one. Number two, you'll see spark just a spark. Workloads as a whole. All right. Nobody handles spark workloads like we do. So we put a container around them, and we put them inside the pipeline of moving people through that basic, uh uh, ml ai pipeline of getting a model through its system through its train and then actually deployed to our MLS pipeline. This is a key fundamental for delivering value in the data space as well. And then, lastly, this is This is really important. When you think about the data fabric that we offer, um, the data fabric itself, it doesn't necessarily have to be bolted with the container platform to container at the actual data. Fabric itself can be deployed underneath a number of our for competitive platforms who don't handle data. Well, we know that we know that they don't handle it very well at all. And we get lots and lots of calls for people say, Hey, can you take your as Merrill data for every and solve my large scale, highly challenging data problems, we say yes. And then when you're ready for a real world full time but enterprise already, container platform would be happy to privilege. >>So you're saying if I'm inferring correctly, you're one of the values? Is your simplifying that whole data pipeline and the whole data science science project? Unintended, I guess. >>Okay, >>that's so so >>absolutely So where does the customer start? I mean, what what are the engagements like? Um, what's the starting point? >>It's being is probably one of the most trusted enterprise supplier for many, many years, and we have a phenomenal workforce of the both. The PowerPoint next is one of the leading world leading support organization. There are many places to start with. The right one is Obviously all these services are available on the green leg as we just start apart and they can start on a pay as you go basis. We have many customers that. Actually, some of the grandfather from the early days of pleaded and map are and they're already running, and they actually improvised on when, as they move into their next generation modernization, um, you can start with simple as metal container platform with persist with the story compared to this operation and can implement as as little as $10 and to start working. Um, and finally, there is a a big company like HP E. As an enterprise company defined next services. It's very easy for the customers to be able to get that support on the day to operation. >>Thank you for watching everybody's day volonte for the Cube. Keep it right there for more great content from Esmeralda. >>A mhm, okay.

>>Yeah, yeah, hello again and welcome to our last session of the day before we head to the meat. The experts roundtables how T Mobile is building a data driven organization with thought spot and whip prone. Today we'll hear how T Mobile is leaving Excel hell by enabling all employees with self service analytics so they can get instant answers on curated data. We're lucky to be closing off the day with these two speakers. Evo Benzema, manager of business intelligence services at T Mobile Netherlands, and Sanjeev Chowed Hurry, lead architect AT T Mobile, Netherlands, from Whip Chrome. Thank you both very much for being with us today, for today's session will cover how mobile telco markets have specific dynamics and what it waas that T Mobile was facing. We'll also go over the Fox spot and whip pro solution and how they address T mobile challenges. Lastly, but not least, of course, we'll cover Team Mobil's experience and learnings and takeaways that you can use in your business without further ado Evo, take us away. >>Thank you very much. Well, let's first talk a little bit about T Mobile, Netherlands. We are part off the larger deutsche Telekom Group that ISS operating in Europe and the US We are the second largest mobile phone company in the Netherlands, and we offer the full suite awful services that you expect mobile landline in A in an interactive TV. And of course, Broadbent. Um so this is what the Mobile is appreciation at at the moment, a little bit about myself. I'm already 11 years at T Mobile, which is we part being part of the furniture. In the meantime, I started out at the front line service desk employee, and that's essentially first time I came into a touch with data, and what I found is that I did not have any possibility of myself to track my performance. Eso I build something myself and here I saw that this need was there because really quickly, roughly 2020 off my employer colleagues were using us as well. This was a little bit where my efficient came from that people need to have access to data across the organization. Um, currently, after 11 years running the BR Services Department on, I'm driving this transformation now to create a data driven organization with a heavy customer focus. Our big goal. Our vision is that within two years, 8% of all our employees use data on a day to day basis to make their decisions and to improve their decision. So over, tuition Chief. Now, thank >>you. Uh, something about the proof. So we prize a global I T and business process consulting and delivery company. Uh, we have a comprehensive portfolio of services with presents, but in 61 countries and maybe 1000 plus customers. As we're speaking with Donald, keep customers Region Point of view. We primary look to help our customers in reinventing the business models with digital first approach. That's how we look at our our customers toe move to digitalization as much as possible as early as possible. Talking about myself. Oh, I have little over two decades of experience in the intelligence and tell cope landscape. Calico Industries. I have worked with most of the telcos totally of in us in India and in Europe is well now I have well known cream feed on brownfield implementation off their house on big it up platforms. At present, I'm actively working with seminal data transform initiative mentioned by evil, and we are actively participating in defining the logical and physical footprint for future architectures for criminal. I understand we are also, in addition, taking care off and two and ownership off off projects, deliveries on operations, back to you >>so a little bit over about the general telco market dynamics. It's very saturated market. Everybody has mobile phones already. It's the growth is mostly gone, and what you see is that we have a lot of trouble around customer brand loyalty. People switch around from provider to provider quite easily, and new customers are quite expensive. So our focus is always to make customer loyal and to keep them in the company. And this is where the opportunities are as well. If we increase the retention of customers or reduce what we say turned. This is where the big potential is for around to use of data, and we should not do this by only offering this to the C suite or the directors or the mark managers data. But this needs to be happening toe all employees so that they can use this to really help these customers and and services customers is situated. This that we can create his loyalty and then This is where data comes in as a big opportunity going forward. Yeah. So what are these challenges, though? What we're facing two uses the data. And this is, uh, these air massive over our big. At least let's put it like that is we have a lot of data. We create around four billion new record today in our current platforms. The problem is not everybody can use or access this data. You need quite some technical expertise to add it, or they are pre calculated into mawr aggregated dashboard. So if you have a specific question, uh, somebody on the it side on the buy side should have already prepared something so that you can get this answer. So we have a huge back lock off questions and data answers that currently we cannot answer on. People are limited because they need technical expertise to use this data. These are the challenges we're trying to solve going forward. >>Uh, so the challenge we see in the current landscape is T mobile as a civil mentioned number two telco in Europe and then actually in Netherlands. And then we have a lot of acquisitions coming in tow of the landscape. So overall complexity off technical stack increases year by year and acquisition by acquisition it put this way. So we at this time we're talking about Claudia Irureta in for Matic Uh, aws and many other a complex silo systems. We actually are integrated where we see multiple. In some cases, the data silos are also duplicated. So the challenge here is how do we look into this data? How do we present this data to business and still ensure that Ah, mhm Kelsey of the data is reliable. So in this project, what we looked at is we curated that around 10% off the data of us and made it ready for business to look at too hot spot. And this also basically help us not looking at the A larger part of the data all together in one shot. What's is going to step by step with manageable set of data, obviously manages the time also and get control on cost has. >>So what did we actually do and how we did? Did we do it? And what are we going to do going forward? Why did we chose to spot and what are we measuring to see if we're successful is is very simply, Some stuff I already alluded to is usual adoption. This needs to be a tool that is useable by everybody. Eso This is adoption. The user experience is a major key to to focus on at the beginning. Uh, but lastly, and this is just also cold hard. Fact is, it needs to save time. It needs to be faster. It needs to be smarter than the way we used to do it. So we focused first on setting up the environment with our most used and known data set within the company. The data set that is used already on the daily basis by a large group. We know what it's how it works. We know how it acts on this is what we decided to make available fire talksport this cut down the time around, uh, data modeling a lot because we had this already done so we could go right away into training users to start using this data, and this is already going on very successfully. We have now 40 heavily engaged users. We go went life less than a month ago, and we see very successful feedback on user experience. We had either yesterday, even a beautiful example off loading a new data set and and giving access to user that did not have a training for talk sport or did not know what thoughts, what Waas. And we didn't in our he was actively using this data set by building its own pin boards and asking questions already. And this shows a little bit the speed off delivery we can have with this without, um, much investments on data modeling, because that's part was already done. So our second stage is a little bit more ambitious, and this is making sure that all this information, all our information, is available for frontline uh, employees. So a customer service but also chills employees that they can have data specifically for them that make them their life easier. So this is performance KP ice. But it could also be the beautiful word that everybody always uses customer Terry, 60 fuse. But this is giving the power off, asking questions and getting answers quickly to everybody in the company. That's the big stage two after that, and this is going forward a little bit further in the future and we are not completely there yet, is we also want Thio. Really? After we set up the government's properly give the power to add your own data to our curated data sets that that's when you've talked about. And then with that, we really hope that Oh, our ambition and our plan is to bring this really to more than 800 users on a daily basis to for uses on a daily basis across our company. So this is not for only marketing or only technology or only one segment. This is really an application that we want to set in our into system that works for everybody. And this is our ambition that we will work through in these three, uh, steps. So what did we learn so far? And and Sanjeev, please out here as well, But one I already said, this is no which, which data set you start. This is something. Start with something. You know, start with something that has a wide appeal to more than one use case and make sure that you make this decision. Don't ask somebody else. You know what your company needs? The best you should be in the driver seat off this decision. And this is I would be saying really the big one because this will enable you to kickstart this really quickly going forward. Um, second, wellness and this is why we introduce are also here together is don't do this alone. Do this together with, uh I t do this together with security. Do this together with business to tackle all these little things that you don't think about yourself. Maybe security, governance, network connections and stuff like that. Make sure that you do this as a company and don't try to do this on your own, because there's also again it's removes. Is so much obstacles going forward? Um, lastly, I want to mention is make sure that you measure your success and this is people in the data domain sometimes forget to measure themselves. Way can make sure everybody else, but we forget ourselves. But really try to figure out what makes its successful for you. And we use adoption percentages, usual experience, surveys and and really calculations about time saved. We have some rough calculations that we can calculate changes thio monetary value, and this will save us millions in years. by just automating time that is now used on, uh, now to taken by people on manual work. So, do you have any to adhere? A swell You, Susan, You? >>Yeah. So I'll just pick on what you want to mention about. Partner goes live with I t and other functions. But that is a very keating, because from my point of view, you see if you can see that the data very nice and data quality is also very clear. If we have data preparing at the right level, ready to be consumed, and data quality is taken, care off this feel 30 less challenges. Uh, when the user comes and questioned the gator, those are the things which has traded Quiz it we should be sure about before we expose the data to the Children. When you're confident about your data, you are confident that the user will also get the right numbers they're looking for and the number they have. Their mind matches with what they see on the screen. And that's where you see there. >>Yeah, and that that that again helps that adoption, and that makes it so powerful. So I fully agree. >>Thank you. Eva and Sanjeev. This is the picture perfect example of how a thought spot can get up and running, even in a large, complex organization like T Mobile and Sanjay. Thank you for sharing your experience on how whip rose system integration expertise paved the way for Evo and team to realize value quickly. Alright, everyone's favorite part. Let's get to some questions. Evil will start with you. How have your skill? Data experts reacted to thought spot Is it Onley non technical people that seem to be using the tool or is it broader than that? You may be on. >>Yes, of course, that happens in the digital environment. Now this. This is an interesting question because I was a little bit afraid off the direction off our data experts and are technically skilled people that know how to work in our fight and sequel on all these things. But here I saw a lot of enthusiasm for the tool itself and and from two sides, either to use it themselves because they see it's a very easy way Thio get to data themselves, but also especially that they see this as a benefit, that it frees them up from? Well, let's say mundane questions they get every day. And and this is especially I got pleasantly surprised with their reaction on that. And I think maybe you can also say something. How? That on the i t site that was experienced. >>Well, uh, yeah, from park department of you, As you mentioned, it is changing the way business is looking at. The data, if you ask me, have taken out talkto data rather than looking at it. Uh, it is making the interactivity that that's a keyword. But I see that the gap between the technical and function folks is also diminishing, if I may say so over a period of time, because the technical folks now would be able to work with functional teams on the depth and coverage of the data, rather than making it available and looking at the technical side off it. So now they can have a a fair discussion with the functional teams on. Okay, these are refute. Other things you can look at because I know this data is available can make it usable for you, especially the time it takes for the I t. G. When graduate dashboard, Uh, that time can we utilize toe improve the quality and reliability of the data? That's yeah. See the value coming. So if you ask me to me, I see the technical people moving towards more of a technical functional role. Tools such as >>That's great. I love that saying now we can talk to data instead of just looking at it. Um Alright, Evo, I think that will finish up with one last question for you that I think you probably could speak. Thio. Given your experience, we've seen that some organizations worry about providing access to data for everyone. How do you make sure that everyone gets the same answer? >>Yes. The big data Girlfriends question thesis What I like so much about that the platform is completely online. Everything it happens online and everything is terrible. Which means, uh, in the good old days, people will do something on their laptop. Beirut at a logic to it, they were aggregated and then they put it in a power point and they will share it. But nobody knew how this happened because it all happened offline. With this approach, everything is transparent. I'm a big I love the word transparency in this. Everything is available for everybody. So you will not have a discussion anymore. About how did you get to this number or how did you get to this? So the question off getting two different answers to the same question is removed because everything happens. Transparency, online, transparent, online. And this is what I think, actually, make that question moot. Asl Long as you don't start exporting this to an offline environment to do your own thing, you are completely controlling, complete transparent. And this is why I love to share options, for example and on this is something I would really keep focusing on. Keep it online, keep it visible, keep it traceable. And there, actually, this problem then stops existing. >>Thank you, Evelyn. Cindy, That was awesome. And thank you to >>all of our presenters. I appreciate your time so much. I hope all of you at home enjoyed that as much as I did. I know a lot of you did. I was watching the chat. You know who you are. I don't think that I'm just a little bit in awe and completely inspired by where we are from a technological perspective, even outside of thoughts about it feels like we're finally at a time where we can capitalize on the promise that cloud and big data made to us so long ago. I loved getting to see Anna and James describe how you can maximize the investment both in time and money that you've already made by moving your data into a performance cloud data warehouse. It was cool to see that doubled down on with the session, with AWS seeing a direct query on Red Shift. And even with something that's has so much scale like TV shows and genres combining all of that being able to search right there Evo in Sanjiv Wow. I mean being able to combine all of those different analytics tools being able to free up these analysts who could do much more important and impactful work than just making dashboards and giving self service analytics to so many different employees. That's incredible. And then, of course, from our experts on the panel, I just think it's so fascinating to see how experts that came from industries like finance or consulting, where they saw the imperative that you needed to move to thes third party data sets enriching and organizations data. So thank you to everyone. It was fascinating. I appreciate everybody at home joining us to We're not quite done yet. Though. I'm happy to say that we after this have the product roadmap session and that we are also then going to move into hearing and being able to ask directly our speakers today and meet the expert session. So please join us for that. We'll see you there. Thank you so much again. It was really a pleasure having you.

>> Hi, I'm Kyle Rourke, VP of Platform Strategy here at Snowflake. And I could not be more excited to be here today with Margaret Sherman, who is the Head of Data Strategy for Sonos. Now, all throughout the day-to-day we've been hearing from lots of customers from all over the world and hearing about their journeys and how they've transformed their business by embracing the data cloud. And of course, this next story is one I am personally very excited about because I'm a huge Sonos customer. And I'm sure many of you are as well. >> Thanks Kyle. As you mentioned, I'm the Head of Data Strategy at Sonos. And what that means is that I set the data priorities for the company, as well as guide the company on where to invest now and in the future, to get the most out of our data resources. I've spent about four years at Sonos. Previously I was the head of product data and worked a lot with setting up Snowflake and the IoT data. And I've been in technology for 20 years and the last 10 years I've spent in data analytics and machine learning. >> That's very cool. Now, how does your company view data in general? How do you guys... How does your team fit in, in the overall strategy of how Sonos leverages data? >> Yeah. So Sonos is a sound experience company and we really pioneered multi-room wireless audio. And we made that experience amazing and truly changed how people listen. And our mission is to inspire the world to listen better. And everything that we do is in service to that. And data is a part of that. So we really believe that data is fundamental to helping us achieve our mission. Data helps us build a better business, build better products and ultimately we think it helps us make our customers happier. >> Now, before Snowflake, maybe let's go back in time. You've been at Sonos for the last four years. Maybe go back in time a little bit for the time pre Snowflake. What were some of your challenges that you guys faced before you started with us? >> Yeah, it was really challenging. So I was actually leading the product data team at the time and we use Snowflake primarily for our IoT data. And so we're collecting tons of data, but we're really struggling to leverage it. Essentially what would happen is, if we wanted to answer a single question about what was happening with the customer experience, we would have to have data engineers go and write some code, spin up clusters. This could take weeks just to extract the data and get it into a shape where analysts and scientists could go and work with it. And so we really went from being in a place where it was taking weeks just to answer a single question, to now we can do things in hours. So it really changed things for us. >> Wow. And so the benefits of obviously being able to go act on information very, very quickly. What are some of the other benefits that's driven for you guys? >> I mean, our data people love it, obviously, because if you think about the process of data science, it's very iterative. So you're going to ask a question, you're going to go and investigate your data. You're going to do some data processing, you're going to do some visualizations and then you're going to come up with more questions. You're going to want to dig in. You're going to want to pull more data and you're going to want to join it together, do different cuts and pivots. And before that was just off the table, because as you can imagine, if every time you have to go weeks to go do that, it's just impossible. Whereas now, our data scientists can churn through these problems very quickly. And the data engineers love it because they're not sitting around waiting for jobs to finish for forever. They are able to get through their code faster. And as one of my engineers likes to say, she was telling me, she said, "Snowflake, she's a beast." (Kyle and Margaret laughing) It's like crunching through the data. >> You mentioned IoT. And I think that's obviously a very challenging space for a lot of customers and there's a lot of interest in it. Maybe give me some more thoughts on how much data are you guys are bringing in? Is it small, large? Has the volume been something that you could handle with Snowflake? >> Yeah, I mean, that was why we chose Snowflake. So prior to having Snowflake, we were really struggling with the vol... I mean, we've very large volumes of data, as you can imagine from an IoT device because we're collecting from over 10 million homes across the world. So it's quite a bit of data. And all of that doesn't fit in a traditional sort of data warehouse. We were trying to push some of it into SQL and we were essentially taking just a handful of our telemetry events. And we were boiling them down to daily and weekly aggregates. And even trying to push that into SQL was just too much for it to handle. And with Snowflake, we had processing jobs that were timing out in SQL server after running for hours and hours, and then Snowflake could just crunch through it in a few minutes. So-- >> Kyle: Wow. >> It was, yeah. I mean, I literally almost fell off my chair. (Margaret laughs) They showed me the comparison numbers. >> Well, so now... So it's been a good experience for you, but let's talk about your customers. And I think, I can say as a customer myself, I've always had a great experience with Sonos, it's probably why I keep buying more and more and more of them. But talk to me about how has data really helped you guys drive that customer experience in some very tangible ways? >> As a Sonos customer, we really hope that you enjoy the great sound and freedom of choice and ease of use that the product brings. But obviously behind all of that, behind that really easy experience, it's very complex. You've got a lot going on, when you're interacting with your Sonos system. So, it's not just a single piece of hardware, you've got a mobile device potentially that you're using to control it, you have third-party voice services, you have music service providers, wifi, network traffic, all of these things are going on. So for us to really make sure that we're creating an amazing experience cause we're super customer obsessed. >> So Margaret, one of the things that I've always experienced as a customer of Sonos, is that, frankly for me it just always works. And that's one of the best parts of the customer experience. Whether I'm pulling up Spotify on my phone to go listen to it, or whether I'm plugging in a soundbar into the TV, everything seems to just work. So maybe just walk me through why it's been such a good experience maybe for me. >> Yeah. So some of the kind of at a high level, an example of what we do is we use Snowflake to... And because of the power of Snowflake, we're able to bring together different kinds of telemetry about what's happening in our products and our services. And then we can try to pinpoint reliability issues and determine what's happening, like what's causing them. That was kind of the first thing that we attacked with Snowflake, was really to go in and dig in and join some different events together and start slicing and dicing and looking for what are the main problems that we're seeing and what are the fixes to them. And the product team was able to find some reliability issues that they were able to fix. And they shipped the fixes and we were able to significantly reduce the rate at which some of these errors were occurring. >> Just by having all the data in one place and being able to go actually act on it quickly and of course in a cost effective manner, it really did let you guys really pinpoint any issue when it did occur and go and go support it, fixed quickly. >> Yeah. I mean, we were able to find things that we didn't even know were happening because we could really drill into the data. >> So it wasn't just having one place, it was also just being able to go dig into a different level of fidelity that you didn't have before? >> I mean, you should see some of the tableau dashboards that we've put on top of Snowflake. So, it's pretty impressive. (laughs) >> That's awesome. That's awesome. So what really set it apart? I mean you've been in the business, you're an expert in this business and there's a lot of options out there. What really set apart Snowflake from everything else at the time, and even now in your opinion? >> Just like you said about Sonos, it just works. (Kyle and Margaret laughing) We were able to stand it up really easily. We were able to load data into it really easily. It's pretty flexible in terms of what you can do. So for example, we use the variant column quite a bit, and that allows us to take kind of this semi-structured data and throw it in there and have an index. And then we can work with it as we want to. We don't have to have like a real complex data pipeline upstream before we throw things into Snowflake. If we don't want to, or we can, we really like, obviously I mentioned the speed. I keep mentioning that cause it's really powerful and it holds a ton of data. But even better is the cost. So we're pushing tons of data into Snowflake and we're not having to pay that much for that cost. We're basically paying S3 costs. But then you pay for what you use. So you're just paying for your processing costs. And even that is pretty easy to optimize. And you guys provide tools for that and support. I mean, you've helped me save thousands dollars a month this year (laughs) and it's really great. >> What's the plan for the future? Where do you see Sonos and Snowflake and the data cloud? Where do you see all that intersecting in the future? >> Yeah, I mean we really want Snowflake to be kind of the center of our data platform and bring all of our data together. We want to live the data dream. (Kyle and Margaret laughing) (indistinct) our data together and do all sorts of cool analysis. So we have a bunch of different projects that we want to be able to do. For example, one thing that we're looking at doing is bringing together our product telemetry data and our customer support data so that we can try to find patterns in terms of the types of errors or sequence of events that happen before somebody calls us, so that we can potentially intervene and fix the problem before somebody even has to reach out to our care team. And then another place that we're looking at using Snowflake is connecting it to salesforce. So for people who are interested in hearing from us, we could do things like when you set up a new product, we can send you information about how to use that product, or if there's new features available for the product that you have, we could send you information about that. And with Snowflake as our backend, it really helps us be able to tailor the customer experience. >> So Margaret, you've talked a lot about Snowflake and using Snowflake and it just works. I mean, you guys are running a very, very large implementation of Snowflake like using IoT data coming from, as you mentioned, millions and millions of devices. What's been the overall lift to the organization just to go manage the whole thing and keep it going and keep it up and running? >> It almost runs itself. (Margaret laughing) I'm almost surprised because we obviously use a lot of different third party tools and most of them require quite a bit of intervention on a regular basis. I would say Snowflake, it stays up and running and you have great tools for managing the whole system. And it's easy to see what's happening, it's easy to see when different clusters are spinning up and spinning down, we have tableau dashboards that we use to monitor all of our usage. And you guys provide all the data for that to make that really easy. So that's really great. Can't say it enough, without Snowflake. (laughs) >> So Margaret, thank you so much. It was great hearing your story about Sonos and how you leverage data cloud. So again, thank you for being a customer. And thank you again for being here today. >> Thanks for having me. It was a pleasure.

>> Narrator: From around the globe. It's theCUBE, with digital coverage of Actifio Data Driven 2020, brought to you by Actifio. >> Welcome back. I'm Stuart Miniman and this is theCUBE's coverage of Actifio Data Driven 2020. We wish everybody could join us in Boston, but instead we're doing it online this year, of course, and really excited. We're going to be digging into the value of data, how DataOps, data scientists are leveraging data. And joining me on the program, Scott Buckles, he's the North American Business Executive for database data science and DataOps with IBM, Scott, welcome to theCUBE. >> Thanks Stuart, thanks for having me, great to see you. >> Start with the Actifio-IBM partnership. Anyone that knows that Actifio knows that the IBM partnership is really the oldest one that they've had, either it's hardware through software, those joint solutions go together. So tell us about the partnership here in 2020. >> Sure. So it's been a fabulous partnership. In the DataOps world where we are looking to help, all of our customers gain efficiency and effectiveness in their data pipeline and getting value out of their data, Actifio really compliments a lot of the solutions that we have very well. So the folks from everybody from the up top, all the way through the engineering team, is a great team to work with. We're very, very fortunate to have them. How many or any specific examples or anonymized examples that you can share about joint (indistinct). >> I'm going to stay safe and go on the anonymized side. But we've had a lot of great wins, several significantly large wins, where we've had clients that have been struggling with their different data pipelines. And I say data pipeline, I mean getting value from understanding their data, to developing models and and doing the testing on that, and we can get into this in a minute, but those folks have really needed a solution where Actifio has stepped in and provided that solution. To do that at several of the largest banks in the world, including one that was a very recent merger down in the Southeast, where we were able to bring in the Actifio solution and address our, the customer's needs around how they were testing and how they were trying to really move through that testing cycle, because it was a very iterative process, a very sequential process, and they just weren't doing it fast enough, and Actifio stepped in and helped us deliver that in a much more effective way, in a much more efficient way, especially when you into a bank or two banks rather that are merging and have a lot of work to convert systems into one another and converge data, not an easy task. And that was one of the best wins that we've had in the recent months. And again, going back to the partnership, it was an awesome, awesome opportunity to work with them. >> Well, Scott, as I teed up for the beginning of the conversation, you've got data science and DataOps, help us understand how this isn't just a storage solution, when you're talking about BDP. How does DevOps fit into this? Talk a little bit about some of the constituents inside your customers that are engaging with the solution. >> Yeah. So we call it DataOps, and DataOps is both a methodology, which is really trying to combine the best of the way that we've transformed how we develop applications with DevOps and Agile Development. So going back 20 years ago, everything was a waterfall approach, everything was very slow , and then you had to wait a long time to figure out whether you had success or failure in the application that you had developed and whether it was the right application. And with the advent of DevOps and continuous delivery, the advent of things like Agile Development methodologies, DataOps is really converging that and applying that to our data pipelines. So when we look at the opportunity ahead of us, with the world exploding with data, we see it all the time. And it's not just structured data anymore, it's unstructured data, it's how do we take advantage of all the data that we have so that we can make that impact to our business. But oftentimes we are seeing where it's still a very slow process. Data scientists are struggling or business analysts are struggling to get the data in the right form so that they can create a model, and then they're having to go through a long process of trying to figure out whether that model that they've created in Python or R is an effective model. So DataOps is all about driving more efficiency, more speed to that process, and doing it in a much more effective manner. And we've had a lot of good success, and so it's part methodology, which is really cool, and applying that to certain use cases within the, in the data science world, and then it's also a part of how do we build our solutions within IBM, so that we are aligning with that methodology and taking advantage of it. So that we have the AI machine learning capabilities built in to increase that speed which is required by our customers. Because data science is great, AI is great, but you still have to have good data underneath and you have to do it at speed. Well, yeah, Scott, definitely a theme that I heard loud and clear read. IBM think this year, we do a lot of interviews with theCUBE there, it was helping with the tools, helping with the processes, and as you said, helping customers move fast. A big piece of IBM strategy there are the Cloud Paks. My understanding you've got an update with regards to BDP and Cloud Pak. So to tell us what the new releases here for the show. >> Yeah. So in our (indistinct) release that's coming up, we will be to launch BDP directly from Cloud Pak, so that you can take advantage of the Activio capabilities, which we call virtual data pipeline, straight from within Cloud Pak. So it's a native integration, and that's the first of many things to come with how we are tying those two capabilities and those two solutions more closely together. So we're excited about it and we're looking forward to getting it in our customer's hands. >> All right. And that's the Cloud Pak for Data, if I have that correct, right? >> That's called Cloud Pak for data, correct, sorry, yes. Absolutely, I should have been more clear. >> No, it's all right. It's, it's definitely, we've been watching that, those different solutions that IBM is building out with the Cloud Paks, and of course data, as we said, it's so important. Bring us inside a little bit, if you could, the customers. What are the use cases, those problems that you're helping your customers solve with these solution? >> Sure. So there's three primary use cases. One is about accelerating the development process. Getting into how do you take data from its raw form, which may or may not be usable, in a lot of cases it's not, and getting it to a business ready state, so that your data scientists, your business, your data models can take advantage of it, about speed. The second is about reducing storage costs. As data has exponentially grown so has storage costs. We've been in the test data management world for a number of years now. And our ability to help customers reduce that storage footprint is also tied to actually the acceleration piece, but helping them reduce that cost is a big part of it. And then the third part is about mitigating risk. With the amount of data security challenges that we've seen, customers are continuously looking for ways to mitigate their exposure to somebody manipulating data, accessing production data and manipulating production data, especially sensitive data. And by virtualizing that data, we really almost fully mitigate that risk of them being able to do that. Somebody either unintentionally or intentionally altering that data and exposing a client. >> Scott, I know IBM is speaking at the Data Driven event. I read through some of the pieces that they're talking about. It looks like really what you talk about accelerating customer outcomes, helping them be more productive, if you could, what, what are some of key measurements, KPIs that your customers have when they successfully deploy the solution? >> So when it comes to speed, it's really about, we're looking at about how are we reducing the time of that project, right? Are we able to have a material impact on the amount of time that we see clients get through a testing cycle, right? Are we taking them from months to days, are we taking them from weeks to hours? Having that type of material impact. The other piece on storage costs is certainly looking at what is the future growth? You're not necessarily going to reduce storage costs, but are you reducing the growth or the speed at which your storage costs are growing. And then the third piece is really looking at how are we minimizing the vulnerabilities that we have. And when you go through an audit, internally or externally around your data, understanding that the number of exposures and helping find a material impact there, those vulnerabilities are reduced. >> Scott, last question I have for you. You talk about making data scientists more efficient and the like, what are you seeing organizationally, have teams come together or are they planning together, who has the enablement to be able to leverage some of the more modern technologies out there? >> Well, that's a great question. And it varies. I think the organizations that we see that have the most impact are the ones that are most open to bringing their data science as close to the business as possible. The ones that are integrating their data organizations, either the CDO organization or wherever that may set it. Even if you don't have a CDO, that data organization and who owned those data scientists, and folding them and integrating them into the business so that they're an integral part of it, rather than a standalone organization. I think the ones that sort of weave them into the fabric of the business are the ones that get the most benefit and we've seen have the most success thus far. >> Well, Scott, absolutely. We know how important data is and getting full value out of those data scientists, critical initiative for customers. Thanks so much for joining us. Great to get the updates. >> Oh, thank you for having me. Greatly appreciated. >> Stay tuned for more coverage from Activio Data Driven 2020. I'm Stuart Miniman, and thank you for watching theCUBE. (upbeat music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of Actifio data driven 2020 brought to you by Actifio. >> Welcome back, I'm Stu Miniman. This is the cubes coverage of Actifio data driven, happy to welcome to the program, the Co-founder and Chief Product Officer David Chang with Actifio. Thanks so much for joining us. Great to have you back on theCUBE. >> Great to be here, Stu, thank you. >> All right, so the big theme of the event is the next normal, of course, we've been talking about transformation of data for many years, but the global pandemic has put a real emphasis on some of the transformations that customers are going through and alluding to that next normal because definitely things have changed a bit. What, give us if you could kind of a high level, you know what you've been seeing, you've been there since the start for Actifio. So, you know, what is that next normal for customers? >> Yeah, absolutely, so I would say over the span of the last two years, we've seen definitely a accelerated ramp into the Clouds. But I think this whole pandemic has really accelerated. I mean, this really telltale sign came in, we actually, prior to the pandemic hit, we closed a large European customer. And within a span of two weeks, they were saying, you know, "I can't get access to my data center for all the important work that I have to do." And with Actifio like to move everything into the Cloud. So within the span of three weeks, we were able to move a lot of their critical workloads with them. So I think that gave us the telltale sign that this thing is really truly accelerating. >> Yeah, it absolutely there's that acceleration. It's tough to move data, though. It's not like we can just say, "Okay, hey, you know, we've got petabytes, you know, that the laws of physics still are in place." And also with that move to Cloud, you know, backup and recovery, you know, disaster recovery, you know, still critically important so and any learnings that you've had this year or things where you've had to, you know, help out customers, as they say, "We need to move fast, but we also need to stay secure. And we need to make sure that our data is safe." >> Absolutely, so I think there's a major difference between the lift and shift model in terms of your way at your application infrastructure, and then the actual foundation, building block you're using those pieces are very difficult to lift and shift, because Cloud fundamentally present different set of building blocks. A great example here is that object storage, you know, it's the most scalable and lowest cost storage available in the public sort of Cloud hyperscaler infrastructure. And without that, trying to move to the Cloud would be very difficult indeed, trying to make the infrastructure match. >> So let's dig in and talk a little bit about how Cloud really transforms storage, you know, back in the storage industry, we've talked for a long time that you know, object was the future and that's, what Cloud was built on. So you've got large scalability, you've got some great cost efficiencies. You know, what does that mean for the Actifio solutions and your customer? >> Yeah, I think from the very beginning, I would say recall this conversation three or four years ago, when we were looking at what are some of the next generation architecture we want to build the Actifio technology on? It was very clearly that object storage needs to be front and center in everything we do. It's not a it's a maybe a little known fact. But Amazon AWS service initially started with the S3 architecture and that was the very first service they brought live within the AWS sort of product portfolio. So it is as fundamental to the Cloud as you know, EC2 more so than containers and so on and so forth. And the fact that you have this almost linear scalability horizontally to exabytes of storage and the fact that you can essentially leverage all the performance you need to get out the object storage that's all built into the environment. Those are some of the critical pieces and obviously, the low cost, you know, compared with SSD or spinning drives on the on the EC2 environment, those are all some of the critical elements on why object storage is so critical in this whole Cloud migration, if you will. >> Yeah, I wonder if we could talk a little bit about the application sort of things because of course, the architecture matters, but it's really the the outcomes, it's the reason we have infrastructures for the applications and of course one of the most mission critical applications. We've talked about data, it's those databases. We've seen a lot of transformation in the database world. Most customers I talked to now, it's not their one central source of truth. They now have many databases, especially in the Cloud we've seen that kind of Cambrian explosion of options out there. What does that meant for your customers? You know, take us inside, you know, that most important database world. >> Yeah, I think any customer with their interest to go into the Cloud or minimize the on premise environment anyway, the very first thing they think about is what are I most critical application I need to move right? Database are typically it. You know, there are companies that has a lot of, I would say, projects around migrating some of the traditional databases into NoSQL, or even hosted services like RDS. But I would say the vast majority of the database population that's in fact, that's essentially in production today are some of the traditional databases. So that tend to be also tend to be the most difficult problem in terms of trying to migrate the workload to the Cloud or DR or business continuity into the Cloud. >> So David, how about you know what is new from Actifio now? What should customers be looking at when we talk about the storage capabilities? >> So I would say the first thing is that Actifio allow our customers to kind of maintain the legacy databases they use. And by using Actifio, we normalize the entire Cloud infrastructures. So you can get all the same RPOs and RTOs that you're used to on premise into the Cloud. And through the adoption or of object storage down deep into their foundation blocks of our architecture, now, you can have sort of the best of both world. You can have this on demand capability or using from the public Clouds. You are, you know, getting capability as you need them. But also you can leverage sort of object storage without changing your application architecture, to get that performance and get to the sort of the cost point that you need to make that entire business viable. I think relatively recently, we did ESG sort of project that really validated that you can get 95 to 97% of the performance of SSD, but rather on object storage. And from a cost saving perspective, that cost say that cost actually went down by 88%. So it is indeed the best of both worlds, if you will. >> Yeah, you know, maybe explain that more a little bit more, if you could, yeah. Because, right, you want that scalability, you want high performance, but, you know, there's always been those architectural trade offs. So what is it that Actifio does, you're talking about the object storage that pairing with the Cloud capabilities? Help us understand, you know, what is differentiated about that solution? >> Yeah, absolutely, so I think in some ways, object storage has been getting a bad rap in terms of people's perception of slow performance and so on, so forth. But I think the real reason is because other vendors aren't using it incorrectly if you will. A lot of things we've seen in the past has, like legacy backup vendors taking sort of a looking at object storage as they tape replacement. With all object storage system, there is a fundamental limit on a per object performance you can get out the entire object infrastructure. But really the secret sauce Actifio came up with is to design an infrastructure that natively translate block or file storage that for example, Oracle SQL consumes, and then taking that data, sort of, if you will, from the application perspective, and divided into hundreds of thousands if not millions of objects and that can be spread across the entire object storage infrastructure. And this is how we get you get the performance if you will. That's very very similar if not almost identical to SSD even on object storage. >> Yeah, I saw a blog post on the Actifio site making a comparison to the SnowFlake database, of course, you know, super hot company lots of adoption in their Cloud service, help us understand a little bit, you know that that comparison that your team is making? >> Yeah, absolutely, I think it's a very interesting insight. I think both Actifio and SnowFlake probably independently arrived at the same conclusion about four or five years ago, that object storage is the foundation building block. And this is how you scale massive infrastructure at a cost that's effective for our business models, right? So I think, in many ways, if you look at how SnowFlakes works is they leverage this almost infinite scalability of object storage to consume sort of this data lake to store this data lake, and therefore they can effectively offer that basic service to your customer at a very low cost point. And then when they actually decide, the customer decide to use that information, this is where the business model works and they actually did start charging the customer. So that foundation building block of object storage on, you know, in terms of the fundamental building block for the SnowFlake service, I would argue is also the reasons why they're so popular today. >> Yeah, and David, you know, we've seen, you know, quite a change in the landscape since the early days of Actifio, it's interesting to hear you talk about those analogies with some of those, you know, Cloud native solutions. Give us a little bit of inside, you're the Chief Product officer. You know, what's the biggest change you'd say of Actifio today versus, you know, maybe how, when people first heard of their copy data management, you know, technology? >> Yeah, I would say I think we were kind of fortunate that when we started the company, the fundamental premise of being very efficient, very scalable, and instant reuse is a sort of fundamental premise of our product and architecture that has held true through technology evolution, you know, three or four different waves in the last, I would say 10 years. So I think what's currently the biggest difference between I would say, now versus Actifio five years ago, is that everything with everything we do, we're thinking Cloud first. This is how, you know, essentially, the Actifio platform has evolved into this normalization platform for enterprise customer to achieve the same RPOs and RTO the same applications and be able to using the some of the same building blocks across both their you know, hybrid infrastructure and also public Cloud infrastructure. >> Yeah, absolutely, that hybrid discussion has really dominated a lot of discussion the last couple of years. A challenge for, you know, the engineering teams is architecting into those environments. It's not just once you've got Amazon, you've got Azure, you've got Google, you've got others out there. What do you say? It doesn't feel like we have a standardization. And there's specific work that you need to do. But your ultimate customer, they want to be able to do it the same way no matter where they are. Give us a little bit of what you know, what you're seeing is some of the challenges and how Actifio is facing that. >> Yeah, I think there are fundamentally two ways to go to the Cloud. I think one is to entirely consume a log or higher log of functionality that Amazon Google and Azure has, right? That mean that does mean rewriting your application from scratch to take advantage of that. I think some of the benefit there is you have some very low entry cost and you don't have to worry about operationally how to keep that going. But I think more commonly, what we're seeing customer enterprise customer do is to taking their existing stack, rewriting portions of this and kind of build it on EC2 you know, and a container environment. And those are sort of I think, more of the more popular choices that people are making in terms of making the move to the Cloud at least from my enterprise customer perspective. And that is an area that Actifio could really help, by again, normalizing what they're familiar with on premise to the Cloud and we can provide the same service level and provide really this level of flexibility for you to shift workloads back and forth to make that work for your business case. >> Yeah, I'm curious, I remember back you talked about Actifio five years ago, and some of the early days it was like, "Well, you know, the traditional storage companies might not like Actifio because at the end of the day, they're going to sell less capacity. And that's really how they price things." Feels like the Cloud providers, think about it very different, you don't really think as much about, you know, "I don't buy capacity, I have scalability, I build out my applications in a certain way." Do you see that Cloud model taking over any other comparisons you'd make kind of the Cloud world versus the data center world? >> Yeah, I think it really I think that really the switches is very, very telling, right? It's very, I would say in some ways surprised a lot of people at the pace and and that it has happened. But I think it is, that pace is pretty solid at this point. I mean, we are seeing broad adoption of sort of that strategy all over the world, and it's only accelerating. >> All right, final question I have let's bring it on home that next normal, what do you want customers to have as their takeaway from this year's data driven event? >> I think they are, I think the probably the most important thing we want to communicate to our customer and potential prospect is that you can have the best of both world, right? It's not a one or two or other decision you have to make, you could be in the Cloud and enjoy a lot of the same benefits and saw a lot of the same service level that you're used to, but taken advantage of that, you know, there is a separate, very large company running world class operations for you in the Cloud. The elasticity of that capability is very important as well. But with Actifio without having to rewrite your application per se, you can have advantage if you will to of the new world, still maintaining the presence of the old and you can manage both environment in the same way. >> David Chang, thank you so much for the updates. Great to catch up with you and thanks for having us at the event. >> Thank you, Stu for having me. >> I'm Stu Miniman, and thank you for watching theCUBE. (upbeat music)

>> Announcer: From around the globe, it's theCUBE! With digital coverage of Actifio Data Driven 2020. Brought to you by Actifio. >> We're back, This is theCUBE's coverage, our ongoing coverage of Actifio's Data Driven, of course we've gone virtual this year. Ash Ashutosh is here, he's the founder, president, and CEO of Actifio. Ash, great to see you again. >> Likewise, Dave, always, always good to see you. >> We were at a little meetup, you and I, in Boston, I always enjoy our conversations. Little did we know that a few months later, we'd only be talking at this type of distance, and of course, it's sad, I mean, Data Driven is one of our favorite events, it's intimate, it's customer content-driven. The theme this year is, you call it the next normal. Some people call it the new abnormal. The next normal, what's that all about? >> I think it's pretty fascinating to see, when we walked in in March, all of us were shocked by the effect of this pandemic. And for a while, we all scrambled around, trying to figure out, how do you react to this one? And everybody reacted very differently, but most people had this tendency to think that this is going to be a pretty brutal environment with lots of unknown variables, and it is important for us to try to figure out how to get our hands around this. By the time we came around about six weeks into that, almost all of us have figured out, this is not something you fight against, this is not something you wait for it to go away, but this is one that you figure out how to live it, and you figure out how to work around it. And that, we believe, is the next normal. It's not about trying to create a new abnormal, it's not about creating a new normal, but it's truly one that basically says "There is a path forward, there's a way to create this next normal," and you just figure out how to live with the environment we have, and phenomenal outcomes of companies that have done remarkably well, as a result of these actions, Actifio being one of them. >> It's quite amazing, isn't it, I mean, I've talked to a lot of tech companies, CEOs, and their customers, and it's almost like, the first reaction was of course they cared about their employees and their broader families. Number one, number two was, many companies, as you know, saw a tailwind, and initially didn't want to be seen as ambulance chasing, and then of course the entrepreneurial spirit kicked in, and they said, "Okay, hey, "we can only control what we can control." And tech companies in particular have just done exceedingly well. I mean, I don't think anybody really predicted that early on. >> Yeah. I think at the heart we are all human beings and the first reaction was to take care of, four constituencies, right? One, take care of your family, take care of your community, take care of your employees, take care of your customers. And that was the hardest part. The first four to six weeks was to figure out how do you do each of those four. Once you figured that part out, or you figured out ways to get around to making sure you can take care of those, you really found the next normal. You really started figuring out how to continue to innovate, continue to support each of those four constituencies, and people have done different things. I know it's amazing how CUBE continues to operate. As far as a user is concerned, they're all watching remote. Yes, we don't have the wonderful desk and we all get to chat and look in the eye. But the content, the message is as powerful as what it was a few months ago. So I'm sure this is how we're all going to figure out how to make through. There's a new next normal. >> Yeah, and digital transformation kind of went from push to pull, I mean every conference you'd go to, they'd say, "Well, look at Uber, look at Airbnb," and they put up the examples. "You have to do this too." And then all of a sudden the industry dragged you along. So I'm curious as to how, and I guess the other point there is digital means data. We've said that many, many times, if you didn't have a digital strategy during the height of the lockdown, you couldn't transact business and still many restaurants are still trying to figure this out, but so how did it affect you and your customers? >> Yeah, it's really interesting. And we spend a lot of time with several of our customers who are managing some of the largest IT organizations. And we talk about a very interesting phenomenon that happened somewhere beginning of this year, about 20 years ago, we used to worry about this thing called the digital divide. Those who have access to the network and internet, and those who don't. And now there is this data divide, the divide between organizations that know how to leverage, exploit, and absolutely accelerate the business using data, and those who don't. And I think we're seeing this effect show very clearly among organizations that are able to come back and address some of this stuff. And it's fascinating. Yes, we all have the examples of the likes of people who are doing delivery. People who are doing E-tailing, but there are so many little things, you're seeing organizations, and just the other day, we had a video from Sentry Data Systems, which is helping accelerate COVID-19 research because you're able to get copies of the data faster, they're able to get access to data, to their researchers much, much faster, sometimes from several days to a few minutes. It's that level of effect, it's not just down to some subtle, you know, you almost think of it as nice to have, but it's must have life threatening stuff, essential stuff, or just addressing today, I was reading a wonderful article about this supercomputer and that's doing analysis of COVID-19, and how it's figured out most of these symptoms, then able to figure it out by just crunching a ton of data. And almost every one of those symptoms the supercomputer has predicted, has been accurate. It's about data, right? It is absolutely about data, and which is why I think this is a phenomenal time for companies to absolutely go change, make this transformation about data acceleration, data leverage, data exploitation. And there's a ton of it all around us. >> Yeah, and part of that digital transformation, the mandate is to really put data at the core. I mean, we've certainly seen this with the top market cap companies. They've got data at the core, and now, as I say, it's become a mandate. And you know, there's been several things that we've clearly noticed. I mean, you saw the work from home required laptops and, you know, end point security and things of that, VDI made a comeback, and certainly cloud was there, but I've been struck by the reality of multi-cloud. I was kind of a multi-cloud skeptic early on. I said many times, I thought it was more of a symptom than it was a strategy, but that's completely flipped. Recently in our ETR surveys, we saw multicloud popping up all over the place. I wonder what you're seeing when you talk to your customers and other CIOs. >> Yeah. So fascinating. No, we released our first cloud product sometime around 2018, end of 2018. >> Dave: GO, right? >> Yeah, Actifio GO, OnVault, which is a phenomenal way to completely change the way you think about using object storage in the cloud. For over two years, we saw about 20% of our business, by the beginning of this year, 20% of our business was built on leveraging the cloud. Since March, so that was the end of our, almost the end of the Q1, to now, we're just in the middle of Q3. In six months, we added 12 more percent of the business. Literally we did it in six months, what we did not do before for 18 months before that, significantly more than what we did for a year and a half before that. And there are really three reasons, and you see this over and over again, we have a large customer we closed in January. Ironically we were deploying out of UK, a very large marketing organization, got everything deployed. They were running their backup and DR in a separate data center. And they had a practical problem of not being able to access the second site, literally in the middle of deployment, we steer that customer to GCP, or Google Cloud, because there was simply no way for them to continue protecting the data, being able to develop new applications with that data, they simply had no access. So there was, this was the number one reason, the inability for an organization to physically access or put their employees at risk, and have portal for the cloud be the infrastructure. That's number one. So that first of all drove the reason for the cloud. And then there's a second reason. There are practical reasons on why some cloud platforms are good at one workload. The other ones are not so good at some of the workloads. And so if I'm an organization that has, that spans everything, I've got a power PC, an X86 machine, a VM, I've got container platforms, I got Oracle, I got SAP. There is no single cloud platform that supports all my workload as efficiently, it's available in all the regions I want. So inevitably I have to go adopt different cloud platforms. So that's the second practical reason. And then there's a strategic reason. No vendor, no customer, wants to be locked into any one cloud platform. At least two, you're going to go pay, more likely three. So those are the reasons. And then interestingly enough, we were on a panel with us global CIOs. And in addition to just the usual cloud providers that we all know and love inside the US, across the world, in Europe, in Asia, there's a rise of regional cloud providers. So you take all these factors, right? You've got absolute physical necessity. You got practical constraints of what can the cloud provider support, the strategic reasons of why, either because, I don't want to be locked into a cloud provider, or because there's a rise of, you know, data nationalism that's going on, that people want to keep their data within the country bounds. All of these reasons are the foundations of why multicloud is almost becoming a de facto. It's impossible for a decent size organization to assume they would just depend on one cloud anymore. >> The other big trend we're seeing, I wonder if you could comment, is this notion of the data life cycle, of the data pipeline. It's a very complex situation for a lot of organizations. Their data is siloed. We hear that a lot. They have data scientists, data engineers, developers, data quality engineers, just a lot of different constituencies and lines of business, and it's kind of a mess. And so what they're trying to do is bring that together. So they've done that, data scientists complain, they spend all their time wrangling data, but ultimately the ones that are succeeding to putting data at the core as we've just been discussing, are seeing amazing outcomes, by being able to have a single version of the truth, have confidence in that data, create self-serve for their lines of business and actually reduce the end to end cycle times that's driving your major monetization, whether that's cost cutting or revenue. And I'm curious as to what you're seeing, you guys do a lot of work, heavy work in DevOps and hardcore database. Those are key components of that data life cycle. What are you seeing in that regard regarding that data pipeline? >> Yeah, that's a phenomenal point. If you really want to go back and exploit data within an organization, if you really want to be a data driven organization, the very first thing you have to do is break down the silos. Ironically, every organization has all the data required to make the decisions they want to. They just can't either get to it, or it's so hard to break the silos that it's just not worth trying to make it happen. And 10 years ago, we set out on this mission, rather than keep these individual silos of data, why don't we flip it open and make it into a pipeline which looks like a data cloud, where essentially anybody who's consuming it has access to it based on the governance rules, based on the security rules that the operations people have set. And based on the kind of format they want to see data, not everybody may want to see the data in a database format. Maybe you want the database format converted to a CSB format before you run analytics. And this idea of making data the new infrastructure, this idea of having the operations people provide this new layer container. It's finally come to roost. I mean, it's fascinating. I was looking at the numbers last quarter. We just finished up Q2. Now 45% of our customer base uses Actifio for, or reuses the backup data for things that accelerate the business, things that make the business move faster, more productive, or even survive. That was the mission. That was what we set out to do 10 years ago. You know, we were talking to an analyst this morning and now there's this question of, you know, "Hey, looks like there's a theme of backup data being reused." We said, "Yeah, that's kind of what we've been saying for 10 years." Backup cannot be an insurance, backup cannot be a destination. It has to be something that you can use as an asset. And that I think is finally coming to the point where you can use backup as a single source of truth, only if you designed it right from the beginning for that purpose, you cannot just, there are lots of ways to fake it, make it, try to pretend like you're doing it, but that was the true purpose of making data the new infrastructure, making it a cloud, making it something that is truly an asset. And it's fascinating to see our businesses. You take any of our large accounts, and the way they've gone about transforming, not just basic backup and DR. Yes, we are the world's fastest backup and most scalable DR solution. That's a starting point. But to be able to use that to develop applications eight, 10 times faster, to run analytics 100 X faster? The more data you have, the more people who use data you have, the better this return becomes. >> You know, that is interesting to hear you talk about that, because that has been the Holy Grail of backup was to go beyond insurance to actually create business value. And you're actually seeing some underlying trends, we talked about that data pipeline, and one of the areas that is the most interesting is in database, which was so boring for so many years, and you're seeing new workloads emerge. They take the data warehouse beyond, you know, reporting, never really lived up to its promise of 360 degree view. You mentioned analytics, that's really starting to happen. And it's all about data. You know, John Furrier used to say that data is the new development kit. You call it the new infrastructure and it's sort of the same type of theme. So maybe some of the trends you're seeing in database, I'd love to talk about that for a little bit, and then pick your brains on some other tech like object storage is another one that we've really seen take off. >> Yeah, so I think our journey with object storage began in 2016, 2017, as we started to adopt cloud platform in response to the user requirements, we did more like most companies have done and unfortunately continue to do, we take the on-prem product and then just move it onto the cloud. And one of the things we saw was there was a fundamental difference of how the design points of a cloud engineering is all about, what the design it for. Object storage is one of those primitives, the fundamental storage primitives that the cloud providers actually produced, that nobody really exploited. It was used as a graveyard for data. It's a replacement for the place where data goes to die. And then we look at it really closely and say, "Well, this is actually a massively scalable, very low cost storage, but it has some problems." It has an interface that you cannot use with traditional servers. It has some issues around, you know, not being able to read, modify, write the data, so it feels like you're consuming a lot of storage. So we went on to solve those problems. It took us a good two years to come back with something called OnVault, that fundamentally treats object storage like this massively scalable high performing disk. Except for just ridiculous low cost and optimize the capacity. So this thing called OnVault, as we patent it, has really become the foundation of how everything in cloud works without using CPU. Today there is simply nothing at a lower TCO, that actually, if you want to do basic backup, the more importantly use that to do this massive analytics. Now you're talking about data warehouse, data lakes, right? Because now there's something called data lakehouse. All of these are still silos. All of these are people trying to take some data from somewhere, put it into another new construct and have it be controlled by somebody else. This is autosync, it's just, you just move the silos from someplace to another place, and sort of creating a pipeline. If you want to really create a pipeline, object storage has been an integral part of that pipeline, not a separate bucket by itself. And that's what we did. And same thing with databases. You know, most business, most of the critical business runs on databases, and the ability to find a way to leverage those and move them around, leverage in terms of whichever format the database is accessed, whichever location it's accessed, doesn't matter how big it is. Lots of work has gone into trying to figure that one out. And we had some very, very good partners in some of our largest customers who helped take the journey with us. Pretty much all of the global 2000 accounts you see across the board, were an integral part of our process. >> You know, you mentioned the word journey and it triggered a thought, your discussion with Ravi, the CIO of Seagate, who's a customer of yours. And what he said, I liked what he said, he, of course he used the term journey, we all do. But he said, "You know what? I kind of don't like that term because I want to inject a sense of urgency," essentially what he was saying, "I want speed." You know, journey's like, "Okay, kids get in the car, we're going to drive across country. We're going to make some stops." And so while there's a journey, he also was really trying to push the organization hard. And he talked about culture as some of the most difficult things. Like many CIOs said, "No, the technology is almost the easy part. It's true when it works." >> That's true. >> I thought that was a great discussion that you had. What were your, some of your takeaways? >> I think Ravi's a very astute IT executive who's been around the block for so long. And one of the fascinating things, when I asked him this question about, "Hey, what's the biggest challenge, we've just gone through this a couple of times, what is the biggest challenge?" Taking an organization as venerable, as well known as Seagate is, I mean, this is a data company. This is at the heart of half the world's data is on Seagate stuff. How do you take this old company that's been around for long, in the middle of Silicon Valley, and make it into a fast growing transformation company that's responding to the newer challenges? And I thought he was going to come back with, "Well, you know, I got to go through these pieces, I pick this technology that technology," and surely that's exactly what I expected he would end up with. He goes "It has nothing to do with technology." In this day and age, when you can have an Elon Musk can send a car to Mars, there's not many technologies that we can't really solve. Maybe COVID-19 is the next frontier we got to go solve. But frankly, he hit upon the one thing that matters to every company. It is the fundamental culture to create a bias to action. It's a fundamental culture where you have to come back and have a deliverable that moves the ball forward every day, every month, every quarter, as opposed to have this series of, like you said, a journey that says, and we all know this, right? People talk about, "Oh, we're going to do this in phase one, we're going to do this in phase two and do this in phase three," nothing ever happens in phase three. Nobody gets around to phase three. So I think he did a great job of saying, "I fundamentally had to go change the culture." That was my biggest takeaway. And this, I've heard this so many times, the most effective IT execs who've made the transformation, it actually shows in the people that they have. It's not the technology, it's the people. And his history is replete with organizations that have done remarkably well, not by leveraging the heck out of the technology, but truly by leveraging the change in the people's mindset. And of course that mindset leverages technology where appropriate, but Ravi is a insightful person, always such a delight to talk to him, it's a delight for him to have chosen us as a foundational technology for him to go pull his data warehouses and completely transform how he's doing manufacturing across the globe. >> Yeah, I want to add some color to what you just said, because some key takeaways from what you just said, Ash, is, you know, you're right. When you look back at the history of the computer industry, there used to be very well known processes, but the technology was the big mystery and the big risk. And you think about with COVID, were it not for technology, we didn't know what was coming. We were inventing new processes literally every day, every week, every month. And so technology was pretty well understood, and enabled that. And when you think, when we talked earlier about putting data at the core, it was interesting to hear Ravi. He basically said, "Yeah, we had a big data team in the US, a big data team in Europe." We actually organized around silos. And so you guys played a role, you were very respectful about, you know, touting Actifio with him. You did ask him, you know, what role you play, but it was interesting to hear him talk about how he had to address that both culturally and of course, there's technology underneath to enable that unification of data, that silo busting, if you will. And you guys played a role in that. >> Yeah, well, I always enjoy conversation with the folks who have taken a problem, identified what needs to be done, and then just get it done. And that's more fascinating than, yeah, of course Actifio plays a small part in a lot of things, and we're proud to have played a small part in his big initiative. And that's true of the thousands of customers we talk about, but it's such a fascinating story to have leaders who come back and make this transformation happen and to understand how they went about making those decisions, how they identified where the problem was. These are so hard when we all see them in our own lives. We see there's a problem, but sometimes it takes a while to try to understand how do you identify them and what do you have to do? And more importantly, actually do it. And so whenever I get an opportunity with people like Ravi, I think understanding that, and if there's a way to help, we always make sure that we play our own small part and we're privileged to be a part of those kinds of journeys. >> I think what's interesting about Actifio and the company that you created is essentially that we're talking about the democratization of data, that whole data pipeline, that discussion that we had, the self service of that data to the lines of business, and, you know, you guys clearly play a role there. The multicloud discussion fits into that. I mean these are all trends that are tailwinds for companies that can help sort of flatten the data globe, if you will. Your final thoughts, Ash? >> Yeah, you said something that is so much at the heart of every IT exec that we are talking to. If data truly is the fundamental asset that I finally end up with as an organization, then democratization of data, where I do not lock this into another silo, another platform, another cloud, another application, has to be part of my foundation design. And therefore my ability to use each of these cloud platform for the services they provide while I am able to move the data to where I need it to be, that is so critical. So you almost start to think about the one position an organization now has. And we talked about this with a group of CIOs. There might be some pretty soon, not too far off, but if data is truly an asset, I might actually have a data market, just like you have a stock market, where I can start to sell my data, imagine a COVID-19, there's so many organizations that have so much data, and many of them have contributed to this research because this is an existential issue, but you can see this turning into a next level. So yes, we have got activists help move the data to one level higher where it's become a foundational construct for an organization. The next part is, can I actually turn this into an asset where I actually monetize some of this stuff? And it will be not too long when you and I could talk about how there's this new exchange and what's the rate of data for this company versus that company, and there'll be future trading options, who knows, it's going to be very interesting. >> Well, I think you're right on, this notion of a data marketplace is coming and it's not that far away. Well, Ash, it's always great to talk to you. I hope next year at Data Driven, we can be face to face, but I mean, look, this has been, we've dealt with it. It's actually created opportunities for us to kind of reinvent ourselves. So congratulations on the success that you've had and thank you for coming on theCUBE. >> No, thank you for hosting us and always a big fan of theCUBE. You guys, we've engaged with you since the early days, and it is fascinating to see how this company has grown. And it's probably many people don't even know how much you've grown behind the scenes and all the technologies and culture that you've created yourself. So it's hopefully one day we'll switch the table and then I'd be on the other side and ask you about transformation, digital transformation of CUBE itself. >> I'd love to do that, and thanks again, and thank you everybody for watching our continuous coverage of Actifio Data Driven. Keep it right there. We'll be back with our next guest right after this short break. >> Ash: Thank you, Dave. (calm music)

from around the globe it's thecube with digital coverage of actifio data driven 2020 brought to you by actifio hi and welcome to the cube's coverage of actifio data driven 2020 i'm stu miniman my co-host for this event is dave vellante but joining me to help kick off this discussion is david floyer he is the co-founder and chief technology officer of wikibon of course the research arm of siliconangle media which includes also thecube david great to see you thanks so much for joining us great to see you stu all right so we we've got a really nice lineup of course last year dave and i were in boston with the actifio team they had a really good lineup uh you know analysts thought leaders and of course lots of users you know love to talk to those users uh you and i are quite familiar with actifio uh really the company that that created copy data management as a category and a solution out there so why don't we start there david you know what what's the importance of copy data management you know here in 2020 you know many years after uh when actifio had created it well this year has really uh amplified the importance of copy data management and being able to manage across different locations across different clouds manage the copies manage the the reuse of data in different places um the the the covert has really emphasized the importance for example of putting just backup onto a cloud because it's on many occasions it's not going to be possible to get into your own data centers or if you're sharing a data center so uh automation and uh use of clouds multiple clouds has really driven uh i've become of a supreme importance uh since covert had started and and that's how it's going to be from here on in that's not going to change yeah david absolutely i mean we said for many years when you you know adopt cloud you know i still need to think about my data protection i need to think about security uh those aren't just covered uh because i have you know lovely object storage or it you know spreads things out amongst the different cloud regions um and even this year's you brought up covid uh we've been having so many conversations with with companies uh in many cases they're accelerating or new groups are diving in and therefore we need to make sure that they take the proper control precautions so you know my my disaster recover me my backup is so important uh maybe flush out a little bit for us if you would you know cloud we've been looking at uh that you know hybrid and multi-cloud architectures how people should be building it and of course data the critical component uh that we look at there what what should people be looking at well absolutely if you're going to have a multi-cloud strategy you uh you have to there are several things which are really important you have to be able to operate across each cloud natively in the cloud it's not it's not good enough to uh just be an appendage if you like um so and equally important is that you have to make sure that you're taking advantage of the characteristics of the cloud in particular object storage backup has always gone to object storage but object storage itself is not that fantastic if you're trying to just recover something from a from a lot of different objects unless you put an architecture around that unless you make it such that you can uh take all the workloads and be able to address them in the cloud itself and uh in in particular what's very interesting is there are two fundamental philosophies of moving to the cloud one of which is that you migrate everything you you convert all of your databases to a database that's operating in the cloud that you go to um and the other one is to say well that type of lift and shift is not good enough what you want to be able to do is be able to use the same databases the same applications that you're using at the moment avoid that enormous expensive cost of moving everything and then be able to operate on those databases using the cloud principles the cloud object store and have the same level of performance yeah absolutely david i know i'm looking forward to uh you know dave's got uh you know ash the ceo of actifio uh on today tomorrow uh i'll be talking to david chang who's the co-founder uh also onto the product there to really understand you know how is activio building an architecture that meets what you were just talking about uh and david you know things i i've heard you talking about for many years you know uh migrations obviously are something that anybody in it dreads uh i i used to say in the storage world uh it you know upgrade came with that four-letter word it was migration because you you had to do that and you know databases of critical importance um one of the other uh discussions i have is with ibm and ibm has had a long partnership with activio um but they're also they're they're getting involved with that data usage so maybe if you could expound a little bit you know how is it just you know the early days copy data management i looked at it it was a you know financial savings it was okay hey we've got way too many copies out there how can we enable them to be used better and not have you know just lots and lots of big capacity that the the storage vendors uh as it was you know hard disk and then flash converting there so you know how are we actually unlocking the value of data in today's world well there are two aspects of that one of which is you want the the original data wherever possible you uh you you want to have be able to access that data as quickly as possible so if you have for example a system of record and you want to be able to access that system of record uh it may be one day you want to be able to bring it right to one day before the day before not have a week waiting for it coffee management is essential to be able to access that data and the same data for everybody and know that and know from a compliance point of view you have the right data so that's the first stage but then from a development point of view you want to have the flexibility of using real-time data whenever you can so you want to be able to access any data you want from anywhere and know that it's the correct data and and move your business processes from asynchronous business processes to as synchronous as you can and you can only do that with automation through uh real-time data management yeah uh absolutely david and it's even it's even more pertinent right now as everyone is you know the discussion is you know work from home is becoming work from anywhere uh so it's it's not just oh hey i can get into the data warehouse uh and know that i have uh you know a low latency connection when i'm sitting in the corporate uh internet now you know developers uh typically are dispersed people need to be able to access it um talk a little bit about uh the data pipeline the discussion we've been hearing from uh you know the cdo events that we've gone to as well as discussions you know how does you know actifio in the industry as a whole streamline that data pipeline that you started talking about yeah that that's absolutely essential uh you you you have to have processes and procedures that identify the data where it's going to go uh and and have essentially a data plane managed data plane which is taking it from where it need where it is to where it needs to go sharing the metadata across that fabric um those are the ways that you build a consistent data pipeline where people know what the provenance of that data is and the less copies that you have and the more single copies of that data a a a copy of record a single version of the truth then the less complicated the systems become and even more the the systems between the systems the the human interaction that's required to to manage that data goes down so it and it makes development so much easier so a data pipeline is absolutely essential and it's part of that data plane and it's part of the overall architecture that has to be there we've lived in silos for so long and getting out of silos is not it's not easy at all and uh you've got to have the right tools to be able to do that yeah uh the the keynote speaker uh that actifio has for the event is gene kim somebody we've had on the cube a few times and excited to have him back on at this event uh what i thought was really interesting david i read his first book uh his first fiction book i should say he's also written many non-fiction books uh the phoenix project was really the go-to book to kind of understand devops i've i've recommended so many friends uh people in the industry his new one the unicorn project is really about software development but what i found really interesting because i i didn't get to read it earlier this year because there was just no travel but made sure i did read it ahead of this event and the lesson that it called out to me was you know moving faster using these modern tools you know breaking through silos was all well and good but the the real turning point for the company was enabling that use of data and as you said that real time not looking historically but be able to react fast so you know not giving away the secrets of the book there but uh you know a retail organization that could trial things could update in real time what the inventory was and having everybody in the company get access to that so the product people the marketing people uh the field people all accessing that single source of truth and that being fed throughout the organization really invigorated and drove uh the the ability for a company to react and move fast which really is the the clarion call for business today so david yeah you know any any final word from you as to you know we've we've been beating that drum for years that you know data data data um is is critically important whether you're taking that specific example if you can take that all of that data and then start updating the pricing according to that data you've suddenly made repricing a dynamic event uh one that's going to respond to the customer and they their characteristics uh good or bad and the availability of those uh availability and the uh and the the pipeline of products if you understand all of that then suddenly your ability to increase revenue by being able to reprice more quickly uh automatically become an amazingly uh effective in terms of revenue increase yeah absolutely i i feel like uh i remember back in the early days of hadoop it was you know how can i make an ad better to increase increase click rate but the promise of unlocking data today is to really understand and customize for that environment so some of it is we can maximize profitability there will be certain clients um which are willing to pay for more premium products and others uh that you need to have that value option but when you understand the data you understand the customer you understand the need for the portfolio of solutions you have data can just be that key enabler all right well hey david floyer thank you so much for helping us kick off our coverage here i want to tell everybody make sure to you know tune in for the rest of it uh dave vellante and myself going through the interviews of course on demand with actifio as well as i'm sorry live with actifio as well as on demand on thecube.net as always for david floyer dave vellante i'm stu miniman thank you for joining us for activio data driven and thank you for watching thecube [Music] you

>> Narrator: From around the globe, It's theCube, with digital coverage, of Actifio data-driven 2020, brought to you by Actifio. >> Welcome back, I'm Stu Miniman, and this is theCube coverage of Actifio Data-driven 2020. Really excited to, dig into a fun topic. I have a Cube alumni with us he is a DevOps author, and researcher Gene Kim. Unicorn Project is the most recent, Gene, great to see you, thanks so much for joining us. >> Stu, great to see you again, here at the Actifio conference, this is all fantastic. >> Yeah, so your new book, it was much awaited out there, you know, Unicorn's always discussed out there, but you know, the Phoenix Project, as I said, is really this seminal, book when people say, What is that DevOps thing and how do I do it? So, why don't you give us a little bit as to The Unicorn Project, why is it important? Why we're excited to dig into this and, we'll, we'll tie it into the discussion we're having here for the next normal, at Actifio. >> For sure, yeah, in fact, yeah. As you might have heard in the keynote address, you know, the what, what vexed me, after the Phoenix project came out in 2013 is that there is still looming problems that still remain, seven years after the Phoenix project was written. And, you know, these problems I think are very important, around you and what does it really take to enable developers to truly be productive, instead of being locked in a tundra of technical debt. Two is, you know, how do we unlock truly the power of data so that we can help everyone make better decisions, whether it's a developer, or anyone, within the business units and the organizations that we serve. And then three is like, what are really the behaviors that we need from leadership to make these amazing transformations possible? And so The Unicorn Project really is, the fifth project retold, but instead of through the eyes of Ops leadership, is told through the eyes, of a phenomenal developer. And so it was amazing to revisit the, the Phoenix project universe, I in the same timeline, but told from a different point of view. And it was such a fun project to work on, just because, you know, to relive the story, and just expose all these other problems, not happening, not on the side, but from, the development and data side. >> Yeah. They've always these characters in there that, I know I personally, and many people I talked to can, you know, really associate with, there was a return of certain characters, quite prominent, like Brent, you know, don't be the bottleneck in your system. It's great, if you're a fighter firefighter, and can solve everything, but if everything has to come through you, you know, Pedro is always going off, he's getting no sleep and, you know, you'd just get stressed out. You talked a bit more, about the organization and there are the five ideals in the book. So maybe if you can, you know, strongly recommend, of course, anybody at ending active you, got a copy of the books they'll be able to read the whole thing, but, you know, give us the bumper sticker on some of those key learnings. >> Yeah, for sure, yeah. So the five ideals represents five ideas, I think are just very important, for everyone, the organization, serves, especially leadership. The first ideal is locality and simplicity. In other words, when you need to get something done, we should be able to get it done within our team, without having to do a lot of communication coordination, with people outside of our team. The worst, the most horrible feeling is that in order, to do a small little thing, you actually have, to have a, coordinated action that spans 15 teams, right. And that's why you can't get anything done, right? And so that's so much the hallmark of large complex organizations. The second ideal is that what I think the outcomes are, which is focused flow and joy, you know, I've not just now started to for the first time in 20 years, self identify, not as an ops person, but as a developer. And, I really now understand, why we got into technology in the first place. This so that we can solve the business problem at hand unencumbered by minute share. And that allows for a sense of focus flow and even joy. And I love how Dr. Mihaly Csikszentmihalyi, describe it. He said, flow is a state that we feel when we love our work, so much that we lose track of time, and maybe even sense of self. And so I think we all in technology understand, you know, that that is how it is on the best of days and how terrible it is, you know, when we don't have that sense of flow. Third ideal is improvement of daily work, being even more important than daily work itself. The notion is greatness is never free, we must create it and must prioritize it, for the psychological safety. And the fifth is customer focus. So those are all the things I think are so important, for modern leaders, because it really defines the future of work. >> Yeah, we love that flow and it happens otherwise we're stuck, in that waiting place as you quoted Dr. Csi. So one of my favorite books there, there also. So Gene, for this audience here, there was, you know, yes, CICD is wonderful and I need to be able to move and ship fast, but the real transformational power, for that organization was unlocking the value of data, which is, I think something that everybody here can. So maybe to talk a little bit about that you know, we, there there's, we've almost talked too much, you know, data is the new oil and things like that, but it's that, you know, that allowing everybody to tap in and leverage, you know, real time what's happening there were just at the early parts of the industry being able to unlock that future. >> Oh yeah, I love that phrase. Data is new oil, especially since oil, you know, the last 50 years, the standard Port 500 was dominated by, you know, resource extraction oil company and so forth. And now that is no longer true, it's dominated by the tech giants. And, Columbia there was a Columbia journalism review article that said, data's not only the new oil, is really the new soil. And for me, you know, my area of passion for the last seven years has been studying the DevOps enterprise community where, we're taking all the learnings that were really pioneered by the tech giants, Facebook, Amazon, Netflix, Google, Microsoft, and seeing how they're being adopted by the largest, most complex organizations on the planet, the best known brands across every industry vertical. And it's so true that, you know, where the real learning gets exploited right, is through data. I realized, this is how we get to know our customers better. This is how we understand their wants and needs. This is how we test, and make offers to them to see if they like it or not to see if they value it or not. And, and so for me, one of the best examples, of this was, the target transformation and Adidas how it was just an amazing example of, to what links they went to, to liberate developers from, being shackled by ancient systems of records, data warehouses, and truly enabled developers to get access to the data they need modify it, even delete information, all without having to be dependent on, you know, integration teams that were essentially holding them hostage for six to nine months. And, these programs really enable some of the most strategic programs at their organizations, you know, enabling hundreds of projects over the years. So, I think that is really, just showing to what extent, the value that is created by unlocking data for individuals. And sorry Stu, one more thing that I'm just always dazzled by my friend, Chris Berg. He told me that, somewhere between a third and a half of all company employees use data in their daily work. They either use data or manipulate data as part of the daily work, which, you know, that, population is actually larger than the number of developers in an organization. So it just shows you how big this problem is, and how much value we can create by addressing this problem. >> Well, it's interesting if it's only a third, we still have work to do. What we've been saying for years is, you know, when you talk about digital transformation, the thing that separates those that have transformed and those that haven't is data needs to be at the core. I just can't be doing things the way I was or doing things off intuition, you know, being data-driven, I'm sure you know, the same Gene, if you're not, if you don't have data, you know, you're just some other person with an opinion. >> Yeah, yeah. That's it this is a great point. And in Risto Siilasmaa's amazing book, Transforming Nokia, I mean, he was, he said exactly that. And he said something that was even more astonishing. He said, there's not only at the core, but data also has to be at the edges. You know, he was describing at Amazon, anyone can do an experiment @booking.com. Anyone can do an experiment to see, if they can create value for the customer. They don't need approvals from, committees or their manager. This is something that is really truly part of everyone's daily work. And so, to me, that was a huge aha moment that says, you know, to what degree, you know. Our cultures need to change so that we can not only, use data, but also create learnings and create new data, you know, that the rest of the organization can learn from as well. >> Yeah. One of the other things I definitely, you know, felt in your book, you synthesize so much of the learnings that you've had over the years from like the DevOps enterprise summit. The question I have for you is, you know, you hear some of these, you know, great stories, but the question is, our companies, are they moving fast enough? Have they transformed the entire business or have they taken, you know, we've got one slice of the business that is kind of modernized and we're going to get to the other 30 pieces along the way, but you know, there's wholesale change, you know, 2020 has had such a big impact. What's your thoughts on, you know, how we are doing in the enterprise on pace of change these days? >> That's a great question. I mean, I think some people, when they ask me, you know, how far are we into kind of total adoption of DevOps? It's a newer better way of working. And I would say probably somewhere between 5 and 7%, right, and the math I would take them through is, you know, there are about 20 million developers on the planet of which at best, I think, a million of them are working in a DevOps type way. But yet now that's only growing. I think it was an amazing presentation at DevOps surprise summit in London that was virtual from nationwide building society, the largest organization of its kind. It's a large financially mutually owned organization for housing in the UK. And, they touched about how, you know, post COVID post lockdown suddenly they found themselves able to do them reckless things that would have normally taken four years, in four weeks. And I think that's what almost every organization is learning these days is, when survival is at stake, you know, we can throw the rules out of the window, right. And do things in a way that are safe and responsible, but, you know, create satisfy the business urgent needs, like, you know, provisioning tens of thousand people to work from home safely. You know, I think the shows, I think it's such a powerful proof point of what technology can do when it is unleashed from, you know, perhaps unnecessarily burdensome rules and process. And I think the other point I would make Stu is that, what has been so rewarding is the population of these technology leaders presenting at DevOps enterprise, they're all being promoted, they're all being, being given new responsibilities because they, are demonstrating that they have the best longterm interest of the organization at heart. And, they're being given even more responsibilities because, to make a bigger impact through the organization. So I'm incredibly optimistic about the direction we're heading and even the pace we're going at. >> Well, Gene definitely 2020 has put a real highlight on how fast things have changed, not just work from home, but, but the homeschooling, you know, telehealth, there are so many things out there where there was no choice, but to move forward. So the, the second presentation you participated in was talking about that next normal. So give us a little bit of, you know, what does that mean? You know, what, what we should be looking at going forward? >> Yeah, it was great to catch up with my friend Paul Forte, who I've known for many, many years, and now, now a VP of sales at the Actifio and yeah, I think it is amazing that academic Dr. Colada Perez, she said, you know, in every turning point, you know, where, there's such a the stage for decades of economic prosperity usually comes, by something exactly like what we're going through now, a huge economic recession or depression, following a period of intense re regulations there's new, technology that's unlocking, you know, new ways of working. And she pointed exactly to what's happening in the Covid pandemic in terms of, how much, the way we're working is being revolutionized, not by choice, but out of necessity. And, you know, as she said, you know, we're now learning to what degree we can actually do our daily work without getting on airplanes or, you know, meeting people in person. So, I'm a hue, I have so many friends in the travel industry, right. I think we all want normalcy to return, but I think we are learning, you know, potentially, you know, there are more efficient ways to do things, that don't require a day of travel for a couple hour meeting and day to return, right. So, yeah, I think this is being demonstrated. I think this will unlock a whole bunch of ways of interacting that will create efficiency. So I don't think we're going, as you suggested, right. There will be a new normal, but the new normal is not going to be the same as your old normal. And I think it will be, in general for the better. >> So, Gene, you, you've gone to gotten to see some of the transformation happening in the organizations when it comes to developers, you know, the, the DevOps enterprise summit, the, the state of DevOps, you know. I think five years ago, we knew how important developers were, but there was such a gap between, well, the developers are kind of in the corner, they don't pay for anything. They're not tied to the enterprise. And today it feels like we have a more cohesive story that there, there is that if you put in The Unicorn Project, it's, you know, business and IT, you know. IT, and the developers can actually drive that change and the survival of the business. So, you know, are we there yet success or net developers now have a seat at the table? Or, you know, what do you see on that, that we still need to do? >> Yeah, I think we're still, I mean, I think we're getting there, we're closer than ever. And as my friend, Chris O'Malley the CEO of the famously resurgent mainframe vendor Compuware said, you know, it is, everyone is aware that you can't do any major initiatives these days without some investment in technology, right? In fact, you can't invest in anything without technology. So I think that is now better understood than ever. And, yeah, just the digital, it's a whole digital disruption, I think is really, no one needs to be convinced that if we organize large complex organizations, don't change, they're at a risk of, you know, being decimated by the organizations that can change using an exploiting technology, you know, to their benefit and to the other person's detriment. So, and that primarily comes through software and who creates software developers. So I, by the way, I love the Stripe it was a CFO for Stripe who said, the largest, constraint for them is, and their peers is not access to capital, it is access development talent. I think when you have CFOs talking like that, right. It does says it's suggested something really has changed in the economic environment that we all compete in. >> So, I mentioned that on the research side, one of the things I've loved reading over the years is that, fundamental discussion that, going faster does not mean, that I am sacrificing security, or, you know, the product itself, you know, in the last couple of years, it's, you know, what separates those really high performing companies, and, you know, just kind of the middle of the ground. So, what, what, what advice would you give out there, to make sure that I'm moving my company more along to those high performing methods. >> Yeah, but just to resonate with that, I was interviewing a friend of mine, Mike Nygaard, long time friend of mine, and we were talking on and we were recalling the first time we both heard the famous 2009 presentation doing 10 deploys a day, every day at flicker, by John Allspaw and Paul Hammond. And we were both incredulous, right there? We thought it was irresponsible reckless, and maybe even immoral what they were doing, because, you know, I think most organizations were doing three a year, and that was very problematic. How could one do 10 deploys a day. And I think, what we now know, with the size of evidence, especially through the state of DevOps research, is that, you know, for six years, 35,000 plus respondents, the only way that you can be reliable, and secure, is to do smaller deployments more frequently, right? It makes you, be able to respond quicker in the marketplace, allows you to have better stability and reliability in the operational environment, allows you to be more secure. It allows you to be able to, you know, increase market share, increase productivity, and, you know, have happier employees. So, you know, at this point, I think the research is so decisive, that, you know, we can, as a whole book accelerate, that really makes the case for that, that this is something that I now have moral certainty or even absolute certainty oh, right. It's, you know, self evident to me, and it, I think we should have confidence that that really is true. >> Wonderful work, Gene, thanks so much for giving us the update. I really appreciate it, some really good sessions here in Actifio, as well as the book. Thanks so much, great to talk to you. >> Stu is always a pleasure to see you again, and thank you so much. >> Alright, that's our coverage from Actifio Data-driven, be sure to check out thecube.net for all of the, on demand content, as well as, as I said, if you were part of the show, definitely recommend reading Gene's book, The Unicorn Project. I'm Stu Miniman. And thank you for watching the cube. (soft upbeat music)

>> Narrator: From around the globe, It's theCUBE with digital coverage of Actifio Data Driven 2020. Brought to you by Actifio. >> Hi, my name is Gene Kim and I am looking forward to the amazing Actifio Data Summit. Everyone who applies... Three, two, one. Hi, my name is Gene Kim. I'm going to smile one more time. Three, two, one. Hi, my name is Gene Kim. I'm looking forward to the Actifio Data Summit, where we're going to learn all about the power of data, everyone who registers between now and then will receive a copy of my book, "The Unicorn Project." I look forward to seeing you there, thank you. (upbeat music)

>> Narrator: From around the globe, it's theCUBE with digital coverage of Actifio Data Driven 2020, brought to you by Actifio. >> Hi everybody this is Dave Vellante, full of preview of Actifio Data Driven, and with me is Brian Reagan who is a long time cube alumni, good friend. Brian, awesome to see you thanks for coming on and help us set up Data Driven >> Dave it's always a pleasure to be here, thanks for having me. >> So this is one of our favorite events of the season, not only because it's historically been in Boston, but it's a really good intimate event, lot of customer content. Unfortunately this year, of course everything has gone virtual but tell us about that, what do you guys got planned for Data Driven this year? >> Well again we're delighted to be able to put the show on, in spite of all of the challenges of travel and face to face. As you know from years past, Data Driven has always been sort of by the customers for the customers, very much an event that is driven around understanding how customers are using data strategically, and how Actifio is helping them do that to power their businesses. This year is no different, I think what we've done is we've taken the best of the physical events, which is really facilitating fireside chats and panels of people using our technology to move the business forward with data, but also added a lot of things that frankly are impossible to do when you're strained by a physical event, which is be able to run a series of on demand technical sessions. Our technical tracks are always standing room only, so now we can offer more content, more discreet package content that can be consumed the day of the event and on for a year plus after the event. So we're excited to really sort of mix the best of both worlds virtual and the forums that have worked so well for us in the physical events. >> Well it's like I said I mean, lots of these events are sort of vendor fests, but what you do with Data Driven is you bring in the customer's voice. And I remember last year in theCUBE, we had Holly st. Clair who was with the state of Massachusetts, she was awesome. We had a guest from DraftKings, which was really, really tremendous. Of course, you see what's happening with those guys now just exploding. >> Exactly. >> But we also had a lot of fun, when of course Ash comes on, and all the Actifio folk, but we had Frank Gens on, the first and only time we've ever had him on theCUBE, he's now retired from IDC, I guess semiretired. We had Duplessie on, which was a lot of fun. So it's just a good vibe. >> Yeah, we made a conscious decision to your point not to avoid the traditional vendor fest, and bludgeoning people with PowerPoint throughout the day, and really wanted to make it spin it around, and have the customers tell their stories in their own words, and really talk about the themes that are both common, in terms of challenges, ways that they've addressed those challenges, but also dig into the real implications of when they do solve these challenges, what are the unintended consequences? It's sort of like the... In a lot of ways I think about the journey that customers went through with VMware and with the ability to spin up VMs effortlessly, was a fantastic first step, and then all of a sudden they realized they had all of these spun up Vms that were consuming resources that they didn't necessarily had thought about at the very beginning. I think that our customers as they progress through their journey with Actifio, once they realize the power of being able to access data and deliver data, no matter how big it is, in any form factor in any cloud, there's incredible power there, but there also comes with that a real need to make sure that the governance and controls and management systems are in place to properly deliver that. Particularly today when everything is distributed, everything is essentially at arms length, so that's part of the fun of these events is really being able to hear all of the ways these unique customers are, adding value, delivering value, gaining value, from the platform. >> What's it's interesting you mentioned VMs, it was like life changing when you saw your first VM get spun up and you're like, wow, this is unbelievable, and then it was so easy to spin up. and then you just save VM creep and copy creep. >> Right. >> And you're seeing some similar things now with cloud I mean example is the cloud data warehouses is so easy to spin those things up now. The CFOs are looking at the bill going Whoa, what are we doing here? >> (laughs) >> You're going to see the same thing >> Exactly. >> with containers as you begin to persist containers, you're going to have the same problem. So you guys created the category, it's always a marketing executives dreams to be able to create a category. You guys created the Copy Data Management category, and of course, you've extended that. But that was really good, it was something that you guys set forth and then all the analysts picked up on it, people now use that as a term and it kind of resonates with everybody. >> Right, right. It was bittersweet but also very satisfying to start to see other vendors come out with their own Copy Data Management offerings, and so yes the validating that in fact this is a real problem in the enterprise continues to be a real problem in the enterprise, and by using technologies that Actifio really pioneered and patented quite a bit of foundational technologies around, we're able to help customers address those copy data challenges, those spiraling costs of managing all of these duplicate, physical instances of data. And to your point, to some degree when you're on-prem in a data center and you've already bought your storage array. Okay, I'm consuming 20% more of the Ray or 100% more of the array than I really need to be, but I've already paid for the array. When it comes to cloud, those bills are adding up hourly, daily, weekly, monthly, and those are real costs, and so in many ways cloud is actually highlighting the power and frankly the problem of copy data, far more than the on-prem phenomenon ever did. >> Yeah I was on the phone with a former CIO, COO now of a healthcare organization, and he was saying to me there's a dark side of CapEx to OPEX, which is now that he's a COO he's like really concerned about the income statement and the variability of those costs, and so to your point I mean it's a big issue, the convenience seems to be outweighing some of that concern but nonetheless lack of predictability is a real concern there. >> Absolutely, absolutely. And I think we see that... You mentioned data lakes, and whether you call it a data lake or you just call it a massive data instance, one of the speakers of Data Driven this year is a customer of our Century Data Systems down in Florida. And they have 120 terabyte database that actually they're using, and this is an incredible story that we're excited to have them share with the world during Data Driven. They're using it to help the federal government get better data faster on COVID treatments and the efficacy of those treatments, and so to even consider being able to rapidly access and manage 120 terabyte instance. It breaks the laws of physics frankly. But again with Copy Data Management, we have the ability to help them really extend and really enhance their business and ultimately enhance the data flows that are hopefully going to accelerate the access to a vaccine for us in North American and worldwide, quite frankly. >> That's awesome, that's awesome. Now let's talk a little bit more about Data Driven what we can expect. Of course, the last couple of years you've been the host of Data Driven. They pulled a Ricky gervais' on you >> (Laughs loudly) like get the golden gloves, he's no longer being invited to host, but I think probably for different reasons, but what are some the major themes that we can expect this year? >> Yeah, we were disappointed that we couldn't get Tina Fey and Amy Poehler. >> (laughs quietly) I think we decided that in a virtual construct, the host duties were pretty amenable. So among the many things I talked about Sentry Data Systems and we have many customers who are going to be joining us and telling their stories. And again from accelerating data analytics to accelerating DevOps initiatives, to accelerating a move to the cloud, we're going to hear all of those different use cases described. One of the things that is different this year and we're really excited. Gene Kim sort of the author and noted DevOps guru, author of The Phoenix Project and The Unicorn Project, he's going to be joining us. We had previously intended to do a road show with Gene this year and obviously those plans got changed a bit. So really excited to have him join us, talk about his point of view around DevOps. Certainly it's a hugely important use case for us, really important for many of our customers, and actually registrant's between now and the event, which is September 15th and 16th, we'll get an eCopy an e-book copy of his Unicorn Project book. So we're eager to have people register and if they haven't already read him then I think they're going to be really pleasantly surprised to see how accessible his materials are, and yet how meaningful and how powerful they can be in terms of articulating the journeys that many of these businesses are going through. >> Yeah, I'm glad you brought that up. I'm stucked I have not read that material, but I've heard a lot about it, and when I signed up I saw that, said great I'm going to get the free book. So I'm going to check that out, >> Yeah It's obviously a very, very hot topic. Well Brian, I really appreciate you coming on, and setting up the event. What are the details? So where do I go to sign up? When is the event? What's the format? Give us the lowdown. >> It is September 15th and 16th, actifio.com will guide you through the registration process. You'll be able to create the event based on the content that you're eager to participate in. And again not only on the 15th and 16th, but then into the future, you'll be able to go back and re access or access content that you didn't have the time to do during the event window. So we're really excited to be able to offer that as an important part of the event. >> Fantastic and of course theCUBE will be there doing its normal wall to wall coverage. Of course, this time virtual, and you'll see us on social media with all the clips and all the work on Silicon Angle. So Brian great to see you and we will see you online in September. >> Thanks, Dave. >> All right, and thank you. Go to actifio.com, sign up register for Data Driven, this is Dave Vellante for theCUBE, we'll see you next time. (upbeat music)

>>from around the globe. It's the queue with digital coverage of active EO data driven 2020 brought to you by activity. >>However, this is Dave Volante for the Cube and appear to really tell you how excited we are about active CIOs. Data driven. We're partnering with active again this year. Of course, the conference has gone virtual. Data driven is a great event. It's a very customer oriented event. Active CEO is a company that deals with some really gnarly data problems at scale. They started in the space of copy data Management and have extended into Dev Ops and Analytics and Cloud. And so the Cube will be there. It's September 15th and 16th, 16th goto active geo dot com. Sign up. There's a free e book on Dev Ops. It's always a great program. We'll see you there. >>Yeah, yeah, yeah, yeah

>>from around the globe. It's the queue with digital coverage of active eo data driven 2020 Brought to you by activity. >>Hi, I'm Brian Reagan from Active Seo. And I'd like to welcome you to join us at Data Driven 2020 this year. Online as in years past, it's all about the customer from the bear voice to you talking about how they're solving their cloud Dev Ops Analytics and data protection challenges using the activity of platform and helping move their business forward this year. We're also excited to welcome Gene Kim Noted Dev Ops author in Guru on his E book. The Unicorn Project is available for free if you register today, so join us September 15th and 16th for data driven 2020. We look forward to seeing you online. >>Yeah, yeah, yeah, yeah, yeah

>> Narrator: Hi, everyone, thanks for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled toward Zero Unplanned Downtime of Medical Imaging Systems using Big Data. My name is Sue LeClaire, Director of Marketing at Vertica, and I'll be your host for this webinar. Joining me is Mauro Barbieri, lead architect of analytics at Philips. Before we begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. And we'll answer as many questions as we're able to during that time. Any questions that we don't get to we'll do our best to answer them offline. Alternatively, you can also visit the vertical forums to post your question there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And yes, this virtual session is being recorded, and we'll be available to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Mauro, over to you. >> Thank you, good day everyone. So medical imaging systems such as MRI scanners, interventional guided therapy machines, CT scanners, the XR system, they need to provide hospitals, optimal clinical performance but also predictable cost of ownership. So clinicians understand the need for maintenance of these devices, but they just want to be non intrusive and scheduled. And whenever there is a problem with the system, the hospital suspects Philips services to resolve it fast and and the first interaction with them. In this presentation you will see how we are using big data to increase the uptime of our medical imaging systems. I'm sure you have heard of the company Phillips. Phillips is a company that was founded in 129 years ago in actually 1891 in Eindhoven in Netherlands, and they started by manufacturing, light bulbs, and other electrical products. The two brothers Gerard and Anton, they took an investment from their father Frederik, and they set up to manufacture and sale light bulbs. And as you may know, a key technology for making light bulbs is, was glass and vacuum. So when you're good at making glass products and vacuum and light bulbs, then there is an easy step to start making radicals like they did but also X ray tubes. So Philips actually entered very early in the market of medical imaging and healthcare technology. And this is what our is our core as a company, and it's also our future. So, healthcare, I mean, we are in a situation now in which everybody recognize the importance of it. And and we see incredible trends in a transition from what we call Volume Based Healthcare to Value Base, where, where the clinical outcomes are driving improvements in the healthcare domain. Where it's not enough to respond to healthcare challenges, but we need to be involved in preventing and maintaining the population wellness and from a situation in which we episodically are in touch with healthcare we need to continuously monitor and continuously take care of populations. And from healthcare facilities and technology available to a few elected and reach countries we want to make health care accessible to everybody throughout the world. And this of course, has poses incredible challenges. And this is why we are transforming the Philips to become a healthcare technology leader. So from Philips has been a concern realizing and active in many sectors in many sectors and realizing what kind of technologies we've been focusing on healthcare. And we have been transitioning from creating and selling products to making solutions to addresses ethical challenges. And from selling boxes, to creating long term relationships with our customers. And so, if you have known the Philips brand from from Shavers from, from televisions to light bulbs, you probably now also recognize the involvement of Philips in the healthcare domain, in diagnostic imaging, in ultrasound, in image guided therapy and systems, in digital pathology, non invasive ventilation, as well as patient monitoring intensive care, telemedicine, but also radiology, cardiology and oncology informatics. Philips has become a powerhouse of healthcare technology. To give you an idea of this, these are the numbers for, from 2019 about almost 20 billion sales, 4% comparable sales growth with respect to the previous year and about 10% of the sales are reinvested in R&D. This is also shown in the number of patents rights, last year we filed more than 1000 patents in, in the healthcare domain. And the company is about 80,000 employees active globally in over 100 countries. So, let me focus now on the type of products that are in the scope of this presentation. This is a Philips Magnetic Resonance Imaging Scanner, also called Ingenia 3.0 Tesla is an incredible machine. Apart from being very beautiful as you can see, it's a it's a very powerful technology. It can make high resolution images of the human body without harmful radiation. And it's a, it's a, it's a complex machine. First of all, it's massive, it weights 4.6 thousand kilograms. And it has superconducting magnets cooled with liquid helium at -269 degrees Celsius. And it's actually full of software millions and millions of lines of code. And it's occupied three rooms. What you see in this picture, the examination room, but there is also a technical room which is full of of of equipment of custom hardware, and machinery that is needed to operate this complex device. This is another system, it's an interventional, guided therapy system where the X ray is used during interventions with the patient on the table. You see on the left, what we call C-arm, a robotic arm that moves and can take images of the patient while it's been operated, it's used for cardiology intervention, neurological intervention, cardiovascular intervention. There's a table that moves in very complex ways and it again it occupies two rooms, this room that we see here and but also a room full of cabinets and hardwood and computers. This is another another characteristic of this machine is that it has to operate it as it is used during medical interventions, and so it has to interact with all kind of other equipment. This is another system it's a, it's a, it's a Computer Tomography Scanner Icon which is a unique, it is unique due to its special detection technology. It has an image resolution up to 0.5 millimeters and making thousand by thousand pixel images. And it is also a complex machine. This is a picture of the inside of a compatible device not really an icon, but it has, again three rotating, which waits two and a half turn. So, it's a combination of X ray tube on top, high voltage generators to power the extra tube and in a ray of detectors to create the images. And this rotates at 220 right per minutes, making 50 frames per second to make 3D reconstruction of the of the body. So a lot of technology, complex technology and this technology is made for this situation. We make it for clinicians, who are busy saving people lives. And of course, they want optimal clinical performance. They want the best technology to treat the patients. But they also want predictable cost of ownership. They want predictable system operations. They want their clinical schedules not interrupted. So, they understand these machines are complex full of technology. And these machines may have, may require maintenance, may require software update, sometimes may even say they require some parts, horrible parts to be replaced, but they don't want to have it unplanned. They don't want to have unplanned downtime. They would hate send, having to send patients home and to have to reschedule visits. So they understand maintenance. They just want to have a schedule predictable and non intrusive. So already a number of years ago, we started a transition from what we call Reactive Maintenance services of these devices to proactive. So, let me show you what we mean with this. Normally, if a system has an issue system on the field, and traditional reactive workflow would be that, this the customer calls a call center, reports the problem. The company servicing the device would dispatch a field service engineer, the field service engineer would go on site, do troubleshooting, literally smell, listen to noise, watch for lights, for, for blinking LEDs or other unusual issues and would troubleshoot the issue, find the root cause and perhaps decide that the spare part needs to be replaced. He would order a spare part. The part would have to be delivered at the site. Either immediately or the engineer would would need to come back another day when the part is available, perform the repair. That means replacing the parts, do all the needed tests and validations. And finally release the system for clinical use. So as you can see, there is a lot of, there are a lot of steps, and also handover of information from one to between different people, between different organizations even. Would it be better to actually keep monitoring the installed base, keep observing the machine and actually based on the information collected, detect or predict even when an issue is is going to happen? And then instead of reacting to a customer calling, proactively approach the customer scheduling, preventive service, and therefore avoid the problem. So this is actually what we call Corrective Service. And this is what we're being transitioning to using Big Data and Big Data is just one ingredient. In fact, there are more things that are needed. The devices themselves need to be designed for reliability and predictability. If the device is a black box does not communicate to the outside world the status, if it does not transmit data, then of course, it is not possible to observe and therefore, predict issues. This of course requires a remote service infrastructure or an IoT infrastructure as it is called nowadays. The passivity to connect the medical device with a data center in enterprise infrastructure, collect the data and perform the remote troubleshooting and the predictions. Also the right processes and the right organization is to be in place, because an organization that is, you know, waiting for the customer to call and then has a number of few service engineers available and a certain amount of spare parts and stock is a different organization from an organization that actually is continuously observing the installed base and is scheduling actions to prevent issues. And in other pillar is knowledge management. So in order to realize predictive models and to have predictive service action, it's important to manage knowledge about failure modes, about maintenance procedures very well to have it standardized and digitalized and available. And last but not least, of course, the predictive models themselves. So we talked about transmitting data from the installed base on the medical device, to an enterprise infrastructure that would analyze the data and generate predictions that's predictive models are exactly the last ingredient that is needed. So this is not something that I'm, you know, I'm telling you for the first time is actually a strategic intent of Philips, where we aim for zero unplanned downtime. And we market it that way. We also is not a secret that we do it by using big data. And, of course, there could be other methods to to achieving the same goal. But we started using big data already now well, quite quite many years ago. And one of the reasons is that our medical devices already are wired to collect lots of data about the functioning. So they collect events, error logs that are sensor connecting sensor data. And to give you an idea, for example, just as an order of magnitudes of size of the data, the one MRI scanner can log more than 1 million events per day, hundreds of thousands of sensor readings and tens of thousands of many other data elements. And so this is truly big data. On the other hand, this data was was actually not designed for predictive maintenance, you have to think a medical device of this type of is, stays in the field for about 10 years. Some a little bit longer, some of it's shorter. So these devices have been designed 10 years ago, and not necessarily during the design, and not all components were designed, were designed with predictive maintenance in mind with IoT, and with the latest technology at that time, you know, progress, will not so forward looking at the time. So the actual the key challenge is taking the data which is already available, which is already logged by the medical devices, integrating it and creating predictive models. And if we dive a little bit more into the research challenges, this is one of the Challenges. How to integrate diverse data sources, especially how to automate the costly process of data provisioning and cleaning? But also, once you have the data, let's say, how to create these models that can predict failures and the degradation of performance of a single medical device? Once you have these models and alerts, another challenge is how to automatically recommend service actions based on the probabilistic information on these possible failures? And once you have the insights even if you can recommend action still recommending an action should be done with the goal of planning, maintenance, for generating value. That means balancing costs and benefits, preventing unplanned downtimes without of course scheduling and unnecessary interventions because every intervention, of course, is a disruption for the clinical schedule. And there are many more applications that can be built off such as the optimal management of spare parts supplies. So how do you approach this problem? Our approach was to collect into one database Vertica. A large amount of historical data, first of all historical data coming from the medical devices, so event logs, parameter value system configuration, sensor readings, all the data that we have at our disposal, that in the same database together with records of failures, maintenance records, service work orders, part replacement contracts, so basically the evidence of failures and once you have data from the medical devices, and data from the failures in the same database, it becomes possible to correlate event logs, errors, signal sensor readings with records of failures and records of part replacement and maintenance operations. And we did that also with a specific approach. So we, we create integrated teams, and every integrated team at three figures, not necessarily three people, they were actually multiple people. But there was at least one business owner from a service organization. And this business owner is the person who knows what is relevant, which use case are relevant to solve for a particular type of product or a particular market. What basically is generating value or is worthwhile tackling as an organization. And we have data scientists, data scientists are the one who actually can manipulate data. They can write the queries, they can write the models and robust statistics. They can create visualization and they are the ones who really manipulate the data. Last but not least, very important is subject matter experts. Subject Matter Experts are the people who know the failure modes, who know about the functioning of the medical devices, perhaps they're even designed, they come from the design side, or they come from the service innovation side or even from the field. People who have been servicing the machines in real life for many, many years. So, they are familiar with the failure models, but also familiar with the type of data that is logged and the processes and how actually the systems behave, if you if you if you if you allow me in, in the wild in the in the field. So the combination of these three secrets was a key. Because data scientist alone, just statisticians basically are people who can all do machine learning. And they're not very effective because the data is too complicated. That's why you more than too complex, so they will spend a huge amount of time just trying to figure out the data. Or perhaps they will spend the time in tackling things that are useless, because it's such an interesting knows much quicker which data points are useful, which phenomenon can be found in the data or probably not found. So the combination of subject matter experts and data scientists is very powerful and together gathered by a business owner, we could tackle the most useful use cases first. So, this teams set up to work and they developed three things mainly, first of all, they develop insights on the failure modes. So, by looking at the data, and analyzing information about what happened in the field, they find out exactly how things fail in a very pragmatic and quantitative way. Also, they of course, set up to develop the predictive model with associated alerts and service actions. And a predictive model is just not an alert is just not a flag. Just not a flag, only flag that turns on like a like a traffic light, you know, but there's much more than that. It's such an alert is to be interpreted and used by highly skilled and trained engineer, for example, in a in a call center, who needs to evaluate that error and plan a service action. Service action may involve the ordering a replacement of an expensive part, it may involve calling up the customer hospital and scheduling a period of downtime, downtime to replace a part. So it has an impact on the clinical practice, could have an impact. So, it is important that the alert is coupled with sufficient evidence and information for such a highly skilled trained engineer to plan the service session efficiently. So, it's it's, it's a lot of work in terms of preparing data, preparing visualizations, and making sure that old information is represented correctly and in a compact form. Additionally, These teams develop, get insight into the failure modes and so they can provide input to the R&D organization to improve the products. So, to summarize these graphically, we took a lot of historical data from, coming from the medical devices from the history but also data from relational databases, where the service, work orders, where the part replacement, the contact information, we integrated it, and we set up to the data analytics. From there we don't have value yet, only value starts appearing when we use the insights of data analytics the model on live data. When we process live data with the module we can generate alerts, and the alerts can be used to plan the maintenance and the maintenance therefore the plant maintenance replaces replacing downtime is creating value. To give an idea of the, of the type of I cannot show you the details of these modules, all of these predictive models. But to give you an idea, this is just a picture of some of the components of our medical device for which we have models for which we have, for which we call the failure modes, hard disk, clinical grade monitoring, monitors, X ray tubes, and so forth. This is for MRI machines, a lot of custom hardware and other types of amplifiers and electronics. The alerts are then displayed in a in a dashboard, what we call a Remote monitoring dashboard. We have a team of remote monitoring engineers that basically surveyors the install base, looks at this dashboard picks up these alerts. And an alert as I said before is not just one flag, it contains a lot of information about the failure and about the medical device. And the remote monitor engineer basically will pick up these alerts, they review them and they create cases for the markets organization to handle. So, they see an alert coming in they create a case. So that the particular call center in in some country can call the customer and schedule and make an appointment to schedule a service action or it can add it preventive action to the schedule of the field service engineer who's already supposed to go to visit the customer for example. This is a picture and high-level picture of the overall data person architecture. On the bottom we have install base install base is formed by all our medical devices that are connected to our Philips and more service network. Data is transmitted in a in a secure and in a secure way to our enterprise infrastructure. Where we have a so called Data Lake, which is basically an archive where we store the data as it comes from, from the customers, it is scrubbed and protected. From there, we have a processes ETL, Extract, Transform and Load that in parallel, analyze this information, parse all these files and all this data and extract the relevant parameters. All this, the reason is that the data coming from the medical device is very verbose, and in legacy formats, sometimes in binary formats in strange legacy structures. And therefore, we parse it and we structure it and we make it magically usable by data science teams. And the results are stored in a in a vertica cluster, in a data warehouse. In the same data warehouse, where we also store information from other enterprise systems from all kinds of databases from SQL, Microsoft SQL Server, Tera Data SAP from Salesforce obligations. So, the enterprise IT system also are connected to vertica the data is inserted into vertica. And then from vertica, the data is pulled by our predictive models, which are Python and Rscripts that run on our proprietary environment helps with insights. From this proprietary environment we generate the alerts which are then used by the remote monitoring application. It's not the only application this is the case of remote monitoring. We also have applications for particular remote service. So whenever we cannot prevent or predict we cannot predict an issue from happening or we cannot prevent an issue from happening and we need to react on a customer call, then we can still use the data to very quickly troubleshoot the system, find the root cause and advice or the best service session. Additionally, there are reliability dashboards because all this data can also be used to perform reliability studies and improve the design of the medical devices and is used by R&D. And the access is with all kinds of tools. So Vertica gives the flexibility to connect with JDBC to connect dashboards using Power BI to create dashboards and click view or just simply use RM Python directly to perform analytics. So little summary of the, of the size of the data for the for the moment we have integrated about 500 terabytes worth of data tables, about 30 trillion data points. More than eighty different data sources. For our complete connected install base, including our customer relation management system SAP, we also have connected, we have integrated data from from the factory for repair shops, this is very useful because having information from the factory allows to characterize components and devices when they are new, when they are still not used. So, we can model degradation, excuse me, predict failures much better. Also, we have many years of historical data and of course 24/7 live feeds. So, to get all this going, we we have chosen very simple designs from the very beginning this was developed in the back the first system in 2015. At that time, we went from scratch to production eight months and is also very stable system. To achieve that, we apply what we call Exhaustive Error Handling. When you process, most of people attending this conference probably know when you are dealing with Big Data, you have probably you face all kinds of corner cases you feel that will never happen. But just because of the sheer volume of the data, you find all kinds of strange things. And that's what you need to take care of, if you want to have a stable, stable platform, stable data pipeline. Also other characteristic is that, we need to handle live data, but also be able to, we need to be able to reprocess large historical datasets, because insights into the data are getting generated over time by the team that is using the data. And very often, they find not only defects, but also they have changed requests for new data to be extracted to distract in a different way to be aggregated in a different way. So basically, the platform is continuously crunching data. Also, components have built-in monitoring capabilities. Transparent transparency builds trust by showing how the platform behaves. People actually trust that they are having all the data which is available, or if they don't see the data or if something is not functioning they can see why and where the processing has stopped. A very important point is documentation of data sources every data point as a so called Data Provenance Fields. That is not only the medical device where it comes from, with all this identifier, but also from which file, from which moment in time, from which row, from which byte offset that data point comes. This allows to identify and not only that, but also when this data point was created, by whom, by whom meaning which version of the platform and of the ETL created a data point. This allows us to identify issues and also to fix only the subset of when an issue is identified and fixed. It's possible then to fix only subset of the data that is impacted by that issue. Again, this grid trusts in data to essential for this type of applications. We actually have different environments in our analytic solution. One that we call data science environment is more or less what I've shown so far, where it's deployed in our Philips private cloud, but also can be deployed in in in public cloud such as Amazon. It contains the years of historical data, it allows interactive data exploration, human queries, therefore, it is a highly viable load. It is used for the training of machine learning algorithms and this design has been such that we it is for allowing rapid prototyping and for large data volumes. In other environments is the so called Production Environment where we actually score the models with live data from generation of the alerts. So this environment does not require years of data just months, because a model to make a prediction does not need necessarily years of data, but maybe some model even a couple of weeks or a few months, three months, six months depending on the type of data on the failure which has been predicted. And this has highly optimized queries because the applications are stable. It only only change when we deploy new models or new versions of the models. And it is designed optimized for low latency, high throughput and reliability is no human intervention, no human queries. And of course, there are development staging environments. And one of the characteristics. Another characteristic of all this work is that what we call Data Driven Service Innovation. In all this work, we use the data in every step of the process. The First business case creation. So, basically, some people ask how did you manage to find the unlocked investment to create such a platform and to work on it for years, you know, how did you start? Basically, we started with a business case and the business case again for that we use data. Of course, you need to start somewhere you need to have some data, but basically, you can use data to make a quantitative analysis of the current situation and also make it as accurate as possible estimate quantitative of value creation, if you have that basically, is you can justify the investments and you can start building. Next to that data is used to decide where to focus your efforts. In this case, we decided to focus on the use cases that had the maximum estimated business impact, with business impact meaning here, customer value, as well as value for the company. So we want to reduce unplanned downtime, we want to give value to our customers. But it would be not sustainable, if for creating value, we would start replacing, you know, parts without any consideration for the cost of it. So it needs to be sustainable. Also, then we use data to analyze the failure modes to actually do digging into the data understanding of things fail, for visualization, and to do reliability analysis. And of course, then data is a key to do feature engineering for the development of the predictive models for training the models and for the validation with historical data. So data is all over the place. And last but not least, again, these models is architecture generates new data about the alerts and about the how good the alerts are, and how well they can predict failures, how much downtime is being saved, how money issues have been prevented. So this also data that needs to be analyzed and provides insights on the performance of this, of this models and can be used to improve the models found. And last but not least, once you have performance of the models you can use data to, to quantify as much as possible the value which is created. And it is when you go back to the first step, you made the business value you you create the first business case with estimates. Can you, can you actually show that you are creating value? And the more you can, have this fitness feedback loop closed and quantify the better it is for having more and more impact. Among the key elements that are needed for realizing this? So I want to mention one about data documentation is the practice that we started already six years ago is proven to be very valuable. We document always how data is extracted and how it is stored in, in data model documents. Data Model documents specify how data goes from one place to the other, in this case from device logs, for example, to a table in vertica. And it includes things such as the finish of duplicates, queries to check for duplicates, and of course, the logical design of the tables below the physical design of the table and the rationale. Next to it, there is a data dictionary that explains for each column in the data model from a subject matter expert perspective, what that means, such as its definition and meaning is if it's, if it's a measurement, the use of measure and the range. Or if it's a, some sort of, of label the spec values, or whether the value is raw or or calculated. This is essential for maximizing the value of data for allowing people to use data. Last but not least, also an ETL design document, it explains how the transformation has happened from the source to the destination including very important the failure and the strategy. For example, when you cannot parse part of a file, should you load only what you can parse or drop the entire file completely? So, import best effort or do all or nothing or how to populate records for which there is no value what are the default values and you know, how to have the data is normalized or transform and also to avoid duplicates. This again is very important to provide to the users of the data, if full picture of all the data itself. And this is not just, this the formal process the documents are reviewed and approved by all the stakeholders into the subject matter experts and also the data scientists from a function that we have started called Data Architect. So to, this is something I want to give about, oh, yeah and of course the the documents are available to the end users of the data. And we even have links with documents of the data warehouse. So if you are, if you get access to the database, and you're doing your research and you see a table or a view, you think, well, it could be that could be interesting. It looks like something I could use for my research. Well, the data itself has a link to the document. So from the database while you're exploring data, you can retrieve a link to the place where the document is available. This is just the quick summary of some of the of the results that I'm allowed to share at this moment. This is about image guided therapy, using our remote service infrastructure for remotely connected system with the right contracts. We can achieve we have we have reduced downtime by 14% more than one out of three of cases are resolved remotely without an engineer having to go outside. 82% is the first time right fixed rate that means that the issue is fixed either remotely or if a visit at the site is needed, that visit only one visit is needed. So at that moment, the engineer we decided the right part and fix this straightaway. And this result on average on 135 hours more operational availability per year. This therefore, the ability to treat more patients for the same costs. I'd like to conclude with citing some nice testimonials from some of our customers, showing that the value that we've created is really high impact and this concludes my presentation. Thanks for your attention so far. >> Thank you Morrow, very interesting. And we've got a number of questions that we that have come in. So let's get to them. The first one, how many devices has Philips connected worldwide? And how do you determine which related center data workloads get analyzed with protocols? >> Okay, so this is just two questions. So the first question how many devices are connected worldwide? Well, actually, I'm not allowed to tell you the precise number of connected devices worldwide, but what I can tell is that we are in the order of tens of thousands of devices. And of all types actually. And then, how would we determine which related sensor gets analyzed with vertica well? And a little bit how I set In the in the presentation is a combination of two approaches is a data driven approach and the knowledge driven approach. So a knowledge driven approach because we make maximum use of our knowledge of the failure modes, and the behavior of the medical devices and of their components to select what we think are promising data points and promising features. However, from that moment on data science kicks in, and it's actually data science is used to look at the actual data and come up with quantitative information of what is really happening. So, it could be that an expert is convinced that the particular range of value of a sensor are indicative of a particular failure. And it turns out that maybe it was too optimistic on the other way around that in practice, there are many other situations situation he was not aware of. That could happen. So thanks to the data, then we, you know, get a better understanding of the phenomenon and we get the better modeling. I bet I answered that, any question? >> Yeah, we have another question. Do you have plans to perform any analytics at the edge? >> Now that's a good question. So I can't disclose our plans on this right now, but at the edge devices are certainly one of the options we look at to help our customers towards Zero Unplanned Downtime. Not only that, but also to facilitate the integration of our solution with existing and future hospital IT infrastructure. I mean, we're talking about advanced security, privacy and guarantee that the data is always safe remains. patient data and clinical data remains does not go outside the parameters of the hospital of course, while we want to enhance our functionality provides more value with our services. Yeah, so edge definitely very interesting area of innovation. >> Another question, what are the most helpful vertica features that you rely on? >> I would say, the first that comes to mind, to me at this moment is ease of integration. Basically, with vertica, we will be able to load any data source in a very easy way. And also it really can be interfaced very easily with old type of ions as an application. And this, of course, is not unique to vertica. Nevertheless, the added value here is that this is coupled with an incredible speed, incredible speed for loading and for querying. So it's basically a very versatile tool to innovate fast for data science, because basically we do not end up another thing is multiple projections, advanced encoding and compression. So this allows us to perform the optimizations only when we need it and without having to touch applications or queries. So if we want to achieve high performance, we Basically spend a little effort on improving the projection. And now we can achieve very often dramatic increases in performance. Another feature is EO mode. This is great for for cloud for cloud deployment. >> Okay, another question. What is the number one lesson learned that you can share? >> I think that would my advice would be document control your entire data pipeline, end to end, create positive feedback loops. So I hear that what I hear often is that enterprises I mean Philips is one of them that are not digitally native. I mean, Philips is 129 years old as a company. So you can imagine the the legacy that we have, we will not, you know, we are not born with Web, like web companies are with with, you know, with everything online and everything digital. So enterprises that are not digitally native, sometimes they struggle to innovate in big data or into to do data driven innovation, because, you know, the data is not available or is in silos. Data is controlled by different parts of the organ of the organization with different processes. There is not as a super strong enterprise IT system, providing all the data, you know, for everybody with API's. So my advice is to, to for the very beginning, a creative creating as soon as possible, an end to end solution, from data creation to consumption. That creates value for all the stakeholders of the data pipeline. It is important that everyone in the data pipeline from the producer of the data to the to the consumers, basically in order to pipeline everybody gets a piece of value, piece of the cake. When the value is proven to all stakeholders, everyone would naturally contribute to keep the data pipeline running, and to keep the quality of the data high. That's the students there. >> Yeah, thank you. And in the area of machine learning, what types of innovations do you plan to adopt to help with your data pipeline? >> So, in the error of machine learning, we're looking at things like automatically detecting the deterioration of models to trigger improvement action, as well as connected with active learning. Again, focused on improving the accuracy of our predictive models. So active learning is when the additional human intervention labeling of difficult cases is triggered. So the machine learning classifier may not be able to, you know, classify correctly all the time and instead of just randomly picking up some cases for a human to review, you, you want the costly humans to only review the most valuable cases, from a machine learning point of view, the ones that would contribute the most in improving the classifier. Another error is is deep learning and was not working on it, I mean, but but also applications of more generic anomaly detection algorithms. So the challenge of anomaly detection is that we are not only interested in finding anomalies but also in the recommended proper service actions. Because without a proper service action, and alert generated because of an anomaly, the data loses most of its value. So, this is where I think we, you know. >> Go ahead. >> No, that's, that's it, thanks. >> Okay, all right. So that's all the time that we have today for questions. I want to thank the audience for attending Mauro's presentation and also for your questions. If you weren't able to, if we weren't able to answer your question today, I'd ask let we'll let you know that we'll respond via email. And again, our engineers will be at the vertica, on the vertica quorums awaiting your other questions. It would help us greatly if you could give us some feedback and rate the session before you sign off. Your rating will help us guide us as when we're looking at content to provide for the next vertica BTC. Also, note that a replay of today's event and a PDF copy of the slides will be available on demand, we'll let you know when that'll be by email hopefully later this week. And of course, we invite you to share the content with your colleagues. Again, thank you for your participation today. This includes this breakout session and hope you have a wonderful day. Thank you. >> Thank you

>> From Boston, Massachusetts. It's theCUBE. Covering Actifio 2019 Data Driven. (upbeat techno music) Brought to you by Actifio. >> Hello everyone and welcome to Boston and theCUBE's special coverage of Actifio Data Driven 19. I'm Dave Vellante. Stu Miniman is here. We've got a special guest, John Furrier is in the house from from Palo Alto. Guys, theCUBE we love to go out on the ground, you know, we go deep. We're here at this data theme, right? We were there in the early days, John, you called me up and say, "Get your butt here, we're going to cover the first of Doop World". And since then things have moved quite fast. Everybody thought, you know, Hadoop Big Data was going to take over the world. Nobody even uses that term anymore, right? It's kind of, now it's AI, and machine intelligence, and block chain, and everything else. So what do you think is happening? Did the early Big Data days fail? You know, Frank Genus this morning called it The experimentation phase. >> I mean, I don't really think Frank has a good handle on what's going on in my opinion, cause I think it's not an experimentation, it's real. That was a wave that was essentially the beginning of, not an experimentation, of realization and reality that data, unstructured data in particular was real and relevant. Hadoop looked good off the tee, mill the fairway as we say, but the thing about the Hadoop ecosystem is that validated big data. Every financial institution jumped on it. Everyone who knew anything about data or had data issues or had a lot of data, knew the value. It's just that the apparatus to build via Hadoop was too expensive. In comes Cloud computing at scale, so, as Cloud was accelerating, you look at the Amazon Web Services Revenue Chart you can almost see the D mark where the inflection point is on the hockey stick of Amazon's revenue numbers. And that is the point in time where Hadoop was on the declining of failure. Hortonworks sold the Cloudera. Cloudera's earnings are at an all-time low. A lot of speculation of their entire strategy, and their venture back company went public, but bet the ranch to be the next data warehouse. That wasn't the business model. The data business was a completely new industry, completely being re-transformed, and, far from experimentation, it is real and definitely growing like a weed, but changing because of the underpinning infrastructure dynamics of Cloud Native, Microservices, and that's only going to get highly accelerated and the people who talk about context of industry like Frank, are going to be off. Their predictions will be off because they don't really see the new picture clear enough, in my opinion, >> So, >> I think he's off. >> So it's not so much of a structural change like it was when we went from, you know, mainframes to PCs, it's more of a sort of flow, evolution into this new area which is being driven, powered by new technologies, we talk about block chain machine intelligence and other things. >> Well, I mean, the make up of companies that were building quote, "Big Data Solutions", were trying to build an apparatus or mechanisms to solve big data problems, but none of them actually had the big data problem. None of them were full of data. None of them had a lot of data. The ones that had problems were the financial institutions, the credit card companies, the people who were doing a lot of large scale, um, with Google, Facebook, and some of the hyperscalers. They were actually dealing with the data tsunami themselves, so the practitioners ended up driving it. You guys at Wikibomb, we pointed this out on theCUBE many times, that the value was going to come from the practitioners not the suppliers of so called technology. So, you know, the Clouderas of the world who thought Hadoop would be relevant and growing as a technology were right on one side, on the other side of the coin was the Cloud decimation of that sector. The Cloud computer just completely blew away that Hadoop market because you didn't have to hire a PhD, you didn't have to hire specialty skills to stand up Hadoop clusters. You could actually throw it in the Cloud and get agile quickly, and get value out of data very very quickly. That has been real, it has not been an experiment. There's been new case studies, new companies born, new brands, so it's not an experiment, it is reality, and it's only going to get more real every day. >> And I add of course now you've got, you mentioned Cloudera and Hortenworks, you also got Matt Bar reeling Stu. Let's talk about Actifio. So they coined the term Copy Data Management, they created the category, of course they do a lot of backup, I mean, everybody in this space does a lot of backup. And then you saw the Silicon Valley companies come in. Particularly Cohesity and Rubric, you know, to a lesser extent he got some other guys like Zerto and Durva, but it was really those two companies, Cohesity and Rubric, they raised more money in their D round than Actifio has since inception. But yet Actifio keeps, you know, plodding along, growing, you know, word is they're profitable, you know, they're not like this really sectioned very East Coast versus kind of West Coast mentality. What's your take on what's going on? >> Yeah, so, Dave right, you look at the early days of Actifio and you say great, Copy Data Management, I have all these copies of data, how do I reduce my cost, get greater utilization than I have and leverage the data? I love the title of the show here, Data Driven. You know, we know at the center of digital transformation if you can't become data driven, like the CMO Brian Regan got up on stage talk about that industrialization of data. How am I going along that journey being this, I collected data versus now, you know, data, you know, is the reason that I make decisions, how I make decisions, I get smarter. The Cloud of course is a huge enabler of this, there's all these services that I can instantly access to be able to get greater insight, and move along with that environment, and if you look underneath all of these backup companies, it's really how I can change that data into business value and drive my business, the metadata underneath and all those pieces, not just the wonky storage and technical solutions that make things better, and I get a faster ROI. It's that data at the core of what we do and how do I get that as a business to accelerate. Because we know IT needs to be able to respond back to the business and data needs to be that rocket fuel. >> Is it the case of data haves and data have-nots? I mean, Amazon has data >> I mean, you're right-- >> and Facebook has data. >> We're talking about Actifio, you brought that up, okay, on this segment, on the inside segment, which is cool, they're here at the event, but they have a good opportunity but they also, they got some challenges. I mean, the thing about Actifio is, to my earlier point, which side of the wave are they on? Are they out too much out front with virtualization and Amazon, the Cloud will take them away, or are they riding the Cloud wave, making that an enabler? And I think what really I like about Actifio is because they have a lot of virtualization capabilities, the question is can they scale that Stu, to containers and microservices, because, the real opportunity in this market, in my opinion, is going to build on the virtualization trend, and make container aware, microservices capabilities because if they don't, then that would be a tell sign. Now either way it's a hot M&A market right now, so I think being in the market, horse on the track as you say. You look at the tableau sales force deal monster numbers we are in clearly a hot IPO market and a major roll up market on the M&A side. I think clearly there's two types of companies, old and new, and that is really what people are looking at, are they part of the old guard, are they the new guard. So, you know, this to me is going to be a tell sign of what they do next, can they make the data driven value proposition, you articulated Stu, actually a reality It's going to come from the technology underneath. >> Well I think it's a really interesting point you're making because, Stu as you probably know, that Amazon announced the Amazon backup service right, and you talked about the backup guys and they're like, "Ah yeah it's backup, but it really doesn't do recovery, it's really not that robust". It's part of me says, "Uh oh"... >> Watch out. >> You better move fast", because Amazon has stated, "Hey if you don't move fast we're going to just keep gobbling", and you've seen Amazon do this. What are your thoughts on that? Can these specialists, can they survive, John's talking about M&A. Can the market support all these guys along with the big, you know, traditional guys like Veritas, and Dell EMC, and IBM and Combol? >> Right, well so Actifio started very much in the data center. They were before this Could wave really took off. It's really only in the last year that they've been sassifying their product. So the question is, does that underlying IP, which wasn't tied to hardware, but, you know, sat at really more of, you know, reminded us of that storage virtualization battles that we talked about for years, Dave, but now they are going in the Cloud. They've got all the partnerships in the Cloud, but they are competing against those new vendors that you talked about like Cohesity and Rubric out there, and there's big money chasing this environment. So, you know, I want to talk to the customers here and find out, you know, where they are using them, and especially some of those first customers using this--. >> Well they clearly need a Cloud play cause that's clearly where the action is. But if you look at what's going on with Amazon, Azure, and Google you see a lot of on premises, Stu, because that's where the customers are. So just because the customers are currently not migrating their existing workloads to the Cloud doesn't mean it's not going to happen. So I think there's an opportunity for any company like Actifio, who may or may not be on the curve on the tech side, one little misfire on a tech bet could cripple the company and also make the company. There's a lot of high risk, reward ratio. How they handle containers. How they build on virtualizations. Virtualization going to to be part of the future with Cloud. These are the kind of the dynamics that are going to be in play, and they got some time on their hands because the on premises growth is because the clients are trying to figure out what to do and they're not going to be migrating, lifting, and shifting workloads all off to the Cloud. New will be Cloud based, but enterprises have proven why we are in multi-Cloud and hybrid-Cloud conversation, that... The enterprise on premises is not going away anytime soon. >> I want to ask you guys, John you specifically, about this sort of new Silicon Valley growth model and how companies are achieving escape velocity. When you and I made our first trip to Barcelona, I was having dinner with David Scott who was the CEO of 3PAR and he said to me, When I came to 3PAR the board said, "Hey we're willing to invest 30 million dollars in this company". And David Scott said to them, "I need way more, I need 80 million dollars". Today 80 million dollars is nothing. You saw, you know, Pure Storage hit escape velocity, was just throwing money, and growing at the problem. You're seeing Cohesity-- >> Well you can debate that. I mean, If you have to build a rocket ship, hit critical mass and you want to fund that, you're going to to need an enterprise. However, there's arguments on the south side that you can actually get fly wheel effect going early with less capital. So again, that's 3PAR-- >> But so that's my point. >> Well so that's 3PAR, that was 2009. >> So, yeah that was early days so that's ancient history. But software is generally supposed to be a capital efficient market, yet these companies are raising many hundreds and hundreds of millions, you know, half a billion dollar raises and they are putting it largely in promotion. Is that the new model, is that sustainable, in your view? >> Well I think you're conflating capital market dynamics with viable companies to invest in. I think there's a robust seed in series A market but the series A market and Silicon Valley is you know, 15 to 25 million, it used to be 3 to 5. So the dynamics are changing on funding. There's just not enough companies, horses on the track, to deploy capital at tranches of 30, 50, 80 million. So the capital markets are clearly going to have the money available so it's a market for the startups and the broke companies. That's separate from actually winning. So you've got slacks going public this weeks, you have other companies who have built business on a sass fly wheel, and then everything else is gravy in terms of the go to market, they got a couple hundred million. I think slack got close to a billion dollars in cash that they've raised. So they're flooded with cash, they'll never spend it all. So there are some companies that can achieve success like that. Others have to buy market share, they got to push and build out a sales force, and it's going to be a function of the role of customer, customization, specialism, and whatnot. But with AI machine leaning there's more efficiencies coming in so I think the modern company can do more with less. >> What do you think of the ride sharing on IPOs, Uber and Lift, do you abol? Do you like 'em or do you think it's just, they're losing too money and can't sustain it? >> I was thinking about that this morning after looking at the article in the Wall Street Journal in our coverage on Silicon angle. You look at Zoom communications, I like models that actually can take a simple concept and an existing mature market and disrupt it by being Cloud efficient and completely sass and data driven. That is an example of success. That to me, Zoom Communications and Zscaler, another company that we talk to, these are companies that were built with a specific value proposition that made the product and they were targeting mature markets with leaders in it. Video conferencing, Webex, Citrix, Zoom came out of nowhere, optimized on simple value proposition, used Cloud scale and data, and crushed it. Uber, Lift, little bit different issue. They're losing money but I would bet on the long term that that is going to be the used case for how people will have transportation. I think that's the long game and I think that without regulatory kind of pressure, without, there's regulatory issues that's really the big risk. But I believe that Uber and Lift absolutely will be long brands and just like Facebook was early on, although they threw off a lot of cash, those guys are building for penetration, and that's where the funding matters. Penetration is critical. Now they're the standard, and people really don't take taxis anymore, but they're really using the ride sharing. And you get the scooters, you get the bikes, they're all sequencing into these adjacent markets which drains more cash but builds the brand, builds the footprint. >> Well that's what I want to ask you. So people compare the early Uber, Lift, Taxi, Ride sharing to Amazon selling books, but there's all these other adjacencies. You have a thought on this? >> Well, just, you know, right, Uber Eats is a huge opportunity for that environment and autonomous vehicles everybody talks about, but it's still quite a ways out. So there are a lot of different- >> Scooters are the same, we're in San Diego, there are 8 gazillion scooters. >> San Diego had fun, you know, going around on their electronic scooters, boy, talk about the gig economy, they pay people at the night, to like go pay by the recharge you do on that, what is the future of work, >> Yeah, that's a great point. >> and how can we have that-- >> Uber going to look a lot like Amazon. You subsidize the front end retail side of the business, but look at the data that they throw up. Uber's data that they're gathering on, not only customer behavior, but just mapping services, 3-D mapping is going to be huge, so you've got these cars that are essentially bots on the road, providing massive mapping and traffic analysis. So you're going to start to see data driven, like Actifio slogan here, be a big part of all design decisions and value proposition from any company out there. And if they're not data driven I think they're going to be toast. >> Probably could because there's that data and that machine learning underneath, that can optimize, you know, where the people are, how I use the system, such a huge wave that we're watching. >> How about one last topic which is heavily data driven, it's Facebook. Facebook is obviously a data driven company, the Facebook crypto play, I love it, I love Facebook. I'm a bull on Facebook, I think it's been beat up. I think, two billion users is hard to replicate, but what's your thoughts on their crypto play? >> Well it's kind of a middle finger to the United States of America but it's a great catalyst for the international market because crypto needed a whale to come in and bring all those users in. Bad timing, in my mind, for Facebook, because given all the anti-trust and regulatory conversations, what better way to show your threat to the world order when you say we're going to run a banking system with a collection of international companies. I think the US is going to look at this and say, "Oh my God! They can't even be trusted to handle personal information and we're going to now let them run a banking system? Run monetary, basically World Bank equivalent infrastructure?" No frickin way! I think this is going to to be a major road to home. I think Facebook has to really make this an ecosystem play if they want to make it work, that's their telegraphic move they're saying, "Hey we want to do for the community but we got our own wallet and we got our own network". But they bring a lot to the table so it's going to be a really interesting dynamic to see the coalescing around Facebook because they could make the market. Look what Instagram did to Snapchat. They literally killed the company, took all their users. That is what's going to happen in the digital money economy when Facebook brings billions of users user experience with money. What happened with Snapchat with Instagram is going to happen to the World Bank if this continues. >> Where do you stand on the government breaking up big tech? >> So Dave, you know, you look in these companies, it's not easy to pull those apart. I don't think our government understands how most of big tech works. You know, take Amazon and AWS, that's one company underneath it. You know, Facebook, Microsoft. You know, Microsoft went through all these issues. Question Dave, we've had lots of debates on Twitter you know, are they breaking the law, are they not doing trust? I have some trust issues with Facebook myself, but most of the big companies up there I don't think the anti-trust kicks in, I don't think it makes sense to pull them apart. >> Stu, the Facebook story and the YouTube story are simply this, they have been hiding under the platform rules, of the Digital Millennium Copyright Act, and they are an editing platform so you can't sue them. Okay, once they become a publisher they could be sued. Just like CNN, Fox News, and everybody else. And we're publishers. So they've been hiding behind the platform. That gig is up. They're going to have to address are you a platform or are you a publisher? You're making editing decisions around what users can see with software, you are essentially editing the feed, that is a publisher role, with that becomes responsibility, and then obviously regulartory. >> Well Facebook is conflicted right now. They're trying to figure out which side of the fence to go on. >> No no no! They want one side! The platform side! They're make billions of dollars! >> Yeah but so they're making decisions about you know, which content to show and whether they monetize it. And when it's controversial content, they'll turn down the ads a little bit but they won't completely eliminate it sometimes. >> So, Dave, the only thing that the partisans in politics seem to agree on though is that big tech has too much power. You know, What's your take on that? >> Well so I think that if they are breaking the law then they should be moderated. But I don't think the answer is to go hard after Elizabeth Warren. Hard after them and break them up. I think you got to start with okay, because you break these companies up what's going to happen is they're going to be worth more, it's going to be AT&T all over again. >> While you guys were at Sysco Live, we covered this at Amazon Web Service and Public Sector Summit. The real issue in government, Stu, is there's too much tech for bad on the PR side, and there's not enough tech for good. Tech is not bad, tech is good. There's not enough promotion around the apps around there. There's real venture funds being created to promote tech for good. That's going to where the tide will turn. When does the tech industry start doing good stuff, not bad stuff. >> All right we've got to wrap. John, thanks for sitting in. Thank you for watching. Be right back, we're here at Actifio Data Driven 2019. From Boston this is theCUBE, be right back. (upbeat techno music)

from Boston Massachusetts it's the cube covering Activia 2019 data-driven to you by Activia welcome to Boston everybody this is Dave Volante and I'm here with stupid man finally still in our hometown you're watching the cube the leader in live tech coverage we're covering actifi Oh data-driven hashtag data-driven 19 activity it was a company that is focus started focused on copy data management they sort of popularized the term the I the concept the idea of data virtualization there's big data digital transformation all the buzz it's kind of been a tailwind for the company and we followed them quite closely over the years poly st. Claire is here she's the CEO of the state of Massachusetts that's chief of ditch and chief data officer Holly thanks for coming on the Q thanks for having me so it's kind of rare that somebody shares the title of chief digital officer of chief data officer I think it's rare right now I think that would change you think it will change I think those two roles will come together I just think data fuels our digital world and it both creates the content and also monitors how we're doing and it's just inevitably I think either they're gonna be joined at the hip or it's gonna be the same person that's interesting I always thought the chief data officer sort of emerged from this wonky back-office role data quality of this careful the word walking okay well yeah let's talk about that but the chief digital officer is kind of the mover the shaker has a little marketing genius but but okay so you see those two roles coming together that maybe makes sense because why because there's there some tension in a lot of organizations between those two roles well I think the challenge with the way that sometimes people think about data is they think about it's only a technical process data is actually very creative and you also have to tell a story in order to be good with it it's the same thing as marketing but it's just a little bit of a different hue a different type of audience a different type of pace there's a technical component to the data work but I'm looking at my organization that I'm surrounded by additional technical folks CTO CSO privacy officer CIO so we have a lot of supports that might take away some of those roles are scrunched in under the data officer or the digital so I used to turn wonky before it kind of triggered you a little bit but but you're a modeler you're a data scientist your development programmer right no but I know enough to I know enough to read code and get in trouble okay so you can direct coders and you have data scientists working for you yeah right so you've got that entire organization underneath you and your your mission is blank fill in the blank so our mission is to use the best information technology to ensure that every users experience with the Commonwealth is fast easy and wicked awesome awesome Holly our team just got back from a very large public sector event down in DC and digging into you know how our agency is doing with you know cloud force initiatives how are they doing the city environments you were state of Massachusetts and you know rolled out that that first chief data if you keep dipped officer gets a little bit of insight inside how Massachusetts doing with these latest waves of innovation uh well you know we have our legacy systems and as our opportunities come up to improve those systems our reinvest in them we are taking a step forward to cloud we're not so dogmatic that it's cloud only but it's definitely cloud when it's appropriate I do think we'll always have some on-prem services but really when it's possible whether it's a staff service off-the-shelf or it's a cloud environment to make sense than we are moving to that in your keynote this morning you you talked about something called data minimalism yeah and wonder if you could explain that for audience because for the longest time it's been well you want to hoard all the data you want to get all the data and you know what do you do with it how do you manage you right right I mean data's only as good as your ability to use it and I often find that we're ingesting all this data and we don't really know what to do with it or really rather our business leaders and decision-makers can't quite figure out how to connect that to the mission or to act properly interrogate the data to get the information they want and so this idea is an idea that's sort of coming a little bit out of Europe and or some of the other trends we see around some cyber security and hacking worlds and the idea is this actually came from fjords Digital Trends for 2019 is data minimalism the idea is that you strongly connect your business objectives to the data collection program that you have you don't just collect data until you're sure that it supports your objectives so you know one of the things that I also talked about in the keynote was not just data minimalism but doing a try test iterate approach we often collect data hoping to see that we can create a change I think we need to prove that we can create the change before we do a widespread scalable data collection program because often we collect data and you still can't see what you're doing has an effect within the data the signals too strong or too too weak or you're asking the wrong question of the data or it's the wrong plectra collection of the technique and that's largely driven from a sort of privacy a privacy privacy the reality of how costly sometimes the kennedys but you know storage of data is cheap but the actual reality of moving it and saving it and knowing where it is and accessing it later that takes time and energy of your of your actual people so I think it's just important for us to think carefully about a resource in government we have a little less resources sometimes in the private sector so we're very strategic on what we do and so I think we need to really think about the data we use if the pendulum swings remember back to the days of you know 2006 the Federal Rules of Civil Procedure said okay you got to keep electronic records for whatever seven years of depending on industry and people said okay let's get rid of it as soon as we can data was viewed as a liability and then of course all the big data height we've talked about a little bit in your in your speech everybody said I could collect everything throw it into a data Lake and we all know those became data swamps so do you feel like the pendulum is swinging and there's maybe a little balance are we reaching an equilibrium is it going to be a you know hard shift back to data as a liability what are your thoughts well I think isn't with any trend there's always a little bit of a pendulum swing as we're learning it's with it with the equilibrium is equilibrium is I think that's a great word I think the piece that I neglected to mention is the relationship to the consumer trust you know for us in government we have to have the trust of our constituents we do have a higher bar than public sector in terms of handling data in a way that's respectful of individuals privacy and their security of their data and so I think to the extent that we are able to lend transparency and show the utility and the data we're using and that will gain the trust of our users or customers but if we continue to do things behind the scenes and not be overt about it I think then that can cause more problems I think we face is organizations to ask ourselves is having more data worth the sort of vulnerability introduces and the possible liability of trust of our of our customers when you betray to test over your customers it's really hard to replace that and so you know to a certain extent I think we should be more deliberate about our data and earn the trust of our customers okay how how does Massachusetts look at the boundary of data between the public sector and the private sector I've talked to you know some states where you know we're helping business off parking by giving you know new mobile apps access to that information you talked a little bit about health care you know I've done interviews with the massive macleod initiative here locally how do you look at that balance of sharing I think it is a real balance you know I don't think we do very much of it yet and we certainly don't share data that were not allowed to by law and we have very strict laws here in Massachusetts the stricter at the ten most states and so I think it's very strategic when we do share data we are looking for opportunities when we can when I talk about demand driven data I look forward to opening the conversation a little bit to ask people what data are they looking for to ask businesses and different institutions we have throughout the Commonwealth what data would help you do your job better and grow our economy and our jobs and I think that's a conversation we need to have over time to figure out what the right balances someday it'll be easier for us to share than others and some will never be able to share the first data scientist I've ever met is somebody I interviewed the amazing Hilary Mason and she said something that I want to circle back to something you said in your talk if she said the hardest part of my job or one of the hardest parts is people come to me with data and and it's the most valuable thing I can do is show them which questions to ask and you have talked about well what's a lot of times you don't know what questions to ask until you look at the data or vice versa what comes first the chicken or the egg what's your experience pin well I do think we need to be driven by the business objectives and goals it doesn't mean there's not an iterative process in there somewhere but you know data wonks we can we can just throw data all day long and still might not give you the answer there forward but I think it's really important for us to be driven by the business and I think executives don't know how to ask the questions of the data they don't know how to interrogate it or honestly more realistically we don't have a date of actually answers the question they want to know so we often have to use proxies for that information but I do think if there's an iterative after you get to a starting point so I do think knowing what the business question is first I know you gotta go but I want to ask your last question bring it back to the state where both Massachusetts residents and your services it sounds like you're picking off some some good wins with a through the fast ROI I mean you mentioned you know driver's license renewals etc how about procurement has procurement been a challenge from the state standpoint you are you looking at sort of the digital process and how to streamline procurement that is a conversation that the secretary what is currently in and I think it's a good one I don't think we have any any solutions yet but I think we have a lot of the issues that were struggling with but we're not alone all public sectors struggling with this type of procurement question so we're working on it all right last question there's quick thoughts on you know what you've seen here I know you're in and out but data-driven yeah it's a great theme it's a really exciting agenda there's people for all these different organizations and approaches to data-driven you know from movie executives and casting to era it's just really exciting to see the program it's Nate Claire thanks so much I'm coming on the queue thank you great to meet you okay keep it right there everybody we'll be back with our next guest right after this short break well the cube is here at data-driven day one special coverage we'll be right back