>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Hello, everyone, and welcome to this week's Cuban sites, powered by ET are in this breaking analysis, we want to accomplish three things. First thing I'll do is we'll recap the current spending outlook. Next, we want to share some of the priorities and sentiments and the outlook that we're hearing from leading tech execs that we've been interviewing in the past couple of weeks on the remote cube. And finally, we'll take a look at really what's going on in the market place, a little bit of a look forward and what we expect in the coming weeks and months ahead. Now, as you know, E. T. R was really the first to quantify with real survey data the impact of covert 19 on I t spend. So I just want to review that for a moment. This CTR graphic right here shows that results from more than 1200 CIOs and I T practitioners. That shows that they expect their I t spending how they're they're spending on the change in 2020 now, look at the gray bar shows a very large number of organizations that they're plowing ahead without any change. In overall, I spend about 35% now shown in the green bars before 21% of respondents are actually increase their budgets this year. And the red bars, of course, they show the carnage. Really, 28% of customers are expecting a decrease of more than 10% year on year. Now, as we've reported, the picture would look a lot worse were it not for the work from home infrastructure, offset by E spending on collaboration tools and related networking security. VPN, VD I interest infrastructure, etcetera. Now remember each year launched this survey on March 11th and ran it through early April. So it caught the change in sentiment literally in real time on a daily basis. And that's what I'm showing here in this graphic. What it does is it overlays key events that occurred during that time frame and what E. T. R did was they modeled and rear end the data excluding the responses prior to each event. So, of course, the forecast got progressively worse over time. But as you can see on the Purple Line. There was a little bit of an uptick in sentiment from the stimulus package, and it looked like, you know, there's another. It looks like there's another economic cash injection coming soon. Now, as we've reported, the card forecast calls for around 4% decline in I t spend from 2020. That's down from plus 4% prior to Corona virus. It's ER has now entered its self imposed quiet period for two weeks. But what we're doing here is showing some of the sectors that we're watching closely for big changes. We're gonna drill into these over the next several weeks. Now, of course, is we've reported we're seeing a substantial cut in I t spend across the board. Capex will be down. We would expect sectors like I t consulting and outsourcing to be way, way down as organizations put a lot of projects on the back burner. But there are bright spots is shown here in the green. One that we really haven't highlighted to date is cloud really haven't dug into that and also data center related services around Cloud Cloud, we think, is definitely going to remain strong and these related services to get connect clouds via Coehlo services and really reducing latency across clouds and on Prem, we think will remain strong. Now I want to shift gears a little bit and talk about some of the learnings and takeaways from our conversations with CSOs over the past couple of weeks. One of the great things about the Cube is we get to build relationships with many, many people. Over the past 10 years, I've probably personally interviewed close to 5000 people, so we've reached out to a number of those execs over the last couple of weeks to really try and understand how they're managing through this cove in 19 Crisis. So let me summarize just some of the things that we heard. And then I'll let the execs speak to you directly first, of course, like tech execs, are there half full people perpetual optimist, if you will. It was interesting to hear how many of the people that I spoke with, that they actually had early visibility on this crisis. Why? Because a lot of our operations, we're actually in China and other parts of Asia, so they saw this coming to an extent, and they saw it coming to the U. S. And so you know, there were somewhat ready and you're here. They all had on air of confidence about their long term viability and putting their put their employees ahead of profits. But the same time, once they see that their employees are okay, they want to get them focused and productive. Now what they've also done is they've increased the cadence and the frequency of their communications. Yeah, and most, if not all, are trying to get back with a free no strings attached software and other similar programs. But the bottom line is, they really don't know what's coming. They don't know when this thing will end. They don't know what a recovery really is gonna look like when people are going to feel safe traveling again what the overall economic impact is gonna be. So I think it's best summarized to say they're hoping for the best, but planning for the worst. But let's listen to this highlight clip that we put together of five execs that I talked to along with John Furrier Melissa DiDonato of Susa. Frank Sluman, who had snowflake and he's formerly the chairman and CEO of service. Now Jeremy Burton is the CEO of a company called Observe. He used to be the CMO of Dell and EMC. Before that, brand products Sanjay Poonam as the CEO of VM Ware and ST ST Vossen heads up Cisco's collaboration business. Roll the clip. >>What keeps me up at night now and how I wake up every morning is wondering about the health of my employees, that a couple of employees, one that was quite ill in Italy. We were phoning him and calling and emailing him from his hospital bed. And that's what's really keeping me going. What's inspiring me to leave this incredible company is the people and the culture that they built that I'm honoring and taking forward as part of the open source value system. My first movers, Let's not overreact. Take a deep breath. Let's really examine what we know. Let's not jump to conclusions. Let's not try to project things that were not capable of projecting death hard because, you know, we tend to have sort of levels off certainty about what's gonna happen in the next week in the next month, and so on. All of a sudden that's out of the window creates enormous anxiety with people. So, in other words, you've got a sort of a reset to Okay, what do we know? What can we do? What we control, Um, and and not let our minds sort of, you know, go out of control. So I talk to are people time of maintain a sense of normalcy focused on the work. Stay in the state in the moment. And ah, I don't turn the news feed off. Right, Because the hysteria you get through that through the media really not helpful. Just haven't been through, you know, a couple of recessions where, you know, we all went through 9 11 You know, the world just turn around and you come out the other side. And so the key thing is, you said it very much is a cliche, but you gotta live in the moment. What can I do right now? What can I affect right now? How can I make sure that you know what I'm working on is a value for when we come out the other side. And when you know more code balls come along. I think you'd better reason about that with the best information you have at the time. I always tell people the profits of VM Ware wheat. If you are not well, if your loved ones not well, if you take a picture of that first, we will be fine. You know this to show fast, but if you're healthy, let's turn our attention because we're not going to just sit in a little mini games. We're gonna so, customers, How do we do that? A lot of our customers are adjusting to this pool, and as a result they have to, you know, either order devices, but the laptop screens things were the kinds to allow work for your environment to be as close to productive as they're working today. I do see some, some things coming. Problem right? Do I expect the volumes off collaboration to go down? You know, it's never going to go back to the same level. The world as we know it is going to change forever. We are going to have a post code area, and that's going to be changed for the better. There's a number of employees who have been skeptical, reticent, working from home were suddenly going to say just work from home. Thing is not so bad after all. >>So you can hear from the execs who all either currently or one point of lead large companies in large teams. They're pretty optimistic now. The other thing that's Lukman told me, by the way, is he approves investments in engineering with no qualms because that's the future of the company. But he's much more circumspect with regard to go to market investments because he wants to see a high probability of yield from the sales teams before making investments there. I also want to share some perspectives that I've learned from small early stage companies, and we've all seen the Sequoia Black Swan memo and you might remember there onerous rest in peace, good times the alert that they put out in 2008. It basically they're essentially advising companies to stop spending on non essential items. By the way, another slew of society also somewhat scoffed at this advice, and he told me on the Cube, you should always stop spending money on non essential items. At any rate, I've talked to a number of early stage investors and portfolio companies, and I'll share a little bit of their play Bach playbook that they're using during this crisis, and it might have some value to the cut, cut cut narrative that you're hearing out there. I think the summary for these early stage startups is first focus on those customers that got you to where you are today. In other words, don't lose sight of your core. The second thing is, try to hone your go to market and align it with current conditions. In other words, paint a picture of the ideal customer and the value proposition that you deliver specifically in the context of the current market. The third thing is, they're updating their forecast more frequently and running sensitivity analysis much more often so that they can better predict outcomes. I e. Reset. You're likely best case and worst case models. The third is essentially reset your near term and midterm plans and those goals and re balance your expense portfolio to reflect these new targets. And this is important by the way, to communicate to your investors. When I've seen is those companies with annual recurring revenue there actually in pretty good shape, believe it or not, in almost all cases, I've seen targets lowered. But there are some examples of startups that are actually increasing their outlook. Think, Zoom, even those who is not a startup anymore. But generally I've seen resets of between 5 to 10% downward, which you know what often is in pretty much in line with the board level goals. And I've seen more drastic reductions as well of up to 50% now. So we've heard some pretty good stories from larger tech companies and some of these VC funded startups. Now I want to talk about small business broadly and what we're hearing from small business owners and also the banks that serve them. Look, I'm not going to sugar coat this many small businesses, as you well know, in deep trouble. They're gonna go out of business. They're laying off people on. There are a number of unemployed the aid package that the government's putting forth the small businesses. It's not working its way through the banking system. Not nearly fast enough, despite the Treasury secretaries efforts, The bottom line is banks don't want to make these loans to small businesses. Right now, there's too much that they don't understand. They're making no money on these loans they're being overwhelmed with. Volume will give you some examples. Bank of America, when the small business payroll program first hit signal that would Onley help companies with both ah banking relationship and an existing lending relationship with the bank UPS is another example said it was only gonna directly help companies with over 500 employees. And for small businesses, it was outsourcing that relationship to another firm, which, of course, meant you had to go through a new rectal exam, if you will, with that new firm. In a way, you can't blame the banks. They're being asked to execute on these programs without clear guidance on how they're supposed to enforce guidelines. And what happens if they make a mistake? Is the federal government gonna pull their guaranteed backing? What are those guidelines? They seem to be changing all the time. And what's the banks, liability and authority to enforce them? Why don't I spend time talking about this? Well, nearly half of US employees work for small businesses, and nearly 17 million workers as of this date have filed for unemployment, and I'll say the banks got bailed out in the financial crisis of 2008 and they need to step up, period, and the government needs to help them, all right. The other buzz kill data that I want to bring up is our national debt. Now many have invoked that there's no such thing as a free lunch, including the famous Milton Friedman, the Economist who I'm gonna credit. Others have said it, but I'll give it to him. Why? Because he espoused controlling the money supply and letting the market's fix themselves bailouts. The banks, airlines, Boeing, automakers, etcetera, those air antithetical to his underlying philosophy. Currently, the U. S national debt is $24 trillion. That's $194,000. Protects player Americans. Personal debt is now 20 trillion. Our unfunded liabilities, like Social Security, Medicare, etcetera now stands at a whopping 139 trillion. And that equates to about 422,000 per citizen. Think about this. The average liquid savings for US family is 15 K, and the U. S debt is now 111% of GDP. So we've been applying Kenzie and Economics for a while now. I'm gonna say it seems to have been working. Think about the predictions of inflation after the 8 4000 and nine crisis. They proved to be wrong. But my concern is I don't see how we grow our way out of this debt, and I worry about that. I've worried about this for a long time, but look, we're knee deep into it and it looks like there's no turning back so well, I'll try to keep my rhetoric to a minimum and stay positive here because I think there is light at the end of the tunnel. We're starting to see some some good opportunities emerging here just in terms of flattening the curve and the like. One of the things that pretty positive about is there gonna be some permanent changes from Cove it. It's kind of ironic that this thing hit as we're entering a new decade decade and as I said before, I expect digital transformations to be accelerated because of this crisis and the many companies that have talked digital from the corner office. But I haven't necessarily really walked the walk, I think will now I think is going to be more cloud more subscription less wasted labor, more automation, more work from home unless big physical events, at least in the next couple of years. So that's kind of the new expectation. As always, we're going to continue to report from our studios in Palo Alto and Boston, and we really welcome and appreciate your feedback. Remember, these segments are all available as podcasts, and we're publishing regularly on silicon angle dot com and on wiki bond dot com. Check out ctr dot plus for all the spending action, and you can feel free to comment on my LinkedIn post or DME at development or email me at David Volante Wiki. Sorry, David Vellante is silicon angle dot com. This is Dave Volante for the Cube Insights powered by CTR. Thanks for watching everyone. We'll see you next time. >>Yeah, yeah, yeah, yeah.

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi I'm Peter Burris analyst at wiki bond welcome to another wiki bond the cube digital community event this one sponsored by HP and focusing on hybrid storage like all of our digital community events this one will feature about 25 minutes of video followed by a crowd chat which will be your opportunity to ask your questions share your experiences and push forward the community's thinking on the important issues facing business today so what are we talking about today again hybrid storage let's get going so what is hybrid storage in a lot of shops most people have associated the cloud with public cloud but as we gain experience with the challenges associated with transforming to digital business in which we use data as a singular value producing asset increasingly IT professionals are starting to realize this important relationship between data storage and cloud services and in many respects that's really what we're trying to master today is a better understanding of how the business is going to use data to affect significant changes in how it behaves in the marketplace and it's that question of behavior that question of action that question of location that is pushing business to think differently about how its cloud architectures are going to work we're going to keep data proximate to where it's created to where it's going to be used to where it's going to be able to generate value which demands that we have storage resources in place close to that data proximate to that activity near that value producing activity and that the cloud services will have to follow in many respects that's what we're talking about when we talk about hybrid cloud today we're talking about the increasing recognition that we're going to move cloud services to the data default and not move the data into the cloud public cloud specifically so it's this ongoing understanding as we gain experience with this powerful set of technologies that data architecture is going to be increasingly distributed that storage therefore will be increasingly distributed and that cloud services will flow to where the data is required utilizing storage technologies that can best serve that set of workload so it's a more complex world that demands new levels of simplicity ease of use and optimization so that's where we're going to start our conversation so these crucial questions of how data storage and cloud are going to come together to create hybrid architectures was the basis for a great cubed conversation between silicon angle wiki bonds david Volante and HPE sun dip aurora let's hear what they had to say talk about let's talk about the break down those three things cost efficiency ease of use and resource optimization let's start with cost efficiency so obviously there's TCO there's also the way in which I consume the people I presume are looking for a different pricing model is that are you hearing that yeah absolutely so as part of the cost of of running their business and being able to operate like a cloud everybody is looking at a variety of different procurement and utilization models one of the ways HPE provides utilization model that can map to their cloud journey a public cloud journey is through Greenlake the ability to use and consume data on-demand consume compute on demand across the entire portfolio of products HPE has essentially is what a Greenlake journey looks like and let's go into ease-of-use so what do you mean by that I mean people look they think cloud they think swipe the credit card and start you know deploying machines what do you mean by easy for us ease of use translates back to how do you map to a simpler operating and support model for us the support model is the is the key for customers to be able to to realize the benefits of going to that cloud to get to a simpler support model we use AI ops and for us a offs means using a product called info site info site is a product that is uses deep learning and machine learning algorithms to look at a wide net of call home data from physical resources out there and then be able to take that data and make it actionable and the action behind that is predictiveness the prescriptive nosov creating automated support tickets enclosing automated support tickets without anybody ever having to pick up a phone and call IT support that info site model now is being expanded across the board to all HP products it started with nimble now info site is available on three part it's available on synergy and a recent announcement said it's also available on pro alliance and we expect that info set becomes the glue the automation a I do that goes across the entire portfolio of HP products so this is a great example of applying AI to data so it's like call home taking to a whole new level isn't it yeah it absolutely is and in fact what it does is it uses the call home data that we've had for a long time with products like 3par which essentially was amazing data but not being auctioned on in an automated fashion it takes that data and creates an automation tasks around it and many times that automation task leads to much simpler support experience all right third item you mentioned was resource optimization let's let's drill down into that I infer from that there's there are performance implications is maybe governance compliance you know physical placement can you elaborate that's in color yes I think it's all of the above that he just talked about it's definitely about applying the right performance level to the right set of applications we call this application of air storage the ability to be able to understand which application is creating the data allows us to understand how that data needs to be accessed which in turn means we know where it needs to reside one of the things that HP is doing in the storage domain is creating a common storage fabric with the cloud we call that the fabric for the cloud the idea there is that we have a single layer between the on-premises and off premises resources that allows us to move data as needed depending on the application needs and depending on the user needs so this crucial new factors that have to be incorporated through everyone's thinking of cost efficiency ease of use and resource optimization it's going to place new types of stress on the storage hierarchy it's gonna require new technologies to better support digital transformation David Flor an analyst here in wiki bon has been a leading thinker of the relationship between the storage hierarchy and workloads and digital thinking for quite some time I had a great conversation with David not too long ago let's hear what he had to say about this new storage hierarchy and the new technologies they're gonna make possible these changes have you've been looking at this notion of modern storage architectures for 10 years now and you've been relatively prescient in understanding what's going to happen you were one of the first guys to predict well in advance of everybody else that the crossover between flash and HDD was gonna happen sooner rather than later so I'm not gonna spend a lot of time quizzing you what do you see as a modern storage architecture let's just let it rip ok well let's start with one simple observation the days of stand-alone systems for data have gone we're in a software-defined world and you want to be able to run those data architectures anywhere where you the data is and that means in your data center where you've is created or in the cloud or in a public cloud or at the edge you want to be able to be flexible enough to be able to do all of the data services where the best place is and that means everything has to be software German Software Defined is the first proposition of a modern day in a storage so so the second thing is that there are different types of technology you have the very fastest storage which is in the in in the DRAM itself you have env dim which is the next one down from that expensive but a lot cheaper than the dim and then you have different sorts of flash you have the high-performance flash and you have the 3d flash you know as many layers as you can which is much cheaper flash and then at the bottom you have HD DS and an even tape as storage devices so how the key question is how do you manage that sort of environment well let me start because it still sounds like we still have a storage hierarchy absolutely and it still sounds like that hierarchy is defined largely in terms of access speeds yep and price point size points yes those are the two mason and and bandwidth and latency as well with it which are tied into the richer tied into those yes so what you if you're gonna have this everywhere and you need services everywhere what you have to have is an architecture which takes away all of that complexity so that you all you see from an application point of view is data and how it gets there and how it's put away and how it's stored and how it's protected that's under the covers so the first thing is you need a virtualization of that data layer the physical layer the virtualization of that physical yes and secondly you need that physical layer to extend to all the places that may be using this data you you don't want to be constrained to this data set lives here you want to be able to say ok I want to move this piece of programming to the data as quickly as I can that's much much faster than moving the data to the to the processing so I want to be able to know where all the data is for this particular dataset or file or whatever it is where they all are how they connect together what the latency is between everything I want to understand that architecture and I want a virtualized view of that across that whole the nodes that make up my hybrid cloud so let me be clear here so so we are going to use a software-defined infrastructure that allows us to place the physical devices that have the right cost performance characteristics where they need to be based on the physical realities of latency of you know power availability hardening etc on the network and the network but we want to mask that complexity from the application the application developer an application administrator yes and Software Defined helps do that but doesn't completely do it No well you you want services which say exactly so their service is on top of all that apps that are that are recognizable by the developer by the you know the business person by the administrator as they think about how they use data towards those outcomes not use a storage or use a device but use the data to reach application outcomes that's absolutely right then that's what I call the data plane which is a series of services which enable that to happen and and driven by the application required so we've looked at this and some of the services include you know and and compression deduplication the backup restore security data protection so that's kind of that's kind of the services that now the enterprise by or needs to think about so that those services can be applied with you know by policy yes wherever they're required based on the utilization of the data correct where it's kind of where the event takes place and then you still have at the bottom of that you have the different types of devices you still have you still want of hamsters Mickey you still want hard disk they're not disappearing but if you're gonna use hard disks then you want to use it in the right way if you're using a hard disk you know you want to give it large box you to have it going sequentially in and out all the time so the storage administration and the day the physical schema and everything else is still important in all this but it's less important less the centerpiece of the buying decision correct increasingly it's how well does this stuff prove support the services that the business is using to achieve their outcomes and you want to use course the lowest cost that you can and there will be many different options over more more options open but but the automation of that is absolutely key and that automation from a vendor point of view one of the key things they have to do is to be able to learn from the usage by their customers across as broad a number of customers as they can learn what works what doesn't work learn so that they can put automation into their own software their own software services well sounds like we're talking four things we got we got software-defined still have a storage hierarchy defined by cost and performance but with mainly semiconductor stuff we've got great data services that are relevant to the business and automation that masks the complexity from the artificial AI there is also also made many things fantastic so David's thinking on the new storage hierarchy and how it's going to relate to new classes of workload is a baseline for a lot of the changes happening in the industry today but we still have to turn technology into services that deliver higher levels of value once again let's go back to Dave volantes conversation with Sun dip Arora and here what Sun dip has to say about some of the new digital services some of the new data services they're gonna be essential to supporting these new hybrid storage capabilities we have and what it does it it gives us the opportunity now not just you look at column data from storage but then also look at call home data from the compute side and then what we can do is correlate the data coming back to have better predictability and outcomes on your data center operations as opposed to doing it at the layer of infrastructure you also set out a vision of this this orchestration yeah lair can you talk more about that are we talking about across all clouds whether it's on pram or at the edge or in the public cloud yeah we are we're talking about making it as simple as possible where the customers are not necessarily picking and choosing it allows them to have a strategy that allows them to go across the data center whether it's a public cloud building their own private infrastructure or running on a traditional on-premises sand structure so this vision for us cloud fabric vision for us allows for customers to do that and what about software-defined storage yeah where does that fit into this whole equation yeah I'm glad you mentioned that because that was a third tenant of what HP truly brings to our customers software-defined is is something that allows us to maximize the utilization of the existing resources that our customers have so what we've done is we've partnered with a great deal of really strong software-defined vendors such as comm world cohesive accumulo de terre I know we work very closely with the likes of veeam Zotoh and and the goal there is to do to provide our customers with a whole range of options to drive building a software-defined infrastructure build off the Apollo series of products Apollo servers or storage products for us are extremely dense storage products that allow for both cost and resource optimization so Sunday I made some fantastic points about how new storage technologies are going to be turned into usable services that digital businesses will require as they conceived of their overall hybrid storage approach here's an opportunity hear a little bit more about what HPE thinks about some of these crucial areas let's hear what they have to say in this Chuck talk short take I'm gonna introduce you to HPE primary storage if you want the agility of the public cloud but need the resiliency and speed of high-end storage for mission-critical applications this force is a trade-off of agility for resiliency high-end storage is fast and reliable but falls short on agility and simplicity what if you could have it all what if you could have both agility and resiliency for your mission-critical apps introducing the world's most intelligent storage for mission-critical apps HP primary it delivers an on-demand experience so storage is instantly available Apple wear resiliency backed with a hundred percent availability guarantee predictive acceleration so apps aren't fast some of the time but fast all the time with embedded AI let me tell you more about HPE primarily was engineered to drive unique value in high-end storage there are four areas we focus on global intelligence powered with the most advanced AI for infrastructure info site an all active architecture with multiple nodes for higher resiliency and limitless parallelization a service centric OS that eliminates the risk and simplifies management and timeless storage with a new ownership experience that keeps getting better to learn more go to hp.com slash storage slash prime era so that's been a great series of conversations about hybrid storage and I want to thank Sun dip Arora of HPE David floor of wiki bonds to look at angle jim kanby lists of wiki bonds to look and angle and my colleague David Volante for helping out on the interview side I'm Peter Burris and this has been another wiki bond the cube digital community event sponsored by HPE now stay tuned for our Crouch at which will be your opportunity to ask your questions share your experiences and push for the community's thinking on hybrid storage once again thank you very much for watching let's crouch at

(upbeat music) >> Announcer: Live from San Jose, it's theCUBE presenting Big Data Silicon Valley brought to you by SiliconANGLE Media and its ecosystem partner. >> What am I going to spend time, next 15, 20 minutes or so, talking about. I'm going to answer three things. Our research has gone deep into where are we now in the big data community. I'm sorry, where is the big data community going, number one. Number two is how are we going to get there and number three, what do the numbers say about where we are? So those are the three things. Now, since when we want to get out of here, I'm going to fly through some of these slides but again there's a lot of opportunity for additional conversation because we're all about having conversations with the community. So let's start here. The first thing to know, when we think about where this is all going is it has to be bound. It's inextricably bound up with digital transformation. Well, what is digital transformation? We've done a lot of research on this. This is Peter Drucker who famously said many years ago, that the purpose of a business is to create and keep a customer. That's what a business is. Now what's the difference between a business and a digital business? What's the business between Sears Roebuck, or what's the difference between Sears Roebuck and Amazon? It's data. A digital business uses data as an asset to create and keep customers. It infuses data and operations differently to create more automation. It infuses data and engagement differently to catalyze superior customer experiences. It reformats and restructures its concept of value proposition and product to move from a product to a services orientation. The role of data is the centerpiece of digital business transformation and in many respects that is where we're going, is an understanding and appreciation of that. Now, we think there's going to be a number of strategic capabilities that will have to be built out to make that possible. First off, we have to start thinking about what it means to put data to work. The whole notion of an asset is an asset is something that can be applied to a productive activity. Data can be applied to a productive activity. Now, there's a lot of very interesting implications that we won't get into now, but essentially if we're going to treat data as an asset and think about how we could put more data to work, we're going to focus on three core strategic capabilities about how to make that possible. One, we need to build a capability for collecting and capturing data. That's a lot of what IoT is about. It's a lot of what mobile computing is about. There's going to be a lot of implications around how to ethically and properly do some of those things but a lot of that investment is about finding better and superior ways to capture data. Two, once we are able to capture that data, we have to turn it into value. That in many respects is the essence of big data. How we turn data into data assets, in the form of models, in the form of insights, in the form of any number of other approaches to thinking about how we're going to appropriate value out of data. But it's not just enough to create value out of it and have it sit there as potential value. We have to turn it into kinetic value, to actually do the work with it and that is the last piece. We have to build new capabilities for how we're going to apply data to perform work better, to enact based on data. Now, we've got a concept we're researching now that we call systems of agency, which is the idea that there's going to be a lot of new approaches, new systems with a lot of intelligence and a lot of data that act on behalf of the brand. I'm not going to spend a lot of time going into this but remember that word because I will come back to it. Systems of agency is about how you're going to apply data to perform work with automation, augmentation, and actuation on behalf of your brand. Now, all this is going to happen against the backdrop of cloud optimization. I'll explain what we mean by that right now. Very importantly, increasingly how you create value out of data, how you create future options on the value of your data is going to drive your technology choices. For the first 10 years of the cloud, the presumption is all data was going to go to the cloud. We think that a better way of thinking about it is how is the cloud experience going to come to the data. We've done a lot of research on the cost of data movement and both in terms of the actual out-of-pocket costs but also the potential uncertainty, the transaction costs, etc, associated with data movement. And that's going to be one of the fundamental pieces or elements of how we think about the future of big data and how digital business works, is what we think about data movement. I'll come to that in a bit. But our proposition is increasingly, we're going to see architectural approaches that focus on how we're going to move the cloud experience to the data. We've got this notion of true private cloud which is effectively the idea of the cloud experience on or near premise. That doesn't diminish the role that the cloud's going to play on industry or doesn't say that Amazon and AWS and Microsoft Azure and all the other options are not important. They're crucially important but it means we have to start thinking architecturally about how we're going to create value of data out of data and recognize that means that it, we have to start envisioning how our organization and infrastructure is going to be set up so that we can use data where it needs to be or where it's most valuable and often that's close to the action. So if we think then about that very quickly because it's a backdrop for everything, increasingly we're going to start talking about the idea of where's the workload going to go? Where's workload the dog going to be against this kind of backdrop of the divorce of infrastructure? We believe that and our research pretty strongly shows that a lot of workloads are going to go to true private cloud but a lot of big data is moving into the cloud. This is a prediction we made a few years ago and it's clearly happening and it's underway and we'll get into what some of the implications are. So again, when we say that a lot of the big data elements, a lot of the process of creating value out of data is going to move into the cloud. That doesn't mean that all the systems of agency that build or rely on that data, the inference engines, etc, are also in a public cloud. A lot of them are going to be distributed out to the edge, out to where the action needs to be because of latency and other types of issues. This is a fundamental proposition and I know I'm going fast but hopefully I'm being clear. All right, so let's now get to the second part. This is kind of where the industry's going. Data is an asset. Invest in strategic business capabilities to appreciate, to create those data assets and appreciate the value of those assets and utilize the cloud intelligently to generate and ensure increasing returns. So the next question is well, how will we get there? Now. Right now, not too far from here, Neil Raden for example, was on the show floor yesterday. Neil made the observation that, as he wandered around, he only heard the word big data two or three times. The concept of big data is not dead. Whether the term is or is not is somebody else's decision. Our perspective, very simply, is that the notion is bifurcating. And it's bifurcating because we see different strategic imperatives happening at two different levels. On the one hand, we see infrastructure convergence. The idea that increasingly we have to think about how we're going to bring and federated data together, both from a systems and a data management standpoint. And on the other hand, we're going to see infrastructure or application specialization. That's going to have an enormous implication over next few years, if only because there just aren't enough people in the world that understand how to create value out of data. And there's going to be a lot of effort made over the next few years to find new ways to go from that one expertise group to billions of people, billions of devices, and those are the two dominant considerations in the industry right now. How can we converge data physically, logically, and on the other hand, how can we liberate more of the smarts associated with this very, very powerful approach so that more people get access to the capacities and the capabilities and the assets that are being generated by that process. Now, we've done at Wikibon, probably I don't know, 18, 20, 23 predictions overall on the role that or on the changes being wrought by digital business. Here I'm going to focus on four of them that are central to our big data research. We have many more but I'm just going to focus on four. The first one, when we think about infrastructure convergence we worry about hardware. Here's a prediction about what we think is going to happen with hardware and our observation is we believe pretty strongly that future systems are going to be built on the concept of how do you increase the value of data assets. The technologies are all in place. Simpler parts that it more successfully bind specifically through all its storage and network are going to play together. Why, because increasingly that's the fundamental constraint. How do I make data available to other machines, actors, sources of change, sources of process within the business. Now, we envision or we are watching before our very eyes, new technologies that allow us to take these simple piece parts and weave them together in very powerful fabrics or grids, what we call UniGrid. So that there is almost no latency between data that exists within one of these, call it a molecule, and anywhere else in that grid or lattice. Now again, these are not systems that are going to be here in five years. All the piece parts are here today and there are companies that are actually delivering them. So if you take a look at what Micron has done with Mellanox and other players, that's an example of one of these true private cloud oriented machines in place. The bottom line though is that there is a lot of room left in hardware. A lot of room. This is what cloud suppliers are building and are going to build but increasingly as we think about true private cloud, enterprises are going to look at this as well. So future systems for improving data assets. The capacity of this type of a system with low latency amongst any source of data means that we can now think about data not as... Not as a set of sources that have to be each individually, each having some control over its own data and sinks woven together by middleware and applications but literally as networks of data. As we start to think about distributing data and distributing control and authority associated with that data more broadly across systems, we now have to think about what does it mean to create networks of data? Because that, in many respects, is how these assets are going to be forged. I haven't even mentioned the role that security is going to play in all of this by the way but fundamentally that's how it's likely to play out. We'll have a lot of different sources but from a business standpoint, we're going to think about how those sources come together into a persistent network that can be acted upon by the business. One of the primary drivers of this is what's going on at the edge. Marc Andreessen famously said that software is eating the world, well our observation is great but if software's eating the world, it's eating it at the edge. That's where it's happening. Secondly, that this notion of agency zones. I said I'm going to bring that word up again, how systems act on behalf of a brand or act on behalf of an institution or business is very, very crucial because the time necessary to do the analysis, perform the intelligence, and then take action is a real constraint on how we do things. And our expectation is that we're going to see what we call an agency zone or a hub zone or cloud zone defined by latency and how we architect data to get the data that's necessary to perform that piece of work into the zone where it's required. Now, the implications of this is none of this is going to happen if we don't use AI and related technologies to increasingly automate how we handle infrastructure. And technologies like blockchain have the potential to provide a interesting way of imagining how these networks of data actually get structured. It's not going to solve everything. There's some people that think the blockchain is kind of everything that's necessary but it will be a way of describing a network of data. So we see those technologies on the ascension. But what does it mean for DBMS? In the old way, in the old world, the old way of thinking, the database manager was the control point for data. In the new world these networks of data are going to exist beyond a single DBMS and in fact, over time, that concept of federated data actually has a potential to become real. When we have these networks of data, we're going to need people to act upon them and that's essentially a lot of what the data scientist is going to be doing. Identifying the outcome, identifying the data that's required, and weaving that data through the construction and management, manipulation of pipelines, to ensure that the data as an asset can persist for the purposes of solving a near-term problem or over whatever duration is required to solve a longer term problem. Data scientists remain very important but we're going to see, as a consequence of improvements in tooling capable of doing these things, an increasing recognition that there's a difference between a data scientist and a data scientist. There's going to be a lot of folks that participate in the process of manipulating, maintaining, managing these networks of data to create these business outcomes but we're going to see specialization in those ranks as the tooling is more targeted to specific types of activities. So the data scientist is going to become or will remain an important job, going to lose a little bit of its luster because it's going to become clear what it means. So some data scientists will probably become more, let's call them data network administrators or networks of data administrators. And very importantly as I said earlier, there's just not enough of these people on the planet and so increasingly when we think about again, digital business and the idea of creating data assets. A central challenge is going to be how to create the data or how to turn all the data that can be captured into assets that can be applied to a lot of different uses. There's going to be two fundamental changes to the way we are currently conceiving of the big data world on the horizon. One is well, it's pretty clear that Hadoop can only go so far. Hadoop is a great tool for certain types of activities and certain numbers of individuals. So Hadoop solves problems for an important but relatively limited subset of the world. Some of the new data science platforms that we just talked about, that I just talked about, they're going to help with a degree of specialization that hasn't been available before in the data world, will certainly also help but it also will only take it so far. The real way that we see the work that we're doing, the work that the big data community is performing, turned into sources of value that extend into virtually every single corner of humankind is going to be through these cloud services that are being built and increasingly through packaged applications. A lot of computer science, it still exists between what I just said and when this actually happens. But in many respects, that's the challenge of the vendor ecosystem. How to reconstruct the idea of packaged software, which has historically been built around operations and transaction processing, with a known data model and an unknown or the known process and some technology challenges. How do we reapply that to a world where we now are thinking about, well we don't know exactly what the process is because the data tells us at the moment that the actions going to be taking place. It's a very different way of thinking about application development. A very different way of thinking about what's important in IT and very different way of thinking about how business is going to be constructed and how strategy's going to be established. Packaged applications are going to be crucially important. So in the last few minutes here, what are the numbers? So this is kind of the basis for our analysis. Digital business, role of data is an asset, having an enormous impact in how we think about hardware, how do we think about database management or data management, how we think about the people involved in this, and ultimately how we think about how we're going to deliver all this value out to the world. And the numbers are starting to reflect that. So why don't you think about four numbers as I go through the two or three slides. Hundred and three billion, 68%, 11%, and 2017. So of all the numbers that you will see, those are four of the most important numbers. So let's start by looking at the total market place. This is the growth of the hardware, software, and services pieces of the big data universe. Now we have a fair amount of additional research that breaks all these down into tighter segments, especially in software side. But the key number here is we're talking about big numbers. 103 billion over the course of next 10 years and let's be clear that 103 billion dollars actually has a dramatic amplification on the rest of the computing industry because a lot of the pricing models associated with, especially the software, are tied back to open source which has its own issues. And very importantly, the fact that the services business is going to go through an enormous amount of change over the next five years as service companies better understand how to deliver some of these big data rich applications. The second point to note here is that it was in 2017 that the software market surpassed the hardware market in big data. Again, for first number of years we focused on buying the hardware and the system software associated with that and the software became something that we hope to discover. So I was having a conversation here in theCUBE with the CEO of Transwarp which is a very interesting Chinese big data company and I asked what's the difference between how you do things in China and how we do things in the US? He said well, in the US you guys focus on proof of concept. You spend an enormous amount of time asking, does the hardware work? Does the database software work? Does the data management software work? In China we focus on the outcome. That's what we focus on. Here you have to placate the IT organization to make sure that everybody in IT is comfortable with what's about to happen. In China, were focused on the business people. This is the first year that software is bigger than hardware and it's only going to get bigger and bigger over time. It doesn't mean again, that hardware is dead or hardware is not important. It's going to remain very important but it does mean that the centerpiece of the locus of the industry is moving. Now, when we think about what the market shares look like, it's a very fragmented market. 60%, 68% of the market is still other. This is a highly immature market that's going to go through a number of changes over the next few years. Partly catalyzed by that notion of infrastructure convergence. So in four years our expectation is that, that 68% is going to start going down pretty fast as we see greater consolidation in how some of these numbers come together. Now IBM is the biggest one on the basis of the fact that they operate in all these different segments. They operating the hardware, software, and services segment but especially because they're very strong within the services business. The last one I want to point your attention to is this one. I mentioned earlier on, that our expectation is that the market increasingly is going to move to a packaged application orientation or packaged services orientation as a way of delivering expertise about big data to customers. Splunk is the leading software player right now. Why, because that's the perspective that they've taken. Now, perhaps we're a limited subset. It's perhaps for a limited subset of individuals or markets or of sectors but it takes a packaged application, weaves these technologies together, and applies them to an outcome. And we think this presages more of that kind of activity over the course of the next few years. Oracle, kind of different approach and we'll see how that plays out over the course of the next five years as well. Okay, so that's where the numbers are. Again, a lot more numbers, a lot of people you can talk to. Let me give you some action items. First one, if data was a core asset, how would IT, how would your business be different? Stop and think about that. If it wasn't your buildings that were the asset, it wasn't the machines that were the asset, it wasn't your people by themselves who were the asset, but data was the asset. How would you reinstitutionalize work? That's what every business is starting to ask, even if they don't ask it in the same way. And our advice is, then do it because that's the future of business. Not that data is the only asset but data is a recognized central asset and that's going to have enormous impacts on a lot of things. The second point I want to leave you with, tens of billions of users and I'm including people and devices, are dependent on thousands of data scientists that's an impedance mismatch that cannot be sustained. Packaged apps and these cloud services are going to be the way to bridge that gap. I'd love to tell you that it's all going to be about tools, that we're going to have hundreds of thousands or millions or tens of millions or hundreds of millions of data scientists suddenly emerge out of the woodwork. It's not going to happen. The third thing is we think that big businesses, enterprises, have to master what we call the big inflection. The big tech inflection. The first 50 years were about known process and unknown technology. How do I take an accounting package and do I put on a mainframe or a mini computer a client/server or do I do it on the web? Unknown technology. Well increasingly today, all of us have a pretty good idea what the base technology is going to be. Does anybody doubt it's going to be the cloud? We got a pretty good idea what the base technology is going to be. What we don't know is what are the new problems that we can attack, that we can address with data rich approaches to thinking about how we turn those systems into actors on behalf of our business and customers. So I'm a couple minutes over, I apologize. I want to make sure everybody can get over to the keynotes if you want to. Feel free to stay, theCUBE's going to be live at 9:30. If I got that right. So it's actually pretty exciting if anybody wants to see how it works, feel free to stay. Georgia's here, Neil's here, I'm here. I mentioned Greg Terrio, Dave Volante, John Greco, I think I saw Sam Kahane back in the corner. Any questions, come and ask us, we'll be more than happy. Thank you very much for, oh David Volante. >> David: I have a question. >> Yes. >> David: Do you have time? >> Yep. >> David: So you talk about data as a core asset, that if you look at the top five companies by market cap in the US, Google, Amazon, Facebook, etc. They're data companies, they got data at the core which is kind of what your first bullet here describes. How do you see traditional companies closing that gap where humans, buildings, etc at the core as we enter this machine intelligence era, what's your advice to the traditional companies on how they close that gap? >> All right. So the question was, the most valuable companies in the world are companies that are well down the path of treating data as an asset. How does everybody else get going? Our observation is you go back to what's the value proposition? What actions are most important? what's data is necessary to perform those actions? Can changing the way the data is orchestrated and organized and put together inform or change the cost of performing that work by changing the cost transactions? Can you increase a new service along the same lines and then architect your infrastructure and your business to make sure that the data is near the action in time for the action to be absolute genius to your customer. So it's a relatively simple thought process. That's how Amazon thought, Apple increasingly thinks like that, where they design the experience and they think what data is necessary to deliver that experience. That's a simple approach but it works. Yes, sir. >> Audience Member: With the slide that you had a few slides ago, the market share, the big spenders, and you mentioned that, you asked the question do any of us doubt that cloud is the future? I'm with Snowflake, I don't see many of those large vendors in the cloud and I was wondering if you could speak to what are you seeing in terms of emerging vendors in that space. >> What a great question. So the question was, when you look at the companies that are catalyzing a lot of the change, you don't see a lot of the big companies being at the leadership. And someone from Snowflake just said, well who's going to lead it? That's a big question that has a lot of implications but at this point time it's very clear that the big companies are suffering a bit from the old, from the old, trying to remember what the... RCA syndrome. I think Clay Christensen talked about this. You know, the innovators dilemma. So RCA actually is one of the first creators. They created the transistor and they held a lot of original patents on it. They put that incredible new technology, back in the forties and fifties, under the control of the people who ran the vacuum tube business. When was the last time anybody bought RCA stock? The same problem is existing today. Now, how is that going to play out? Are we going to see a lot of, as we've always seen, a lot of new vendors emerge out of this industry, grow into big vendors with IPO related exits to try to scale their business? Or are we going to see a whole bunch of gobbling up? That's what I'm not clear on but it's pretty clear at this point in time that a lot of the technology, a lot of the science, is being done in smaller places. The moderating feature of that is the services side. Because there's limited groupings of expertise that the companies that today are able to attract that expertise. The Googles, the Facebooks, the AWSs, etc, the Amazons. Are doing so in support of a particular service. IBM and others are trying to attract that talent so they can apply it to customer problems. We'll see over the next few years whether the IBMs and the Accentures and the big service providers are able to attract the kind of talent necessary to diffuse that knowledge into the industry faster. So it's the rate at which that the idea of internet scale computing, the idea of big data being applied to business problems, can diffuse into the marketplace through services. If it can diffuse faster that will have both an accelerating impact for smaller vendors, as it has in the past. But it may also again, have a moderating impact because a lot of that expertise that comes out of IBM, IBM is going to find ways to drive in the product faster than it ever has before. So it's a complicated answer but that's our thinking at this point time. >> Dave: Can I add to that? >> Yeah. (audience member speaking faintly) >> I think that's true now but I think the real question, not to not to argue with Dave but this is part of what we do. The real question is how is that knowledge going to diffuse into the enterprise broadly? Because Airbnb, I doubt is going to get into the business of providing services. (audience member speaking faintly) So I think that the whole concept of community, partnership, ecosystem is going to remain very important as it always has and we'll see how fast those service companies that are dedicated to diffusing knowledge, diffusing knowledge into customer problems actually occurs. Our expectation is that as the tooling gets better, we will see more people be able to present themselves truly as capable of doing this and that will accelerate the process. But the next few years are going to be really turbulent and we'll see which way it actually ends up going. (audience member speaking faintly) >> Audience Member: So I'm with IBM. So I can tell you 100% for sure that we are, I hired literally 50 data scientists in the last three months to go out and do exactly what you're saying. Sit down with clients and help them figure out how to do data science in the enterprise. And so we are in fact scaling it, we're getting people that have done this at Google, Facebook. Not a whole lot of those 'cause we want to do it with people that have actually done it in legacy fortune 500 Companies, right? Because there's a little bit difference there. >> So. >> Audience Member: So we are doing exactly what you said and Microsoft is doing the same thing, Amazon is actually doing the same thing too, Domino Data Lab. >> They don't like they're like talking about it too much but they're doing it. >> Audience Member: But all the big players from the data science platform game are doing this at a different scale. >> Exactly. >> Audience Member: IBM is doing it on a much bigger scale than anyone else. >> And that will have an impact on ultimately how the market gets structured and who the winners end up being. >> Audience Member: To add too, a lot of people thought that, you mentioned the Red Hat of big data, a lot of people thought Cloudera was going to be the Red Hat of big data and if you look at what's happened to their business. (background noise drowns out other sounds) They're getting surrounded by the cloud. We look at like how can we get closer to companies like AWS? That was like a wild card that wasn't expected. >> Yeah but look, at the end of the day Red Hat isn't even the Red Hat of open source. So the bottom line is the thing to focus on is how is this knowledge going to diffuse. That's the thing to focus on. And there's a lot of different ways, some of its going to diffuse through tools. If it diffuses through tools, it increases the likelihood that we'll have more people capable of doing this in IBM and others can hire more. That Citibank can hire more. That's an important participant, that's an important play. So you have something to say about that but it also says we're going to see more of the packaged applications emerge because that facilitates the diffusion. This is not, we haven't figured out, I don't know exactly, nobody knows exactly the exact shape it's going to take. But that's the centerpiece of our big data researches. How is that diffusion process going to happen, accelerate, and what's the resulting structure going to look like? And ultimately how are enterprises going to create value with whatever results. Yes, sir. (audience member asks question faintly) So the recap question is you see more people coming in and promising the moon but being incapable of delivering because they are, partly because the technology is uncertain and for other reasons. So here's our approach. Or here's our observation. We actually did a fair amount of research on this. When you take a look at what we call a approach to doing big data that's optimized for the costs of procurement i.e. let's get the simplest combination of infrastructure, the simplest combination of open-source software, the simplest contracting, to create that proof of concept that you can stand things up very quickly if you have enough expertise but you can create that proof of concept but the process of turning that into actually a production system extends dramatically. And that's one of the reasons why the Clouderas did not take over the universe. There are other reasons. As George Gilbert's research has pointed out, that Cloudera is spending 53, 55 % of their money right now just integrating all the stuff that they bought into the distribution five years ago. Which is a real great recipe for creating customer value. The bottom line though is that if we focus on the time to value in production, we end up taking a different path. We don't focus as much on whether the hardware is going to work and the network is going to work and the storage can be integrated and how it's going to impact the database and what that's going to mean to our Oracle license pool and all the other things that people tend to think about if they're focused on the technology. And so as a consequence, you get better time to value if you focus on bringing the domain expertise, working with the right partner, working with the appropriate approach, to go from what's the value proposition, what actions are associated with a value proposition, what's stated in that area to perform those actions, how can I take transaction costs out of performing those actions, where's the data need to be, what infrastructure do I require? So we have to focus on a time to value not the time to procure. And that's not what a lot of professional IT oriented people are doing because many of them, I hate say it, but many of them still acquire new technology with the promise to helping the business but having a stronger focus on what it's going to mean to their careers. All right, I want to be really respectful to everybody's time. The keynotes start in about five minutes which means you just got time. If you want to stay, feel free to stay. We'll be here, we'll be happy to talk but I think that's pretty much going to close our presentation broadcast. Thank you very much for being an attentive audience and I hope you found this useful. (upbeat music)