(upbeat music) >> Welcome to this Cube conversation sponsored by Citrix. This is the third and final installment in the Citrix launchpad series. We're going to be talking about the launchpad series for work. Lisa Martin here with Dion Hinchcliffe, VP and principal analyst at Constellation research. Dion, welcome to the program. >> No, thanks Lisa. Great to be here. >> So we have seen a tremendous amount of change in the last 18, 19 months. You know, we saw this massive scatter to work from home a year and a half ago. Now we're in this sort of distributed environment. That's been persisting for a long time. Talk to me about, we're going to be talking about some of the things that Citrix is seeing and some of the things that they're doing to help individuals and teams, but give me your lens from Constellation's perspective. What are some of the major challenges with this distributed environment that you've seen? >> Sure. Well, so we've gone from this, you know, the world of work, the way that it was now, we're all very decentralized, you know, work from anywhere. Remote work is really dominating, you know, white collar types of activities in the workplace and workplaces that in our homes for most of us even today. But that started to change. Some people are going back. Although I just recently spoke to a panel of CIOs that says they have no plans anytime soon, but they're very aware that they need to have workable plans for when we start sending people back to the office and there's this big divide. How are we going to make sure that we have one common culture? We have a collaborative organization when, you know, a good percentage of our workers are in the office, but also maybe as much as half the organization is at home. And so, how to make processes seamless, how to make people collaborate and make sure there's equity and inclusion so that the people at home aren't left out and then people in the office, maybe you don't have an unfair advantage. So those are all the conversations. And of course, because this is a technology revolution, remote work was enabled by technology. We're literally looking at it again for this hybrid work, this, you know, this divided organization that we're going to have. >> You mentioned culture that's incredibly important, but also challenging to do with this distribution. I was looking at some research that Citrix provided, asking individuals from a productivity perspective, and two thirds said, hey, for our organizations that have given us more tools for collaboration and communication, yes, we are absolutely more productive. But the kicker is, the same amount of people, about two thirds that answered the survey said, we've now got about ten tools. So complexity is more challenging. It's harder to work individually. It's harder to work in teams. And so Citrix is really coming to the table here with the launchpad series for work, saying let's help these individuals and these teams, because as we, we think, and I'm sure you have insight Dion on this as well, this hybrid model that we're starting to see emerge is going to be persistent for a while. >> Yeah. For the foreseeable future. Cause we don't know what the future holds. So we'll have to hold the hybrid model as the primary model. And we may eventually go back to the way that we were. But for the next several years, there's going to be that. And so we're trying to wrap our arms around that. And I think that we're seeing with things like the Citrix announcements, a wave of responses saying, all right, let's really design properly for these changes. You know, we kind of just adapted quickly when everyone went to remote last year and now we're actually adding features to streamline, to reduce the friction, to simplify remote work, which does use, you have to use more applications. You have to switch between different things. You have to, you know, your employee experience in the digital world is just more cluttered and complicated, but it doesn't have to be. And so I, you know, we can look to some of these announcements for last year, I think address some of that. >> Let's break some of that down because to your point, it doesn't have to be complex complicated. It shouldn't be. Initially this scatter was, let's do everything we can to ensure that our teams and our people can be productive, can communicate, can collaborate. And now, since this is going to be persistent for quite some time, to your point, let's design for this distributed environment, this hybrid workforce of the future. Talk to me about the, one of the things that Citrix is doing with Citrix workspace, the app personalization, I can imagine as an individual contributor, but also as a team leader, the ability to customize this to the way that I work best is critical. >> And it really is, especially when you know, you have workers, you know, 18 or 19 months worth of new hires that you've never met. They don't really feel like, you know, this is maybe their organization. But if you allow them to shape it a little bit, make it contextual for them. So they don't just come into this cookie cutter digital experience that actually is kind of more meaningful for them. It makes it easier for them to get their job done and things are the way that they want them and where they want them. I think that makes a lot of sense. And so the app personalization announcements is important for remote workers in particular, but all workers to say, hey, can I start tailoring, you know, parts of my employee experience? So they make more sense for me. And I feel like I belong a little bit more. I think it's significant. >> It is. Let's talk about it from a security perspective though. We've seen massive changes in the security landscape in the last year and a half. We've seen some Citrix data that I was looking at, said between 2019 and 2020, ransomware up 435%, malware up 358%. And of course the weakest link being humans. Talk to me from a Citrix workspace perspective about some of the things that they've done to ensure that those security policies can be applied. >> Well, and the part that I really liked about the launchpad announcements around work in terms of security was this much more intelligent analysis. You know, one of the most frustrating things is you're trying to get work done remotely and maybe you're you're in crunch mode and all of a sudden the security system clamps down because they think you're doing something that, you know, you might be sharing information you shouldn't be and now you can't, get your deadline met. I really liked how the analytics inside the new security features really try to make sure they're applying intelligent analysis of behavior. And only when it's clear that a bad actor is in there doing something, then they can restrict access, protect information. And so I have no doubt they'll continue to evolve the product so that it's even even more effective in terms of how it can include or exclude bad actors from doing things inside your system. And so this is the kind of intelligence security increasingly based on AI type technologies that I think that will keep our workers productive, but clamp down on the much higher rate of that activity we see out there. Because we do have so many more endpoints there's a thousand or more times more endpoints in today's organizations because of remote work. >> Right. And one of the things that we've seen with ransomware, I mentioned those numbers that Citrix was sharing. It's gotten so much more personalized, so it's harder and harder to catch these things. One of the things that I found interesting, Dion, that from a secure collaboration perspective, that Citrix is saying is that, you know, we need to go, security needs to go beyond the devices and the endpoints and the apps that an employee is using, which of which we said, there are at least 10 apps that are being used today and it needs to actually be applied at a content level, the content creation level. Talk to me about your thoughts about that. >> I think that's exactly right. So if you know the profile of that worker and the types of things they normally do, and you see unusual behavior that is uncharacteristic to that worker, because you know their patterns, the types of content, the locations of that content that they might normally have access to. And if they're just accessing things, you know, periodically, that's usually not a problem. When they suddenly access a large volume of information and appear to be downloading it, those are the types of issues and especially of content they don't normally use for their work. Then you can intervene and take more intelligent actions as opposed to just trying to limit all content for example. So that knowledge workers can actually get access to all that great information in your IT systems. You can now give them access to it, but when clearly something, something bad is happening, the system automatically does it and steps in. >> I was looking at some of the data with respect to updates to Citrix analytics that it can now auto change permissions on shared files to read only, I think you alluded to this earlier, when it detects that excess sharing is going on. >> And, inappropriate access sharing. So sometimes it's okay for a worker to access, you know, documents. But the big fear is that a bad actor gets access. They get a USB key and they download a bunch of files and they get a whole bunch of IP or important knowledge. Well, when you have a system that's continually monitoring and you know, the unblinking gaze of Citrix security capabilities are looking at the patterns, not just the content alone or just the device alone, but at the, at the usage patterns and saying, I can make this read only because that's clearly the, you know, we don't want them to be able to download this because this activity is completely out of bounds or very unusual. >> Right. One of the things also that Citrix is doing is integrating with Microsoft teams. I was listening to a fun quiz show the other day that said, what were the top two apps downloaded in 2020? And I guessed one of them correctly, Tiktok though. I still don't know how to use it. And the second one was Zoom, and I'm sure Microsoft teams is way up there. I was looking at some stats that said, I think as of the spring of 2020, there were 145 million daily users of Microsoft teams. So that, from a collaboration perspective, something that a lot of folks are dependent on during the pandemic. And now within Teams, I can access Microsoft workspace? Citrix workspace. >> Yes. Well, and it's more significant than it sounds because there's a real hunger to find a center of gravity for the employee experience. What do I put that? Where should they be spending most of their time? Where should I be training them to focus most of their attention? And obviously workers collaborate a lot and Teams as part of Office 365, is a juggernaut? You know, the rise of it during the pandemic has been incredible. And just to show this, I have a digital workplace advisory board. Its companies who are heading, are the farthest along in designing digital employee experiences, and 31% of them said, this January, they're planning on centralizing the employee experience in Teams. Now, if you're a Citrix customer, you have workspace you go, how do I, I don't want to be left out. This announcement allows you to say, you can have the goodness of teams and its capabilities and the power of Citrix workspace, and you have them in one place and really creating a true center of gravity and simplifying and streamlining the employee experience. You don't have this fragmented pieces. Everything's right there in one place, in one pane of glass. And so I like this announcement. It brings Citrix up to parody with a lot of their competitors and actually eclipses several of them as well. So I really like to see this. >> So then from within teams, I can access Citrix workspace. I can share documents with team members and collaborate as well as that kind of the idea. >> Yes. That is the idea, and of course, they'll continue to evolve that, but now you can do your work in Citrix workspace and when documents are involved and you want to bring your team in, they're already right there inside that experience. >> That ability to streamline things, so critical, given the fact that we're still in this distributed environment, I'm sure families are still dealing with some, some amount of remote learning, or there's still distractions from the, do I live at work, do I work from home environment? One of the grips I really felt for when this happened, Dion, was the contact center. I thought these poor people, more people now with shorter and shorter fuses trying to get updates on whatever it was that they were, if they had something ordered and of course all the shipping delays. And the contact center of course went (blowing sound) scattered as well. And we've got people working from home, trying to do their jobs. Talk to me about some of those things that Citrix is doing to enable with Google, those contact center workers to have a good experience so that ultimately the employee experience is good, so is the customer experience? >> The contact center worker has the toughest of all of the different employee profiles I've seen, they have the most they have to learn, the most number of applications. They're typically not highly skilled workers. So they might only just have a, you know, high school education. Yet, they're being asked to cram all of these technologies, each one with a different employee experience, and they don't stay very long as a result of that. You might train them for two months before they're effective and they only stay for six months on average. And so, both businesses really want to be able to streamline onboarding and provisioning a and getting them set up and effective. And they want it too, if you want happy contact center workers making your customers happy and staying around. And so this announcements really allows you to deploy pre-configured Citrix workspaces on, on Chrome OS so that, you know, if you need to field a whole bunch of workers or you have a big dose say you're a relief company and you have a lot of disaster care workers. You can certainly this issue that these devices very easily, they're ready to go with their employee experience and all the right things in place so they can be effective with the least amount of effort. So I guess, it's a big step forward for a worker that is often neglected and underserved. >> Right. Definitely often neglected. And you, you brought up a good point there. And one of the things that, that peaked in my mind, as you talked about, you know, the onboarding experience, the retention, well, these contact center folks are the front lines to the customer. So from a brand reputation perspective, that's on the line, for companies in every industry where people with short fuses are dealing with contact center folks. So the ability to onboard them to give them a much more seamless experience is critical for the brand reputation, customer retention for every industry, I would imagine. >> Absolutely. Especially when you're setting up a contact center or you have a new product launching and you want, you know, you've got to bring, onboard all these new workers, you can do it, and they are going to have the least challenges. They're going to be ready to go right out of the box, be able to receive their package, with their device and their Citrix employee experience, ready to go. You know, just turn the machine on and they're off to the races. And that's the vision and that's the right one. So I was glad to see that as well. >> Yeah. Fantastic. One of the things also that Citrix did, the Citrix workspace app builder, so that Citrix workspace can now be a system of record for certain things like collaboration, surveys, maybe even COVID-19 information, that system of record. Talk to me about why that's so critical for the distributed worker. >> So we've had this, this longstanding challenge in that we've had our systems of record, you know, these are CRM systems, ERP, things like that, which we use to run our business. And then we've had our collaboration tools and they're separate, even though we're collaborating on sales deals and we're collaborating on our supply chain. And so like, the team's announcement was in the same game. We can say, let's close that gap between our systems of record and our collaboration tools. Well, this announcement says, all right, well, we still have these isolated systems of record. How can we streamline them to build and start connecting together a little bit so that we have processes that might cross all of those things, right? It's still going to order comes in from the CRM system. Then you can complete it in the, in the ERP system, you know, ordering that product for them. So they actually get it. You know, and that's probably overkill, that scenario for this particular example. But for example, collecting data from workers saying, let's build some forms and collect some data and then feed it to this process, or this system record. You can do it much more easily than before, before you would have to hire a development team or a contractor to develop another system that would integrate, you know, CRM or ERP or whatever. Now you can do it very quickly inside that builder. First simple, basic applications, and get a lot of the low hanging fruit off your plate and more automated inside of your Citrix workspace. >> And automation has been one of the keys that we've seen to streamlining worker productivity in the last 18 months. Another thing that I was looking at is, you know, the fact that we have so many different apps and we're constantly switching apps, context is constantly changing. Is this sort of system of record going to allow or reduce the amount of context switching that employees have to do? >> Yep. Almost all of these announcements have some flavor to that saying, can we start bringing more systems together in one place? So you're not switching between applications. You don't have different and disconnected sets of data that if you need to, and if they are disconnected, you can connect them, right. That's what the app builder announcement again is about saying, all right, if you're already, always using these three applications to do something, and you're switching between them, maybe you can just build something that connect them into one experience and, you know, maybe a low level of IT person, or even a business user can do that. That's the big trend right now. >> That's so important for that continued productivity, as things will continue to be a little bit unstable, I guess, for awhile. One more thing that I saw that Citrix is announcing is integrations with, Wrike I've been a Wrike user myself. I like to have program project management tools that I can utilize to keep track of projects, but they've done a number of integrations, one of them with Wrike Signature, which I thought was really cool. So for, to secure e-signature within Wrike, based on a program or a project that you're working on. Talk to me about some of the boosts to Wrike that they've done and how you think that's going to be influential in the employee experience. >> Well, first let's just say that the Wrike acquisition was a really important one for Citrix to go above just the basic digital workplace and simple systems of record. This is a really a mass collaboration tool for managing work itself. And so they're, this is taking Citrix up the stack in the more sophisticated work scenarios. And, and when you, we are in more sophisticated work scenarios, you want to be able to pull in different data sets. So, you know, they have the Citrix ShareFile support. You want to be able to bring in really important things like, you know, signing contracts or signing sales deals or mortgage applications, or all sorts of exciting things that actually run in your business. And so, Wrike Signatures, support's really important so that when you have key processes that involve people putting signatures on documents, you can just build collaborative work management flows that, that take all that into account without having to leave the experience. Everything's in one place as much as possible. And this is the big push and we need to have all these different systems. We don't have too many apps. What we have is too many touchpoints, so lets start combining some of these. And so the Wrike integrations, really help you do that. >> Well, and ultimately it seems like what Citrix is doing with the work launchpad series. All the announcements here is really helping workers to work how and where they want to work. Which is very similar to what we say when we're talking about the end user customer experience. When tech companies like Citrix say, we have to meet our customers where they are, it sounds like that's the same thing that's happening here. >> It is. And I would just add on top of that and to make it all safe. So you can bring all these systems together, work from anywhere, and you can feel confident that you're going to do so securely and safely. And it's that whole package I think that's really critical here. >> You're right, I'm glad you brought up that security. All right, Dion take out your crystal ball for me. As we wrap things up, you're saying, you know, going into the future, we're going to be moving from this distributed workforce to this hybrid. What are some of the things that you see as really critical happening in the next six to nine months? >> Well, there's a real push to say, we need to bring in all the workers that we've hired over the last year. Maybe not bringing them in, in person, but can we use these collaborative tools and technologies to bring them, hold them closer so they get to know us. And so, you know, things like, having Microsoft teams integrated right into your Citrix workspace makes it easier for you to collaborate with remote workers and inside any process wherever you are. So whether you're in the office or not, it should bring workers closer, especially those remote ones that are at risk of being left out as they move to hybrid work. And then it's really important. And so the things like the app builder are going to also allow building those connections. And I think that workers and businesses are really going to try and build those bridges, because the number one thing I'm hearing from business leaders and IT leaders is, is it, you know, we're worried about splitting into two different organizations, the ones that are remote and the ones that are in the office and any way that we can bring all of them together in an easy way, in a natural way, situate the digital employee experience so that we really back or back to one company, one common culture, everybody has equal access and equity to the employee experience. That's going to be really important. And I think that Citrix launchpad announcements around work really are a step, a major step in the right direction for that. There's still more things that have to be done and all, all vendors are working on that. But it's nice to see. I really liked what Citrix is doing here to move the ball forward towards where we're all going. >> It is nice to see, and those connections are critically important. I happen to be at an in-person event last week, and several folks had just had been hired during the pandemic and just got to meet some of their teams. So in terms of, of getting that cultural alignment, once again, this is a great step towards that. Dion thank you for joining me on the program, talking about the Citrix launchpad series for work, all the great new things that they're announcing and sharing with us as some of the things that you see coming down the pike. We appreciate your time. >> Thanks Lisa, for having me. >> For Dion Hinchcliffe. I'm Lisa Martin. You're watching this Cube conversation. (upbeat music)

>>we're back, we're assessing the as a service space. H. P. S. Green Lake announcements, my name is Dave balanta, you're watching the cube die on Hinchcliffe is here along with Holger muller, these are the constellation kids, extraordinary analysts guys. Great to see you again. I mean it super experienced. You guys, you deal with practitioners, you deal your technologist, you've been following this business for a long time. Diane, We spoke to Holger earlier, I want to start with you uh when you look at this whole trend to as a service, you see a lot of traditional enterprise companies, hard traditionally hardware companies making that move for for a lot of obvious reasons are they sort of replicating in your view, a market that you know well and sas what's your take on how they're doing generally that trend and how HP is >>operating well. Hp has had a unique heritage. They're coming at the whole cloud story and you know the Hyper Scaler story from a different angle than a lot of their competitors and that's mostly a good thing because most of the world is not yet on the cloud, They actually came from H. P. S original world, their line of servers and networks and so on. Um and and so they bring a lot of credibility saying we really understand the world you live in now but we want to take you to that that as a service future. Uh and and you know, since we understand you so well and we also understand where this is going and we can adapt that to that world. Have a very compelling story and I think that with green like you know, was first started about four years ago, it was off to the side uh you know, with all the other offerings now it's it's really grown up, it's matured a lot and I think you know, as we talked about the announcements, we'll see that a lot of key pieces have fallen into place to make it a very compelling hybrid cloud option for the enterprise. >>Let's talk about the announcement. Was there anything in particular that stood out the move to data management? I think it's pretty interesting is a tam expansion strategy. What's your take on the >>announcement? Well, the you know, the unified analytics uh story I think is really important now. That's the technology piece where they say, they say we can give you a data fabric, you can access your data outside of its silos. It doesn't address a lot of the process and cultural issues around data ownership inside the enterprise, but it's you know, having in the actual platform and as you articulating it as a platform, that's one of the things that was also evident, they were getting better and better at saying this is a hybrid cloud platform and it has all the pieces that you would expect, especially the things like being able to bring your data from wherever it is to wherever people needed to be. Uh you know, that's the Holy Grail, so really glad to see that component in particular. I also like the cloud adoption framework saying we understand how to take you from this parochial world of servers that you have and do a cloud date of hybrid world and then maybe eventually get you get you to a public cloud. We understand all the steps and all the components uh I think that's uh you know, I have a study that fully in depth but it seems to have all the moving parts >>chime in anything stand out to, you >>know, I think it's great announcements and the most important things H. P. S and transformation and when you and transformation people realize who you've been, the old and they're here. Maybe the mass of the new but an experienced technology but I will not right away saying oh it's gonna happen right. It's going to happen like this is gonna be done, it's ready, it's materials ready to use and so on. So this is going to give more data points, more proof points, more capabilities that HB is moving away from whatever they were before. That's not even say that to a software services as a service as you mentioned provider. It's >>been challenging, you look at the course of history for companies that try to go from being a hardware company to a software company, uh HP itself, you know, sort of gave up on that IBM you could say, you know semi succeeded but they've they've struggled what's different >>That will spend 30 billion, >>30 >>four. Exactly. So and of course Cisco is making that transition. I mean every traditional large companies in that transition. What about today? Well, first of all, what do you think about HP es, prospects of doing so? And are there things today in the business that make that, you know more faster, whether it's containers or the cloud itself or just the scale of the internet? >>I mean it's fascinating topic, right? And I think many of the traditional players in the space failed because they wanted to mimic the cloud players and they simply couldn't muster up the Capex, which you need to build up public cloud. Right? Because if you think of the public cloud players then didn't put it up for the cloud offering, they put it up because they need themselves right, amazon is an online retailer google as a search and advertising giant Microsoft is organic load from from from office, which they had to bring to the cloud. So it was easier for them to do that. So no wonder they failed. The good news is they haven't lost much of their organic load. Hp customers are still HP customer service, celebrity security in their own premises and now they're bringing the qualities of the cloud as a service, the pay as you go capabilities to the on premise stack, which helps night leader to reduce complexity and go to what everybody in the post pandemic world wants to get to, which is I only pay for what I use and that's super crucial because business goes up and down. We're riding all the waves in a much, much faster way than ever before. Right before we had seven year cycles, it was kind of like cozy almost now we're down to seven weeks, sometimes seven days, sometimes seven hour cycles. And I don't want to pay for it infrastructure, which was great for how my business was two years ago. I want to pay for it as I use it now as a pivot now and I'm going to use >>Diane. How much of this? Thank you for that whole girl. How much of this is what customers want and need versus sort of survival tactics on the vendors >>part. So I think that there, if you look at where customers want to go, they know they have to go cloud, they had to go as a service. Um, and that they need to make multiple steps to get there. And for the most part, I see green light is being a, a highly credible market response to say, you know, we understand IT better, we helped build you guys up over the last 30 years. We can take you the rest of the way, here's all the evidence and the proof points, which I think a lot of the announcements provide uh, and they're very good on cloud native, but the area where the story, um, you may not be the fullest strength it needs to be is around things like multi cloud. So when I talked to almost any large organization C I O. They have all the clouds need to know, how do I make all this fit together? How do I reconcile that? So for the most part, I think it's closely aligned with actual customer requirements and customer needs. I think these have additional steps to go >>is that, do you feel like that's a a priority? In other words, they got to kind of take a linear path. They got to solve the problem for their core customer base or is it, do you feel like that's not even necessarily an aspiration? And it seems like customers, I want them to go. There is what I'm >>inferring that you're, so I do. Well let's go back to the announcement specifically. So there's there are two great operational announcements, one around the cloud physics and the other one around info site. It gives a wealth of data, you know, full stack about how things are operating, where the needs are, how you might be able to get more efficiencies, how you can shut down silicon, you're not using a lot of really great information, but all that has to live with a whole bunch of other consoles and everybody is really craving the single piece of glass. That's what they want is they want to reduce complexity as holder was saying and say, I want to be able to get my arms around my data center and all of my cloud assets. But I don't want to have to check each cloud. I want it in one place. So uh, but it's great to see those announcements position them for that next step. They have these essential components that are that look, you know, uh, they look best to breed in terms of their capabilities are certainly very modern now. They have to get the rest of that story. >>Hope you were mentioning Capex. I added it up I think last year the big four include Alibaba, spent 100 billion on the Capex and generally the traditional on prem players have been defensive around cloud. Not everything is moving to the cloud, we all know that. But I, I see that as a gift in a way that the companies like HP can build on top of into Diane's point that, you know, extend cross clouds out to the edge, which is, you know, a trillion dollar opportunity, which is just just massive. What are your thoughts on HBs opportunities there and chances of maybe breaking away from the pack >>I think definitely well there's no matter pack left, like there's only 23, it's a triumvirate of maybe it's a good thing from a marketing standpoint. There's not a long list of people who give me hardware in my data center. But I think it increases their chances, right? Like I said, it's a transformation, there's more credibility, there's more data point, there's more usage. I can put more workloads on this. And I see, I also will pay attention to that and look at that for the transformation. No question. >>Yeah. And speaking of C. I. O. S. What are you hearing these days? What's their reaction to this whole trend toward as a service? Do they, do they welcome it? Do they feel like okay it's a wait and see. Uh I need more proof points. What's the sentiment? >>Well, you have to divide the Ceo market basically two large groups. One is the the ones that are highly mature. They tend to be in larger organizations are very sophisticated consumers of everything. They see the writing on the wall and that for most things certainly not everything as a service makes the most sense for all the reasons we know, agility and and and speed, you know, time to value scalability, elasticity, all those great things. Uh And then you have the the other side of the market which they really crave control. They have highly parochial worlds that they've built up um that are hard to move to the cloud because they're so complex and intertwined because they haven't had that high maturity. They have a lot of spaghetti architecture. They're not really ready to move the cloud very quickly. So the the second audience though is the largest one and it's uh you know, the hyper scales are probably getting a lot of the first ones. Um, but the bigger markets, really the second one where the folks that need a lot of help and they have a lot of legacy hardware and software that they need to move and that H P. E understands very well. And so I think from that standpoint they're well positioned to take advantage of an untapped market are relatively untapped market in comparison. Hey, >>in our business we all get pulled in different directions because it would get to eat. But what are some of the cool things you guys are working on in your research that you might want people to know about? >>Uh, I just did a market overview for enterprise application platforms. I'm a strong believer that you should not build all your enterprise software yourself, but you can't use everything that you get from your typical SAs provider. So it's focusing on the extent integration and build capabilities. Bill is very, very important to create the differentiation in the marketplace and all the known sauce players basically for their past. Right? My final example is always to speak in cartoons, right? The peanuts, right? There's Linus of this comfort blanket. Right? The past capability of the SARS player is the comfort blanket, right? You don't fit 100% there or you want to build something strategic or we'll never get to that micro vertical. We have a great enterprise application, interesting topic. >>Especially when you see what's happening with Salesforce and Service now trying to be the platform platforms. I have to check that out. How about >>Diane? Well and last year I had a survey conducted a survey with the top 100 C IOS and at least in my view about what they're gonna do to get through this year. And so I'm redoing that again to say, you know, what are they gonna do in 2022? Because there's so many changes in the world and so, you know, last year digital transformation, automation cybersecurity, we're at the top of the list and it'll be very interesting. Cloud was there too in the top five. So we're gonna see what, how it's all going to change because next year is the year of hybrid work where we're all we have to figure out how half of our businesses are in the office and half are at home and how we're gonna connect those together and what tools we're gonna make, that everybody's trying to figure >>out how to get hybrid. Right, so definitely want to check out that research guys. Thanks so much for coming to the cubes. Great to see you. >>Thanks. Thanks Dave >>Welcome. Okay and thank you for watching everybody keep it right there for more great content from H. P. S. Green Lake announcement. You're watching the cube. Mm this wasn't

>>on >>the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Okay. Welcome back, everyone. That's the cubes. Live coverage here in Palo Alto, California. I'm John for your host with David Lantana in Boston. Massachusetts. Uh, we got a great panel here. Analysts just gonna break it down. Keynote analysis. Day one, we got Ah, longtime Web services expert analyst Diane Hinchcliffe, principal researcher at N V. P. It constantly research, but he goes way back. Dan, I remember, uh, 2000 and one time frame you and I'm >>reading Last time you and I hang out with Michael Arrington's house back in the TechCrunch days >>back when, you know you were on this was Web services. I mean, that's always, uh, serves on the architectures. They called it back then. This was the beginning. This really was the catalyst of cloud. If you think about virtualization and Web services in that era, that really spawned where we are today so great to >>have you on as an Amazon got their start saying that everyone could get whatever they want to on a P. I now right, >>all right? Well, we've been riding this wave. Certainly it's cotton now more clear for the mainstream America. And I quoted you in my story, uh, on Andy Jassy when I had my one on one with them because I saw your talk with star Bit of the weekend and in the way you kicked it off was the Pandemic four was forced upon everybody, which is true, and that caught my attention was very notable because you talked to a lot of C E. O s. Does jazz sees pitch resonate with them? In your opinion, what's your take on on that on that posture? Because we heard, hey, you know, get busy building or you're dying, right? So get busy building. That's what >>I thought that was a good message. But I mean on and certainly I saw tweets and said, Hey, he's just he's just directly talking to the CEO. But if you ask me, he's still talking to the CTO, right? The technology officer who's got a feels all this technology and bend it into the shape that it will serve the business. You talk to a CEO who wants is trying to get on the cloud their biggest challenges. I know I need armies of people who know all these brand new services. You saw the development velocity of all the things that they announced and things they re emphasized there was There was a lot of things that were bringing back again because they have so many things that they're offering to the public. But the developer skills or not, they're the partner skills are not there. So you talked to CEO, says All right, I buy in and and I have had to transform overnight because of the pandemic, my customers have moved, my workers have moved on, and I have to like, you know, redirect all my I t Overnight and Cloud is the best way to do that. Where's my where's all the skills for the training programs, the department programs that allow me to get access to large amounts of talent? Those are the types of things that the CEO is concerned about is from an operational perspective. We didn't hear anything about, like a sales force type trailhead where we're going to democratize cloud skills to the very far end of your organization. >>Yeah, they're just kind of scratching the service. They didn't mention that, you know, far Gates away to get into server list. I mean, this is ultimately the challenge Dave and Deena like, don't get your thoughts on this because I was talking Teoh a big time CTO and a big time see so and that perspectives were interesting. And here's the Here's the Here's what I want you to react Thio the sea level Say everything is gonna be a service. Otherwise we're gonna be extinct. Okay, that's true. I buy that narrative, Okay, Make it as a service. That's why not use it. And then they go to the C t. And they say, implement, They go Well, it's not that easy. So automation becomes a big thing. And then so there's this debate. Automate, automate, automate. And then everything becomes a service. Is it the cart before the horse? So is automation. It's the cart before the horse, for everything is a service. What do you guys think about that? >>We'll see. I mean, CEO is to Diane's point, are highly risk averse and they like services. And those services generally are highly customized. And I think the tell in the bevy of announcements the buffet have announces that we heard today was in the marketplace what you guys thought of this or if you caught this. But there was a discussion about curated professional services that were tied to software, and there were classic PDM services. But they were very, you know, tight eso sort of off the shelf professional services, and that's kind of how Amazon plays it. And they were designed to be either self serve. It's a Diane's point. Skill sets aren't necessarily there or third parties, not directly from Amazon. So that's a gap that Amazon's got too close. I mean, you talk about all the time without post installations, you know, going on Prem. You know who's gonna support and service those things. You know, that's a that's a white space right now. I think >>e think we're still reading the tea leaves on the announcements. But there was one announcement that was, I thought really important. And that was this VM Ware cloud for a W s. It says, Let's take your VM ware skills, which you've honed and and cultivated and built a talent base inside your organization to run VMS and let's make that work for a W s. So I thought the VM Ware cloud for a W s announcement was key. It was a sleeper. It didn't spend a lot of time on it. But the CEO ears are gonna perk up and say, Wait, I can use native born skills. I already have to go out to the cloud So I didn't think that they did have 11 announcement I thought was compelling in that >>in the spending data shows of VM Ware Cloud on AWS is really gaining momentum by the way, As you see in that open shift So you see in that hybrid zone really picking up. And we heard that from AWS today. John, you and I talked about it at the open I produces in >>Yeah, I want to double down on that point you made because I want to get your thoughts on this a Z analyst because you know, the VM ware is also tell. Sign to what I'm seeing as operating and developing Dev ops as they be called back in the day. But you gotta operate, i t. And if Jassy wants to go after this next tier of spend on premise and edge. He's gotta win the global i t posture game. He's gotta win hybrid. He's got to get there faster to your point. You gotta operate. It's not just develop on it. So you have a development environment. You have operational environment. I think the VM Ware thing that's interesting, cause it's a nice clean hand in glove. VM Ware's got operators who operate I t. And they're using Amazon to develop, but they work together. There's no real conflict like everyone predicted. So is that the tell sign is the operational side. The challenge? The Dev, How does Amazon get that global I t formula down? Is it the VM Ware partnership? >>I think part of it is there, finally learning to say that the leverage that the vast pool of operational data they have on their literally watching millions of organizations run all the different services they should know a lot and I say made that point today, he said, Well, people ask us all the time. You must have all these insights about when things were going right or wrong. Can you just tell us? And so I think the announcement around the Dev ops guru was very significant, also saying you don't necessarily have to again teach all your staff every in and out about how to monitor every aspect of all these new services that are much more powerful for your business. But you don't yet know how to manage, especially at scale. So the Dev Ops guru is gonna basically give a dashboard that says, based on everything that we've known in the past, we could give you insights, operational insights you can act on right away. And so I think that is again a tool that could be put in place on the operational side. Right. So b m where for cloud gives you migration ability, uh, of existing skills and workloads. And then the Dev Ops crew, if it turns out to be everything they say it is, could be a really panacea for unlocking the maturity curve that these operators have to climb >>on. AWS is in the business now of solving a lot of the problems that it sort of helped create. So you look at, for instance, you look at the sage maker Data Wrangler trying to simplify data workloads. The data pipeline in the cloud is very very complex and so they could get paid for helping simplify that. So that's a wonderful, virtuous circle. We've seen it before. >>Yeah. I mean, you have a lot of real time contact lens you've got, um, quick site. I mean, they have to kind of match the features. And And I want to get your guys thoughts on on hybrid because I think, you know, I'm still stuck on this, Okay? They won the as path and their innovations Great. The custom chips I buy that machine learning all awesome. So from the classic cloud I as infrastructure and platform as a service business looking good. Now, if you're thinking global, I t I just don't just not connecting the dots there. See Outpost? What's riel today for Amazon? Can you guys share E? I mean, if you were watching this keynote your head explode because you've got so many announcements. What's actually going on if you're looking at this is the CEO. >>So the challenge you have is the CEO. Is that your you have 10, 20 or 30 or more years of legacy hardware, including mainframes, right. Like so big insurance companies don't use mainframe because their claims systems have been developed in their very risk averse about changing them. Do you have to make all of this work together? Like, you know, we see IBM and Redhead are actually, you know, chasing that mainframe. Which angle, which is gonna die out where Amazon, I think is smart is saying, Look, we understand that container is gonna be the model container orchestration is gonna be how I t goes forward. The CEO is now buy into that. Last year, I was still saying, Are we gonna be able to understand? Understand? Kubernetes is the regular average i t person, which are not, you know, Google or Facebook level engineers Are there gonna be able to do do containers? And so we see the open sourcing of of the AWS is, uh, kubernetes, uh, server on. We see plenty of container options. That's how organizations could build cloud native internally. And when they're ready to go outside because we're gonna move, they're gonna move many times slower than a cloud native company to go outside. Everything is ready there. Um, I like what I'm seeing without posts. I like what I'm seeing with the hybrid options. The VM ware for cloud. They're building a pathway that says you can do real cloud. And I think the big announcement that was that. That s a really, uh, spend time on which is that PCs for everywhere. Um, a saying you're gonna be able to put Amazon services are compute services anywhere. You need it, e think that's a smart message. And that allows people to say I could eventually get toe one model to get my arms around this over time >>day. What does that mean for the numbers? I know you do a lot of research on spend customer data. Um, CEO is clearly no. This is gonna be the world's never go back to the same way it was. They certainly will accelerate cloud toe. What level depends upon where they are in their truth, as Jassy says. But >>what does >>the numbers look at? Because you're looking at the data you got Microsoft, You got Amazon. What's the customer spend look like where they're gonna be spending? >>Well, so a couple things one is that when you strip out the the SAS portion of both Google and Azure, you know, as we know, I asked him pass A W S is the leader, but there's no question that Microsoft is catching up. Says that we were talking about earlier. Uh, it's the law of large numbers Just to give you a sense Amazon this year we'll add. Q four is not done yet, but they'll add 10 billion over last year. And Jesse sort of alluded to that. They do that in 12 months. You know, uh, azure will add close to nine billion this year of incremental revenue. Google much, much smaller. And so So that's, you know, just seeing, uh, as you really catch up there for sure, you know, closing that gap. But still Amazon's got the lead. The other thing I would say is die on you and I were talking about this Is that you know Google is starting. Thio do a little bit better. People love their analytics. They love the built in machine learning things like like big query. And you know, even though they're much, much smaller there, another hedge people don't necessarily want to goto Microsoft unless they're Microsoft Shop. Google gives them that alternative, and that's been a bit of a tailwind for Google. Although I would say again, looking at the numbers. If I look back at where Azure and AWS were at this point where Google is with a few billion dollars in cloud the growth rates, I'd like to see Google growing a little faster. Maybe there's a covert factor there. >>Diane. I want to get your thoughts on this transition. Microsoft Oracle competition Um, Jesse knows he's got a deal with the elite Salesforce's out there. Oracle, Microsoft. Microsoft used to be the innovator. They had the they had the phrase embracing extend back in the day. Now Amazon's embracing and extending, but they gotta go through Oracle and Microsoft if they wanna win the enterprise on premise business and everybody else. Um, eso welcome to the party like Amazon. You What's your take on them versus Microsoft? Calling them out on sequel server licensing practices almost thrown him under the bus big time. >>Well, I think that's you know, we saw the evidence today that they're actually taking aim at Microsoft now. So Babel Fish, which allows you to run Microsoft sequel server workloads directly on Aurora. Uh, that that is what I call the escape pod that gives organizations an easy way That isn't Will parliament to redesign and re architect their applications to say, Just come over to AWS, right? We'll give you a better deal. But I think you've got to see Amazon have, um, or comprehensive sales plan to go into the C. E. O s. Go after the big deals and say, You know, we want to say the whole cloud suite, we have a stack that's unbeatable. You see our velocities, you know, best in class. Arguably against Microsoft is the big challenger, but we'll beat you on on a total cost of ownership. You know, your final bill. At the end of the day, we could we commit to being less than our competitors. Things like that will get the attention. But, you know, uh, Amazon is not known for cutting customized deals. Actually, even frankly, I'm hearing from very CEO is a very large, like Fortune 20 companies. They have very little wiggle room with Microsoft's anybody who's willing to go to the big enterprise and create custom deals. So if you build a sales team that could do that, you have a real shot and saying getting into the CEO's office and saying, You know, we want to move all the I t over and I'm seeing Microsoft getting winds like that. I'm not yet seeing Amazon and they're just gonna have to build a specialized sales team that go up against those guys and migration tools like we saw with Babel fish that says, If you want to come, we can get you over here pretty quick. >>I want to chime in on Oracle to John. I do. I think this is a blind spot somewhat for AWS, Oracle and mainframes. Jesse talks that talks like, Oh yeah, these people, they wanna get off there. And there's no question there are a number of folks that are unhappy, certainly with Oracle's licensing practices. But I talked to a lot of Oracle customers that are running the shops on Oracle database, and it's really good technology. It is world class for mission critical transaction workloads. Transaction workloads tend to be much, much smaller data set sizes, and so and Oracle's got, you know, decades built up, and so their their customers air locked in and and they're actually reasonably happy with the service levels they're getting out of Oracle. So yes, licensing is one thing, but there's more to the story and again, CEO or risk averse. To Diane's point, you're not just gonna chuck away your claim system. It's just a lot of custom code. And it's just the business case isn't there to move? >>Well, I mean, I would argue that Well, first of all, I see where you're coming from. But I would also argue that one of the things that Jesse laid out today that I thought was kind of a nuanced point was during the vertical section. I think it was under the manufacturing. He really laid out the case that I saw for startups and or innovation formula, that horizontal integration around the data. But then being vertically focused with the modern app with same machine learning. So what he was saying, and I don't think he did a good job doing it was you could disrupt horizontally in any industry. That's a that's a disruption formula, but you still could have that scale. That's cloud horizontal scalability, cloud. But the data gives you the ability to do both. I think bringing data together across multiple silos is critical, but having that machine learning in the vertical is the way you could different so horizontally. Scalable vertical specialization for the modern app, I think is a killer formula. And I think >>I think that's a I think it's a really strong point, John, and you're seeing that you're seeing in industries like, for instance, Amazon getting into grocery. And that's a data play. But I do like Thio following your point. The Contact Center solutions. I like the solutions play there and some of the stuff they're doing at the edge with i o T. The equipment optimization, the predictive maintenance, those air specialized solutions. I really like the solutions Focus, which several years ago, Amazon really didn't talk solution. So that's a positive sign, >>Diane, what do you think? The context And I think that was just such low hanging fruit for Amazon. Why not do it? You got the cloud scale. You got the Alexa knowledge, you know, got machine learning >>zone, that natural language processing maturity to allow them to actually monitor that. You know that that contact lens real time allows them a lot of supervisors to intervene them conversations before they go completely south, right? So allowing people to get inside decision windows they couldn't before. I think that's a really important capability. And that's a challenge with analytics in general. Is that generates form or insights than people know how to deal with? And it solutions like contact lens Real time? This is Let's make these insights actionable before it's broken. Let's give you the data to go and fix it before it even finishes breaking. And this is the whole predictive model is very powerful. >>Alright, guys, we got four minutes left. I wanted Segway and finish up with what was said in the keynote. That was a tell sign that gives us some direction of where the dots will connect in the future. There's a lot of stuff that was talked about that was, you know, follow on. That was meat on the bone from previous announcements. Where did Jassy layout? What? I would call the directional shift. Did you see anything particular that you said? Okay, that is solid. I mean, the zones was one I could see. What clearly is an edge piece. Where did you guys see? Um, some really good directional signaling from Jassy in terms of where they really go. Deal with start >>e I felt like Jassy basically said, Hey, we invented cloud. Even use these words we invented cloud and we're gonna define what hybrid looks like We're gonna bring our cloud model to the edge. And the data center just happens to be another edge point. And hey, I thought he laid down the gauntlet. E think it's a very powerful message. >>What do you think Jesse has been saying? That he laid out here, That's >>you laid out a very clear path to the edge that the Amazons marching to the edge. That's the next big frontier in the cloud. It isn't well defined. And that just like they defined cloud in the early days that they don't get out there and be the definitive leader in that space. Then they're gonna be the follower. I think so. We saw announcement after announcement around that you know, from the zones Thio the different options for outpost um, the five g announcement wavelength. All of those things says we're gonna go out to the very tippy edge is what I heard right out to your mobile devices. Right after the most obscure field applications imaginable. We're gonna have an appliance So we're gonna have a service that lets you put Amazon everywhere. And so I think the overarching message was This is a W s everywhere it z gonna go after 100% of I t. Eventually on DSO you can move to that. You know, this one stop shop? Um and you know, we saw him or more discussions about multi cloud, but it was interesting how they stand away from that. And this is what I think One area that they're going to continue to avoid. So it was interesting, >>John, I think I think the edges one by developers. And that's good news for Amazon. And good news for Microsoft. >>We'll see the facilities is gonna be good for me. I think guys, the big take away You guys nailed two of them there, but I think the other one was I think he's trying to speak to this new generation in a very professorial way. Talk about Clay Christensen was a professor at his business school at Harvard. We all know the book. Um, but there was this There was this a posture of speaking to the younger generation like hey, the old guy, the old that was running the mainframe. Wherever the old guys there, you could take over and run this. So it's kind of like more of a leadership preach of preaching like, Hey, it's okay to be cool and innovative, right now is the time to get in cloud. And the people who are blocking you are either holding on to what they built or too afraid to shift. Eso I think a Z we've seen through waves of innovation. You always have those people you know who are gonna stop that innovation. So I was very interesting. You mentioned that would service to the next generation. Um, compute. So he had that kind of posture. Interesting point. Yeah, just very, very preachy. >>E think he's talking to a group of people who also went through the through 2020 and they might be very risk averse and not bold anymore. And so, you know, I think that may have helped address that as well. >>All right, gentlemen, great stuff. Final word in the nutshell. Kena, What do you think about it in general? Will take away. >>Yeah, I I think we saw the continued product development intensity that Amazon is going to use to try and thrash the competition? Uh, the big vision. Um, you know, the real focus on developers first? Um and I think I t and C e O's second, I think before you could say they didn't really think about them too much at all. But now it's a close second. You know, I really liked what I saw, and I think it's It's the right move. I'd like to Seymour on on hybrid cloud migration than that, even when we saw them. >>All right, leave it there. Don. Thanks for coming on from this guest analyst segment. Appreciate you jumping in Cuba. Live. Thank you. >>Thanks. Alright. >>With acute virtual. I'm your host John per day Volonte here covering A W s live covering the keynote in real time State more for more coverage after the break

>>Live from Seattle, Washington. It's the cube covering Smartsheet engage 2019 brought to you by Smartsheet. >>Welcome back everyone to Seattle, Washington. We are here at Smartsheet engaged 2019 I'm your host, Rebecca Knight along with my cohost Jeff Frick. You're watching the cube. We are here with a cube alum, a cube veteran, Dion Hinchcliffe, VP and principal analyst at constellation research at at Washington DC. Thank you so much for returning to the cube. Absolutely. Thanks for having me. So we're here to talk with you about the future of work, which is a huge topic but a fascinating one. I want you to start by giving sort of a broad brush of what you see are the biggest changes right now happening in the workplace is driven by the new, the rise of digital technologies. >>Sure. I mean while it digital is infusing everything in the workplace these days, right? And so we've had the past waves of productivity tools and then mobile devices came through and then eventually augmented reality and virtual reality are going to literally change how we perceive the workplace. And then we have just everyday trends like remote working. And now people can work from anywhere, right? It's fantastic. And that's, that's really revolutionized a lot of things. There are things in 2% of the workforce per year is becoming a remote work force. Companies like ADP have a quarter of their workforce working from home, right? Accenture, same thing. They're getting rid of office space and they, they work out of their house unless there's a client site. And because you can create a, create the experience that you want. And one of the really big trends is this is this trend towards being able to shape the employee experience the way that you want to, using the tools that you prefer. >>And some people call this shadow it, other people call it innovation, right? And so that's one of the, one of the big changes. And then we have things like the gig economy, which is allowing people to build the lifestyles they want doing any kind of work they want when they want to, when they feel like it on their own terms. And that's, that's really quite exciting too. So all these, this confluence of forces all enabled them driven by technology. But it's also leading to a lot of what we call cognitive overload workers that are not lifelong learners are feeling overwhelmed by this. And that's another big challenge. >>Well, you also get this tools proliferation, which they're just not, they're just not word and, and Excel anymore. But you've got a tab open with Salesforce, you've got a tab open with Slack, you've got Gmail open, you've got docs open and you've got Smartsheet open. You might have a JIRA open. I mean, so how is that gonna sort itself out as we just kind of keep adding new tabs of apps that we have to keep up >>and we need all this technology to do better work. I mean the, these apps provide value except that it's increased in the onboarding time for workers. It's making it hard for us to train people. In some companies it's hard to retain people because they feel like they have to go to work and there's this onslaught of technologies they have to have tabs open and get their jobs done. And they do. And so we're seeing things like, you know, we're at the Smartsheet conference where, you know, how can we centralize work a little bit better, streamline it by integrating the tools and creating more focus in on what we're doing. And that's a very big trend. So my latest digital workplace trends report, we say this, we're seeing these hubs form, you know like Slack is another work hub that's become very popular inside of organizations. >>They have over 1100 application integrations that allow people to spend their time in one place and kind of work through all these other systems from one hub. So we're dealing with this complexity, you know, starting to be able to do this now, but it's early days still a big challenge. So what's a, what are you seeing now? So what's the, what is the answer then? I mean we have you just described all of these trends that are taking place that are making, making the work modern workplace so much more complex, dealing with workers who have, they're dealing with cognitive overload leaders who want more with less. What are some of the answers? What are some of the most exciting tools that you're seeing right now? We talked already about Smartsheet and Slack. We see the new digital experience platforms are emerging and low code and no code is also becoming popular. >>I'd be able to take the pieces of the applications you want and create more streamline experiences. So the CIO of Accenture, Andrew Wilson, solve his problem right away there. They're knowledge workers are just being choked by all of these tools, but yet we need the value they provide. So he began to divide up the employee experience, the 100 top moments and then he built experiences that enabled, you know, project management and onboarding and all of these key activities to be friction-free built out of their existing applications, but streamlined to just what they needed to do. And he used this as his top priority as a digital leader is to say, we've got to take as much complexity away so we can get at the values with streamlining and simplification. And we now have tools that allow that shaping to happen very quickly. It's almost reminds me of kind of the competition for Deb's right now. >>It's the competition for employees. And then we've talked a lot about the consumerization of it in mobile devices for the customer experience, but there hasn't been as much talk about leveraging that same kind of expected behavior, right? Or expected inner engagement interaction with the apps on the actual employee engagement side, which is probably as fierce of a battle as it is to get customers. Cause I think there's a lot more than 2% customers out available. But yeah, we only get 2% unemployment in the Bay area. Now it's creating effectively negative unemployment, right? Anything under 3%. So this is the challenges. Employee experience is usually low on the priority list for CEOs. They usually have analytics and cloud and cybersecurity and all these things that they have to get done that are higher priority. Yet customer experience is, is one of those priorities. But how does an employee give a good customer experience when they have a poor experience to deliver it with? Right. We're seeing you can do with talented people, is expecting to do a great job. And then give them a bunch of hard to use tools, right. Which is what's happening. So we are now finally seeing that prioritization go up a little bit because employee experience is part of delivering great customer experience and it's how you, how you create that experience to begin with. So small >>and leaders are seeing that as a priority of retaining their top people because they understand that their workers need to feel satisfied with their work life. >>Yeah. And now we have data on a lot of these things we didn't have before and I'm sure you've seen the numbers that are, most employees are disengaged at work. The majority, right between 50 and 60% depending on whose data you're looking at. That's an enormous untapped investment that workers are not performing the way that they could if they had better employee experiences. And what's disengaging is, as I mentioned, you know, giving a talented person allows you tools or allows you experience, right and expect them to do great is right. It doesn't happen. >>How much do you think AAL or excuse me, AI and machine learning will be able to offload enough of the mundane to flip the bit on how engaged they are in their job. >>Yeah, it's, it's interesting cause there's, you know, there's two sides of the coin there. Some people like a, a job that they can just kind of phone in and it's kind of rote and they can come in, they don't have to think too hard and then they can go home to their family and some people are hired on that basis. Right. Um, because that's the challenge. AI and machine learning will absolutely automate most rote work. If you look at like Adobe sensei, I was at the Adobe conference and, and they were talking about how all of these creative types, you'll have all these mundane tasks automated for them. And I could see everybody looking at each other going, I get paid to do. >>Right, right. >>So you know, it, you'll see things like robotic process automation is working. I mean, I hear anecdotes all the time from CIO is how they had, they cut like 25% out of their call center because they handed it over to the box. Right. You know, as bill processing, that's one of the, and sorting and matching bills, the invoices, it's a manual job even in today's world until very recently. So we are seeing that happen about the most rote level and it just, but it's just going to climb up from there. >>What do you see down the road though? I mean in terms of those, in terms of those employees who are raising their saying hands saying weed, I kind of want that job. I are you, are you seeing what's going to happen to those people? Are they going to have to learn new skills? Are they, are they going to be invested in by their companies? >>Well you hope so. You know, it's interesting. We see that all the big vendors now have these big education programs. Salesforce has Trailhead. SAP just announced open SAP where they giveaway massively open online courses. And you know, Microsoft has done this with Microsoft developers network way back in the day, trying to educate people. I mean you can get re-skilled for nothing for free now if you want to do it. But this is the challenges. Even though every technological revolution in the past, and it looks like this one too has totally changed the employment picture. Uh, uh, by and large it creates more jobs than we lose. And that looks like it's going to happen here. But the people who lose the jobs aren't the ones that tend to gain the jobs, the new jobs, right? Yeah. The, it's hard to take somebody who's, who's sorting bills and say, I need you to develop a new AI algorithms because that's where the next strategic jobs are going to be directing the AI to do all these things. Right. And so I think the short term is going to be dislocation and it's happening so fast that unless society, government, and enterprises really intervene that to upskill these folks, we are going to have a challenge. >>Well, we're in this really weird time too, in between, I mean, the classic one is long haul trucking, right? Which is perfect for autonomous vehicles, you know, to carry a lot of that freight and everyone pretty much agrees that's going to happen. At the same time, there's, there's a huge shortage of available truck drivers today. Uh, like there never has been. So as these weird, and again, it's probably not the best thing for a young kid to get into, right? Because it's not, doesn't have a lot great long. >>Right? Right. >>Well, and you know, you look at Uber and their stated direction is, is they want to get rid of all these drivers, right? They want it, they want self-driving taxis. And you know, we're getting close to where that might actually happen, right? Uh, and so the unskilled labor is going to be hit by far the worst. You have to become skilled labor in, in the digital economy. Uh, and so a big part of the future of work is going to be finding ways to, to get the skills into people's hands. You know, like Facebook and other large organizations don't even require a college degree. What they want people, the people that can deliver, they can take these things and create the, you know, the, the great products of the future. And so, you know, those everyone has to become a knowledge worker. >>And, and as Laird Hamilton said on the main stage today, it's the, it's the, the formula of learning to really understand when you're starting from a point of, wow, I don't know much about that. I bet. I guess I'd better learn about it. And then learning a lot about it along the way. We all have to be able to adapt and adopt those new, >>no, absolutely. Now the, uh, uh, and so w we see up-skilling and cross skilling becoming more transdisciplinary. So business people are becoming it folks now and it folks really business people, you know, we've had this business, it divide for a long time and cracks me up. I still go to big companies in the it departments using its own building. Right. But those days are going away. And now seeing that, you know, now as it people over on the business side that live there now. Right. You know, so we're seeing this kind of, this blending where digital is infusing everything and so you have to become digitally competent. Uh, and this is where we have to make that simpler. This is going back to the, you know, the, the, the digital workplace, the average user has had the number of applications they have to learn double or triple in the last just the last five years. Right. So it's a big challenge. >>So what should kids be majoring in today? What's your, >>Oh, a game design. Know the gaming industry is bigger than the movie by a large, large margin. Right. And, and that, that's where all the experience of these immersive experiences in virtual reality and augmented reality really come from. And then you can go into business. Right. You know, >>even sociology majors can design games. >>Yeah. It's just, you know, it's just get, like you said, it's, it's the poor tweeners right. That get bumped on the old and aren't necessarily in a position to take care of the new, yeah. I'll have to take care of. And unfortunately, uh, not a lot of great record of retraining today, but maybe that's going to have to be a much more significant investment because there just aren't the people to fill those positions, period. Right? Yeah. Well, and there's these big market places now you can build the career of your dreams. You'd go to Upwork or Gigster. I mean, these are big job markets where you can go and find work and do it from anywhere using a tablet you bought for $50 off Amazon. Right, right. You know, it just that most of you aren't even aware of that. They can do that. Right, right, right. >>So it's this fast changing world. Put a few bucks away for insurance and you've put a few bucks away in your 401k and you, yeah. You know, not just living off the cash plus a little bit to cover your costs, which unfortunately a lot of their, like the Uber drivers and the Lyft drivers are anyway, you know, they're not really banking that thing for building a, a career. Well, I've crawled to those platforms and it's interesting, entrepreneurial activities, very common in places like Asia, right? Where if, you know, they come here, they build businesses right away. Right. And they're used to that. So w and we lost some of that, but I think we were gave a economy is giving a lot of that back to us. We have to relearn it again, you know? Right. >>Well Deon, thank you so much for coming on the cube. It was a pleasure having you. Absolutely. Thanks. So Jeff. Thanks Rebecca. I'm Rebecca Knight for Jeff Frick. Stay tuned to more of the cubes live coverage of NJ engaged 2019.

>>live from Seattle, Washington. It's the key nude covering Smartsheet engaged 2019. Brought to you by smartsheet >>Welcome back, everyone to Seattle. Washington. We're here at smartsheet engaged 2019. I'm your host, Rebecca Night, along with my co host, Jeff. Rick, You're watching the Cube? We're here with a Cuba Lama Cube veteran Dion Hinchcliffe, VP and principal analyst at Constellation Research at a Washington D. C. Thank you so much for returning to the Cube. >>Absolutely. Thanks for having me. >>So we're here to talk with you about the future of work, which is a huge topic, but a fascinating one. I want you to start by giving sort of a broad brush of what you see are the biggest changes right now happening in the work force driven by the new the rise of digital technologies. >>Sure. I mean, well, it digital is infusing everything in the workplace these days, right? So, you know, we've had the past waves of productivity tools and mobile devices came through and then eventually augmented reality and virtual reality. You're gonna literally change how we perceive the workplace on then We have just, you know, everyday translate remote working and now people can work from anywhere, right. It's fantastic, and that's that's really revolutionized a lot of things. Things in 2% of the workforce per year is becoming a remote work force. Companies like 80 p have 1/4 of their work force working from home, right X century, something to get rid of office space. And they work out of their house. Unless there's a client site on because you can credit, create the experience that you want and one of the really big trends is this is this trend towards be able to shape the employees experience the way that you want to using the tools that you prefer. And so people call this shadow I t. Other people call it innovation, right? And so that's one of the big changes. Then we have things like the gig economy, which is allowing people to build the lifestyles they want. We're doing any kind of work they want when they want to, when they feel like it on their own terms on that's that's really quite exciting to use all these. This confluence of forces are enabled him driven by technology, but it's also leading to a lot of what we call cognitive overload workers. They're not lifelong learners are feeling overwhelmed by this, and that's another big challenge. >>But you also get this tools >>proliferation, which they're just not. They're just not word and excel anymore. But you've got a tab open with Salesforce. You've got a A tab open with slack. You got Gmail open. You've got Doc's open. He got smart cheat open. You might have Jiro open. I mean, so how is that gonna sort itself out as we just kind of keep adding new tabs of AB? So we have to keep in our >>way. And we need all this technology to do better work. Thes APS provide value, except that it's increasing the on boarding time for workers. It's making it hard for us, the train people. In some company. It's hard to retain people because they feel like they have to go to work. And there's this onslaught of technology. They have to have 30 tabs open to get their jobs done, and they do. And so we're seeing things that you know we're at the smartsheet conference where how can we centralize work a little bit better? Streamline it by integrating the tools and credit more focused on what we're doing. And that's a very big trend. S Oh, my latest digital workplace trends report. We say that we're seeing these hubs for me, like Slack is another workup that's become very popular inside of organizations. They have over 1100 application integrations that allow people to spend their time in one place and kind of work through all these other systems from one hub. So we're dealing with this complexity starting to be able to do this now. But it's early days still a big challenge. >>So so So what are you seeing now? So what? So what is the answer then? I mean, we have You've just described all of these trends that are taking place that they're making making the modern workplace so much more complex, dealing with workers who they're dealing with, cognitive overload leaders who want more with less What? What are some of the answers? What are some of the most exciting tools that you're seeing right now? >>Boys, we talked already about smartsheet and slack. We see the new digital experience platforms are emerging on low code and know code is also becoming popular to be able to take the pieces of the applications you want and create more streamlined experiences. So the CEO of Accenture, Andrew Wilson, you solve this problem right away Their their knowledge. Workers were being choked by all of these tools, but yet we need the value they provide. So he began to divide up the employees experience of the 100 top moments, and then he built experiences that enabled project management and on boarding and all these key activities to be friction free, built out of their existing applications. Streamlines, too, just what they needed to dio. And he views this as his top priority as a digital leaders and say, We've got to take much complexity away so we can get at the values with streamlining the simplification on. We now have tools that allow that shaping that happen very quickly. >>It's almost reminds me it's kind of the competition for Deb's right now competition for employees, and we've talked a lot about the consumer ization Oh, I t and mobile devices for the customer experience. But there hasn't been as much talk about leveraging that same kind of expected behaviour writer expected in her engagement interaction with the APS on the actual employee engagement side, which is probably as fierce of a battle as it is to get customers because I think there's a lot more than 2% customers out available, but we only got 2% unemployment in the Bay Area. Now. It's crazy, >>effectively, negative unemployment, right, right? Is that anything under 3%? Yes, so you know this is the challenges employees experience is usually low on the priority list for CEOs usually have analytics and cloud in cyber security and all these things that they have to get done that are higher priority. Yet customer experiences is one of those priorities. But how does an employee give a good customer experience when they have a poor experience to do it, deliver it with right? The worst thing you could do with talented people is expected to do a great job and then give him a bunch of hard to use tools, right? Which is what's happening. So we are now finally seeing that privatization go up a little bit because employees experiences part of delivering great customer experience. That is how you how do you create that experience to begin with so small progress >>and leaders air seeing that as a priority of retaining their top people because they understand that they're workers need to feel satisfied with their work life. >>Yeah, and now we have data on a lot of these things we didn't have before, you know? And I'm sure you've seen the numbers. Most employees air disengaged at work, the majority right between 50 and 60% depending on whose data you're looking at. That's an enormous untapped investment that that that workers are not performing the way that they could if they had better employees experiences and what's disengaged. As I mentioned, giving a talented person lousy tools are allows the experience and expecting the two greatest. It doesn't happen. How >>much do you >>think? A. L Excuse me. Aye, aye. And machine learning will be able to offload enough of the mundane to flip the bit on how engaged they are in their job. >>Yeah, it's interesting because there's, you know, there's two sides to a coin there. Some people like a job that they could just kind of phone in, and it's kind of wrote, and they can come in. They don't have to think too hard and then go home to their families. So people are hired on that basis, right? Because that's the challenge a I and machine learning will absolutely automate. Most wrote work if you look at like a dill bee sense A. I was at the adobe conference and they were talking about how all of these creative types you have all these mundane tasks automated for them, and I could see everybody looking at each other going. I >>could pay to >>do >>that creative rate. >>So you see the things like robotic process automation is working. I mean, I hear anecdotes all the time from CEOs how they how they cut 25% out of the call center because they handed it over to the box, right? You know, Bill processing. That's one of the, you know and sorting matching bills, the invoices, a manual job, even in today's world until very recently. So we are seeing that happen about the most wrote level and just, but it's just gonna climb up from there. >>What do you see down the road, though? I mean in terms of those in terms of those employees were raising their saying can saying I kind of want that job. Are you? Are you seeing what's gonna happen to those people? Are they going to have to learn new skills? Are they are they going to be invested in by their companies? >>We hope so. You know, it's interesting. We see that all the big vendors have these big education programs. Sales force has trailhead s a P just announced open ASAP where they give away massively open online courses on. And Microsoft has done this with Microsoft Developers Network way back in the day, trying to educate people. You can get Reese killed for nothing for free now if you want to do it. But this is the challenges, even though every technological revolution in the past it looks like this one, too, has you are really changed the employment picture. By and large, it creates more jobs than we lose on. That looks like it's gonna happen here. But the people who lose their jobs, not the ones that tend to gain the job, gets a new job. They often it's hard to take somebody who's who's sorting bills and say, I need you to develop a new way I algorithm because that's where you have executed jobs. They're gonna be directing the eye to do all these things right on. So I think the short term is gonna be dislocation. And it's happening so fast that unless society, government and enterprises really intervene that toe up skill, these folks, we are gonna have a challenge. >>We're in this really weird time to in between. I mean, the classic one is long haul trucking, right, which is perfect for autonomous vehicles. T carry a lot of that freight, and everyone pretty much agrees that's gonna happen. At the same time there's there's a huge shortage of available truck drivers today, like there never has been. So is he's weird, and it's probably not the best thing for a young kid to get into right, because doesn't have a lot of great long term, >>right? >>Well, you look at uber on their stated direction is they want to get rid of all these drivers they want. They want self driving taxis on, you know, we're getting close to where that might actually happen right on. So the unskilled labor is gonna be hit by far the worst you have to become skilled labor in the digital economy on a big part of the future of work is going to be finding ways to get the skills into people's hands on Facebook and other larger. They don't even require a college degree what they want people to people that can deliver that could take these things and create the, you know, the great products of the future. On DSO, you know, those everyone has to become a knowledge worker >>and in as layered, Hamilton said. On the main stage today, it's the formula of learning to really understand when you're starting from a point of Wow, I don't know much about that. I guess I better learn about it and then learning a lot about it along the way, we all have to be able to adapt and adopt those >>absolutely no the and so that way see up Skilling and cross killing becoming more trans disciplinary. So business people are becoming I t folks now and I t folks really business people. We had this business I t divide for a long time. It cracks me up. I still go to big companies in the I T department using its own building, right? But those days were going away. And I'll see that, you know now is that people over on the business side that live there now, right? So we're seeing this kind of blending where digital is infusing everything, and so you have to become digitally confident on this is where we have to make that simpler. This is going back to the digital workplace. The average user, as had the number of applications they have thio to learn double or triple in just the last five years. Right? So it's a big challenge. >>So what should kids be majoring in today? What's your >>Oh, uh, game design gaming industry is bigger than the movie industry by a large large margin, right? And that that's where all the experience of these immersive experiences and virtual reality and augmented reality >>a come >>from and then you can go into business, right? You know, >>even sociology majors, design games. >>Yeah, it's just, you know, just get like it's the poor tweeners, right that get bumped on the old and aren't necessarily in a position to take care of the new. And I want to take care of it. Unfortunately, not a lot of great record of retraining to date. But maybe that's gonna have to be a much more significant investment because there just aren't the people to fill those positions, period. >>Well, and there's a big market places now. You can build the career of your dreams. You goto up work or gig stir. I mean, these are big job markets where you go and find work and do it from anywhere. Using a tablet you bought for $50 off Amazon, right? You know, just that most you weren't even aware that they could do that. Right? So >>the world put a few bucks away for insurance and you put a few bucks away in your for one k and you, you know, just living off the cash, plus a little bit to cover your cost, which, unfortunately rather like the uber drivers in the lift drivers are Anyway, you know, they're not really thinking that thing for building a career. >>Well, I've crawled to those platforms and it's interesting. Entrepreneurial activity is very common in places like Asia, right? Where? Where you know, they come here, they build businesses right away, right, And they're used to that and we lost some of that. But I think we gave economy is giving a lot of that back to us. We have to relearn it again, you know. >>Great. Well, Dionne, thank you so much for coming on the Cube. It was a pleasure having you. >>Absolutely Thanks, Jeff. Thanks for >>I'm Rebecca Knight for Jeff. Rick. Stay tuned For more of the cubes. Live coverage of NJ engaged 2019.

>> Welcome back everyone. CUBE live coverage here in Las Vegas for AWS Amazon Web Services, reinvent 2021. In person event on the floor, back in business, theCUBE. Two live sets pumping out content left and right. Three and a half days of wall to wall overage, over 120 interviews, stream 28 hours literally on the main site as well as on the CUBE zone. Go to CUBEreinvent.com to get all the action, all the videos will be there. Of course theCUBE.net. I'm John Furrier, your host, with Dave Nicholson my cohost this week and Sarbjeet Johal cloud strategist, influencer, all around great guy, CUBE alumni, here to break down reinvent in context to the cloud industry. Sarbjeet, great to see you, thanks for coming on. >> Good to see you guys in person finally. >> I'm so excited. I did all these interviews the past two years in person and I've been remote, now were in person, great to do it, everyone's excited. 27,000 people here at reinvent. Stand in line for classes. By the way, they're not offering these classes online, only the leadership classes and the keynote. If you're not here, you're not getting the classes. >> I like the vibe actually. I thought it would be more subdued but it is better than what I thought and energy is here. It's not like 2019, it's not. >> That's 60,000 people, you couldn't even get through the hallway. Any company would love to have 27,000 people but I got to say, this year we were just talking earlier on the segment this morning, I wanted to get your thoughts on this, you go back 15 years ago when AWS rolled out, you have EC2, S3, SQS, you had to roll your own. Basically your alternative was better than building a data center or hosting on a colo. So great, check, you don't have to buy the technology tax. I think you had to fill in the glue layers, you had to kind of roll your own and build it up. Now everyone is scaling up and next gen cloud is a completely different architecture. You got serverless, you got all the glue layers pretty much there, and you can still add stuff on it, so a completely different mindset. Changing the startup speed game. Changing the enterprise. Looking pretty good. What's your reaction to the new architecture in cloud vis a vis where it came from? >> My reaction to the new architecture is that number one it's just new. We change stuff all the time in software stacks and what I was grasping within myself sitting in my hotel in the morning listening to Warner's keynote was that we have started to accumulate the technology debt even in cloud. We cooked up some some stuff with the scripts and we automated stuff with programing, language of your choice, or CLIs. Then became the cloud formation automation, orchestration of your cloud stack, if you will. Then Hashicorp are like, so Hashicorp are sitting on the side there. But now there's another abstraction layer on top of that which was announced during Warner's keynote today. I think the new abstraction layers leave the pervious architectures a little stale. It's always like, what should you do? Should you refactor your existing stacks or should you not touch that? Just go from now on on the new architecture? I think it's getting busy, complicated, a lot of number of services. >> What do you think other people are saying? I saw you did a little snippet with Dion Hinchcliffe online, nice Tweet there, you got a big video coming out. As you talk to other folks and influencers and people in the front lines, what are they saying about Amazon Reinvent this year? >> I think almost everybody's saying that number of services is expanding exponentially. I was thinking that 200 plus number of services or whatever that number is today, it's mind boggling. I totally understand that when you have two teams that they want to take the credit for creating a new service and they want to publish it. They want to do a press release and all that. But my request to all cloud providers, mainly three, is to not call everything a new service. Call that feature of a service. So number of services has to be reduced, collapsed if you will. We need umbrella services and then under that there should be features of services, that's one thing. Another feedback I got from some second tier partners is that they have the competency program for partners. They announced that. They had that earlier but new competencies. It leaves the second or third tier partners in the cold. Only the first tier partners can get those competencies because for that they have to send a lot of money, train people, then they get that check box, oh, you can do this. >> This whole services thing and what you call a service, if you called everything a service a new feature of DNS or a new thing here and there, serverless, there's be thousands of features, services. I think Amazon, I think they culled it down to like, 200, is the number we hear. >> But isn't that part of the role of the partner, the services provider, the consultancy, to act as a bridge between all of those services and features, whatever you want to call them and figuring out exactly what the end user customer actually needs? The idea that AWS is messaging here is targeted directly towards end user customers. There's a lot to be desired there because how do you translate that? I'm thinking, compare and contrast that with the Steve Jobs approach of there shall be three. There will be a large, a medium and a small. I know that this is more complex, but when you come out and you say, 475 different kinds of instances, you're leaving that to your partners to translate. To your point, if you're segregating those partners into categories where only a top tier has access to everything, interesting place to be. >> A couple of discussions I had with partners was that I actually suggested them to create a bank of reference architectures, we call that in Amazon terms. But it's not only technical side of things, but business as well. They need to create some principle based architectures and have a bank of that and then prescribe that to their customers base. I think that's the only way to simplify these things because as you said, if you have 200 different types of instances, for instance, (laughs), it is hard. It is really hard. >> I want to get your thoughts, we talk about this on Twitter all the time so the folks watching, if you want to follow our rants and raves on Twitter, follow us on Twitter you'll get all the action, all the influencers are there. Competition. I've been ranting all week and been saying it for a long time, Microsoft's not even close to Amazon. I'm a bit over the top but I'll just say that if Amazon goes unchecked, Microsoft's ecosystem's going to get decimated. Why would I want to run software, my software, on a suboptimal performance infrastructure? Microsoft had Windows back in the day and had the system software and the application suite but they encouraged developers to build on top of Windows. Their "dot net" or ecosystem. That game's over. I guess Window's runs on Amazon too, whatever. But now the cloud is the Windows. The cloud is the system software. So developers are running on top of the cloud. >> Yes. >> So who wins? >> I think Open wins. Not Open-source. Open-source and Open are different things, we always discuss that. I think Open wins, the close systems have this problem of protectionism which doesn't work, with our little kids at home or your economy as whole. When you protect your local industry, the economy goes down. I've seen that, I'm an economist by education as you guys know. >> Yes. >> I think it's the same, when you protect too much of whatever you have, I think it's has a worse effect. But there's one narrative, Satya sort of narrates if you will, he says that, hey, when you use Windows, you keep everything, 100%. We are not taking a cut. When you're sitting in a cloud marketplace, somebody's getting a cut. That's the argument. >> Terry Chen said, because he puked on what I said, he said better could win. >> Yes. >> That's one thing. Okay, I buy that. Azure could be better in some use cases. But I think over all Amazon wins hands down currently. Certainly with the custom processors. >> You haven't mentioned GCP. >> Actually GCP. >> What can you say about it? >> What you could say is that AWS right now has either constructed or is benefiting from the highest barrier to entry to any business in the history of our planet. You can look at the investment that GCP is making to the tune of six billion dollars a year to go after market share. Are they going after current market share which is arguably the 20% of IT that's in cloud now? Or are they going for future market share which is a piece of the larger pie? When you talk about who wins, I think it's still possible for- >> Hold on, hold on. >> You left Oracle out. I think it's still possible. >> Hold on, hold on, hold on. >> I can tell you about Oracle. >> Hold on, hold on. This is a thought exercise, I'm going to ask you guys this question. It may be rhetorical, you don't need to answer it. If you went to all the people out there buying Azure and GCP, no offense guys, and you said, "Put aside all your credits you've been given, how much are you actually using?" If you take the incentives away, why are you on those clouds from a performance perspective? >> Sorry to cut you off. We know that Oracle uses incentives, X codes, leads for sale, and all that stuff, we know that. A lot of people know that. So cloud became shelfware there, we know the story. I'm leaving Oracle to the side. But I think Google has legs. Google's cloud has legs. They are a very enduring focus company. They are more open-source friendly and data science friendly as well. I think they are actually a number two, personally I believe. I'm a developer by heart, so they are number two developer cloud after Amazon. >> I think it's well know, I agree with you by the way. I think people may not know this but it's well known in the industry that Amazon has been mostly afraid of Google more than Microsoft. I think now because of this market share, the ecosystem war that's going to happen in a very short period of time, Microsoft's more of a threat on paper. But Google's got more threat to sling shot back and front technically because if you look at Graviton, the stack that they're building for ISVs and developers, Amazon's clearly winning. Google can pull that off. If they get it, they got to have their own way. >> Let me tell you, the one thing actually, if we want to know what was the fumble this time? I have some, actually I will talk about it in my radio, if you have enough time here. I think Google will do better because they're open and Amazon is complex. I was thinking during the keynotes, what are the clues to Amazon, AWS, leaving which is helping Google and Azure, mainly Google. Google is simple actually, a lot simpler to use, but again having said that, there's one thing actually, the new term I'm trying to define is the feature proximity. Amazon has feature proximity, like the best. When you are doing one thing and you want to do another thing, they have that all right there. They're ahead of the game. They have their 5G, private 5G on all their stuff, it's very futuristic. >> By the way, I got Amazon to agree to get me some private 5G for when we go back home. We're going to setup an outdoor area for some open CUBE action with some 5G. >> Actually we could put that on a nice van with the logos and all that. We could move around. >> We'll park it right there on El Camino, right next to Stanford University. Maybe we could live in one of those things too. >> Make it a taco truck and I'll join you guys. >> (laughs) Taco truck for free food. >> Yeah, let's do that. >> All seriousness guys, I want to get your thoughts as we wrap up this segment on the analysis of the cloud industry. What do you guys think, your opinion, it's going to take, I'll start by saying I think Amazon, if not contested for their leadership in the performance of silicon and the stack for software developers and owners to run the fastest they can run away with this. I think Microsoft and Google better be cranking right now to make it easy and have silicon advantage as well. I think clearly if the ecosystem's going to be at play, because the shift is happening to modernize software development, low code, no code, every shift everyone will go to the best performance, independent of cost and incentives. Amazon's got lower cost too so they got the fly wheel going. >> I can make mine short. I think GCP can also be successful. But I think already the amount of momentum that AWS has, the wind behind it's sails, I was at EMC for many years and we used to joke about our arch nemesis Hitachi Data Systems and saying that they were quite discouraged every morning as they woke up learning that they were a year further behind. Every night they went to sleep. They woke up the next day and they were a year further behind. Watching the announcements coming out of this event this week, I think there are some people at GCP and Microsoft and others who have that sense. But having said that, we're at the dawn of at era of cloud. There's plenty of room for a lot of players. When you give us your thoughts, I'd like your answer to the question, how much are consumers in the driver's seat today? Will the customers be able to demand multi sourcing? >> I think customers, you work with your money. Customers can demand that but at the same time customers can get stuck in a platform and they can't get out. We usually talk about when to lock in. There's one thing that Amazon keeps saying that we are open, we are open and the other vendors are like, these brands. I think that kind of narrative can come bite back to them. It's not a good thing to say. You don't want to be cocky about your features or you are the best and all that stuff. I think you want to stay humble and respect the other guys as well because they are coming right behind you. I think the key is developers. I have the bias towards developers because I was a developers but I totally believe deep down, actually I have tried to put my developer hat off and still think that way about these constructs. Developers are the people who call the shots. If you are not developer friendly you can't do much. >> That's a good point. >> That's my warning to Amazon. Don't go away from developers. You are number one developer cloud, stay there. This refocus is good, but put that to the side, not make that front center. Google has made that front center, I think that's a mistake. >> Yeah, you have the features, the right features, but again, speed, performance. Developers, capture the opportunity. Developers want to move fast. That's the entrepreneurship. Sarbjeet, great to have you on theCUBE, great to see you. >> Thanks for having me here, I enjoyed it. Great set here. >> All right, Dave Nicholson's here. Dave Nicholson, CUBE host. I'm John Furrier. You're watching theCUBE, the world leader in technology coverage. We'll be back with more live coverage from Reinvent after this short break. (upbeat music)

>>Yeah, it's the queue covering the virtual vertical Big Data Conference 2020 Brought to You by vertical. >>Welcome back, everybody. My name is Dave Vellante, and you're watching the Cube's coverage of the verdict of Virtual Big Data conference. The Cube has been at every BTC, and it's our pleasure in these difficult times to be covering BBC as a virtual event. This digital program really excited to have Joy King joining us. Joy is the vice president of product and go to market strategy in particular. And if that weren't enough, he also runs marketing and education curve for him. So, Joe, you're a multi tool players. You've got the technical side and the marketing gene, So welcome to the Cube. You're always a great guest. Love to have you on. >>Thank you so much, David. The pleasure, it really is. >>So I want to get in. You know, we'll have some time. We've been talking about the conference and the virtual event, but I really want to dig in to the product stuff. It's a big day for you guys. You announced 10.0. But before we get into the announcements, step back a little bit you know, you guys are riding the waves. I've said to ah, number of our guests that that brick has always been good. It riding the wave not only the initial MPP, but you you embraced, embraced HD fs. You embrace data science and analytics and in the cloud. So one of the trends that you see the big waves that you're writing >>Well, you're absolutely right, Dave. I mean, what what I think is most interesting and important is because verdict is, at its core a true engineering culture founded by, well, a pretty famous guy, right, Dr Stone Breaker, who embedded that very technical vertical engineering culture. It means that we don't pretend to know everything that's coming, but we are committed to embracing the tech. An ology trends, the innovations, things like that. We don't pretend to know it all. We just do it all. So right now, I think I see three big imminent trends that we are addressing. And matters had we have been for a while, but that are particularly relevant right now. The first is a combination of, I guess, a disappointment in what Hadoop was able to deliver. I always feel a little guilty because she's a very reasonably capable elephant. She was designed to be HD fs highly distributed file store, but she cant be an entire zoo, so there's a lot of disappointment in the market, but a lot of data. In HD FM, you combine that with some of the well, not some the explosion of cloud object storage. You're talking about even more data, but even more data silos. So data growth and and data silos is Trend one. Then what I would say Trend, too, is the cloud Reality Cloud brings so many events. There are so many opportunities that public cloud computing delivers. But I think we've learned enough now to know that there's also some reality. The cloud providers themselves. Dave. Don't talk about it well, because not, is it more agile? Can you do things without having to manage your own data center? Of course you can. That the reality is it's a little more pricey than we expected. There are some security and privacy concerns. There's some workloads that can go to the cloud, so hybrid and also multi cloud deployments are the next trend that are mandatory. And then maybe the one that is the most exciting in terms of changing the world we could use. A little change right now is operationalize in machine learning. There's so much potential in the technology, but it's somehow has been stuck for the most part in science projects and data science lab, and the time is now to operationalize it. Those are the three big trends that vertical is focusing on right now. >>That's great. I wonder if I could ask you a couple questions about that. I mean, I like you have a soft spot in my heart for the and the thing about the Hadoop that that was, I think, profound was it got people thinking about, you know, bringing compute to the data and leaving data in place, and it really got people thinking about data driven cultures. It didn't solve all the problems, but it collected a lot of data that we can now take your third trend and apply machine intelligence on top of that data. And then the cloud is really the ability to scale, and it gives you that agility and that it's not really that cloud experience. It's not not just the cloud itself, it's bringing the cloud experience to wherever the data lives. And I think that's what I'm hearing from you. Those are the three big super powers of innovation today. >>That's exactly right. So, you know, I have to say I think we all know that Data Analytics machine learning none of that delivers real value unless the volume of data is there to be able to truly predict and influence the future. So the last 7 to 10 years has been correctly about collecting the data, getting the data into a common location, and H DFS was well designed for that. But we live in a capitalist world, and some companies stepped in and tried to make HD Fs and the broader Hadoop ecosystem be the single solution to big data. It's not true. So now that the key is, how do we take advantage of all of that data? And now that's exactly what verdict is focusing on. So as you know, we began our journey with vertical back in the day in 2007 with our first release, and we saw the growth of the dupe. So we announced many years ago verdict a sequel on that. The idea to be able to deploy vertical on Hadoop nodes and query the data in Hadoop. We wanted to help. Now with Verdict A 10. We are also introducing vertical in eon mode, and we can talk more about that. But Verdict and Ian Mode for HDs, This is a way to apply it and see sequel database management platform to H DFS infrastructure and data in each DFS file storage. And that is a great way to leverage the investment that so many companies have made in HD Fs. And I think it's fair to the elephant to treat >>her well. Okay, let's get into the hard news and auto. Um, she's got, but you got a mature stack, but one of the highlights of append auto. And then we can drill into some of the technologies >>Absolutely so in well in 2018 vertical announced vertical in Deon mode is the separation of compute from storage. Now this is a great example of vertical embracing innovation. Vertical was designed for on premises, data centers and bare metal servers, tightly coupled storage de l three eighties from Hewlett Packard Enterprises, Dell, etcetera. But we saw that cloud computing was changing fundamentally data center architectures, and it made sense to separate compute from storage. So you add compute when you need compute. You add storage when you need storage. That's exactly what the cloud's introduced, but it was only available on the club. So first thing we did was architect vertical and EON mode, which is not a new product. Eight. This is really important. It's a deployment option. And in 2018 our customers had the opportunity to deploy their vertical licenses in EON mode on AWS in September of 2019. We then broke an important record. We brought cloud architecture down to earth and we announced vertical in eon mode so vertical with communal or shared storage, leveraging pure storage flash blade that gave us all the advantages of separating compute from storage. All of the workload, isolation, the scale up scale down the ability to manage clusters. And we did that with on Premise Data Center. And now, with vertical 10 we are announcing verdict in eon mode on HD fs and vertically on mode on Google Cloud. So what we've got here, in summary, is vertical Andy on mode, multi cloud and multiple on premise data that storage, and that gives us the opportunity to help our customers both with the hybrid and multi cloud strategies they have and unifying their data silos. But America 10 goes farther. >>Well, let me stop you there, because I just wanna I want to mention So we talked to Joe Gonzalez and past Mutual, who essentially, he was brought in. And one of this task was the lead into eon mode. Why? Because I'm asking. You still had three separate data silos and they wanted to bring those together. They're investing heavily in technology. Joe is an expert, though that really put data at their core and beyond Mode was a key part of that because they're using S three and s o. So that was Ah, very important step for those guys carry on. What else do we need to know about? >>So one of the reasons, for example, that Mass Mutual is so excited about John Mode is because of the operational advantages. You think about exactly what Joe told you about multiple clusters serving must multiple use cases and maybe multiple divisions. And look, let's be clear. Marketing doesn't always get along with finance and finance doesn't necessarily get along with up, and I t is often caught the middle. Erica and Dion mode allows workload, isolation, meaning allocating the compute resource is that different use cases need without allowing them to interfere with other use cases and allowing everybody to access the data. So it's a great way to bring the corporate world together but still protect them from each other. And that's one of the things that Mass Mutual is going to benefit from, as well, so many of >>our other customers I also want to mention. So when I saw you, ah, last last year at the Pure Storage Accelerate conference just today we are the only company that separates you from storage that that runs on Prem and in the cloud. And I was like I had to think about it. I've researched. I still can't find anybody anybody else who doesn't know. I want to mention you beat actually a number of the cloud players with that capability. So good job and I think is a differentiator, assuming that you're giving me that cloud experience and the licensing and the pricing capability. So I want to talk about that a little >>bit. Well, you're absolutely right. So let's be clear. There is no question that the public cloud public clouds introduced the separation of compute storage and these advantages that they do not have the ability or the interest to replicate that on premise for vertical. We were born to be software only. We make no money on underlying infrastructure. We don't charge as a package for the hardware underneath, so we are totally motivated to be independent of that and also to continuously optimize the software to be as efficient as possible. And we do the exact same thing to your question about life. Cloud providers charge for note indignance. That's how they charge for their underlying infrastructure. Well, in some cases, if you're being, if you're talking about a use case where you have a whole lot of data, but you don't necessarily have a lot of compute for that workload, it may make sense to pay her note. Then it's unlimited data. But what if you have a huge compute need on a relatively small data set that's not so good? Vertical offers per node and four terabyte for our customers, depending on their use case, we also offer perpetual licenses for customers who want capital. But we also offer subscription for companies that they Nope, I have to have opt in. And while this can certainly cause some complexity for our field organization, we know that it's all about choice, that everybody in today's world wants it personalized just for me. And that's exactly what we're doing with our pricing in life. >>So just to clarify, you're saying I can pay by the drink if I want to. You're not going to force me necessarily into a term or Aiken choose to have, you know, more predictable pricing. Is that, Is that correct? >>Well, so it's partially correct. The first verdict, a subscription licensing is a fixed amount for the period of the subscription. We do that so many of our customers cannot, and I'm one of them, by the way, cannot tell finance what the budgets forecast is going to be for the quarter after I spent you say what it's gonna be before, So our subscription facing is a fixed amount for a period of time. However, we do respect the fact that some companies do want usage based pricing. So on AWS, you can use verdict up by the hour and you pay by the hour. We are about to launch the very same thing on Google Cloud. So for us, it's about what do you need? And we make it happen natively directly with us or through AWS and Google Cloud. >>So I want to send so the the fixed isn't some floor. And then if you want a surge above that, you can allow usage pricing. If you're on the cloud, correct. >>Well, you actually license your cluster vertical by the hour on AWS and you run your cluster there. Or you can buy a license from vertical or a fixed capacity or a fixed number of nodes and deploy it on the cloud. And then, if you want to add more nodes or add more capacity, you can. It's not usage based for the license that you bring to the cloud. But if you purchase through the cloud provider, it is usage. >>Yeah, okay. And you guys are in the marketplace. Is that right? So, again, if I want up X, I can do that. I can choose to do that. >>That's awesome. Next usage through the AWS marketplace or yeah, directly from vertical >>because every small business who then goes to a salesforce management system knows this. Okay, great. I can pay by the month. Well, yeah, Well, not really. Here's our three year term in it, right? And it's very frustrating. >>Well, and even in the public cloud you can pay for by the hour by the minute or whatever, but it becomes pretty obvious that you're better off if you have reserved instance types or committed amounts in that by vertical offers subscription. That says, Hey, you want to have 100 terabytes for the next year? Here's what it will cost you. We do interval billing. You want to do monthly orderly bi annual will do that. But we won't charge you for usage that you didn't even know you were using until after you get the bill. And frankly, that's something my finance team does not like. >>Yeah, I think you know, I know this is kind of a wonky discussion, but so many people gloss over the licensing and the pricing, and I think my take away here is Optionality. You know, pricing your way of That's great. Thank you for that clarification. Okay, so you got Google Cloud? I want to talk about storage. Optionality. If I found him up, I got history. I got I'm presuming Google now of you you're pure >>is an s three compatible storage yet So your story >>Google object store >>like Google object store Amazon s three object store HD fs pure storage flash blade, which is an object store on prim. And we are continuing on this theft because ultimately we know that our customers need the option of having next generation data center architecture, which is sort of shared or communal storage. So all the data is in one place. Workloads can be managed independently on that data, and that's exactly what we're doing. But what we already have in two public clouds and to on premise deployment options today. And as you said, I did challenge you back when we saw each other at the conference. Today, vertical is the only analytic data warehouse platform that offers that option on premise and in multiple public clouds. >>Okay, let's talk about the ah, go back through the innovation cocktail. I'll call it So it's It's the data applying machine intelligence to that data. And we've talked about scaling at Cloud and some of the other advantages of Let's Talk About the Machine Intelligence, the machine learning piece of it. What's your story there? Give us any updates on your embracing of tooling and and the like. >>Well, quite a few years ago, we began building some in database native in database machine learning algorithms into vertical, and the reason we did that was we knew that the architecture of MPP Columbia execution would dramatically improve performance. We also knew that a lot of people speak sequel, but at the time, not so many people spoke R or even Python. And so what if we could give act us to machine learning in the database via sequel and deliver that kind of performance? So that's the journey we started out. And then we realized that actually, machine learning is a lot more as everybody knows and just algorithms. So we then built in the full end to end machine learning functions from data preparation to model training, model scoring and evaluation all the way through to fold the point and all of this again sequel accessible. You speak sequel. You speak to the data and the other advantage of this approach was we realized that accuracy was compromised if you down sample. If you moved a portion of the data from a database to a specialty machine learning platform, you you were challenged by accuracy and also what the industry is calling replica ability. And that means if a model makes a decision like, let's say, credit scoring and that decision isn't anyway challenged, well, you have to be able to replicate it to prove that you made the decision correctly. And there was a bit of, ah, you know, blow up in the media not too long ago about a credit scoring decision that appeared to be gender bias. But unfortunately, because the model could not be replicated, there was no way to this Prove that, and that was not a good thing. So all of this is built in a vertical, and with vertical 10. We've taken the next step, just like with with Hadoop. We know that innovation happens within vertical, but also outside of vertical. We saw that data scientists really love their preferred language. Like python, they love their tools and platforms like tensor flow with vertical 10. We now integrate even more with python, which we have for a while, but we also integrate with tensorflow integration and PM ML. What does that mean? It means that if you build and train a model external to vertical, using the machine learning platform that you like, you can import that model into a vertical and run it on the full end to end process. But run it on all the data. No more accuracy challenges MPP Kilometer execution. So it's blazing fast. And if somebody wants to know why a model made a decision, you can replicate that model, and you can explain why those are very powerful. And it's also another cultural unification. Dave. It unifies the business analyst community who speak sequel with the data scientist community who love their tools like Tensorflow and Python. >>Well, I think joy. That's important because so much of machine intelligence and ai there's a black box problem. You can't replicate the model. Then you do run into a potential gender bias. In the example that you're talking about there in their many you know, let's say an individual is very wealthy. He goes for a mortgage and his wife goes for some credit she gets rejected. He gets accepted this to say it's the same household, but the bias in the model that may be gender bias that could be race bias. And so being able to replicate that in and open up and make the the machine intelligence transparent is very, very important, >>It really is. And that replica ability as well as accuracy. It's critical because if you're down sampling and you're running models on different sets of data, things can get confusing. And yet you don't really have a choice. Because if you're talking about petabytes of data and you need to export that data to a machine learning platform and then try to put it back and get the next at the next day, you're looking at way too much time doing it in the database or training the model and then importing it into the database for production. That's what vertical allows, and our customers are. So it right they reopens. Of course, you know, they are the ones that are sort of the Trailblazers they've always been, and ah, this is the next step. In blazing the ML >>thrill joint customers want analytics. They want functional analytics full function. Analytics. What are they pushing you for now? What are you delivering? What's your thought on that? >>Well, I would say the number one thing that our customers are demanding right now is deployment. Flexibility. What? What the what the CEO or the CFO mandated six months ago? Now shout Whatever that thou shalt is is different. And they would, I tell them is it is impossible. No, what you're going to be commanded to do or what options you might have in the future. The key is not having to choose, and they are very, very committed to that. We have a large telco customer who is multi cloud as their commit. Why multi cloud? Well, because they see innovation available in different public clouds. They want to take advantage of all of them. They also, admittedly, the that there's the risk of lock it right. Like any vendor, they don't want that either, so they want multi cloud. We have other customers who say we have some workloads that make sense for the cloud and some that we absolutely cannot in the cloud. But we want a unified analytics strategy, so they are adamant in focusing on deployment flexibility. That's what I'd say is 1st 2nd I would say that the interest in operationalize in machine learning but not necessarily forcing the analytics team to hammer the data science team about which tools or the best tools. That's the probably number two. And then I'd say Number three. And it's because when you look at companies like Uber or the Trade Desk or A T and T or Cerner performance at scale, when they say milliseconds, they think that flow. When they say petabytes, they're like, Yeah, that was yesterday. So performance at scale good enough for vertical is never good enough. And it's why we're constantly building at the core the next generation execution engine, database designer, optimization engine, all that stuff >>I wanna also ask you. When I first started following vertical, we covered the cube covering the BBC. One of things I noticed was in talking to customers and people in the community is that you have a community edition, uh, free addition, and it's not neutered ais that have you maintain that that ethos, you know, through the transitions into into micro focus. And can you talk about that a little bit >>absolutely vertical community edition is vertical. It's all of the verdict of functionality geospatial time series, pattern matching, machine learning, all of the verdict, vertical neon mode, vertical and enterprise mode. All vertical is the community edition. The only limitation is one terabyte of data and three notes, and it's free now. If you want commercial support, where you can file a support ticket and and things like that, you do have to buy the life. But it's free, and we people say, Well, free for how long? Like our field? I've asked that and I say forever and what he said, What do you mean forever? Because we want people to use vertical for use cases that are small. They want to learn that they want to try, and we see no reason to limit that. And what we look for is when they're ready to grow when they need the next set of data that goes beyond a terabyte or they need more compute than three notes, then we're here for them, and it also brings up an important thing that I should remind you or tell you about Davis. You haven't heard it, and that's about the Vertical Academy Academy that vertical dot com well, what is that? That is, well, self paced on demand as well as vertical essential certification. Training and certification means you have seven days with your hands on a vertical cluster hosted in the cloud to go through all the certification. And guess what? All of that is free. Why why would you give it for free? Because for us empowering the market, giving the market the expert East, the learning they need to take advantage of vertical, just like with Community Edition is fundamental to our mission because we see the advantage that vertical can bring. And we want to make it possible for every company all around the world that take advantage >>of it. I love that ethos of vertical. I mean, obviously great product. But it's not just the product. It's the business practices and really progressive progressive pricing and embracing of all these trends and not running away from the waves but really leaning in joy. Thanks so much. Great interview really appreciate it. And, ah, I wished we could have been faced face in Boston, but I think it's prudent thing to do, >>I promise you, Dave we will, because the verdict of BTC and 2021 is already booked. So I will see you there. >>Haas enjoyed King. Thanks so much for coming on the Cube. And thank you for watching. Remember, the Cube is running this program in conjunction with the virtual vertical BDC goto vertical dot com slash BBC 2020 for all the coverage and keep it right there. This is Dave Vellante with the Cube. We'll be right back. >>Yeah, >>yeah, yeah.

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in Came along with its ecosystem partners. >>Hey, welcome back to the Cube. Lisa Martin at AWS Reinvent in Vegas. Very busy. Sands Expo Center. Pleased to be joined by my co host this afternoon. Justin Warren, founder and chief analyst at Pivot nine. Justin, we're hosting together again. We are. >>It's great to be >>here. It's great to have you that. So. Justin Meyer, please welcome a couple of our cue ball. Um, back to the program. A couple guys from nut up. We have Anthony Lie, the S B, P and G m of the Cloud business unit. Welcome back at the >>very much great to be here >>and color coordinating with Anthony's Jandi Stephenson, Chief Technology officer and GPS Cloud. Welcome back. >>Thank you. Thank you >>very shortly. Dress, guys and very >>thank you. Thank you. It's, uh, the good news Is that their suits anymore. So we're not going to have to wear ties >>comfortable guys net up a w s this event even bigger than last year, which I can't even believe that 65,000 or so thugs. But, Anthony, let's start with you. Talk to us about what's new with the net up AWS partnership a little bit about the evolution of it. >>Yeah. I mean, you know, we started on AWS. Oh, my gosh. Must be almost five or six years ago now and we made a conscious effort to port are operating system to AWS, which was no small task on dhe. It's taken us a few years, but we're really starting to hit our stride Now. We've been very successful, were on boarding customers on an ever increasing rate. We've added more. Service is on. We just continue to love the cloud as a platform for development. We can go so fast, and we can do things in in an environment like aws that, frankly, you just couldn't do on premise, you know, they're they're complexity and EJ ineighty of on premise was always a challenge. The cloud for us is an amazing platform where we can go very, very fast >>and from a customer demand standpoint. Don't talk to me about that, Chief technologist. One of the thing interesting things that that Andy Jassy shared yesterday was that surprised me. 97% of I t spend is still on from So we know that regardless of the M word, multi cloud work customers are living in that multi cloud world. Whether it's by strategy, a lot of it's not. A lot of it's inherited right, but they have to have that choice, right? It's gonna depend on the data, the workload, etcetera. What can you tell us about when you're talking with customers? What what? How are they driving NetApp evolution of its partnership with public provider AWS? >>So actually, I don't know if it's the desired state to be running in a hybrid, mostly cloud fashion, but it's it's It's driven by strategy, and it's usually driven by specific workloads and on the finding the best home for your application or for your workers at any given time. Because it's it's ultimately unrealistic for on premise customers to try to compete with like a machine and keep learning algorithms and the rate of development and rate off basically evolution in the cloud. So you always have to be there to be able to stay competitive, so it's becoming a part of the strategy even though it was probably asked that developers that drove a lot off cloud adoption to begin with. Maybe, maybe not. Not in favor of the c i o r. You have, like a lot of Cloud Cloud sprawling, but there's no longer sprawling it. It's part of the strategy before every company in my way >>heard from any Jesse in the keynote yesterday about the transformation being an important thing. And he also highlighted a lot of enterprise. Nedda has a long history with enterprise, Yes, very solid reputation with enterprise. So it feels to me like this This is an enterprise show. Now that the enterprise has really arrived at with the cloud, what are you seeing from the customers that you've already had for a long time? No, no, no, I'm familiar with it. Trust Net up. We're now exploring the Clouded and doing more than just dipping their toe in the water. What are they actually doing with the cloud and and we'll get up together, you know, >>we see and no one ever growing list of workload. I think when people make decisions in the cloud, they're not making those traditional horizontal decisions anymore. They're making workload by workload by workload decisions and Internet EPPS history and I think, uh, performance on premises, given customers peace of mind now in the cloud, they sort of know that what's been highly reliable, highly scaleable for them on premise, they can now have that same confidence in the cloud. So way started. Like just like Amazon. We started off seeing secondary workloads like D r Back Up Dev ops, but now is seeing big primaries go A s, a p big database workloads, e commerce. Ah, lot of HBC high forming compute. We're doing very well in oil and gas in the pharmaceutical industries where file has been really lacking on the public cloud. I think we leaned in as a company years ago and put put, put a concerted effort to make it there. And I think now the workloads a confident that were there and we can give them the throughput. We give them the performance on the protocols and now we're seeing big, big workloads come over to the public clouds. >>And he did make a big deal about transformation being important. And a lot of that was around the operational model. Let's let's just the pure technology. But what about the operating model? How are you seeing Enterprises Transformer? There's a lot of traditionally just taken a workload, do a bit of lift and shift and put it to the cloud. Where are they now transforming the way they actually operate? Things because of >>cloud? Absolutely. I mean, they have to They have to adopt the new technologies and new ways of doing business. So I mean, I think they are actually celebrating that to answer point. I think this is not a partnership and we're partnering with. We have a very unique story. We're partnering with all of them and have really deep engineering relationship with all of them. And they are now able to go after enterprise type workloads that they haven't been gone. I've been able to go after before, so that's why it's such a strategic strategic relationship that we have with all of them. That sort of brings in in the freedom of choice. You can basically go everywhere anywhere. That, in my opinion, is that true hyper cloud story lot has always been really difficult. But with the data management capabilities of not top, it's really easy to move my greater replicate across on premise toe are hyper scaler off choice. >>I mean, I think you know, if you're in enterprise right now, you know you're a CEO. You're probably scared to death of, like, being uber, you know exactly on. Uh, you know, if you're you know, So speed has now become what we say. The new scale they used to be scaled is your advantage. And now, if you're not fast, you could be killed any day by some of these startups who just build a mobile app. And all of a sudden they've gotten between you and the customer and you've lost. And I think CEOs are now. How fast are we going? How many application developers do we have? And did a scientist do we have? And because of that, that they're seeing Amazon as a platform for speed on. So that's just that paranoia. I think digital transformation is driving everybody to the cloud. >>You're right. If we look at transformation if a business and Andy Jassy and John for your talked about this and that exclusive interview that they did the other day. And Andy, if you're and a legacy enterprise and you're looking at your existing market share segment exactly, and you're not thinking there's somebody else. What assisting on there on the side mirror? Objects in mirror are closer. Not getting ready for that. You're on the wrong. You're going to be on the wrong side of that equation. But if we look at cloud, it has had an impact on traditional story one of naps. Taglines is data driven. If we look at transformation and if we'll even look at the translation of cloud in and of itself, data is at the heart of everything. Yes, and they talk to us about net APS transformation as cloud is something that you're enabling on prime hybrid multi cloud as you talked about. But how is your advantage allowing customers to not only be data driven, but to find value in that data that gives them that differentiation that they need for the guy or a girl that's right behind them. I already did take over. >>Well, I think if you're you know, if you're an enterprise, you know, the one asset you have is data. You have history now >>a liability Now with an asset. >>Can they can they do anything with it. Do they know where it is? Do they know how to use it where it should be, you know, Is it secured? Is it protected all of those things? It's very hard for enterprise to answer those questions. What one end up, I think it's done incredibly well, is by leaning in as much as we did onto AWS way. Give our customers the absolute choice to leave our on premise business and a lot of people, I think years ago thought we were crazy. But because now we've expanded our footprint to allow customers to run anywhere without any fear of lock in, people will start to see us now not as a storage vendor but as a strategic partner, and that that that strategic partnership is really has really come about because of our willingness to let people move the data and manage the data wherever they needed to be. On that something our customers have said, you know, used to be a storage vendor on along with the other storage vendors and now all of a sudden that we're having conversations with you about strategy where the data should be, you know who's using it is. It's secured all of those kinds of conversations we're having with customers. >>You mentioned moving data, and that was something that again came up in the keynote yesterday. And he mentioned that Hey, maybe instead of taking the data to the computer, we should bring the computer's data. That's something that Ned Abbas has long actually talked about. I remember when you used to mention data fabric was something about We want to take your data and then make it available to where the computer is. I'd like you to talk it through that, particularly in light of like a I and ML, which is on the tip of everybody's tongue. It's It's a bit of I think, it's possibly reaching the peak of the hype cycle at the moment s o what our customers actually doing with their data to actually analyze it? Are they actually seeing real value from machine learning? And I are We still isn't just kicking the tires on that. >>I mean, the biggest problem with deep learning and machine learning is having our accumulating enough on being able to have the data or lessening that gravity by being able to move it then you can take advantage off states maker in AWS, the big Cleary and Google, whatever fits your needs. And then, if you want to store the results back on premise, that's what we enable. With it out of harbor having that free flowing work clothes migration has to count for data. It's not enough to just move your application that that that's the key for machine learning and thought the lakes and others, >>absolutely in terms of speed. Anthony mentioned that that's the new scale. How is flash changing the game >>with perspective, you know, flashes a media type, but it's just, you know, the prices have come down now that you know the price performance couple flashes an obvious thing. Um, and a lot of people are, I think now, making on premise decisions to get rid of spinning disc and replaced with Flash because the R. O. I is so good. Tco the meantime between failures, that's that's so many advantages that percent workloads. It's a better decision, of course. You know, AWS provides a whole bunch of media Onda again. It's just you like a kid in a candy store, you know, as a developer, you look at Amazon. You're like, Oh, my God. Back in the day, we had to make, like, an Oracle decision and everything was Oracle. And now you can just move things around and you can take advantage of all sorts of different utilities. And now you piece together an application very differently. And so you're able to sort of really think I think Dion sees point. People are telling us they have to have a date, a strategy, and then, based on the data strategy, they will then leverage the right storage with the right protocols. They'll then bring that to compute whatever compute is necessary. I think data science is, you know, a little fashion, you know, conscious. Right now, you know, everybody wants to say how many did a scientist they have on their teams? They're looking for needles in haystacks. Someone, they're finding them. Some of them are but not doing it, I think it is. Makes companies very, very nervous. So they're going the results, gonna trying as hard as they can to leverage that technology. >>And you'll see where is that data strategy conversation happening if we think about the four essentials that Andy Johnson talked about yesterday for transformation in one of the first things he said was, it has to be topped at senior level decision. Then it's going to be aggressively pushed down through the organization. Are you seeing this data strategy at the CEO level yet? >>Yeah, we are. But I'm also seeing it much lower. I mean, with the data engineers with the developers, because it's asked, is it is extremely important to be developing on top off production data, specifically if you're doing machine and deep learning. So I think it's both. I think the decision authority has actually moved lower in the company where the developers are the side reliability engineers are actually choosing more technology to use. That fits the product that they are actually creating off course. The strategy happens at the tall, but the influencer and the decision makers, in my opinion, has been moving lower and within the organization. So I'm basically contradicting what yes is a. But to me that is also important. The days off a C t o r C E o. Forcing a specific platform or strategy on to developers. Those days are hopefully gone. >>I think if you're a CEO and you know of any company in any industry you have to be a tech company, you know, it used to be a tech industry, and now every company in the world is now tech. Everyone's building APS. Everyone's using data. Everybody's, you know, trying to figure out machine learning. And so I think what's happening is CEOs are are increasingly becoming technically literate. They have to Exactly. They're dead if they're not. I mean, you know whether your insurance company, your primary platform, is now digital if you're a medical company or primary platform additional. So I think that's a great stat. I saw that about two and 1/2 years ago. The number of software engineering jobs in non tech surpassed the number of jobs in tech, so we used to have our little industry and all the software engineers came to work for tech companies. Now there are more jobs outside the tech segment for engineers, and there are in the text >>well, and you brought up uber a minute ago and I think of a couple of companies examples in my last question for you is real. Rapid is about industries. You look at uber for example, what the fact that the taxi cab companies were transitional. And we're really eager to, you know, AP, if I their organizations, and meet the consumer demand. And then you look at Airbnb and how that's revolutionized hospitality or pellet on how it's revolutionized. Fitness Last question, Jonesy, Let's go for you. Looking at all of the transformation that cloud has enabled and can enable what industry you mentioned when the gas. But is there any industry that you see right now that is just at the tipping point to be ableto blow the door wide open if they transform successfully? >>Well, I mean way are working with a lot off pharma companies and genome sequencing companies that have not actually working with sensitive data on if those companies, I mean, these are people's medical histories and everything, so we're seeing them moving now in close into the cloud so those companies can move to the cloud. Anybody can move to the cloud. You mean these sort of compliancy scaremongering? You cannot move to the cloud because of P. C. I or hip power. Those days are over because aws, Microsoft and Google, that's the first thing they do they have? Ah, stricter compliancy than most on premise Homemade tartar sentence. So I see. I see that industry really moving into the cloud. Now >>who knows what a ws re invent 2020 will look like Gentlemen I wish we had more time, but thank you. Both Young and Anthony were talking with Justin and me today sharing what's new with netapp. What? You guys are enabling customers. D'oh! In multiple. Same old way. We appreciate your time where my car is. Justin Warren, I'm Lisa Martin. You're watching the Cube from AWS or reinvent 19 from Vegas. Thanks for watching.

>>live from Seattle, Washington. It's the key nude covering smartsheet engaged 2019. Brought to you by Smartsheet. >>Hello, everyone. We are wrapping up one day of coverage at Smartsheet. Engage here in Seattle. I'm Rebecca Knight. Been coasting all day with Jeff Rick. It's been a pleasure sitting next to you together, and it has just been so much fun. It's a great show. >>And you've never been to Seattle before >>my time in the city? Exactly. So you've >>covered this space, Rebecca, in your in your non key black for a very long time. So first off, you know, kind of general impressions of new way to work. We hear about it every show we got to talk about new way to work. So, you know, kind of your global perspective a little bit and then, you know, kind of some takeaways from some of the conversations today. >>Well, we know that the situation is pretty bleak right now that there are the statistics are horrible just in terms of the number of employees that are really checked out, totally disengaged, would would love to quit, but they need the health insurance. And so we're already sort of starting from from a pretty low place, where in terms of people's engagement at work, and I think a lot of the things that that drive people nuts about their work. Uh, of course, is a bad boss and not a great parking spot and everything, but it's it's it's it's the little things that get in your way of doing your job. And it's it's the things that just drive you nuts about some sort of process that takes forever. And, oh, I have to keep doing this. And I just already sent you that email and how come you're looking at this other version? And it's all those impediments that really drive people crazy and that makes people stressed out and and unhappy in their jobs. So I do think that if you are a company like Smartsheet and you have you realized this and you can slowly chip away at those impediments and the aggregate aggravate aggravations that people feel, I think that's not a bad business model. I think I think they're on to something here. Don't worry, though >>sometimes is just is just additive, right? It's just another thing we talked. It's one of the interviews. And when I'm at work, I have three big monitors, each one split into two screens. I've got mail open calendar, open sales force open, slack open asana open YouTube. Twitter. Um, it's probably a couple. And then if I have to, like, look something up and and you know there's this kind of constant confusion is what it what is the screen that's open when you work? And it used to just be e mail, which is not a good solution at all. So I think if if you know, they can become the place that people do, their work right, and we talked about all the integrations like it's that integrate with slack. So maybe you know, the people that work primarily and slacker primarily there, and maybe the people in some other department are primarily on spark cheat, and somebody else is primarily on another tool. But it just seems still like keep adding, tourists were not necessarily taking a lot of them away. >>Well, that will be the job for Anna Griffin, who is the first ever cmo this company. You just started in April, and she's got her work cut out for her because you're right. There are a lot of screens. That's that does not describe my work day. But I know it describes a lot of people's work day, Um, and that that that will be. What she needs to figure out is how to be your number one You're going to the one that you rely on to get your job done. >>The part that I took away from her interview is really She talked a lot about engagement, and you just talked about engagement, an empowerment, you know, not only not only getting the obstacles out of the way, but making me feel like what I do matters, matters to me, matters to my boss, matters to my clients and matters. And then I think that does finally drive to innovation, which is the Holy Grail that everyone talks about. But it's really not that easy to execute. >>Everyone wants more innovative, of course, >>and then the last thing which she talked about, why part of the reason why she came here? His leadership. But I think the way we really can't have this conversation around engagement without talking about leadership, because it's such a critical piece to the puzzle for everyone to rally around, you know, a mission. So this is the execution details. But you also need some type of a mission that you can feel good about, as well as feeling that you can contribute to. >>Absolutely. And I think that what you were just talking about with the ownership piece and so these air these employees, as we said, they're removing the impediments to their job. But then they're also able to then focus on higher level tasks, assignments, thinking, strategy. They're able to use their brains for what they were hired for, not thinking about certain tasks and other files that are old versions. And so if they if they could do those things and then, as you said, feel like they matter, feel like that work, they matters to their boss. However, you are right in that if you got a bad boss, all bets are off. If it works, still gonna stink and you're there. There's nothing you can do about it. >>The other piece that came up, which I was interesting, is really about prioritization. What and what do you optimizing for? And my favorite part of Clayton Christian since Innovator's dilemma, is the conversation about that you must prioritize. You cannot engineer for everything equally, and you have to force up. That pressurization, I think, is interesting here about Smart Cheat is for all the talk about digital transformation. Most people talk about the products, and service is that they sell. They talk about the engagement with their customers. They don't talk about transforming the life of their employees and the way their employees get stepped on and the way the employees actually engage with the company through the applications. And I thought that was a really interesting and insightful take, especially in the day where everything is a service. And again your people walk out the door every night and you hope they come back the next day. So I think, you know, spinning the digital transformation story into more of an employee enable men and engagement story is pretty powerful. >>You I could not agree more because because that that is the critical piece. If you have a bunch of people coming to work every day who hate their jobs, they're not gonna be giving your customers the experience that you want their customers tohave. So it really does start with Happy workers, right? Andi, I think that I think smart. She really gets that. So that's that's what I am struck by today. >>Yeah, it's just those other ones that we're going to bring along. And Dion may have made a good point and said, You know, some people don't want to be engaged work. Some people don't want >>you >>next level things like that they like their roads in the routine gives him comfort. They come to work, they do the road in the routine and they go home. So it's gonna be interesting. Time for those peoples can reach it in time for people to not necessarily have expertise in a broad range of categories formerly siloed categories like product marketing, product management, finance sales, biz, Dev production. But you least have tohave in a kind of an inch. De Milo gave those teams. So you put together a SWAT team, if you will, to accomplish the task. And that's what I'm curious to see. Some of the 4 51 research that how how he was pointing to kind of a restructuring of the silos of teams and organizations within it within a company that We don't hear much about how that's going to restructure on kind of a dev ops, fast assembly, fast, complete kind of assemble and disassemble around projects, which is what Dev Ops says. We'll see you know how that how that impacts organizational structure. >>And I think that could be very cool and very different, particularly with different. I mean, we know that diverse groups make better decisions than lone geniuses. And so if we have a bunch of people who have different perspectives, different levels of expertise and even if it's not expertise, it's just sort of a general knowledge about a lot of different things, right. We know that if we can get those people working together on a task, it's got a lot of potential. So I think I think you're right, right. >>Last thing is that I think really interesting. Here is the is the acknowledgment of team beyond even the company walls. So you've got your core team, you know, cross departmental collaboration, and then was a mere it over and over here here today, collaboration outside the walls to external teams. And it was Mark talking about putting on these big events mean there's so many external stakeholders in place holders and vendors involved in this humongous dance that becomes our enjoyment of the Final Four event. I think that's really insightful. Kind of take that. You have to have the ability to engage, collaborate with a large group or an extended group for any particular project. And And that really changes the way you think about what the application is high share information >>and that they all have to feel ownership in the process to yes, very >>important. All right, Rebecca. Well, >>this was so much fun. I Jeff, I had a great time working with you, and we had a great team. We had Andrew in Jay and Brendan and Taylor Welcome Taylor to the to the show. It was great. I can't wait to come back and do it again. >>It will be big next time. All right, >>Thanks. That is wrapping up our coverage of engaged 2019. I'm Rebecca Knight for Jeff. Rick. Thanks a lot for watching

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)