>> From theCUBE Studios in Palo Alto and Boston, bringing you data driven insights from theCUBE in ETR, this is Breaking Analysis with Dave Vellante. >> The introduction in socialization of data mesh has caused practitioners, business technology executives, and technologists to pause, and ask some probing questions about the organization of their data teams, their data strategies, future investments, and their current architectural approaches. Some in the technology community have embraced the concept, others have twisted the definition, while still others remain oblivious to the momentum building around data mesh. Here we are in the early days of data mesh adoption. Organizations that have taken the plunge will tell you that aligning stakeholders is a non-trivial effort, but necessary to break through the limitations that monolithic data architectures and highly specialized teams have imposed over frustrated business and domain leaders. However, practical data mesh examples often lie in the eyes of the implementer, and may not strictly adhere to the principles of data mesh. Now, part of the problem is lack of open technologies and standards that can accelerate adoption and reduce friction, and that's what we're going to talk about today. Some of the key technology and architecture questions around data mesh. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR, and in this Breaking Analysis, we welcome back the founder of data mesh and director of Emerging Technologies at Thoughtworks, Zhamak Dehghani. Hello, Zhamak. Thanks for being here today. >> Hi Dave, thank you for having me back. It's always a delight to connect and have a conversation. Thank you. >> Great, looking forward to it. Okay, so before we get into it in the technology details, I just want to quickly share some data from our friends at ETR. You know, despite the importance of data initiative since the pandemic, CIOs and IT organizations have had to juggle of course, a few other priorities, this is why in the survey data, cyber and cloud computing are rated as two most important priorities. Analytics and machine learning, and AI, which are kind of data topics, still make the top of the list, well ahead of many other categories. And look, a sound data architecture and strategy is fundamental to digital transformations, and much of the past two years, as we've often said, has been like a forced march into digital. So while organizations are moving forward, they really have to think hard about the data architecture decisions that they make, because it's going to impact them, Zhamak, for years to come, isn't it? >> Yes, absolutely. I mean, we are moving really from, slowly moving from reason based logical algorithmic to model based computation and decision making, where we exploit the patterns and signals within the data. So data becomes a very important ingredient, of not only decision making, and analytics and discovering trends, but also the features and applications that we build for the future. So we can't really ignore it, and as we see, some of the existing challenges around getting value from data is not necessarily that no longer is access to computation, is actually access to trustworthy, reliable data at scale. >> Yeah, and you see these domains coming together with the cloud and obviously it has to be secure and trusted, and that's why we're here today talking about data mesh. So let's get into it. Zhamak, first, your new book is out, 'Data Mesh: Delivering Data-Driven Value at Scale' just recently published, so congratulations on getting that done, awesome. Now in a recent presentation, you pulled excerpts from the book and we're going to talk through some of the technology and architectural considerations. Just quickly for the audience, four principles of data mesh. Domain driven ownership, data as product, self-served data platform and federated computational governance. So I want to start with self-serve platform and some of the data that you shared recently. You say that, "Data mesh serves autonomous domain oriented teams versus existing platforms, which serve a centralized team." Can you elaborate? >> Sure. I mean the role of the platform is to lower the cognitive load for domain teams, for people who are focusing on the business outcomes, the technologies that are building the applications, to really lower the cognitive load for them, to be able to work with data. Whether they are building analytics, automated decision making, intelligent modeling. They need to be able to get access to data and use it. So the role of the platform, I guess, just stepping back for a moment is to empower and enable these teams. Data mesh by definition is a scale out model. It's a decentralized model that wants to give autonomy to cross-functional teams. So it is core requires a set of tools that work really well in that decentralized model. When we look at the existing platforms, they try to achieve this similar outcome, right? Lower the cognitive load, give the tools to data practitioners, to manage data at scale because today centralized teams, really their job, the centralized data teams, their job isn't really directly aligned with a one or two or different, you know, business units and business outcomes in terms of getting value from data. Their job is manage the data and make the data available for then those cross-functional teams or business units to use the data. So the platforms they've been given are really centralized around or tuned to work with this structure as a team, structure of centralized team. Although on the surface, it seems that why not? Why can't I use my, you know, cloud storage or computation or data warehouse in a decentralized way? You should be able to, but some changes need to happen to those online platforms. As an example, some cloud providers simply have hard limits on the number of like account storage, storage accounts that you can have. Because they never envisaged you have hundreds of lakes. They envisage one or two, maybe 10 lakes, right. They envisage really centralizing data, not decentralizing data. So I think we see a shift in thinking about enabling autonomous independent teams versus a centralized team. >> So just a follow up if I may, we could be here for a while. But so this assumes that you've sorted out the organizational considerations? That you've defined all the, what a data product is and a sub product. And people will say, of course we use the term monolithic as a pejorative, let's face it. But the data warehouse crowd will say, "Well, that's what data march did. So we got that covered." But Europe... The primest of data mesh, if I understand it is whether it's a data march or a data mart or a data warehouse, or a data lake or whatever, a snowflake warehouse, it's a node on the mesh. Okay. So don't build your organization around the technology, let the technology serve the organization is that-- >> That's a perfect way of putting it, exactly. I mean, for a very long time, when we look at decomposition of complexity, we've looked at decomposition of complexity around technology, right? So we have technology and that's maybe a good segue to actually the next item on that list that we looked at. Oh, I need to decompose based on whether I want to have access to raw data and put it on the lake. Whether I want to have access to model data and put it on the warehouse. You know I need to have a team in the middle to move the data around. And then try to figure organization into that model. So data mesh really inverses that, and as you said, is look at the organizational structure first. Then scale boundaries around which your organization and operation can scale. And then the second layer look at the technology and how you decompose it. >> Okay. So let's go to that next point and talk about how you serve and manage autonomous interoperable data products. Where code, data policy you say is treated as one unit. Whereas your contention is existing platforms of course have independent management and dashboards for catalogs or storage, et cetera. Maybe we double click on that a bit. >> Yeah. So if you think about that functional, or technical decomposition, right? Of concerns, that's one way, that's a very valid way of decomposing, complexity and concerns. And then build solutions, independent solutions to address them. That's what we see in the technology landscape today. We will see technologies that are taking care of your management of data, bring your data under some sort of a control and modeling. You'll see technology that moves that data around, will perform various transformations and computations on it. And then you see technology that tries to overlay some level of meaning. Metadata, understandability, discovery was the end policy, right? So that's where your data processing kind of pipeline technologies versus data warehouse, storage, lake technologies, and then the governance come to play. And over time, we decomposed and we compose, right? Deconstruct and reconstruct back this together. But, right now that's where we stand. I think for data mesh really to become a reality, as in independent sources of data and teams can responsibly share data in a way that can be understood right then and there can impose policies, right then when the data gets accessed in that source and in a resilient manner, like in a way that data changes structure of the data or changes to the scheme of the data, doesn't have those downstream down times. We've got to think about this new nucleus or new units of data sharing. And we need to really bring back transformation and governing data and the data itself together around these decentralized nodes on the mesh. So that's another, I guess, deconstruction and reconstruction that needs to happen around the technology to formulate ourselves around the domains. And again the data and the logic of the data itself, the meaning of the data itself. >> Great. Got it. And we're going to talk more about the importance of data sharing and the implications. But the third point deals with how operational, analytical technologies are constructed. You've got an app DevStack, you've got a data stack. You've made the point many times actually that we've contextualized our operational systems, but not our data systems, they remain separate. Maybe you could elaborate on this point. >> Yes. I think this is, again, has a historical background and beginning. For a really long time, applications have dealt with features and the logic of running the business and encapsulating the data and the state that they need to run that feature or run that business function. And then we had for anything analytical driven, which required access data across these applications and across the longer dimension of time around different subjects within the organization. This analytical data, we had made a decision that, "Okay, let's leave those applications aside. Let's leave those databases aside. We'll extract the data out and we'll load it, or we'll transform it and put it under the analytical kind of a data stack and then downstream from it, we will have analytical data users, the data analysts, the data sciences and the, you know, the portfolio of users that are growing use that data stack. And that led to this really separation of dual stack with point to point integration. So applications went down the path of transactional databases or urban document store, but using APIs for communicating and then we've gone to, you know, lake storage or data warehouse on the other side. If we are moving and that again, enforces the silo of data versus app, right? So if we are moving to the world that our missions that are ambitions around making applications, more intelligent. Making them data driven. These two worlds need to come closer. As in ML Analytics gets embedded into those app applications themselves. And the data sharing, as a very essential ingredient of that, gets embedded and gets closer, becomes closer to those applications. So, if you are looking at this now cross-functional, app data, based team, right? Business team, then the technology stacks can't be so segregated, right? There has to be a continuum of experience from app delivery, to sharing of the data, to using that data, to embed models back into those applications. And that continuum of experience requires well integrated technologies. I'll give you an example, which actually in some sense, we are somewhat moving to that direction. But if we are talking about data sharing or data modeling and applications use one set of APIs, you know, HTTP compliant, GraQL or RAC APIs. And on the other hand, you have proprietary SQL, like connect to my database and run SQL. Like those are very two different models of representing and accessing data. So we kind of have to harmonize or integrate those two worlds a bit more closely to achieve that domain oriented cross-functional teams. >> Yeah. We are going to talk about some of the gaps later and actually you look at them as opportunities, more than barriers. But they are barriers, but they're opportunities for more innovation. Let's go on to the fourth one. The next point, it deals with the roles that the platform serves. Data mesh proposes that domain experts own the data and take responsibility for it end to end and are served by the technology. Kind of, we referenced that before. Whereas your contention is that today, data systems are really designed for specialists. I think you use the term hyper specialists a lot. I love that term. And the generalist are kind of passive bystanders waiting in line for the technical teams to serve them. >> Yes. I mean, if you think about the, again, the intention behind data mesh was creating a responsible data sharing model that scales out. And I challenge any organization that has a scaled ambitions around data or usage of data that relies on small pockets of very expensive specialists resources, right? So we have no choice, but upscaling cross-scaling. The majority population of our technologists, we often call them generalists, right? That's a short hand for people that can really move from one technology to another technology. Sometimes we call them pandric people sometimes we call them T-shaped people. But regardless, like we need to have ability to really mobilize our generalists. And we had to do that at Thoughtworks. We serve a lot of our clients and like many other organizations, we are also challenged with hiring specialists. So we have tested the model of having a few specialists, really conveying and translating the knowledge to generalists and bring them forward. And of course, platform is a big enabler of that. Like what is the language of using the technology? What are the APIs that delight that generalist experience? This doesn't mean no code, low code. We have to throw away in to good engineering practices. And I think good software engineering practices remain to exist. Of course, they get adopted to the world of data to build resilient you know, sustainable solutions, but specialty, especially around kind of proprietary technology is going to be a hard one to scale. >> Okay. I'm definitely going to come back and pick your brain on that one. And, you know, your point about scale out in the examples, the practical examples of companies that have implemented data mesh that I've talked to. I think in all cases, you know, there's only a handful that I've really gone deep with, but it was their hadoop instances, their clusters wouldn't scale, they couldn't scale the business and around it. So that's really a key point of a common pattern that we've seen now. I think in all cases, they went to like the data lake model and AWS. And so that maybe has some violation of the principles, but we'll come back to that. But so let me go on to the next one. Of course, data mesh leans heavily, toward this concept of decentralization, to support domain ownership over the centralized approaches. And we certainly see this, the public cloud players, database companies as key actors here with very large install bases, pushing a centralized approach. So I guess my question is, how realistic is this next point where you have decentralized technologies ruling the roost? >> I think if you look at the history of places, in our industry where decentralization has succeeded, they heavily relied on standardization of connectivity with, you know, across different components of technology. And I think right now you are right. The way we get value from data relies on collection. At the end of the day, collection of data. Whether you have a deep learning machinery model that you're training, or you have, you know, reports to generate. Regardless, the model is bring your data to a place that you can collect it, so that we can use it. And that leads to a naturally set of technologies that try to operate as a full stack integrated proprietary with no intention of, you know, opening, data for sharing. Now, conversely, if you think about internet itself, web itself, microservices, even at the enterprise level, not at the planetary level, they succeeded as decentralized technologies to a large degree because of their emphasis on open net and openness and sharing, right. API sharing. We don't talk about, in the API worlds, like we don't say, you know, "I will build a platform to manage your logical applications." Maybe to a degree but we actually moved away from that. We say, "I'll build a platform that opens around applications to manage your APIs, manage your interfaces." Right? Give you access to API. So I think the shift needs to... That definition of decentralized there means really composable, open pieces of the technology that can play nicely with each other, rather than a full stack, all have control of your data yet being somewhat decentralized within the boundary of my platform. That's just simply not going to scale if data needs to come from different platforms, different locations, different geographical locations, it needs to rethink. >> Okay, thank you. And then the final point is, is data mesh favors technologies that are domain agnostic versus those that are domain aware. And I wonder if you could help me square the circle cause it's nuanced and I'm kind of a 100 level student of your work. But you have said for example, that the data teams lack context of the domain and so help us understand what you mean here in this case. >> Sure. Absolutely. So as you said, we want to take... Data mesh tries to give autonomy and decision making power and responsibility to people that have the context of those domains, right? The people that are really familiar with different business domains and naturally the data that that domain needs, or that naturally the data that domains shares. So if the intention of the platform is really to give the power to people with most relevant and timely context, the platform itself naturally becomes as a shared component, becomes domain agnostic to a large degree. Of course those domains can still... The platform is a (chuckles) fairly overloaded world. As in, if you think about it as a set of technology that abstracts complexity and allows building the next level solutions on top, those domains may have their own set of platforms that are very much doing agnostic. But as a generalized shareable set of technologies or tools that allows us share data. So that piece of technology needs to relinquish the knowledge of the context to the domain teams and actually becomes domain agnostic. >> Got it. Okay. Makes sense. All right. Let's shift gears here. Talk about some of the gaps and some of the standards that are needed. You and I have talked about this a little bit before, but this digs deeper. What types of standards are needed? Maybe you could walk us through this graphic, please. >> Sure. So what I'm trying to depict here is that if we imagine a world that data can be shared from many different locations, for a variety of analytical use cases, naturally the boundary of what we call a node on the mesh will encapsulates internally a fair few pieces. It's not just the boundary of that, not on the mesh, is the data itself that it's controlling and updating and maintaining. It's of course a computation and the code that's responsible for that data. And then the policies that continue to govern that data as long as that data exists. So if that's the boundary, then if we shift that focus from implementation details, that we can leave that for later, what becomes really important is the scene or the APIs and interfaces that this node exposes. And I think that's where the work that needs to be done and the standards that are missing. And we want the scene and those interfaces be open because that allows, you know, different organizations with different boundaries of trust to share data. Not only to share data to kind of move that data to yes, another location, to share the data in a way that distributed workloads, distributed analytics, distributed machine learning model can happen on the data where it is. So if you follow that line of thinking around the centralization and connection of data versus collection of data, I think the very, very important piece of it that needs really deep thinking, and I don't claim that I have done that, is how do we share data responsibly and sustainably, right? That is not brittle. If you think about it today, the ways we share data, one of the very common ways is around, I'll give you a JDC endpoint, or I give you an endpoint to your, you know, database of choice. And now as technology, whereas a user actually, you can now have access to the schema of the underlying data and then run various queries or SQL queries on it. That's very simple and easy to get started with. That's why SQL is an evergreen, you know, standard or semi standard, pseudo standard that we all use. But it's also very brittle, because we are dependent on a underlying schema and formatting of the data that's been designed to tell the computer how to store and manage the data. So I think that the data sharing APIs of the future really need to think about removing this brittle dependencies, think about sharing, not only the data, but what we call metadata, I suppose. Additional set of characteristics that is always shared along with data to make the data usage, I suppose ethical and also friendly for the users and also, I think we have to... That data sharing API, the other element of it, is to allow kind of computation to run where the data exists. So if you think about SQL again, as a simple primitive example of computation, when we select and when we filter and when we join, the computation is happening on that data. So maybe there is a next level of articulating, distributed computational data that simply trains models, right? Your language primitives change in a way to allow sophisticated analytical workloads run on the data more responsibly with policies and access control and force. So I think that output port that I mentioned simply is about next generation data sharing, responsible data sharing APIs. Suitable for decentralized analytical workloads. >> So I'm not trying to bait you here, but I have a follow up as well. So you schema, for all its good creates constraints. No schema on right, that didn't work, cause it was just a free for all and it created the data swamps. But now you have technology companies trying to solve that problem. Take Snowflake for example, you know, enabling, data sharing. But it is within its proprietary environment. Certainly Databricks doing something, you know, trying to come at it from its angle, bringing some of the best to data warehouse, with the data science. Is your contention that those remain sort of proprietary and defacto standards? And then what we need is more open standards? Maybe you could comment. >> Sure. I think the two points one is, as you mentioned. Open standards that allow... Actually make the underlying platform invisible. I mean my litmus test for a technology provider to say, "I'm a data mesh," (laughs) kind of compliant is, "Is your platform invisible?" As in, can I replace it with another and yet get the similar data sharing experience that I need? So part of it is that. Part of it is open standards, they're not really proprietary. The other angle for kind of sharing data across different platforms so that you know, we don't get stuck with one technology or another is around APIs. It is around code that is protecting that internal schema. So where we are on the curve of evolution of technology, right now we are exposing the internal structure of the data. That is designed to optimize certain modes of access. We're exposing that to the end client and application APIs, right? So the APIs that use the data today are very much aware that this database was optimized for machine learning workloads. Hence you will deal with a columnar storage of the file versus this other API is optimized for a very different, report type access, relational access and is optimized around roles. I think that should become irrelevant in the API sharing of the future. Because as a user, I shouldn't care how this data is internally optimized, right? The language primitive that I'm using should be really agnostic to the machine optimization underneath that. And if we did that, perhaps this war between warehouse or lake or the other will become actually irrelevant. So we're optimizing for that human best human experience, as opposed to the best machine experience. We still have to do that but we have to make that invisible. Make that an implementation concern. So that's another angle of what should... If we daydream together, the best experience and resilient experience in terms of data usage than these APIs with diagnostics to the internal storage structure. >> Great, thank you for that. We've wrapped our ankles now on the controversy, so we might as well wade all the way in, I can't let you go without addressing some of this. Which you've catalyzed, which I, by the way, I see as a sign of progress. So this gentleman, Paul Andrew is an architect and he gave a presentation I think last night. And he teased it as quote, "The theory from Zhamak Dehghani versus the practical experience of a technical architect, AKA me," meaning him. And Zhamak, you were quick to shoot back that data mesh is not theory, it's based on practice. And some practices are experimental. Some are more baked and data mesh really avoids by design, the specificity of vendor or technology. Perhaps you intend to frame your post as a technology or vendor specific, specific implementation. So touche, that was excellent. (Zhamak laughs) Now you don't need me to defend you, but I will anyway. You spent 14 plus years as a software engineer and the better part of a decade consulting with some of the most technically advanced companies in the world. But I'm going to push you a little bit here and say, some of this tension is of your own making because you purposefully don't talk about technologies and vendors. Sometimes doing so it's instructive for us neophytes. So, why don't you ever like use specific examples of technology for frames of reference? >> Yes. My role is pushes to the next level. So, you know everybody picks their fights, pick their battles. My role in this battle is to push us to think beyond what's available today. Of course, that's my public persona. On a day to day basis, actually I work with clients and existing technology and I think at Thoughtworks we have given the talk we gave a case study talk with a colleague of mine and I intentionally got him to talk about (indistinct) I want to talk about the technology that we use to implement data mesh. And the reason I haven't really embraced, in my conversations, the specific technology. One is, I feel the technology solutions we're using today are still not ready for the vision. I mean, we have to be in this transitional step, no matter what we have to be pragmatic, of course, and practical, I suppose. And use the existing vendors that exist and I wholeheartedly embrace that, but that's just not my role, to show that. I've gone through this transformation once before in my life. When microservices happened, we were building microservices like architectures with technology that wasn't ready for it. Big application, web application servers that were designed to run these giant monolithic applications. And now we're trying to run little microservices onto them. And the tail was riding the dock, the environmental complexity of running these services was consuming so much of our effort that we couldn't really pay attention to that business logic, the business value. And that's where we are today. The complexity of integrating existing technologies is really overwhelmingly, capturing a lot of our attention and cost and effort, money and effort as opposed to really focusing on the data product themselves. So it's just that's the role I have, but it doesn't mean that, you know, we have to rebuild the world. We've got to do with what we have in this transitional phase until the new generation, I guess, technologies come around and reshape our landscape of tools. >> Well, impressive public discipline. Your point about microservice is interesting because a lot of those early microservices, weren't so micro and for the naysayers look past this, not prologue, but Thoughtworks was really early on in the whole concept of microservices. So be very excited to see how this plays out. But now there was some other good comments. There was one from a gentleman who said the most interesting aspects of data mesh are organizational. And that's how my colleague Sanji Mohan frames data mesh versus data fabric. You know, I'm not sure, I think we've sort of scratched the surface today that data today, data mesh is more. And I still think data fabric is what NetApp defined as software defined storage infrastructure that can serve on-prem and public cloud workloads back whatever, 2016. But the point you make in the thread that we're showing you here is that you're warning, and you referenced this earlier, that the segregating different modes of access will lead to fragmentation. And we don't want to repeat the mistakes of the past. >> Yes, there are comments around. Again going back to that original conversation that we have got this at a macro level. We've got this tendency to decompose complexity based on technical solutions. And, you know, the conversation could be, "Oh, I do batch or you do a stream and we are different."' They create these bifurcations in our decisions based on the technology where I do events and you do tables, right? So that sort of segregation of modes of access causes accidental complexity that we keep dealing with. Because every time in this tree, you create a new branch, you create new kind of new set of tools and then somehow need to be point to point integrated. You create new specialization around that. So the least number of branches that we have, and think about really about the continuum of experiences that we need to create and technologies that simplify, that continuum experience. So one of the things, for example, give you a past experience. I was really excited around the papers and the work that came around on Apache Beam, and generally flow based programming and stream processing. Because basically they were saying whether you are doing batch or whether you're doing streaming, it's all one stream. And sometimes the window of time, narrows and sometimes the window of time over which you're computing, widens and at the end of today, is you are just getting... Doing the stream processing. So it is those sort of notions that simplify and create continuum of experience. I think resonate with me personally, more than creating these tribal fights of this type versus that mode of access. So that's why data mesh naturally selects kind of this multimodal access to support end users, right? The persona of end users. >> Okay. So the last topic I want to hit, this whole discussion, the topic of data mesh it's highly nuanced, it's new, and people are going to shoehorn data mesh into their respective views of the world. And we talked about lake houses and there's three buckets. And of course, the gentleman from LinkedIn with Azure, Microsoft has a data mesh community. See you're going to have to enlist some serious army of enforcers to adjudicate. And I wrote some of the stuff down. I mean, it's interesting. Monte Carlo has a data mesh calculator. Starburst is leaning in, chaos. Search sees themselves as an enabler. Oracle and Snowflake both use the term data mesh. And then of course you've got big practitioners J-P-M-C, we've talked to Intuit, Orlando, HelloFresh has been on, Netflix has this event based sort of streaming implementation. So my question is, how realistic is it that the clarity of your vision can be implemented and not polluted by really rich technology companies and others? (Zhamak laughs) >> Is it even possible, right? Is it even possible? That's a yes. That's why I practice then. This is why I should practice things. Cause I think, it's going to be hard. What I'm hopeful, is that the socio-technical, Leveling Data mentioned that this is a socio-technical concern or solution, not just a technology solution. Hopefully always brings us back to, you know, the reality that vendors try to sell you safe oil that solves all of your problems. (chuckles) All of your data mesh problems. It's just going to cause more problem down the track. So we'll see, time will tell Dave and I count on you as one of those members of, (laughs) you know, folks that will continue to share their platform. To go back to the roots, as why in the first place? I mean, I dedicated a whole part of the book to 'Why?' Because we get, as you said, we get carried away with vendors and technology solution try to ride a wave. And in that story, we forget the reason for which we even making this change and we are going to spend all of this resources. So hopefully we can always come back to that. >> Yeah. And I think we can. I think you have really given this some deep thought and as we pointed out, this was based on practical knowledge and experience. And look, we've been trying to solve this data problem for a long, long time. You've not only articulated it well, but you've come up with solutions. So Zhamak, thank you so much. We're going to leave it there and I'd love to have you back. >> Thank you for the conversation. I really enjoyed it. And thank you for sharing your platform to talk about data mesh. >> Yeah, you bet. All right. And I want to thank my colleague, Stephanie Chan, who helps research topics for us. Alex Myerson is on production and Kristen Martin, Cheryl Knight and Rob Hoff on editorial. Remember all these episodes are available as podcasts, wherever you listen. And all you got to do is search Breaking Analysis Podcast. Check out ETR's website at etr.ai for all the data. And we publish a full report every week on wikibon.com, siliconangle.com. You can reach me by email david.vellante@siliconangle.com or DM me @dvellante. Hit us up on our LinkedIn post. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (bright music)

>> 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. >> 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)

>> 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)

>>from around the globe. It's the cue with digital coverage of active Eo data >>driven 2020 >>brought to you by activity. Okay, Ash, tell us what's in it for me as an attendee of active FiOS data driven day. What's what's in it for you at Data Driven is very, very simple. You have probably one of the most unique events that is completely customer driven. The presentations, the discussions, the shading of talks, the platforms, the topics. They're all decided by the costumers act. If he was fortunate enough to have the opportunity to host this, provide a platform and there's a reason why recorded data doing we didn't call it active. Yo next or active, feel something else. It is truly about being able to share, learn, and you'll not hear a single presentation that talks about our road map. Our new lunch. We could have all the time talk about that at some point in future. But the ability to have this concentrated time you have some of the most notable industry executives talk about should listen. So what they have done to change their business things have done in terms of people technologies. Fascinating. I would not miss it. Oh, sure,

>> 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: From the CUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world this is theCUBE Conversation. >> Hi everybody, This is Dave Vellante and welcome to this CUBE Conversation. We've been following a company called Actifio for quite some time. Now they've really popularized the concept of copy data management. Really innovative Boston based Waltham based company. And with me Brian Regan who's the chief marketing officer and Paul Forte who's the newly minted chief revenue officer of Actifio. Guys great to see you. I wish we were face to face at your June event but this will have to do. >> You're welcome. >> Thanks Dave. >> You bet Dave. >> Yeah, so Brian you've been on theCUBE a bunch. I'm going to start with Paul if that's okay. Paul, let's talk a little bit about your background. You've done a number of stints at a variety of companies. Big companies like IBM and others as well. What attracted you to Actifio? >> Yes Dave I would say in all honesty, I've been a software guy and candidly a data specific leader for many many years. And so IT infrastructure particularly associated around data has always been sort of my forte for on and onwards there. And so Actifio was just smack dab in the middle of that. And so when I was looking for my next adventure I had an opportunity to meet with Ash our CEO and founder and describe and discuss kind of what Actifio was all about. And candidly, the number of connections that we had that were the same. There are a lot of OEM relationships with people that I actually worked with and for some that work for me historically. So it was almost this perfect world. And I'm a Boston guy so it is in my old backyard. And yeah it was a perfect match for what I was looking for. Which was really a small growth company that was trying to get to the next level that had compelling technology in a space that I was super familiar with and could understand and articulate the value proposition. >> Well as we say in Boston, Paulie we got to get you back here. (laughs) >> I know (mumbles) so I'll pack my car. >> (laughs) Yeah. So Brian... >> For 25 years, I still got it. >> let's talk about the climate right now. I mean nobody expected this of course. And it's funny I saw Ash at an event in Boston last fall. We were talking like "Hey, what are you expecting for next year?" "Yeah a little bit of softening" but nobody expected this sort of black swan. But you guys I just got your press release. You put it out. You had a good quarter. You had a record first quarter. What's going on in the marketplace. How are you guys doing? >> Yeah, well I think that today more than ever businesses are realizing that data is what is actually going to carry them through this crisis. And that data whether it's changing the nature of how companies interact with their customers, how they manage through their supply chain and frankly how they take care of their employees, is all very data centric. And so businesses that are protecting that data that are helping businesses get faster access to that data and ultimately give them choice as to where they manage that data. On premises, in the cloud and hybrid configuration. Those are the businesses that are really going to be top of a CIO's mind. I think RQ1 is a demonstration that customers voted with their wallets and they are confident in Actifio as an important part of their data supply chain. >> Paul I want to come back to you. First of all I want to let people know you're an Ex-Army Ranger. So thank you for your service, that's awesome. >> You're welcome (mumbles). >> I was talking to Frank Slootman, I interviewed in the other day and he was sharing with me sort of how he manages and he says "Yeah I manage by a playbook". He's a situational manager and that's something that he learned in the military. Well it's weird. This is a situation. (Paul laughs) And that really is kind of how you're trained. And of course we've never seen anything like this but you're trained to deal with things that you've never seen before. So how you seeing organizations generally, Actifio specifically kind of manage through this crisis. What are some of the moves that you'are advising, recommending? Give us some insight there. >> Yeah, so it's really interesting. It's funny that you mentioned my military background. So I was just having this discussion with one of my leaders the other day. That one of the things that they trained for in the military, is the eventuality of chaos. So when you do an exercise we will literally tap the leader on the shoulder and say okay you are now dead. And without that person being allowed to speak they take a knee and the (mumbles) unit has to go on. And so what happens is you learn by muscle memory like how to react in times of crisis and you know this is a classic example of leadership in crisis. And so it's just interesting. So to me you have a playbook. I think everybody needs to start with a playbook and then start with the plan. I can't remember if it was Mike Tyson but one of my famous quotes was "Plan is good until somebody punches you in the face". (Dave laughs) >> That's the reality of what just happened to business across the globe. This is just a punch in the face. And so you've got a playbook that you rely on and then you have to remain nimble and creative and candidly opportunistic. And from a leadership perspective, I think you can't lose your confidence. Right, so I've watched some of my friends and I've watched some other businesses cripple in the midst of this pandemic because they're afraid instead of looking at this. In my first commentary in our first staff meeting Brian, if I remember it was this, okay so what makes Actifio great in this environment? Not why is it not great? And so we didn't get scared. We jumped right into it. We adjusted our playbook a little bit and candidly we just had a record quarter. And we took down deals. Honestly Dave we took down deals in every single geography around the globe to include Italy. It was insane, it was really fun. >> Okay, so this wasn't just one monster deal that gave you that record quarter. It was really a broad based demand. >> Yeah, so if you dug underneath the covers you would see that we had the largest number of transactions ever in the first quarter. We had the largest average selling price in the first quarter ever. We had the largest contribution from our nano partners and our OEM partners ever. And we had the highest number ever. And so it was really a nice truly balanced performance across the globe and across the size of deal sets and candidly across industries. >> Interesting, you used the term opportunistic and I get right on. You obviously don't want to be chasing ambulances. At the same time, we've talked to a lot of CEOs and essentially what they're doing and I'd like to get your feedback on this Brian. You're kind of reassessing the ideal profile of a customer. You're reassessing your value proposition in the context of the current pandemic. And I noticed that you guys in your press release talked about cyber resiliency. You talked about digital initiatives, data center, transformations etc. So maybe you could talk a little bit about that, Brian. Did you do those things, how did you do those things? What kind of pace were you guys at? How did you do it remotely with everybody working from home? Give us some color on that. >> Sure, and if Ash, if he were here he would probably remind us that Actifio was born in the midst of the 2008 financial crisis. So we have essentially been book ended by two black swans over the last decade. The lessons we learned in 2008 are every bit is as relevant today. Everything starts with cost containment and cost reduction. Hence in protection of the business and so CIOs in the midst of this shock to the system. I think we're very much looking at what are the absolutely vital and critical initiatives and what is a "nice to have" and I'm going to hit pause on nice to have and invest entirely in the critical initiative. And the critical initiatives tended to be around getting people safely working remotely. Getting people safe access to their systems and their applications and their data. And then ultimately it also became about protecting the systems from malicious individuals in the state actors. Unfortunately as we've seen in other times of crisis this is when crime and cyber crime particularly tends to spike, particularly against industries that don't have the strong safeguards in place to really ensure the resiliency in their applications. So we very much went a little bit back to the 2008 playbook around helping people get control of their costs, helping people continue to do the things they need to do at a much more infrastructural light manner. But also really emphasized the fact that if you are under attack or if you are concerned that you're infected but you don't know when, instant access to data and a time machine that can take you back and forth to those points in time is something that is something that is incredibly valuable. >> So let's dig into cyber resiliency. So specifically what is Actifio doing for its customers from a product standpoint, capabilities, maybe it's part of the 10C announcement as well but can you give us some specifics on where you fit in. Let's take that use case, cyber resiliency? >> Yeah, absolutely. So I think there's a stack of capabilities when it comes to cyber resiliency. At the lowest level, you need a time machine because most people don't know when they're infected. And so the ability to go back in time, test the recoverability of data, test the validity of the data is step one. Step two is once you found the clean point, being able to resume operations, being able to resume the applications operation instantly or very rapidly is the next phase. And that's something that Actifio was founded on this notion of instant access to data. And then the third phase and this is really where our partnerships really shine is you probably want to go back and mitigate that risk. You want to go back and clean that system. You want to go back and find the infection and eliminate it. And that's where our partnership with IBM for example, resiliency services and their cyber incident recovery solutions which takes the Actifio platform and then wrappers in a complete manage services around it. So they can help the customer not only get their systems and applications back on their feet but clean the systems and allow them to resume operations normally on a much safer and more stable ground. >> Okay, so that's interesting. So Paul was it kind of new adoptions? Was it increases from existing customers combination? Can you talk to that? >> Yeah, totally. So ironically to really come clean the metrics that we had in the first quarter were very similar to do with the metrics that we see historically. So the mix with mean our existing customer base and then our new customer acquisition were very similar to our historical metrics which candidly we were a little surprised by. We anticipated that the majority of our business would come from that safe harbor of your existing customer base. But candidly we had a really nice split which was great which meant that our value proposition was resonating not only with our existing customer base where you would expect it but also in any of our new customers as well who had been evaluating us that either accelerate it or just continue down the path of adoption during the timeframe of COVID-19. Across industries I would say that again there were some industries I would say that pushed pause. And so the ones that you can imagine that accelerated during this past period were the ones you would think of, right? So financial institutions primarily as well as some of the medical. So some of those transactions, healthcare and medical they accelerated along with financial institutions. And then I would say that we did have some industries that pushed pause. You can probably guess what some of those are. Among the majority of those were the ones that were dealing with the small and midsize businesses or consumer-facing businesses, things like retail and stuff like that. Well we typically do have a pretty nice resonance and a really nice value proposition but there were definitely some transactions that we saw basically just pause. Like we're going to come back. But overall yeah the feedback was just in general. It felt like any other quarter and it felt like just pretty normal. As strange as that sounds. 'Cause I know speaking to a lot of my friends in peer companies, peer software companies, they didn't have that experience but we did pretty well. >> That's interesting, you're right. Certain industries, airlines, I'm interviewing a CIO of a major resort next week. Really interested to hear how they're dealing with this but those are obviously depressed and they've dialed everything down. But we were one of the first to report that work from home pivot, it didn't, it didn't buffer the decline in IT spending that were expected to be down maybe as much as 5% this year but it definitely offset it. What about Cloud? We're seeing elevated levels in Cloud demand. Guys have offerings there. What are you seeing in Cloud guys? >> Do you want to take it Brian? >> Yeah, I'll start and then Paul please weigh in. I think that the move to the cloud that we've been witnessing and the acceleration of the move to cloud that we've been we've been witnessing over the past several years probably ramped up in intensity over the last two months. The projects that might have been on the 18 to 24 month roadmap have of all of a sudden been accelerated into maybe this year of our roadmap. But in terms of the wholesale everything moves to Cloud and I abandoned my on-premises estate. I don't think we've seen that quite yet. I think that the world is still hybrid when it comes to Cloud. Although I do think that the beneficiaries of this are probably the non-number one and number two Cloud providers but the rest of the hyper-scalers who are fighting for market shares because now they have an opportunity to perhaps, Google for example, a strategic partner of ours has a huge offering when it comes to enabling work from home and the remote work. So leveraging that as a platform and then extending into their enterprise offerings, I think it gives them a wedge that the Amazon might not have for example. So it's an acceleration of interest but I think it's just a continuation of the trend that we've been seeing for years. >> Yeah, and I would add a little bit Dave. The IBM held their Think Conferences past week. I don't know if you had an opportunity to participate. They're one of our OEM partners and... >> Dave: Oh Yeah, we covered it. >> When our CEO presented his opening his opening remarks it was really about digital transformation and he really put it down to two things and said any business that's trying to transform is either talking about hybrid Clouds or they're talking about AI and machine learning. And that's kind of it, right? And so every digital business is talking in one of those categories. And when I look to Q1 it's interesting that we really didn't see anything other than as Brian talked about all of the cloud business which is some version of an acceleration. But outside of that the customers that are in those industries that are in position to accelerate and double down during this opportunity did so and those that did not just peeled back a little bit. But overall I would agree with IBM's assessment of the market that those are kind of the two hotspots and hybrid Cloud is hot and the good news is, we've got a nice value prop right in the middle of it. >> Yeah, Alvin Chris has talked about, and he has it, maybe not a thing but he talked earlier in his remarks on the earnings call just in public statements that IBM must win the battle the architectural battle, the hybrid Cloud. And also that he wants to lead with a more technical sell essentially, which is to mean those two things are great news for you guys, obviously Red Hat is the linchpin of that. I want to ask you guys about your conference, Data-Driven. So we were there last year it was a really great intimate event. Of course you can't have the physical events anymore. So you've pushed to September or you're going all digital? Give us the update on that Brian. >> We're eager to have theCube participate in our September event. So I'm sure we'll be talking more about that in the coming weeks, but also >> Dave: Awesome, love it. (Brian laughs) >> Exactly, so you can tell Frank to put that in there. So we've been participating in some of the other conferences most notably last week learning a lot and really trying to cherry pick the best ideas and the best tactics we're putting on the digital event. I think that as we look to September and as we look to put on a really rich digital event one of the things that is first and foremost in our minds is we want to actually produce more on demand digital content particularly from a technology standpoint. Our technology sessions last year were oversubscribed. The digital format allows people to stream whenever they can and frankly as many sessions as they might want. So I think we can be far more efficient in terms of delivering technical content for the users of our technology. And then we're also eager to have as we've done with data driven in years past, our customers tell the story of how they're using data. And this year certainly I think we're going to hear a lot of stories about in particular how they use data during this incredible crisis and hopefully renewal from the crisis. >> Well one of my favorite interviews last year at your show was the guy from DraftKings. So hopefully they'll be back on and we'll have some football to talk about, well let's hope. >> Amen. >> I Want to end with just sort of this notion of we've been so tactical the last eight weeks. Right? You guys too I'm sure. Just making sure you're there for customers, making sure your employees are okay. But as we start to think about coming out of this into a Post-COVID Era and it looks like it's going to be with us for a while but we getting back to Quaseye opening. So I'm hearing hybrid is here to stay. We agree for sure. Cyber resiliency is very interesting. I think one of the things we've said is that companies may sub-optimize near term profitability to make sure that they've got the flexibility and business resiliency in place. That's obviously something that is I think good news for you guys but I'll start with Paul and then maybe Brian you can bring us home. How do you see this sort of emergence from this lockdown and into the Post-COVID Era? >> Yeah, this is a really interesting topic for me. In fact I've had many discussions over the last couple of weeks with some of our investors as well as with our executive staff. And so my personal belief is that the way buying and selling has occured, for IT specifically at the enterprise level, it's about to go through a transformation, no different than we watched the transformation of SAS businesses when you basically replaced a cold calling sales person with an inside and inbound marketing kind of effort followed up with SDR and BDR. Because what we're finding is that our clients now are able to meet more frequently because we don't have the friction of airplane ride or physical building to go through. And so that whole thing has been removed from the sales process. So it's interesting to me that one of the things that I'm starting to see is that the amount of activity that our sales organization is doing and the amount of physical calls that were going on, they happen to be online. However, way higher than what we can (mumbles), you coupled that with the cost savings of not traveling around the globe and not being in offices. And I really think that those companies that embrace this new model, are going to find ways to penetrate more customers in a less expensive way. And I do believe that the professional sales enterprise sales person of tomorrow is going to look different than it looks today. And so I'm super excited to be in a company that is smack dab in the middle of selling to enterprise clients and watching us learn together how we're going to buy, sell and market to each other in this post-COVID way. 'Cause the only thing I really do know it's just not going to be the way it used to be. What is it going to look like? I think all of us are placing bets and I don't think anybody has the answer yet. But it's going to look different for sure. >> They're very, very thoughtful comments. And so Brian, you know our thinking is the differentiation in the war. Gets one in digital. How is that affecting your marketing and your things around that? >> We fortunately decided coming into 2020, our fiscal 21, that we were actually going to overweigh digital anyway. We felt that, it was far more effective, we were seeing far better conversion rates. We saw way better ROI in terms of very targeted additive digital campaigns or general purpose ABM type of efforts. So our strategy had essentially been set and what this provided us is the opportunity to essentially redirect all of the other funds into digital. So we have essentially a two pronged marketing attack, right now, which is digital creating inbounds and BDRs that are calling on those inbounds that are created digitally. And so it's going to be a really interesting transition back when physical events if and when they do actually back and spawn, how much we decide to actually go back into that. To some extent we've talked about this in the past Dave. The physical events and the sheer spectacle and the sheer audacity of having to spend a million dollars just to break through that was an unsustainable model. (laughs) And so I think this is hastening perhaps the decline or demise of really silly marketing expense and getting back to telling customers what they need to know to help and assist their buying journey and their investigation journey into new technology. >> There in the IT world is hybrid. And I think the events world is also going to be hybrid. Intimate, they're going to live on but they're also going to have a major digital component to them. I'm very excited that there's a lot of learnings now in digital especially around events and by September, a lot of the bugs are going to be worked out. You know we've been going, feels like 24/7, but really excited to have you guys on. Thanks so much, really looking forward to working with you in September at Data-Driven. So guys thanks a lot for coming on theCUBE. >> Oh my gosh, thank you Dave. So nice to be here, Thank you. >> All right, stay safe. >> Thanks Dave, always a pleasure. You too. >> Thank you everybody, thank you. And thanks for watching. This is Dave Vellante for theCUBE and we'll see you next time. (gentle music)

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone and welcome this week's episode of theCUBE Insights powered by ETR. And welcome to the Trillionaire's Club. In this Breaking Analysis, I want to look at how the big tech companies have really changed the recipe for innovation in the Enterprise. And as we enter the next decade, I think it's important to sort of reset and re-look at how innovation will determine the winners and losers going forward, including not only the sellers of technology but how technology applied will set the stage for the next 50 years of economic growth. Here's the premise that I want to put forth to you. The source of innovation in the technology business has been permanently altered. There's a new cocktail of innovation, if you will, that will far surpass Moore's Law in terms of it's impact on the industry. For 50 years we've marched to the cadence of that Moore's Law, that is the doubling of transistor counts every 18 months, as shown in the left-hand side of this chart. And of course this translated as we know, into a chasing of the chips, where by being first with the latest and greatest microprocessor brought competitive advantage. We saw Moore's Law drive the PC era, the client server era, and it even powered the internet, notwithstanding the effects of Metcalfe's Law. But there's a new engine of innovation or what John Furrier calls the "Innovation Cocktail," and that's shown in the right-hand of this slide where data plus machine intelligence or AI and Cloud are combinatorial technologies that will power innovation for the next 20 plus years. 10 years of gathering big data have put us in a position to now apply AI. Data is plentiful but insights are not and AI unlocks those insights. The Cloud brings three things, agility, scale, and the ability to fail quickly and cheaply. So, it's these three elements and how they are packaged and applied that will in my view determine winners and losers in the next decade and beyond. Now why is this era now suddenly upon us? Well I would argue there are three main factors. One is cheap storage and compute combined with alternative processor types, like GPUs that can power AI. And the era of data is here to stay. This next chart from Dave Moschella's book, "Seeing Digital," really underscores this point. Incumbent organizations born in the last century organized largely around human expertise or processes or hard assets like factories. These were the engines of competitive advantage. But today's successful organizations put data at the core. They live by the mantra of data driven. It is foundational to them. And they organize expertise, processes and people around the data. All you got to do to drive this point home is look at the market caps of the top five public companies in the U.S. Stock Market, Apple, Microsoft, Google, Amazon, and Facebook. I call this chart the Cuatro Comas! as a shout out to Russ Hanneman, the crazy billionaire supporting, was a supporting character in the Silicon Valley series. Now each of these companies, with the exception of Facebook, has hit the trillion dollar club. AWS, like Mr. Hanneman, hit the trillion dollar club status back in September 2018 but fell back down and lost a comma. These five data-driven companies have surpassed big oil and big finance. I mean, the next closest company is Berkshire at 566 billion. And I would argue that if it hadn't been for the fake news scandal, Facebook probably would be right there with these others. Now, with the exception of Apple, these companies, they're not highly valued because of the goods they pump out, rather, and I would argue even in the case of Apple, their highly valued because they're leaders in digital and in the best position to apply machine intelligence to massive stores of data that they've collected. And they have massive scale, thanks to the Cloud. Now, I get that the success of some of these companies is largely driven by the consumer but the consumerization of IT makes this even more relevant, in my opinion. Let's bring in some ETR data to see how this translates into the Enterprise tech world. This chart shows market share from Microsoft, AWS, Apple iPhone, and Google in the Enterprise all the way back to 2010. Now I get that the iPhone is a bit of a stretch here but stick with me. Remember, market share in ETR terms is a measure of pervasiveness in the data set. Look at how Microsoft has held it's ground. And you can see the steady rise of AWS and Google. Now if I superimpose traditional Enterprise players like Cisco, IBM, or Hewlett or even Dell, that is companies that aren't competing with data at the core of their business, you would see a steady decline. I am required to black out January 2020 as you probably remember, but that data will be out soon and made public shortly after ETR exits its self-imposed quiet period. Now Apple iPhone is not a great proxy but Apple, they're not an Enterprise tech company, but it's data that I can show but now I would argue again that Apple's real value and a key determinate of their success going forward, lies in how it uses data and applies machine intelligence at scale over the next decade to compete in apps and digital services, content, and other adjacencies. And I would say these five leaders and virtually any company in the next decade, this applies. Look, digital means data and digital businesses are data driven. Data changes how we think about competition. Just look at Amazon's moves in content, grocery, logistics. Look at Google in automobiles, Apple and Amazon in music. You know, interestingly Microsoft positions this as a competitive advantage, especially in retail. For instance, touting Walmart as a partner, not a competitor, a la Amazon. The point is, that digital data, AI, and Cloud bring forth highly disruptive possibilities and are enabling these giants to enter businesses that previously were insulated from the outsiders. And in the case of the Cloud, it's paying the way. Just look at the data from Amazon. The left bar shows Amazon's revenue. AWS represents only 12% of the total company's turnover. But as you can see on the right-hand side, it accounts for almost half of the company's operating income. So, the Cloud is essentially funding Amazon's entrance into all these other businesses and powering its scale. Now let's bring in some ETR data to show what's happening in the Enterprise in the terms of share shifts. This chart is a double-Y axis that shows spending levels on the left-hand side, represented by the bars, and the average change in spending, represented by the dots. Focus for a second on the dots and the percentages. Container orchestrations at 29% change. Container platforms at 19.7%. These are Cloud-native technologies and customers are voting with their wallets. Machine learning and AI, nearly 18% change. Cloud computing itself still in the 16% range, 10 plus years on. Look at analytics and big data in the double digits still, 10 years into the big data movement. So, you can see the ETR data shows that the spending action is in and around Cloud, AI, and data. And in the red, look at the Moore's Law techs like servers and storage. Now, this isn't to say that those go away. I fully understand you need servers, and storage, and networking, and database, and software to power the Cloud but this data shows that right now, these discreet cocktail technologies are gaining spending momentum. So, the question I want to leave you with is, what does this mean for incumbents? Those that are not digital-natives or not born in the Cloud? Well, the first thing I'd point out is that while the trillionaires, they look invincible today, history suggests that they are not invulnerable. The rise of China, India, open-source, peer-to-peer models, open models, could coalesce and disrupt these big guys if they miss a step or a cycle. The second point I would make is that incumbents are often too complacent. More often than not, in my experience, there is complacency and there will be a fallout. I hear a lot of lip service given to digital and data driven but often I see companies that talk the talk but they don't walk the walk. Change will come and the incumbents will be disrupted and that is going to cause action at the top. The good news is that the incumbents, they don't have to build the tech. They can compete with the disruptors by applying machine intelligence to their unique data sets and they can buy technologies like AI and the Cloud from suppliers. The degree to which they are comfortable buying from these supplies, who may also be competitors, will play out over time but I would argue that building that competitive advantage sooner rather than later with data and learning to apply machine intelligence and AI to their unique businesses, will allow them to thrive and protect their existing businesses and grow. These markets are large and the incumbents have inherent advantages in terms of resources, relationships, brand value, customer affinity, and domain knowledge that if they apply and transform from the top with strong leadership, they will do very, very well in my view. This is Dave Vellante signing out from this latest episode of theCUBE Insights powered by ETR. Thanks for watching everybody. We'll see you next time and please feel free to comment. In my LinkedIn, you can DM me @dvellante and don't forget we turned this into a podcast so check that out at your favorite podcast player. Thanks again.

>> 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 theCUBE! Covering Actifio 2019 Data Driven. Brought to you by Actifio. >> Here we are in Boston, Massachusetts. I'm Stu Miniman, this is theCUBE at the special, at Data Driven '19, Actifio's user event. Happy to bring on a CUBE alum who's a partner of Actifio, Phil Buckellew, who's General Manager of IBM Cloud Object Storage. Phil, thanks for coming back. >> Great, great to be here Stu. >> All right, so object storage. Why don't you give us first just kind of an encapsulation of kind of the state of your business today. >> Sure, object storage is really an extremely important business for the industry today because really it's a new way accessing data, it's been around obviously for a decade or so but really, it's increasingly important because it's a way to cost-effectively store a lot of data, to really to be able to get access to that data in new and exciting ways, and with the growth in the volume of data, of particularly unstructured data, like 103 zettabytes by 2023 I think I heard from the IDC guys, that really kind of shows how important being able to handle that volume of data really is. >> So Phil, I go back, think about 12 years ago, all the technologists in this space were like, "The future of storage is object," and I was working at one of the big storage companies and I'm like, "Well we've been doing block and file," and there was this big gap out there, and kind of quietly object's taken over the world because underneath a lot of the cloud services there, object's there, so IBM made a big acquisition in this space. Talk about, you know, customers that I talk to it's not like they come out and say, "Oh jeez, I'm buying object storage, "I'm thinking about object storage." They've got use cases and services that they're using that happen to have object underneath. Is that what you hear from your users? >> Yeah, there's a couple of different buying groups that exist in the object storage market today. The historic market is really super large volumes. I mean, we're unique in that IBM acquired the Cleversafe company back in 2015 and that technology is technology we've expanded upon and it really, it's great because it can go to exabyte scale and beyond and that's really important for certain use cases. So some customers that have high volumes of videos and other unstructured data, that is really a super good fit for those clients. Additionally, clients that really have the need for highly resilient, because the other thing that's important the way that we built our object storage is to be able to have a lot of resiliency, to be able to run across multiple data centers, to be able to use erasure coding to ensure the data's protected, that's really a large part of the value, and because you can do that at scale without having downtime when you upgrade, those are really a lot of core benefits of object storage. >> Right, that resiliency is kind of built into the way we do it and that was something that was just kind of a mind shift as opposed to, okay I've got to have this enterprise mindset with an HA configuration and everything with N plus whatever version of it. Object's going to give you some of that built-in. The other thing I always found really interesting is storing data is okay, there's some value there, but how do I gain leverage out of the data? And there's the metadata underneath that helps. You talk about video, you talk about all these kinds there. If I don't understand what I've got and how I'd leverage it, it's not nearly as valuable for me, and that's something, you know really that one of the key topics of this show is, how do I become data driven, is the show, and that I have to believe is something critically important to your customers. >> Absolutely, and really object storage is the foundation for modern cloud-native data lakes, if you will, because it's cost-effective enough you can drop any kind of storage in there and then you can really get value from those assets wherever you are, and wherever you're accessing the data. We've taken the same technology that was the exabyte scale on-premise technology, and we've put it in the IBM public cloud, and so that really allows us to be able to deliver against all kinds of use cases with the data sets that clients want, and there's a lot of great innovation that's happening especially on the cloud side. We've got the ability to query that data, any kind of rectangular data with standard ANSI SQL statements, and that just really allows clients to unlock the potential of those data sets, so really good innovation going on in that space to unlock the value of the data that you put inside of object storage. >> All right, Phil let's make the connection. Actifio's here, IBM OEM's the solution. So, talk about the partnership and what customers are looking for when they're looking at their IPs. Sure, so, quite a ways prior to the partnership our object storage team partnered up with the Actifio team at a large financial services customer that recognized the growth in the volume of the data that they had, that had some unique use cases like cyber resiliency. They get attacked with ransomware attacks, they needed to have a standard way to have those data sets and those databases running in a resilient way against object storage that can still be mounted and used, effectively immediately, in case of ransomware attacks, and so that plus a lot of other traditional backup use cases is what drew the IBM Cloud Object Storage team and the Actifio team together. Successful deployments at large customers are really where we got our traction. And with that we also really began to notice the uptick in clients that wanted to use, they wanted to do test data management, they wanted, they needed to be able to have DevOps team that needed to spin up a replica of this database or that database very fast, and, you know, what we found was the combination of the Actifio product, which we've OEM'd as IBM Virtual Data Pipeline, allows us to run those virtual databases extremely cost-effectively backed by object storage, versus needing to make full replicas on really expensive block storage that takes a long time. >> Well yeah, we'd actually done research on this a number of years ago. Copies are great, but how do I leverage that right? From the developer team it's, I want to have something that mirrors what I have in production, not just some test data, so the more I can replicate that, the better. Phil, please, go ahead. >> There's some really important parts of that whole story, of being able to get that data flow right, to be able to go do point-in-time recoveries of those databases so that the data is accurate, but also being able to mask out that PII or sensitive information, credit card data or others that you really shouldn't be exposing to your testers and DevOps people. Being able to have the kind of-- (Phil laughs) >> Yeah, yeah, shouldn't because, you know, there's laws and lawsuits and security and all these things we have. >> Good, good, absolutely. >> So, Phil, we're talking a lot about data, you've actually got some new data to share with us, a recent survey that was done, should we share some of your data with us? >> Yeah, we did some, we did a, the ESG guys actually worked with us to build out a piece of research that looked at what would it cost to take a 50 terabyte Oracle 12c database and effectively spin up five copies the way you traditionally would so that different test teams can hammer away against that data set. And we compared that to running the VDP offering with our Cloud Object Storage solution. You know, distances apart, we had one where the source database is in Dallas and the destination database is in Washington, D.C. over a 10 gigabyte link, and we were able to show that you could set up five replicas of the database in like 90 minutes, compared with the two weeks that it would take to do full replication, because you were going against object storage, which runs about 2.3 cents per gigabyte per month, versus block storage fully loaded, which runs about 58 cents per gigabyte per month. The economics would blow away. And the fact that you could even do queries, because object storage is interesting. Yes, if you're using, if you have microsecond response times for small queries you got to run some of that content on block storage, but for traditional queries, we look at, like, really big queries that would run against 600 rows, and we were half the time that you would need on traditional block storage. So, for those DevOps use cases where you're doing that test in development you can have mass data, five different copies, and you can actually point back in time because really, the Actifio technology is really super in that it can go do point-in-time, it was able to store the right kind of data so the developers can get the most recent current copies of the data. All in, it was like 80% less than what you would have paid doing it the traditional way. >> Okay, so Phil, you started talking a little bit about some of the cloud pieces, you know, Actifio in the last year launched their first SaaS offering Actifio GO. How much of these solutions are for the cloud versus on-premises these days? >> Absolutely, so one of the benefits of using a virtual data approach is being able to leverage cloud economics 'cause a lot of clients they want to do, you know, they want to be able to do the test in dev which has ups and downs and peaks and valleys when you need to use those resources, the cloud is really an ideal way to do those types of workloads. And so, the integration work that we've done with the Actifio team around VDP allows you to replicate or have virtual copies of those databases in the cloud where you want to do your testing, or we can do it in traditional on-prem object storage environments. Really, whatever makes most sense for the client is where we can stand up those environments. >> The other thing I wonder if you could expand on a little bit more, you talked about, like, cloud-native deployment and what's happening there. How does that tie into this discussion? >> Well, obviously modern architectures and ways of Agile, ways of building things, cloud-native with microservices, those are all extremely important, but you've got to be able to access the data, and it's that core data that no matter how much you do with putting Kubernetes around all of your existing applications you've still got to be able to access that core data, often systems record data, which is sitting on these standard databases of record, and so being able to have the VDP technology, be able to replicate those, stand those up like in our public cloud right next to all of our Kubernetes service and all the other technologies, it gives you the kind of full stack that you need to go do that dev in test, or run production workloads if you prefer from a public cloud environment, without having all of the burdens of running the data centers and maintaining things on your own. >> Okay, so Phil, everybody here for this two day event are going to get a nice, you know, jolt of where Actifio fits. You know, lots of orange here at the show. Give us the final word of what does it mean with orange and blue coming together. >> Well absolutely, we think this is going to be great for our clients. We've got, you know, tons of interested clients in this space because they see the value of being able to take what Actifio's done, to be able to virtualize that data, combine it with some of the technologies we've got for object storage or even block storage, to be able to serve up those environments in a super cost-effective way, all underlined by one of our core values at IBM, which is really trust and being responsible. And so, we often say that there's no AI, which all of this data leads up to, without information architecture and that's really where we specialize, is providing that governance, all the masking, all of the things that you need to feel confident that the data you've got is in the right hands, being used the right way, to be able to give you maximum advantage for your business, so we're super excited about the partnership. >> Phil, definitely a theme we heard at IBM Think, there is no AI without the IA, so, Phil Buckellew, thanks so much for joining us, sharing all the updates on what IBM is doing here with Actifio. >> Great, great to be here. >> All right, and we'll be back with more coverage here in Boston, Massachusetts at Actifio Data Driven 2019. I'm Stu Miniman and thanks for watching theCUBE. (futuristic music)

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Hi. We're right outside of the Boston Haba. You're watching >> the cube on stew Minimum in. And this is active Geo data driven. 2019 due date. Two days digging into, You >> know, the role of data inside Cos on, you know, in an ever changing world, happy to welcome to the program of first time guests are China Oven countrymen who's a research manager at I. D. C. Coming to us from across the pond in London. Thanks so much for joining us. Pleasure. So tell us a little bit. I d c. We know. Well, you know, the market landscapes, you know, watching what's happening. Thie said it 77 Zita bites that was put up in the keynote. Came came from I D. C. Tells you you're focused. >> Yeah, so I'm part of the data protection and storage research team, But I have, ah, European focus. I covered the Western European markets where data protection is almost off a neurotic interest to us. So a lot of our investment is actually made on the context of data protection. And how do I become data driven without compromising on security and sovereignty and data locality. So that's something that I look at. I'm also part of our broader multi cloud infrastructure team on also develops practice. I'm looking at all these modern new trends from data perspective as well. So it's kind of nice being >> keeping you busy, huh? Yeah. So about a year ago, every show that I went to there would be a big clock up on the Kino stage counting down until gpr went way actually said on the Q. Many times it's like we'll know when GPR starts with lawsuits. Sister and I feel like it was a couple of days, if not a couple of weeks before some of the big tech firms got sued for this. So here we are 2019. It's been, you know, been a while now since since since this launch. How important is GDP are you know what? How is that impacting customers and kind of ripple effect? Because, you know, here in the States, we're seeing some laws in California and beyond that are following that. But they pushed back from the Oh, hey, we're just gonna have all the data in the world and we'll store it somewhere sure will protect it and keep it secure. But but But >> yeah, yeah, so it's suggestive. Here is a game changer and it's interesting you said this big clock ticking and everybody has been talking about it. So when the European Commission >> announced repairs >> coming, organizations had about two years to actually prepare for it. But there were a lot of naysayers, and they thought, This is not gonna happen. The regulators don't have enough resources to actually go after all of these data breaches, and it's just too complicated. Not everyone's going complaints just not gonna happen. But then they realised that the regulators we're sticking to it on towards the end. Towards the last six months in the race to GDP, and there was this helter skelter running. Their organizations were trying to just do some Die Ryan patch of exercise to have that minimum viable compliance. So there they wanted to make sure that they don't go out of business. They don't have any major data breaches when Jean Pierre comes a difference that that was the story of 2018 although they have so much time to react they didn't on towards the end. They started doing a lot of these patch up work to make sure they had that minimum by the compliance. But over time, what we're seeing is that a lot off a stewed organizations are actually using GDP are as to create that competitive differentiations. If you look at companies like Barclays, they have been so much on top of that game on DH. They include that in their marketing strategies and the corporate social responsibility to say that, Hey, you know our business is important to us, but your privacy and your data is much more valuable to us, and that kind of instantly helps them build that trust. So they have big GDP, our compliance into their operations so much and so well that they can actually sell those kind of GPR consultancy services because they're so good at it. And that's what we are seeing is happening 2019 on DH. Probably the next 12 to 18 months will be about scaling on operational izing GDP are moving from that minimum viable compliance. >> Its interest weighed a conversation with Holly St Clair, whose state of Massachusetts and in our keynote this morning she talked about that data minimalist. I only want as much data as I know what I'm going to do. How I'm goingto leverage it, you know, kind of that pendulum swing back from the I'm goingto poured all the data and think about it later. It is that Did you see that is a trend with, you know, is that just governments is that, you know, you seeing that throughout industries and your >> interesting. So there was seven gpr came into existence. There were a lot of these workshops that were happening for on for organizations and how to become GDP. And there was this Danish public sector organization where one of the employees went to do that workshop was all charged up, and he came back to his employer and said, Hey, can you forget me on it Took that organization about 14 employees and three months to forget one person. So that's the amount of data they were holding in. And they were not dilating on all the processes were manual which took them so long to actually forget one person on. So if you don't cleanse a pure data act now meeting with all these right to be forgotten, Andi, all these specific clauses within GPR is going to be too difficult. And it's going to just eat up your business >> tryingto connecting the dots here. One of the one of the big stumbling blocks is if you look at data protection. If I've got backup, if I've got archive, I mean, if I've taken a snapshot of something and stuck that under a mountain in a giant tape and they say forget about me Oh, my gosh, Do I have to go retrieve that? I need to manage that? The cost could be quite onerous. Help! Help us connect the dots as to what that means to actually, you know, what are the ramifications of this regulation? >> Yeah, So I think so. Judy PR is a beast. It's a dragon off regulations. It's important to dice it to understand what the initial requirements are on one was the first step is to get visibility and classified the data as to what is personal data. You don't want to apply policies to all the data because I might be some garbage in there, so you need to get visibility on A says and classified data on what is personal data. Once you know what data is personal, what do you want to retain? That's when you start applying policies too. Ensure that they are safe and they're anonymous. Pseudonym ized. If you want to do analytics at a later stage on DH, then you think about how you meet. Individual close is so see there's a jeep airframe, but you start by classifying data. Then you apply specific policies to ensure you protect on back up the personal data on. Then you go about meeting the specific requirements. >> What else can you tell us about kind of European markets? You know, I I know when I look at the the cloud space, governance is something very specific to, and I need to make sure my data doesn't leave the borders and like what other trends in you know issues when you hear >> it from Jenny Peered forced a lot ofthe existential threat to a lot of companies. Like, say, hyper scale. Er's SAS men does so they were the first ones to actually become completely compliant to understand their regulations, have European data data hubs, and to have those data centres like I think At that time, Microsoft had this good good collaboration with T systems to have a local data center not controlled by Microsoft, but by somebody who is just a German organizations. You cannot have data locality more than that, right? So they were trying different innovative ways to build confidence among enterprises to make sure that cloud adoption continues on what was interesting. That came out from a research was that way thought, Gee, DPR means people's confidence and cloud is going to plunge. People's confidence in public cloud is going to pledge. That didn't happen. 42% of organizations were still going ahead with their cloud strategies as is, but it's just that they were going to be a lot more cautious. And they want to make sure that the applications and data that they were putting in the cloud was something that they had complete visibility in tow on that didn't have too much of personal data and even if it had, they had complete control over. So they had a different strategy off approaching public cloud, but it didn't slow them down. But over time they realised that to get that control ofthe idea and to get that control of data. They need to have that multiple multi cloud strategy because Cloud had to become a two way street. They need to have an exit strategy. A swell. So they tried to make sure that they adopted multiple cloud technologies and have the data interoperability. Ahs Well, because data management was one of their key key. Top of my prayer. >> Okay, last question I had for you. We're here at the active you event. What? What do you hear from your customers about Octavio? Any research that you have relevant, what >> they're doing, it's going interesting. So copy data management. That's how active you started, right? They created a market for themselves in this competition, a management and be classified copy data management within replication Market on replication is quite a slow market, but this copy data management is big issue, and it's one of the fastest growing market. So So So they started off from a good base, but they created a market for themselves and people started noticing them, and now they have kind of grown further and grown beyond and tried to cover the entire data management space. Andi, I think what's interesting and what's going to be interesting is how they keep up the momentum in building that infrastructure, ecosystem and platform ecosystem. Because companies are moving from protecting data centers to protecting centers of data on if they can help organizations protect multiple centers of data through a unified pane of glass, I have a platform approach to data management. Then they can help organizations become data drivers, which gives them the competitive advantage. So if they can keep up that momentum there going great guns, >> Thank you so much for joining us in Cheshire, sharing the data that you have in the customer viewpoints from Europe. So we'll be back with more coverage here from Active EO data driven 2019 in Boston. Mess fuses on stew Minimum. Thanks for watching the Q. Thank you.

>> From Boston, (upbeat music) Massachusetts, it's the Cube, covering Actifio 2019, Data Driven. Brought to you by Actifio. >> Welcome back to Boston everybody. You're watching the Cube, the leader in on the ground tech coverage. My name is Dave Vellante. Stu Miniman is here. John Furrier is also in the house. This is Actifio's Data Driven conference, the second year that they've done this conference, #DataDriven19. Ash Ashutosh is here. He's the founder and CEO of Actifio, a good friend to the Cube, great to see you again. Thanks for coming on. >> Likewise Dave. Always good to see you. >> Yeah, so second year. You chose Boston, that's great. Last year was Miami at the very swanky Fontainebleau Hotel. >> Yup. >> It's a great location. >> Yup. >> Right in the harbor here. So you've got a nice crowd, and you guys focus on the substance, you know. Not a lot of Actifio marketing stuff coming out, as you market through substantive content. Explain that theory. >> Yeah. Well, I think from inception, there's a very fundamental culture the company has had is about driving customer success, and that is the number one and probably the only one that we drive by. And if you truly are focused on customer success, when you bring a whole bunch of customers together, having more customers talk about their success, so that they help and share with other customers who are looking for some of these initiatives, almost becomes natural. People become tired of seeing and sometimes even participating in our own user conferences, where you would bring a whole bunch of very enthusiastic users, lock the doors, and start talking about your vision, and start talking about your roadmap, your new line, your new partnership. One, we believe we should be doing that throughout the year with our customers. Two, we felt it was a lot better if the customer actually talked about how it mattered to them versus how it mattered to us as Actifio. So that was the theme for why Data Driven, in general, and even before that, you used to have some colleague cloud summit as you were transitioning into use of hybrid cloud in 2016. Across the board, I think this is one theme you'll hear from Actifio and the users who are here is we pay a very, very close attention to what users want, and we give them a forum to explain that to share with other users across the world. >> Well, it sounds like a great way to build a company, you know, focus on the customer and the customer success. Sounds simple, it's not. It's very challenging, and you've been a successful entrepreneur. When I've asked you in the past and David, you know, kind of why you started the company, you focused on a problem, and you guys created the category of copy data management, which is a problem. We had copies everywhere, copy creep, and you felt as though, okay, we can help people not only organize that but maybe even get more out of their data. >> Yeah. >> And so, and that has evolved, and obviously on that journey, people wanted to use you for backup. I mean, that's the big problem. >> Yeah. >> And so you created the category. You kind of monetized the backup space and tried to change the way people thought about that, and then all of a sudden, all this VC money sort of flowing into the whole space. >> Yup. >> From your standpoint, what's going on in the marketplace? Why is it so hot today? >> Yeah. Well I think, as you'll see at this conference, there is absolutely no doubt about how data is a strategic asset, and you'll see the more reason acquisitions of Tableau, of Looker, or even Qualtrics, where the use of data, which is what actually users see, has become one of the killer apps for anybody who is running a cloud. Your own business here, right. It's a use of data, and that's the first app that's out there, that's happening across the board. But right behind that, there's an entire ecosystem about supplying that data to these applications that becomes really important. And we figured this out almost nine years ago. We figured out that for an enterprise, having data available as a strategic asset, wherever, whenever they need, and whoever, as long as it complies with the operations requirements. Instantly is absolutely what we should provide. Now in order to do that, the first place to make it available for users was to capture it. And the best place to start was backup, and we always treated copied data, journey begins with capturing data, and backup happens with the best use case, one that you already spend money on. And that's how we always treated backup as a starting point for the journey. We have over 3,600 enterprise users who range from some of the largest financial services, energy, retail, airline industries, service providers, and the focus has been on companies that are at least $500 millions of (mumbles) more normally for a billion or more who really view data as a strategic asset in their digital transformation. And almost 78 percent of our business now comes from people, they are (mumbles) applications faster. So a small person did almost 20 percent now is coming from people using Actifio data for running machine only analytics faster. And almost 100 percent of them obviously collect the data from backup. That's how we view the market. We view it as application, analytics, machine learning, DevOps, down, and infrastructure happens to be a place where you start. It's not lost on anybody in the market that data is important. It's not lost on investors who see this as an opportunity to pursue in a different way. And so you have different approaches being taken, one that starts with more infrastructure, (mumbles) has provided infrastructure to keep all this (mumbles). And we've always focused on the one thing that really matters to the customer, which is applications, and one that matters to every other application that's using this application, which is the data for this application the point in time. So you see a lot of backup-centric appliances. You see a lot of consolidation appliances. So it's a bottom-up approach. It's a great approach for people who want to buy another single-purpose storage. We fundamentally believe you're not going to be a lot on the storage system. We think this, there's a lot of companies who do a phenomenal job, and we're better off being suppliers of a multi-cloud data management, multi-cloud copy data management, and to leverage all this infrastructure. >> No box. >> Completely no box. In fact, that is the reason why we think 2016, when we saw the emergence of cloud in our user community, it took us two years, but we have the world's best multi-cloud, just copy data and data management. The largest software company, enterprise software company in the world uses Actifio today to manage their SaaS offerings in four different public-wide platforms. We couldn't do that if you had a box. You could not. I mean-- >> Because it wouldn't scale. >> Well, firstly, you can't take your box and go into a cloud. They already have infrastructure. >> Right. >> You can't bring the scale out stuff, because they already have scale out. You can't take your scale out and put in another scale out. And if you start from bottom up, you're fundamentally providing infrastructure on top of an infrastructure that's already provided as a service. What you really needed to do was to allow the applications to come back and use any infrastructure that is most relevant for their workload, for their use case, and most importantly, for that particular time. It's really important, especially if data is persistent. It stays there for 20, 30, forever. And the opportunity for me to come back and leverage infrastructure there just happens to be the right one. That's what we try to describe. >> We always say at the Cube that the difference between a business and a digital business is how the business uses data, how it leverages data. >> Yeah, yeah absolutely. >> So that's been a real tailwind for you. You guys have been on the, you know, data virtualization, it was part of that. You know, it seems to me that one of the challenges that incumbents have is their data is locked inside. Frank James talked about it today, and sort of his maturity model. Actually no, it was Brian Regan, >> Yup. >> talking about the extension maturity model. >> Yup. >> Through the early stages, it's siloed. And it's not easy to go, you know, from that siloed data that's built maybe around a modeling plant or a bank, you know, to sort of this virtualized vision. So that's something that you guys caught early on. Clearly, digital transformation has been a tailwind for you guys, but how are your customers capitalizing on your solutions to transform themselves into a data driven company? >> Yeah, well the first thing you're seeing is, as I mentioned 2016. In 2016, 100 percent of our use cases were people who wanted a backup NDR solution that was a 100x faster and 50 percent or 90 percent cheaper and manage large sets of data. From 2016 into now, we have a massive shift of almost, between 56 percent on DevOps, another 20 percent on machine (mumbles). Think about it, you have a bunch of customers, large enterprises, whose number one focus is now around how to use data, and these are people who are consumers of data, not custodians of data, who are our previous customers. The best part is as you saw their own evolution of DevOps, the merge of the consumers and custodians managing as an agile system, that's exactly what's happening in our customer base. These are people who maybe have a role of a chief data officer, whose job is to supply data but also make sure it complies with governance rules. So there's a big shift of how data is now the new infrastructure. Data is now the one that I have to provide and enable access to wherever I need. And that does require a very, very different approach then build a box, you know, build something that centralizes all this silos into one place. When you build a box, fundamentally, you create another silo, 'cause you just broke in the whole idea about I need something that just drops down that is more global as a single lane space versus you know a box that is providing a single lane space and somehow, I'm going to assume that nobody else exists in the world. >> Yeah. I want to come back to sort of building a company and your philosophy there. A couple of questions I have for you. So you mentioned cloud and how you embraced cloud early on. You know, Amazon announces a backup service. You know, we talk to the backup vendors, and they say, yeah, but it's recovery, it's wonky, it's, you know, it's really not that robust. But it's Amazon, and you know, if you don't move fast, you know Amazon's going to gobble you up. You saw with the (mumbles), you know. It was down to cloud era, and (mumbles) reeling, it's like, that was going to take over the world. How do you think about that, maybe not in terms of competition, but in terms of staying ahead, of getting, you know, Uber'd by Amazon? >> Yeah. >> Thoughts on that. >> I think, number one, as Amazon and every other cloud provider has proven, and one that started nine years ago, enterprise cloud is hybrid. It's hybrid not just on frame and cloud, but it's also on frame and multi-cloud. Number one. Two, it's about applications. It is not about infrastructure. It is not about providing a single function that ties to a single platform. I as a customer, and we have several of those, I want to be able to manage my enterprise applications exactly the same way whatever cloud platform I choose to have, and that opens up a very different engineering, marketing, sales challenges, and most importantly, keeping the focus on the user. Now if I'm Amazon, I have a focus on my platform, not exactly the 50 other platforms you want to support. >> Right. >> And that's what we focus on. We focus on the 50 other platforms you want to support at the moment. Second, you know, there's this whole notion of a stacked fallacy. You might have heard of this paradigm where it's a lot easier for people on top of the stack to come down. It's a lot harder to go from bottom up. So if you're Amazon, and you're trying to drive infrastructure as a service, it takes a little while to go up the stack. It's a lot easier for somebody like us to come down from the stack, which is why we also announced Actifio GO, our SaaS offering. >> Right. >> That today, our version runs in Amazon, providing a much more robust, much more multi-cloud, much more heterogenous, and much more enterprise class and enterprise grade solution. And we also announced one for Actifio GO for TCV for IBM cloud. >> Yeah. >> And that's how our customers want it. >> And it's a much more facile experience for the customers. It seems to me that it makes sense what you're saying is you're happy to build on top of Amazon's infrastructure. For them, you know, frankly, people always say, oh, is Amazon going to get into apps? To me, yeah, maybe some day. They don't have to. Give developers tools to build apps seems to me. Last question I have is just the philosophy of building a company. You know, you've raised I think $200 million since inception. That's a lot of money. Software's a capital efficient business, but it fails in comparison to some of what the west coast companies have done. You know, you guys, you know, I'm from Massachusetts, where maybe more conservative. You are very deliberately building a company. How do you think about, you know, the craziness in the west coast. I call it craziness, but it obviously works. You (mumbles) storage, you know, they hit escape velocity, TSX had a very successful IPO. >> Yeah. >> You're kind of slow and steady. Your philosophy there, explain that. >> Yeah, I think a couple of things. One, it was about creating a sustaining company that was growing responsibly. And two, it's also the speed of how much our customers in the market can absorb a paradigm like what we are trying to drive. And most importantly, the class of customer you're focused on. These are, like I said, $1 billion plus in revenue and above. >> Yeah. >> Sales process for them is longer, which is actually where the money goes. The money isn't on software development. It's about supporting these customers on their initiatives. Any of these customers are somewhere about eight years with us and continue to expand. Some of the largest financial institutions have started with about $500,000 and almost $20 million with us. So that journey of making the customer successful costs money, but it builds long-standing customer whose foundation is built on Actifio. We are the data provider for these customers. We are not a widgit who throws something in there and calls you in three years when your maintenance is up. That is not the business we're building. So I don't think it's about east coast, west coast as much as it's about what we deliver requires being at the customer's side, working with them for years, as they go through the transformation, and I don't think we can do that by supporting 10,000 users at the same time. Maybe we can support 1,000, 2,000. And that's just the product and the market is going now. >> True to your mission, close to the customers, you know, clear differentiation at the app levels, I'm going to just say top down. You guys didn't talk about it, but you know, database affinity, some of the unique things you have going on there. Ash, it's great to see you. Congratulations on all your success, and you'll keep it going. Really appreciate it. Have a good day. >> All right, you're welcome. >> Thank you again. Welcome again for Data Driven 19. >> All right. It's great to be here. Actifio Data Driven 19, day one, the Cube, from Boston. We'll be right back right after this short break. >> Thank you. (upbeat music)

>> From Boston, Massachusets, it's The Cube. Covering Actifio 2019: Data Driven, Brought to you by Actifio. >> Welcome back to Boston, everybody. We're here at the Intercontinental Hotel at Actifio's Data Driven conference, day one. You're watching The Cube. The leader in on-the-ground tech coverage. My name is is Dave Valante, Stu Minamin is here, so is John Ferrer, my friend Frank Gens is here, he's the Senior Vice President and Chief Analyst at IDC and Head Dot Connector. Frank, welcome to The Cube. >> Well thank you Dave. >> First time. >> First time. >> Newbie. >> Yep. >> You're going to crush it, I know. >> Be gentle. >> You know, you're awesome, I've watched you over the many years, of course, you know, you seem to get competitive, and it's like who gets the best rating? Frank always had the best ratings at the Directions conference. He's blushing but I could- >> I don't know if that's true but I'll accept it. >> I could never beat him, no matter how hard I tried. But you are a phenomenal speaker, you gave a great conversation this morning. I'm sure you drew a lot from your Directions talk, but every year you lay down this, you know, sort of, mini manifesto. You describe it as, you connect the dots, IDC, thousands of analysts. And it's your job to say okay, what does this all mean? Not in the micro, let's up-level a little bit. So, what's happening? You talked today, You know you gave your version of the wave slides. So, where are we in the waves? We are exiting the experimentation phase, and coming in to a new phase that multiplied innovation. I saw AI on there, block-chain, some other technologies. Where are we today? >> Yeah, well I think having mental models of the6 industry or any complex system is pretty important. I mean I've made a career dumbing-down a complex industry into something simple enough that I can understand, so we've done it again now with what we call the third platform. So, ten years ago seeing the whole raft of new technologies at the time were coming in that would become the foundation for the next thirty years of tech, so, that's an old story now. Cloud, mobile, social, big data, obviously IOT technologies coming in, block-chain, and so forth. So we call this general era the third platform, but we noticed a few years ago, well, we're at the threshold of kind of a major scale-up of innovation in this third platform that's very different from the last ten or twelve years, which we called the experimentation stage. Where people were using this stuff, using the cloud, using mobile, big data, to create cool things, but they were doing it in kind of a isolated way. Kind of the traditional, well I'm going to invent something and I may have a few friends help me, whereas, the promise of the cloud has been , well, if you have a lot of developers out on the cloud, that form a community, an ecosystem, think of GitHub, you know, any of the big code repositories, or the ability to have shared service as often Amazon, Cloud, or IBM, or Google, or Microsoft, the promise is there to actually bring to life what Bill Joy said, you know, in the nineties. Which was no matter how smart you are, most of the smart people in the world work for someone else. So the questions always been, well, how do I tap into all those other smart people who don't work for me? So we can feel that where we are in the industry right now is the business model of multiplied innovation or if you prefer, a network of collaborative innovation, being able to build something interesting quickly, using a lot of innovation from other people, and then adding your special sauce. But that's going to take the scale of innovation just up a couple of orders of magnitude. And the pace, of course, that goes with that, is people are innovating much more rapid clip now. So really, the full promise of a cloud-native innovation model, so we kind of feel like we're right here, which means there's lots of big changes around the technologies, around kind of the world of developers and apps, AI is changing, and of course, the industry structure itself. You know the power positions, you know, a lot of vendors have spent a lot of energy trying to protect the power positions of the last thirty years. >> Yeah so we're getting into some of that. So, but you know, everybody talks about digital transformation, and they kind of roll their eyes, like it's a big buzzword, but it's real. It's dataware at a data-driven conference. And data, you know, being at the heart of businesses means that you're seeing businesses transition industries, or traverse industries, you know, Amazon getting into groceries, Apple getting into content, Amazon as well, etcetera, etcetera, etcetera, so, my question is, what's a tech company? I mean, you know, Bennyhoff says that, you know, every company's a sass company, and you're certainly seeing that, and it's got to be great for your business. >> Yeah, yeah absolutely >> Quantifying all those markets, but I mean, the market that you quantify is just it's every company now. Banks, insurance companies, grocers, you know? Everybody is a tech company. >> I think, yeah, that's a hundred percent right. It is that this is the biggest revolution in the economy, you know, for many many decades. Or you might say centuries even. Is yeah, whoever put it, was it Mark Andreson or whoever used to talk about software leading the world, we're in the middle of that. Only, software now is being delivered in the form of digital or cloud services so, you know, every company is a tech company. And of course it really raises the question, well what are tech companies? You know, they need to kind of think back about where does our value add? But it is great. It's when we look at the world of clouds, one of the first things we observed in 2007, 2008 was, well, clouds wasn't just about S3 storage clouds, or salesforce.com's softwares and service. It's a model that can be applied to any industry, any company, any offering. And of course we've seen all these startups whether it's Uber or Netflix or whoever it is, basically digital innovation in every single industry, transforming that industry. So, to me that's the exciting part is if that model of transforming industries through the use of software, through digital technology. In that kind of experimentation stage it was mainly a startup story. All those unicorns. To me the multiplied innovation chapter, it's about- (audio cuts out) finally, you know, the cities, the Procter & Gambles, the Walmarts, the John Deere's, they're finally saying hey, this cloud platform and digital innovation, if we can do that in our industry. >> Yeah, so intrapreneurship is actually, you know, starting to- >> Yeah. >> So you and I have seen a lot of psychos, we watched the you know, the mainframe wave get crushed by the micro-processor based revolution, IDC at the time spent a lot of time looking at that. >> Vacuum tubes. >> Water coolant is back. So but the industry has marched to the cadence of Moore's Law forever. Even Thomas Friedman when he talks about, you know, his stuff and he throws in Moore's Law. But no longer Moore's Law the sort of engine of innovation. There's other factors. So what's the innovation cocktail looking forward over the next ten years? You've talked about cloud, you know, we've talked about AI, what's that, you know, sandwich, the innovation sandwich look like? >> Yeah so to me I think it is the harnessing of all this flood of technologies, again, that are mainly coming off the cloud, and that parade is not stopping. Quantum, you know, lots of other technologies are coming down the pipe. But to me, you know, it is the mixture of number one the cloud, public cloud stacks being able to travel anywhere in the world. So take the cloud on the road. So it's even, I would say, not even just scale, I think of, that's almost like a mount of compute power. Which could happen inside multiple hyperscale data centers. I'm also thinking about scale in terms of the horizontal. >> Bringing that model anywhere. >> Take me out to the edge. >> Wherever your data lives. >> Take me to a Carnival cruise ship, you know, take me to, you know, an apple-powered autonomous car, or take me to a hospital or a retail store. So the public cloud stacks where all the innovation is basically happening in the industry. Jail-breaking that out so it can come, you know it's through Amazon, AWS Outpost, or Ajerstack, or Google Anthos, this movement of the cloud guys, to say we'll take public cloud innovation wherever you need it. That to me is a big part of the cocktail because that's you know, basically the public clouds have been the epicenter of most tech innovation the last three or four years, so, that's very important. I think, you know just quickly, the other piece of the puzzle is the revolution that's happening in the modularity of apps. So the micro services revolution. So, the building of new apps and the refactoring of old apps using containers, using servos technologies, you know, API lifecycle management technologies, and of course, agile development methods. Kind of getting to this kind of iterative sped up deployment model, where people might've deployed new code four times a year, they're now deploying it four times a minute. >> Yeah right. >> So to me that's- and kind of aligned with that is what I was mentioning before, that if you can apply that, kind of, rapid scale, massive volume innovation model and bring others into the party, so now you're part of a cloud-connected community of innovators. And again, that could be around a Github, or could be around a Google or Amazon, or it could be around, you know, Walmart. In a retail world. Or an Amazon in retail. Or it could be around a Proctor & Gamble, or around a Disney, digital entertainment, you know, where they're creating ecosystems of innovators, and so to me, bringing people, you know, so it's not just these technologies that enable rapid, high-volume modular innovation, but it's saying okay now plugging lots of people's brains together is just going to, I think that, here's the- >> And all the data that throws off obviously. >> Throws a ton of data, but, to me the number we use it kind of is the punchline for, well where does multiplied innovation lead? A distributed cloud, this revolution in distributing modular massive scale development, that we think the next five years, we'll see as many new apps developed and deploye6d as we saw developed and deployed in the last forty years. So five years, the next five years, versus the last forty years, and so to me that's, that is the revolution. Because, you know, when that happens that means we're going to start seeing that long tail of used cases that people could never get to, you know, all the highly verticalized used cases are going to be filled, you know we're going to finally a lot of white space has been white for decades, is going to start getting a lot of cool colors and a lot of solutions delivered to them. >> Let's talk about some of the macro stuff, I don't know the exact numbers, but it's probably three trillion, maybe it's four trillion now, big market. You talked today about the market's going two x GDP. >> Yeah. >> For the tech market, that is. Why is it that the tech market is able to grow at a rate faster than GDP? And is there a relationship between GDP and tech growth? >> Yeah, well, I think, we are still, while, you know, we've been in tech, talk about those apps developed the last forty years, we've both been there, so- >> And that includes the iPhone apps, too, so that's actually a pretty impressive number when you think about the last ten years being included in that number. >> Absolutely, but if you think about it, we are still kind of teenagers when you think about that Andreson idea of software eating the world. You know, we're just kind of on the early appetizer, you know, the sorbet is coming to clear our palates before we go to the next course. But we're not even close to the main course. And so I think when you look at the kind of, the percentage of companies and industry process that is digital, that has been highly digitized. We're still early days, so to me, I think that's why. That the kind of the steady state of how much of an industry is kind of process and data flow is based on software. I'll just make up a number, you know, we may be a third of the way to whatever the steady state is. We've got two-thirds of the way to go. So to me, that supports growth of IT investment rising at double the rate of overall. Because it's sucking in and absorbing and transforming big pieces of the existing economy, >> So given the size of the market, given that all companies are tech companies. What are your thoughts on the narrative right now? You're hearing a lot of pressure from, you know, public policy to break up big tech. And we saw, you know you and I were there when Microsoft, and I would argue, they were, you know, breaking the law. Okay, the Department of Justice did the right thing, and they put handcuffs on them. >> Yeah. >> But they never really, you know, went after the whole breakup scenario, and you hear a lot of that, a lot of the vitriol. Do you think that makes sense? To break up big tech and what would the result be? >> You don't think I'm going to step on those land mines, do you? >> Okay well I've got an opinion. >> Alright I'll give you mine then. Alright, since- >> I mean, I'll lay it out there, I just think if you break up big tech the little techs are going to get bigger. It's going to be like AT&T all over again. The other thing I would add is if you want to go after China for, you know, IP theft, okay fine, but why would you attack the AI leaders? Now, if they're breaking the law, that should not be allowed. I'm not for you know, monopolistic, you know, illegal behavior. What are your thoughts? >> Alright, you've convinced me to answer this question. >> We're having a conversation- >> Nothing like a little competitive juice going. You're totally wrong. >> Lay it out for me. >> No, I think, but this has been a recurring pattern, as you were saying, it even goes back further to you know, AT&T and people wanting to connect other people to the chiraphone, and it goes IBM mainframes, opening up to peripherals. Right, it goes back to it. Exactly. It goes back to the wheel. But it's yeah, to me it's a valid question to ask. And I think, you know, part of the story I was telling, that multiplied innovation story, and Bill Joy, Joy's Law is really about platform. Right? And so when you get aggregated portfolio of technical capabilities that allow innovation to happen. Right, so the great thing is, you know, you typically see concentration, consolidation around those platforms. But of course they give life to a lot of competition and growth on top of them. So that to me is the, that's the conundrum, because if you attack the platform, you may send us back into this kind of disaggregated, less creative- so that's the art, is to take the scalpel and figure out well, where are the appropriate boundaries for, you know, putting those walls, where if you're in this part of the industry, you can't be in this. So, to me I think one, at least reasonable way to think about it is, so for example, if you are a major cloud platform player, right, you're providing all of the AI services, the cloud services, the compute services, the block-chain services, that a lot of the sass world is using. That, somebody could argue, well, if you get too strong in the sass world, you then could be in a position to give yourself favorable position from the platform. Because everyone in the sass world is depending on the platform. So somebody might say you can't be in. You know, if you're in the sass position you'll have to separate that from the platform business. But I think to me, so that's a logical way to do it, but I think you also have to ask, well, are people actually abusing? Right, so I- >> I think it's a really good question. >> I don't think it's fair to just say well, theoretically it could be abused. If the abuse is not happening, I don't think you, it's appropriate to prophylactically, it's like go after a crime before it's committed. So I think, the other thing that is happening is, often these monopolies or power positions have been about economic power, pricing power, I think there's another dynamic happening because consumer date, people's data, the Facebook phenomenon, the Twitter and the rest, there's a lot of stuff that's not necessarily about pricing, but that's about kind of social norms and privacy that I think are at work and that we haven't really seen as big a factor, I mean obviously we've had privacy regulation is Europe with GDPR and the rest, obviously in check, but part of that's because of the social platforms, so that's another vector that is coming in. >> Well, you would like to see the government actually say okay, this is the framework, or this is what we think the law should be. I mean, part of it is okay, Facebook they have incentive to appropriate our data and they get, okay, and maybe they're not taking enough responsibility for. But I to date have not seen the evidence as we did with, you know, Microsoft wiping out, you know, Lotus, and Novel, and Word Perfect through bundling and what it did to Netscape with bundling the browser and the price practices that- I don't see that, today, maybe I'm just missing it, but- >> Yeah I think that's going to be all around, you know, online advertising, and all that, to me that's kind of the market- >> Yeah, so Google, some of the Google stuff, that's probably legit, and that's fine, they should stop that. >> But to me the bigger issue is more around privacy.6 You know, it's a social norm, it's societal, it's not an economic factor I think around Facebook and the social platforms, and I think, I don't know what the right answer is, but I think certainly government it's legitimate for those questions to be asked. >> Well maybe GDPR becomes that framework, so, they're trying to give us the hook but, I'm having too much fun. So we're going to- I don't know how closely you follow Facebook, I mean they're obviously big tech, so Facebook has this whole crypto-play, seems like they're using it for driving an ecosystem and making money. As opposed to dealing with the privacy issue. I'd like to see more on the latter than the former, perhaps, but, any thoughts on Facebook and what's going on there with their crypto-play? >> Yeah I don't study them all that much so, I am fascinated when Mark Zuckerberg was saying well now our key business now is about privacy, which I find interesting. It doesn't feel that way necessarily, as a consumer and an observer, but- >> Well you're on Facebook, I'm on Facebook, >> Yeah yeah. >> Okay so how about big IPOs, we're in the tenth year now of this huge, you know, tail-wind for tech. Obviously you have guys like Uber, Lyft going IPO,6 losing tons of money. Stocks actually haven't done that well which is kind of interesting. You saw Zoom, you know, go public, doing very well. Slack is about to go public. So there's really a rush to IPO. Your thoughts on that? Is this sustainable? Or are we kind of coming to the end here? >> Yeah so, I think in part, you know, predicting the stock market waves is a very tough thing to do, but I think one kind of secular trend is going to be relevant for these tech IPOs is what I was mentioning earlier, is that we've now had a ten, twelve year run of basically startups coming in and reinventing industries while the incumbents in the industries are basically sitting on their hands, or sleeping. So to me the next ten years, those startups are going to, not that, I mean we've seen that large companies waking up doesn't necessarily always lead to success but it feels to me like it's going to be a more competitive environment for all those startups Because the incumbents, not all of them, and maybe not even most of them, but some decent portion of them are going to wind up becoming digital giants in their own industry. So to me I think that's a different world the next ten years than the last ten. I do think one important thing, and I think around acquisitions MNA, and we saw it just the last few weeks with Google Looker and we saw Tab Low with Salesforce, is if that, the mega-cloud world of Microsoft, Ajer, and Amazon, Google. That world is clearly consolidating. There's room for three or four global players and that game is almost over. But there's another power position on top of that, which is around where did all the app, business app guys, all the suite guys, SAP, Oracle, Salesforce, Adobe, Microsoft, you name it. Where did they go? And so we see, we think- >> Service Now, now kind of getting big. >> Absolutely, so we're entering a intensive period, and I think again, the Tab Low and Looker is just an example where those companies are all stepping on the gas to become better platforms. So apps as platforms, or app portfolio as platforms, so, much more of a data play, analytics play, buying other pieces of the app portfolio, that they may not have. And basically scaling up to become the business process platforms and ecosystems there. So I think we are just at the beginning of that, so look for a lot of sass companies. >> And I wonder if Amazon could become a platform for developers to actually disrupt those traditional sass guys. It's not obvious to me how those guys get disrupted, and I'm thinking, everybody says oh is Amazon going to get into the app space? Maybe some day if they happen to do a cam expans6ion, But it seems to me that they become a platform fo6r new apps you know, your apps explosion.6 At the edge, obviously, you know, local. >> Well there's no question. I think those appcentric apps is what I'd call that competition up there and versus kind of a mega cloud. There's no question the mega cloud guys. They've already started launching like call center, contact center software, they're creeping up into that world of business apps so I don't think they're going to stop and so I think that that is a reasonable place to look is will they just start trying to create and effect suites and platforms around sass of their own. >> Startups, ecosystems like you were saying. Alright, I got to give you some rapid fire questions here, so, when do you think, or do you think, no, I'm going to say when you think, that owning and driving your own car will become the exception, rather than the norm? Buy into the autonomous vehicles hype? Or- >> I think, to me, that's a ten-year type of horizon. >> Okay, ten plus, alright. When will machines be able to make better diagnosis than than doctors? >> Well, you could argue that in some fields we're almost there, or we're there. So it's all about the scope of issue, right? So if it's reading a radiology, you know, film or image, to look for something right there, we're almost there. But for complex cancers or whatever that's going to take- >> One more dot connecting question. >> Yeah yeah. >> So do you think large retail stores will essentially disappear? >> Oh boy that's a- they certainly won't disappear, but I think they can so witness Apple and Amazon even trying to come in, so it feels that the mix is certainly shifting, right? So it feels to me that the model of retail presence, I think that will still be important. Touch, feel, look, socialize. But it feels like the days of, you know, ten thousand or five thousand store chains, it feels like that's declining in a big way. >> How about big banks? You think they'll lose control of the payment systems? >> I think they're already starting to, yeah, so, I would say that is, and they're trying to get in to compete, so I think that is on its way, no question. I think that horse is out of the barn. >> So cloud, AI, new apps, new innovation cocktails, software eating the world, everybody is a tech company. Frank Gens, great to have you. >> Dave, always great to see you. >> Alright, keep it right there buddy. You're watching The Cube, from Actifio: Data Driven nineteen. We'll be right back right after this short break. (bouncy electronic music)

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> We're back with the Cuban active FiO Data driven day one day Volante with student a man you're watching The Cube. Steve Duplessis here is the, uh, let's see. Uh, I'm going to say benevolent. Dictator of Enterprise Strategy Group. Chief analyst, Founder Welcome. Welcome back to the Cube. >> Thanks. Nice friend. Nice to be here, you fellows, and we don't Great. Congratulations. Newly newly closed. That's awesome. I want Yeah, thank you very much. >> Great. Looking good. You're here for your honeymoon. >> He said this is it? After a few marriages. This is the honeymoon. >> Yeah. That's good to know that the honeymoon's not over. So let's talk data, Tio. It's happening. >> That is a terrible question, Dave. >> So yeah, Data. Okay, everybody talks. Data you here, bro. My data is the new oil. Fate is a competitive advantage. And >> you like that. >> You do like what Data's in oil. >> So it's funny because we're I think I'm way older than you. You look better. >> God, no. >> But if you go back in time as long as we were doing this, it's been kind of hilarious, really. In retrospect, when you watch way watch these massive industries get created like the AMC just created because all they were about building bigger buckets to put data, zeros and ones. But no context, completely useless, just big buckets. So we valued Wow, you built a big fast bucket. Then IBM and her tachy whoever was gonna leap frog your next built a faster, bigger bucket. And that was with the world considered valuable. And it's now fast forward to the modern day and oh, maybe with the thing that's really valuable with those zeros and he's in contact. Maybe it's not really the bucket. It's, uh so valuable anymore. So >> So, do you think the with the bucket builders still bucket builders air they actually becoming data Insite creators? Or is it just still build a better bucket? That's cheaper. Faster >> till it's a great question. I think >> that we're first of all, you You still have to have the buckets, right? It's a relative who's going to make a smarter bucket builder. I don't know. >> You need someplace to put it, so >> you're gonna have to put it some place and you're gonna have to deliver it in the good news, you know, storage and or infrastructural say is the most brilliant business ever. From a capacity demand perspective, no one ever needs less, right. You always need Mauritz justa matter what you're gonna do with it, how you're going to address that. So it's we've propagated for 50 years and infrastructure business that build a bigger, faster bucket. Build a bigger, faster processor, build a bigger, faster. And every time you you solve one of those particular problems as long as data doesn't abate and it never does, is only is there's more versus Les. It's just every time we fix one problem way, you stick your finger in the dike and another poll springs out. So right now we're at the we've got more processing capabilities that week, ever possible. Use not true, right? We'll figure out a way to use it so that the last five years of and for the >> next five years waiting talk about analytics, wouldn't talk about io ti. We didn't talk about any of those things that are all just precursors to folk crap. We could make a whole bunch more NATO and do stuff with >> so So computers. Kind of a similar dynamic. It's sort of sensational. But is the relatively crappy business compared to storage rights? Storage is 60% plus gross margin. Business servers. I don't know. You're lucky if you get in in a low twenty's. Um, why is that? >> Hello, Number one. It's essentially monogamous. So 20% is wonderful if your intel and you get it. All right. Well, it sells. Got great gross margins, right? Everybody else's does it. You go down the supply chain. That's where you're gonna add value. So that's difficult for anything. Hard to get gross margins out of like spending. She had a box. >> So, Steve Yes, she's now 20 years old. >> I know >> when I think back 20 years ago. You know, short. You know this capacity price per dollar price per gigabyte. You know, all that stuff has changed a lot. The other thing, You know, I think back 20 years talk about automation and intelligent infrastructure. We were using those terms back that sure, one of things that they did. That that's right. Well, that's what I wanted to ask you about is like, right back then when you talked about well, how intelligent wasn't what could it do? And automation was There was a lot of times, you know, I'm just building a little script. I'm doing something like that. At least you know, from what we see, it feels like, you know, today's automation and intelligence is light times away from what we were talking about. 20 years. Sure, and it's true. What do you see in that? Well, >> so remember where we came from When we were talking originally about automation and orchestration, we were talking about how to manage a box, how to expand a box, how to manage infrastructure. Now it's data operations. Right now it's that that's the whole point of activity. Right to be in with is all right, if you are good enough and smart enoughto have the data sort of everything. What kind of matters? There you've gotta have the data and what can you up? What can you automate an orchestra from a data out perspective? Not from a box, not from a Let's scale out or scale up or something like that again, that's just a bigger bucket. It's a better bucket, but to be able to actually take data and say, You know what? I don't even know necessarily what I'm going to want to use this for, but I know that I gotta have. It's gotta be You have to be able to go click, click, click and get it. If if and when I figure out who I want to find out how lowering the price of Sharman and Seattle at a Wal Mart is going to affect my revenue or my supply chain or whatever. >> So one of the things I've talked with you in the past about is the pace of change of the industry. And, you know, I've said, you know, we know things are changing rather fast, but the average company, how much were they? Actually are they good at adopting change? And you've called me on stupid enterprises slow getting any faster, you know? Are they Are they open to change? Mohr. You know, what do you see in 2019? Is is it any different than it was in, You know, two thousand nine? >> That's a great question. So thie answer is yes, they're getting better. We are finally getting better. Problem, though, is a CZ industry insider watcher or a Boyar is ur is you see it and know what should happen 10 years. It takes 10 years in general for the world to actually catch upto the stuff that we're talking about. So it's not really that helpful to the poor schlub that's running on operation that build sneakers in Kansas, right? That's not really that helpful that we're talking about. This is what you could be doing and should be doing. The pace of change is much faster now because and give the em where most of the credit. Because once that went into place, all of the sudden and that you gotta remember there, everyone thinks vm where was an instant home run? It was 10 years of the same cold sitting in the corner in a queue, a environment before. Finally, we ran out of room in the data center, and that's the only reason they were able to come out. But once it was there, and it enabled you to stop associating the physical to the to the logical once, we could just just dis aggregate that stuff that I think opened up a tidal wave of kind of what else can we do? And people have adopted now. Now it's pervasive. So VM where's everywhere? Now? We're moving in the next level of kind of woman. Why can't I just build a containerized app that I can execute anywhere? No matter of fact, I don't even want it in my data center on. No one has to know that necessarily. So as modernization exercises have started to take off, they just they pick up, they actually pick up steam. So what we know empirically is those that are are halfway down. Call it the transformation or the modernization curve are going three times faster than those just starting. And those guys are going three times faster than the ones that are sitting there in idle doing stuff. The same >> city with the inertia going on. What do you make of this Bubblicious Back up market. Let's talk about that a little bit. You got these big install bases? The veritas, Conmebol, Delhi emcee, IBM, Tivoli install base. Everybody wants a piece of that action. Well, I guess cohesive rubric also want a piece of each other. Sure, which is kind of, you know, they get that urinary Olympics going on. I'd like to say And then you got these guys, which is kind of, you know, playing. Uh, I said to Ashleigh kind of East Coast, West Coast, There's no no, it's not East Coast, West Coast, but there's definitely more conservativism on this side of the of the flyover states. What's your take on what's going on in the landscape right now? >> So back up is awesome from the again, still probably the single most consistently line item budget thing for five decades. It's a guaranteed money in and out, and by and large it still sucks. My general rule is still it's crazy that we haven't been able to solve that particular problem. But regardless, the reason that it's so important is, besides the obvious. Yeah, you need to protect stuff, case. Something goes away and something bad happened good. But really, it's That's the inn. Just point for everything you do, you create data today. I'm backing it up on our later so that backup becomes the injust engine and it also is kicking off point. So at tapioca it started as wow, this is a better backup, most trap for lack of a better term. But really what? It was is didn't matter what with was back up or something else. It's I need tohave the data in order to do other stuff with it, and back up is just a natural, easiest way to be able to do that. So I think what's finally happening is we're moving from Christophe Would would say it's really about intelligence intelligence more so than just capturing those bits and being able to assemble and put it back together. It's understanding the context of those bits so that I can say stew in test. Dev has a different use case than Dave in whatever analytics, etcetera, etcetera. But they both need a copy of the exact scene data, the exact same state at the exact same point in time, etcetera. So if lungs backup's going to be kind of a tip of the spear in terms of going from what I will say, production or live data to the first copy, there's almost always back up. It's gonna matter. >> Christoph, Christoph Bertrand want your analyst? And so we saw, uh, c'mon, Danni Allen put a slideshow $15,000,000,000 tam and back up being a big chunk of that, probably half of it um, how does that jibe with your gut feel in terms of the opportunity beyond backup Dev ops? You know, I don't know. Ransomware insights. So you think that's low? High? Makes sense. >> I think I could justify the number. And what history has taught me is that it's probably low because we we're only talking about a handful of use cases that we've all glommed onto. But there will be remembered, like 11 years ago, there was no iPhone. You know what? How bad that changed. Everything that we do over there. And when did you know at some point during that particular journey, the phone became Who gives a shit about the phone? Excuse. But it's a text machine and it's an instagram thing, and it's a video production facility and all these other things, and the phone's almost dead. I only use it when my mom calls me kind of thing. So, you know, really, it's difficult to imagine. I certainly don't have the mental capabilities to imagine what the next 10 things after Dev Ops and this that and the other. But it's still all predicated on the same you got Somebody's gonna have a copy of that data and you're gonna be able to access it. You've got to be able to put it where you need it for whatever the reason again, a disaster is an important thing to recover from. But so is being ableto farm That data for nuggets of gold. >> Well, I guess I asked the question because, you know, it's a logical question is, is the market big enough to support all these companies that are in, You know, that gardener thing that they do? And I hope so because we love competition. >> I think I >> can answer it >> this way. Everything. Even the oldest guard Veritas, for God's sakes, 1000 years old, t sm 1000 years old con vault code base, 1000 years old. You're all big companies, right? And they're not perishing anytime soon. And I don't run. Love the startup Love the active FiOS or the cohesive sees coming in. But what they're really trying to do is not, you know, they might have started, as in a common ground, backup is a common warzone, but because there's money there like this consistent money there go get. But they soon turn in Teo other value propositions. And that's not is true with the incumbent back up guys because of their own legacy, right? It's hard to turn 1,000,000 year 1,000,000 lines of code into something. It wasn't designed, innit? >> Yeah, and it's not trivial to disrupt that base. But I guess if you get, you know, raising I don't know how much the industry is raised, but it's well over $1,000,000,000 now. I mean, activity has raised 200,000,000 and that's like chump change. Compared to some of the other races that you've seen. Cody City was to 60 and their last rubric was even, you know, crazy, crazy, even >> count the private money that beam God is that, you know, that was half 1,000,000,000 >> right? Well, that's a That's an off camera discussion. All right, we gotta go. So, Steve, thanks so much for for coming. Thank you. Great to >> have you. All right. All right, everybody. We'll be back with our next guest. You wanted the Cube from active field data driven from Boston, right on the harbor. Right back