(upbeat music) >> Hello and welcome to this special CUBE conversation. I'm John Furrier, host of theCUBE here, remote in Washington, DC, not in Palo Alto, but we're all around the world with theCUBE as we are virtual. We're here recapping the Citrix Launchpad: Cloud (accelerating IT modernization) announcements with CUBE alumni Kevin Jackson, Kevin L. Jackson, CEO of GC Global Net. Kevin, great to see you. Thanks for coming on. >> No, thank you very much, John. It's always a pleasure to be on theCUBE. >> It's great to have. You always have great insights. But here, we're recapping the event, Citrix Launchpad: Cloud (accelerator IT modernization). And again, we're seeing this theme constantly now, IT modernization, application modernization. People are now seeing clearly what the pandemic has shown us all that there's a lot of projects that need to be up-leveled or kill. There's a lot of things happening and going on. What's your take of what you heard? >> Well, you know, from a general point of view, organizations can no longer put off this digitalization and the modernization of their IT. Many of these projects have been on a shelf waiting for the right time or, you know, the budget to get right. But when the pandemic hit, everyone found themselves in the virtual world. And one of the most difficult things was how do you make decisions in the virtual world when you can't physically be with someone? How do you have a meeting when you can't shake someone's hand? And they all sort of, you know, stared at each other and virtually, of course, to try to figure this out. And they dusted off all of the technologies they had on the shelf that they were, you know, they were told to use years ago, but just didn't feel that it was right. And now it became necessary. It became the way of life. And the thing that really jumped at me yesterday, well, jumped at me with Launchpad, the Launchpad of the cloud is that Citrix honed in on the key issues with this virtual world. I mean, delivering applications, knowing what the internet state is so that you could select the right sources for information and data. And making security holistic. So you didn't have to, it was no longer sort of this bolted on thing. So, I mean, we are in the virtual world to stay. >> You know, good call out there. Honing in was a good way to put it. One quote I heard from Tim (Minahan) was, you know, he said one thing that's become painfully evident is a lot of companies are going through the pandemic and they're experiencing the criticality of the application experience. And he says, "Application experience is the new currency." Okay, so the pandemic, we all kind of know what's going on there. It's highlighting all the needs. But this idea of an application experience is the new currency is a very interesting comment because, I mean, you nailed it. Everyone's working from home. The whole work is shifting. And the applications, they kind of weren't designed to be this way 100%. >> Right, right. You know, the thing about the old IT was that you would build something and you would deploy it and you would use it for a period of time. You know, a year, two years, three years, and then there would be an upgrade. You would upgrade your hardware, you would upgrade your applications, and then you go through the process again, you know? What was it referred to as, it wasn't modernization, but it was refresh. You know, you would refresh everything. Well today, refresh occurs every day. Sometimes two or three times a day. And you don't even know it's occurring. Especially in the application world, right? I think I was looking at something about Chrome, and I think we're at like Chrome 95. It's like Chrome is updated constantly as a regular course of business. So you have to deploy this, understand when it's going to be deployed, and the customers and users, you can't stop their work. So this whole application delivery and security aspect is completely different than before. That's why this, you know, this intent driven solution that Citrix has come up with is so revolutionary. I mean, by being able to know the real business needs and requirements, and then translating them to real policies that can be enforced, you can really, I guess, project the needs, requirement of the organization anywhere in the world immediately with the applications and with this security platform. >> I want to get your reactions to something because that's right on point there, because when we look at the security piece and the applications you see, okay, your mind goes okay, old IT, new IT. Now with cloud, with the pandemic showing that cloud scale matters, a couple themes have come from that used to be inside the ropes concepts. Virtualization, virtual, and automation. Those two concepts are going mainstream because now automation with data and virtual, virtual work, virtual CUBE, I mean, we're doing virtual interviews. Virtualization is coming here. So building on those things. New things are happening around those two concepts. Automation is becoming much more programmable, much more real time, not just repetitive tasks. Virtual is not just doing virtual work from home. It's integrating that virtual experience into other applications. This requires a whole new organizational structure mindset. What's your thoughts on that? >> Well, one of the things is the whole concept of automation. It used to be a nice to have. Something that you could do maybe to improve your particular process, not all of the processes. And then it became the only way of reacting to reality. Humans, it was no longer possible for humans to recognize a need to change and then execute on that change within the allotted time. So that's why automation became a critical element of every business process. And then it expanded that this automated process needed to be connect and interact with that automated process and the age of the API. And then the organization grew from only relying on itself to relying on its ecosystem. Now an organization had to automate their communications, their integration, the transfer of data and information. So automation is key to business and globalization creates that requirement, or magnifies that requirement. >> One of the things we heard in the event was, obviously Citrix has the experience with virtual apps, virtual desktop, all that stuff, we know that. But as the cloud grows in, they're making a direct statement around Citrix is going to add value on top of the cloud services. Because that's the reality of the hybrid, and now soon to be multi-cloud workflows or architectures. How do you see that evolve? Is that something that's being driven by the cloud or the app experience or both? What's your take on that focus of Citrix taking their concepts and leadership to add value on top of the cloud? >> To be honest, I don't like referring to the cloud. It gives an impression that there's only a single cloud and it's the same no matter what. That couldn't be further from the truth. A typical organization will consume services from three to five cloud service providers. And these providers aren't working with each other. Their services are unique, independent. And it's up to the enterprise to determine which applications and how those applications are presented to their employees. So it's the enterprise that's responsible for the employee experience. Integrating data from one cloud service provider to another cloud service provider within this automated business process or multiple business processes. So I see Citrix is really helping the enterprise to continually monitor performance from these independent cloud service provider and to optimize that experience. You know, the things like, where is the application being consumed for? What is the latency today on the internet? What type of throughput do I need from cloud service provider A versus cloud service provider B? All of this is continually changing. So the it's the enterprise that needs to constantly monitor the performance degradation and look at outages and all of that. So I think, you know, Citrix is on point by understanding that there's no single cloud. Hybrid and multi-cloud is the cloud. It's the real world. >> You know, that's a great call. And I think it's naive for enterprises to think that, you know, Microsoft is sitting there saying hmm, let's figure out a way to really work well with AWS. And vice versa, right? I mean, and you got Google, right? They all have their own specialties. I mean, Amazon web service has got great compliance action going on there. Much back stronger than Microsoft. Microsoft's got much deeper legacy and integration to their base, and Google's doing great with developers. So they're all kind of picking their lanes, but they all exist. So the question in the enterprise is what? Do I, how do I deal with that? And again, this is an opportunity for Citrix, right? So this kind of comes down to the single pane of glass (indistinct) always talks about, or how do I manage this new environment that I need to operate in? Because I will want to take advantage of some of the Google goodness and the Azure and the AWS. But now I got my own on premises. Bare metals grow. You're seeing more bare metal deals going down now because the cloud operations has come on premises. >> Yeah, and in fact, that's hybrid IT, right? I always see that there are an enterprise, when enterprise thinks about modernizing or digitally transforming a business process, you have three options, right? You could put it in your own data center. In fact, building a data center and optimizing a data center for a particular process is the cheapest and most efficient way of executing a business process. But it's only way cheaper and efficient if that process is also stable and consistent. I'll say, but some are like that. But you can also do a managed service provider. But that is a distinctly different approach. And the third option is a cloud service provider. So this is a hybrid IT environment. It's not just cloud. It's sort of, you know, it's not smart to think everything's going to go into the cloud. >> It's distributed computing. We see (indistinct). >> Yeah, yeah, absolutely. I mean, in today's paperless world, don't you still use a pen and paper and pencil? Yes. The right tool for the right job. So it's hybrid IT. Cloud is not always a perfect thing. And that's something that I believe Citrix has looked at. That interface between the enterprise and all of these choices when it comes to delivering applications, delivering the data, integrating that data, and making it secure. >> And I think that's a winning positioning to have this app experience, the currency narrative, because that ultimately is an outcome that you need to win on. And with the cloud and the cloud scale that goes on with all the multiple services now available, the company's business model is app driven, right? That's their application. So I love that, and I love that narrative. Also like this idea of app delivering security. It's kind of in the weeds a little bit, but it highlights this hybrid IT concept you were saying. So I got to ask you as the expert in the industry in this area, you know, as you have intent, what do they call it? Intent driven solution for app delivering security. Self healing, continuous optimization, et cetera, et cetera. The KPIs are changing, right? So I want to get your thoughts on that. Because now, as IT shifts to be much faster, whether it's security teams or IT teams to service that DevOps speed, shifting left everyone talks about, what's the KPIs that are changing? What is the new KPIs that the managers and people can work through as a north star or just tactically? What's your thoughts? >> Well, actually, every KPI has to relate to either the customer experience or the employee experience, and sometimes even more important, your business partner experience. That's the integration of these business processes. And one of the most important aspects that people really don't think about is the API, the application programming interface. You know, you think about software applications and you think about hardware, but how is this hardware deployed? How do you deploy and expand the number of servers based upon more usage from your customer? It's via the API. You manage the customer experience via APIs. You manage your ability to interact with your business partners through the API, their experience. You manage how efficient and effective your employees are through their experience with the IT and the applications through the API. So it's all about that, you know, that experience. Everybody yells customer experience, but it's also your employee experience and your partner experience. So that depends upon this integrated holistic approach to applications and the API security. The web app, the management of bots, and the protection of your APIs. >> Yeah, that really nailed it. I think the position is good. You know, if you can get faster app delivery, keep the security in line, and not bolt it on after the fact and reduce costs, that's a winning formula. And obviously, stitching together the service layer of app and software for all the cloud services is really key. I got to ask you though, Kevin, since you and I have riffed on theCUBE about this before, more importantly now than ever with the pandemic, look at the work edge. People working at home and what's causing the office spaces changing. The entire network architecture. I mean, I was talking to a big enterprise that said, oh yeah, we had, you know, the network for the commercial and the network for dial up now 100% provisioned for everyone at home. The radical change to the structural interface has completely changed the game. What is your view on this? I mean, give us your, where does it go? What happens next? >> So it's not what's next, it's where we are right now. And you need to be able to be, work from anywhere at any time across multiple devices. And on top of that, you have to be able to adapt to constant change in both the devices, the applications, the environment, and a business model. I did a interview with Citrix, actually, from an RV in the middle of a park, right? And it's like, we did video, we did it live. I think it was through LinkedIn live. But I mean, you need to be able to do anything from anywhere. And the enterprise needs to support that business imperative. So I think that's key. It's it's not the future, it's the today. >> I mean, the final question I have for you is, okay, is the frog in the boiling water? At what point does the CIO and the IT leaders, I mean, their minds are probably blown. I can only imagine. The conversations I've been having, it's been, you know, be agile, do it in the cloud, do it at speed, fix the security, programmable infrastructure. What? How fast can I run? This is the management challenge. How are people dealing with this when you talk to them? >> First of all, the IT professional needs to focus on the business needs, the business requirements, the business key performance indicators, not technology, and a business ROI. The CIO has to be right there in the C sweep of understanding what's needed by the business. And there also has to be an expert in being able to translate these business KPIs into IT requirements, all right? And understanding that all of this is going to be within a realm of constant change. So the CIO, the CTO, and the IT professional needs to realize their key deliverable is business performance. >> Kevin, great insight. Loved having you on theCUBE. Thanks for coming on. I really appreciate your time highlighting and recapping the Citrix Launchpad: Cloud announcements. Accelerating IT modernization can't go fast enough. People, they want to go faster. >> Faster, faster, yes. >> So great stuff. Thanks for coming, I appreciate it. >> Thank you, John. I really enjoyed it. >> Okay, it's theCUBE conversation. I'm John Furrier, host of theCUBE. Thanks for watching. (upbeat music)

from the cube studios in palo alto in boston connecting with thought leaders all around the world this is a cube conversation hey welcome back everybody jeff frick here with the cube coming to you from our palo alto studios with a cube conversation with a great influencer we haven't had him on for a while last had him on uh in may i think of 2019 mid 2019. we're excited to welcome back to the program he's kevin l jackson he is the ceo of gc globalnet kevin great to see you today hey how you doing jeff thanks for having me it's uh it's been a while but i really enjoyed it yeah i really enjoy being on thecube well thank you for uh for coming back so we've got you on to talk about citrix we had you last on we had you on a citrix synergy this year obviously covet hit all the all the events have gone virtual and digital and citrix made an interesting move they decided to kind of break their thing into three buckets kind of around the main topics that people are interested in in their world and that's cloud so they had a citrix cloud summit they had a citrix workplace summit and now they just had their last one of the three which is the citrix security summit uh just wrapped up so before we jump into that i just want to get your take how are you doing how you getting through the kind of covid madness from you know the light switch moment that we experienced in march april 2. you know now we're like seven eight months into this and it's not going to end anytime soon well you know it's it was kind of different for me because um i've been working from home and remotely since i guess 2014 being a consultant and with all my different clients i was doing a lot more traveling um but with respect to doing meetings and being on collaborative systems all day long it's sort of like uh old hat and i say welcome to my world but i find that you know society is really changing the things that you thought were necessary in business you know being physically at meetings and shaking hands that's all like you know although we don't do that anymore yeah i used to joke right when we started this year that we finally got to 2020 the year that we know everything right with the benefit of hindsight but it turned out to be the year that we actually find out that we don't know anything and everything that we thought we knew in fact is not necessarily what we thought and um we got thrown into this we got thrown into this thing and you know thankfully for you and for me we're in you know we're in the tech space we can we can go to digital we're not in the hotel business or the hospitality business or you know so many businesses that are still suffering uh greatly but we were able to make the move in i.t and and citrix is a big piece of that in terms of enabling people to support remote work they've always been in remote work but this really changed the game a lot and i think as you said before we turned on the cameras accelerated you know this digital transformation way faster than anybody planned on oh oh yeah absolutely and another one of the areas that was particularly um accelerated they sort of put the rockets on is security which i'm really happy about because of the rapid increase in the number of remote workers i mean historically companies had most of their workforce in their own buildings on on their own property and there was a small percentage that would remote work remotely right but it's completely flipped now and it flipped within a period of a week or a week and a half and many of these companies were really scrambling to make you know their entire workforce be able to communicate collaborate and just get access to information uh remotely right right well david talked about it in the security keynote you know that you know as you said when this light switch moment hit in mid-march you had to get everybody uh secure and take care of your people and get them set up but you know he talked a little bit about you know maybe there were some shortcuts taken um and now that we've been into this thing in a prolonged duration and again it's going to be going on for a while longer uh that there's really an opportunity to to make sure that you put all the proper uh systems in place and make sure that you're protecting people you're protecting the assets and you're protecting you know the jewels of the company which today are data right and data in all the systems that people are working with every single day yeah yeah absolutely they had to rapidly rethink all of the work models and this uh accelerated digital transformation and the adoption of cloud and it was just this this huge demand for remote work but it was also important to uh keep to think about the user experience the employee experience i mean they were learning new things learning new technologies trying to figure out how to how to do new things and that at the beginning of this uh trend this transition people were thinking that hey you know after a few months we'll be okay but now and it's starting to sink in that this stuff is here to stay so you have to understand that work is not a place and i think actually david said that right it's really you have to look at how the worker is delivering and contributing to the mission of the organization to the business model and you have to be able to measure the workers level of output and their accomplishment and be able to do this remotely so back to office is is not going to happen in reality so the employee experience through this digital environment this digital work space it's critical yeah i think one of the quotes he had whether i think was either this one or one of the prior ones is like back to work is not back to normal right we're not going to go back to the way that it was before but it's interesting you touched on employee experience and that's a big piece of the conversation right how do we measure output versus you know just time punching the clock how do we give people that that experience that they've come to expect with the way they interact in technology in their personal lives but there's an interesting you know kind of conflict and i think you've talked about it before between employee experience and security because those two kind of inherently are going to be always in conflict because the employee's going to want more access to more things easier to use and yet you've got to keep security baked in throughout the stack whether it's access to the systems whether it's the individual and and so there's always this built-in kind of tension between those two objectives well the tension is because of history security has always been sort of a a second thought an afterthought uh you know you said due to work oh security we'll catch up to it when we need to but now because of the importance of data and the inherently global connectivity that we have the the need for security has is paramount so in order to attract that in order to address that the existing infrastructures had this where we just bolted security on to the existing infrastructures uh this is when they when the data centers and we said well as long as it's in our data center we can control it but then we with this covet thing we'll just burst out of any data center we have to rely on cloud so this this concept of just bolting on security just doesn't work because you no longer own or control the security right so you have to look at the entire platform and have a holistic security approach and it has to go from being infrastructure-centric to data centric because that's the only way you're going to provide security to your data to those remote employees right right and there's a very significant shift we hear all the time we've got rsa uh all the time to talk about security and that's this concept of zero trust and and the idea that rather than as you said kind of the old school you put a a wall and a moat around the things that you're trying to protect right you kind of start from the perspective of i don't trust anybody i don't trust where they're coming from i don't trust their device i don't trust that they have access to those applications and i don't trust that they have access to that data and then you basically enable that on a kind of a need to know basis across all those different factors at kind of the least the least amount that they need to get their job done it's a really different kind of approach to thinking about security right and but it's a standardized approach i mean before present time you would customize security to the individual or 2d organization or component of the organization because you know you knew where they were and you would you would say well they won't accept this so we'll do that so everything was sort of piecemeal now that work is not a location you have to be much more standardized much more focused and being able to track and secure that data requires things like digital rights management and and secure browsers and some of the work that citrix has done with google has really been amazing they they looked at it from a different point of view they said okay where people are always working through the cloud in different locations from from anywhere but they all work through their browser so you know we could and i think this was something that the vice president at google said uh sunil potty i believe uh vice president of google cloud they said well we can capitalize on that interface without affecting the experience and he was talking about chrome so so citrix and and google have worked together to drive sort of an agent-less experience to order to enhance security so instead of making everything location specific or organizational specific they set a standard and they support this intent-driven security model yeah it's interesting sunil's a really sharp guy we've had him on thecube a ton of times uh over the years but there's another really interesting take on security and i want to get your your feedback on it and that's kind of this coopetation right and silicon valley is very famous for you know coopetation you might be competing tooth and nail with the company across the street at the same time you got an opportunity to partner you might share apis you know it's a really interesting thing and one of the the items that came out of the citrix show was this new thing called the workspace security alliance because what's interesting in security that even if we're competitors if you're suddenly getting a new type of threat where you're getting a new type of attack and there's a new you know kind of profile actually the industry likes to share that information to help other people in the security business as kind of you know us versus the bad guys even if we're you know competing for purchase orders we're competing you know kind of face-to-face so they announced this security alliance which is pretty interesting to basically bring in partners to support uh coopetition around the zero trust framework uh yeah absolutely this is happening across just about every industry though you're going away from uh point-to-point relationships to where you're operating and working within an ecosystem and in security just this week it's been highlighted by the uh the trick trick bot um activity this uh persistent uh malware that i guess this week is attacking um health care uh facilities the actual the u.s department of homeland security put out an alert now and this is a threat to the entire ecosystem so everyone has to work together to protect everyone's data and that improves that that is the way forward and that's really the only way to be successful so uh we have to go from this point-to-point mindset to understanding that we're all in the same boat together and in this uh alliance the workspace security alliance is an indication that citrix gets it right everyone has workers everyone's workers are remote okay and everyone has to protect their own data so why don't we work together to do that yeah that's great that's interesting i had not heard of that alert but what we are hearing a lot of um in in a lot of the interviews that we're doing is kind of a resurfacing of kind of old techniques uh that the bad guys are using to to try to get remote workers because they're not necessarily surrounded with as much security or have as much baked in in their home setup as they have in the office and apparently you know ransomware is really on the rise and the sophistication of the ransom where folks is very high and that they try to go after your backup and all in you know your replication stuff before they actually hit you up for the uh for the want for the money so it's it's there's absolutely that's right yeah go ahead i'm sorry i was just saying that's indicative of the shift that most of your workers are no longer in your facilities than now and at home where companies never really put a lot of investment into protecting that channel that data channel they didn't think they needed to right right one of the other interesting things that came up uh at the citrix event was the use of uh artificial intelligence and machine learning to basically have a dynamic environment where you're adjusting you know kind of the access levels based on the behavior of the individual so what apps are they accessing what you know are they moving stuff around are they downloading stuff and to actually kind of keep a monitor if you will to look for anomalies and behavior so even if someone is trusted to do a particular type of thing if suddenly they're you know kind of out of band for a while then you know you can flag alerts to say hey what's going on is that this person did their job change you know why are they doing things that they don't normally do maybe there's a reason maybe there isn't a reason maybe it's not them so you know i think there's so many great applications for applied machine learning and artificial intelligence and these are the types of applications where you're going to see the huge benefits come from this type of technology oh yeah absolutely i mean the citrix analytics for security is really a um security service right um that monitors the activities of of people on the internet and it this machine learning gives you or gives the service this insight no one company can monitor the entire internet and you can go anywhere on the internet so bob working together leveraging this external service you can actually have automated remediation of your users you can put this specific user security risk score so um companies and organizations can be assured that they are within their risk tolerance right right and of course the other thing you've been in the business for a while that we're seeing that we're just kind of on the cusp of right is 5g and iot so a lot more connected devices a lot more data a lot more data moving at machine speed which is really what 5g is all about it's not necessarily for having a better phone call right so we're just going to see you know kind of again this this growth in terms of attack surfaces this growth in terms of the quantity of data and the growth in terms of the the the rate of change that that data is coming in and and the scale and the speed with the old uh you know velocity and and variety and volume uh the old big data memes so again the other thing go ahead the other thing it's not just data when you have 5g the virtual machines themselves are going to be traveling over this network so it's a whole new paradigm yeah yeah so the uh once again to have you know kind of a platform approach to make sure you're applying intelligence to keep an eye on all these things from zero trust uh uh kind of baseline position right pretty damn important yeah absolutely with with edge computing the internet of things this whole infrastructure based data centric approach where you can focus on how the individual is interacting with the network is important and and uh another real important component of that is the um software-defined wide area network because people work from everywhere and you have to monitor what they're doing right right yeah it's really worked from anywhere not necessarily work from home anymore i just want to you know again you've been doing this for a while get your feedback on on the fact that this is so much of a human problem and so much of a human opportunity versus just pure technology i think it's really easy to kind of get wrapped up in the technology but i think you said before digital transformation is a cultural issue it's not a technology issue and getting people to change the way they work and to change the way they work with each other and to change what they're measuring um as you said kobe kind of accelerated that whole thing but this has always been more of a cultural challenge in a technology challenge yeah the technology in a relative sense of you is kind of easy right but it's the expectations of humans is what they're used to is what they have been told in the past is the right thing no longer is right so you have to teach you have to learn you have to accept change and not just change but rapid change and accelerated change and people just don't like change they're uncomfortable in change so another aspect of this culture is learning to be adaptable and to accept change because it's going to come whether you want it or not faster than you think as well for sure you're right well that's great so kevin i'll give i give you the final word as as you think about how things have changed and again i think i think the significant thing is that we went from you know kind of this light switch moment where it was you know emergency and and quick get everything squared away but now we're in this we're in kind of this new normal it's going to be going for a while we'll get back to some some version of a hybrid uh solution at some point and you and i will be seeing each other at trade shows at some point in time in the in the future but it's not going to go back the way that it was and people can't wait and hope that it goes back the way that it was and really need to get behind this kind of hybrid if you will work environment and helping people you know be more productive with the tools they need it always gets back to giving the right people the right information at the right time to do what they need to do so just kind of get your perspective as we you know kind of get to the end of 2020 we're going to turn the page here rapidly on 2021 and we're going to start 2021 in kind of the same place we are today well to be honest we've talked about a lot of these things but the answer to all of them is agility agility agility is the key to success this is like not locking into a single cloud you're going to have multiple clouds not locking into a single application you have multiple applications not assuming that you're always going to be working from home or working through a certain browser you have to be agile to adapt to rapid change and the organizations that recognize that and uh teach their workers teach their entire ecosystem to operate together in a rapidly changing world with agility will be successful that's a great that's a great way to leave it i saw beth comstack the former vice chair at ge give a keynote one time and one of her great lines was get comfortable with being uncomfortable and i think you nailed it right this is about agility it's about change it's we've seen it in devops where you embrace change you don't try to avoid it you know you take that really at the top level and try to architect to be successful in that environment as opposed to sticking your head in the sand and praying it doesn't absolutely all right well kevin so great to catch up i'm i'm sorry it's been as long as it's been but hopefully it'll be uh shorter uh before the next time we get to see each other yes fine thank you very much i really enjoyed it absolutely all right he's kevin l jackson i'm jeff frick you're watching thecube from our palo alto studios keep conversation we'll see you next time you

>> Narrator: Live from Atlanta, Georgia, it's theCUBE, covering Citrix Synergy, Atlanta, 2019. Brought to you by Citrix. >> Hi, welcome back to theCUBE. Lisa Martin here, at Citrix Synergy 2019 in Atlanta, Georgia, with Keith Townsend, and we're pleased to welcome to theCUBE, Kevin Jackson, the founder of GovCloud. Kevin, welcome to theCUBE. >> Thank you very much for the opportunity. >> So this has been an exciting day. Keith and I have been geeking out all day, starting with the keynote this morning. Talking about employee experience is so relevant, theCUBE covers a ton of technology events, we don't often hear about employee experience as a catalyst to digital transformation, but it is. >> No, absolutely. Citrix, the keynote today was just very impressive. Not because of technology, and not that it wasn't impressive, but it was the focus. Today's world has really been focused around digital transformation. What processes in your organization are the right ones? And Citrix has developed and is delivering tools to help organizations understand those processes which should be digitized, and it's really about the employee experience because companies in the commercial world, in the consumer space, have really focused on consumer experience and customer experience. Those that have been successful in doing that have seen their market share grow. Well, this is all about looking at your employee experience. Instead of looking outside, look inside. If you're able to improve your employee experience, you get more efficiency, you get better employees, and you get better products and services. >> So Kevin, talk to us about the importance of examining your processes prior to automating. I was visiting my parents the other day and they're remodeling their home, and I said, you know, I made a joke about how we automate in ITS, I said you know what, you guys are moving much too slow, I'm going to buy you two more saws, so you can go faster. And a lot of times I feel like that's the way we tackle automation and process improvement. What have you seen out in the field, and where should companies start versus where they do start? >> So one of the biggest problems companies have is their history. They have a process that they've done for years, in their eyes it's been very successful, and I'm not saying it wasn't successful, but it was successful in a different era, successful in a different environment. Today's environment moves much faster. It's much broader. It's not regionalized. It's international. So organizations need to understand what their processes are, and which of their old processes can actually be effective in the new environment. Many of them can be, but they need to be tweaked. They need to be updated. They may need to be entirely changed. When those processes were designed, you didn't have customers with smart phones that can access your products and services. We're going from a physical world to a virtual world. So the first thing is to understand which processes need to be digitized. Maybe the saws were a good thing, but maybe they weren't. Maybe they need a level to go faster and to go better, to improve the quality of the output, not necessarily cutting more wood. >> So these changes are subtle. How does Citrix help kind of break down the processes and help you determine? You know, one of the things that we learnt, early cloud. It's not wise to put everything in the cloud up front, it's what makes a difference and moves the ball. David talked a lot this morning about employees want to move the ball forward. How does Citrix help move the ball forward in determining what processes should be automated? >> Yeah, great question. One of the biggest problems with cloud computing is sort of the adoption of the cloud-first policy. People misunderstood that policy and many companies misunderstand the implementation of that today. Cloud-first doesn't mean put everything in the cloud and get rid of all your legacy, it means evaluate cloud first and make a decision as to what data should go in the cloud and what processes should go in the cloud. Any organization of any significant size is still going to need legacy data centers. They still may need managed services, and cloud computing would be part of that hybrid mix. So what Citrix is doing is providing the tools so that you can get the data about the processes and understand which data should go in the cloud, which data should stay in your legacy data center, and which data could be managed by manage service providers. So customers, Citrix customers that actually leverage this intelligent workspace have the required tool to do digital transformation. >> When you're out talking with customers in different industries, public sector, government, where are they in understanding how critical the employee experience is, from recruiting and onboarding, to actually those employees interfacing with their customers? I mean, it's such a critical function. >> Oh, absolutely, and digital transformation is really not about the technology as much as it's about the culture. Organizations that undergo this journey oftentimes forget about the cultural transformation that needs to occur within the organization. And that means training, that means education, and it also means redefining the roles within the organization. Citrix provides many of the tools for helping employees understand their role, redefining their role, educating employees. So all of this is critical to digital transformation. >> And that's not easy to do, as well. I think this morning, and I've heard this recently from a number of events I've covered, is there are five generations active in the workforce today. So you've got my parents, the baby boomers, you've got the generation younger, too, younger than I am, who were born on smart devices, and there's different expectations, there's different levels of technology expertise, so companies like Citrix have to really balance that employee experience across five generations with very different expectations. >> Yeah, absolutely. I was talking to a colleague of mine and he was relaying a story to me when an employee was working an application, right? And he finished the task, went home, came back the next day and all the work was gone, and the employee was saying, "What happened? "I worked hard on it, it took me hours." And the manager said, "Well did you save it?" And he said, "Well what's that?" (laughs) Because if you're born in the cloud you don't press a button to save, it's automatic. This was a millennial that was born with technology and actually didn't understand the concept of having to save something, because it was always in the cloud. This is cultural, and you need to address this culture when you are improving and modifying your business processes. >> So when you're an organization of any size you can look at this employee experience journey and be overwhelmed, and think, wow. You know what, you could hear a story like that and say, "Where do I start to change?" Like my SAP app, you're still going to have to hit save, that's not going to change tomorrow. So where's the starting point? >> Really, the starting point is data. Collecting data, understanding the data, interpreting the data, because then you can make the appropriate decisions within the context of what your organization or industry is doing. Although I do a lot of public sector, most of my work today is in commercial industry, and employees are in an environment that's forever changing, where their context changes from second to second. They're doing one application then doing another application. They're responding to a client or customer, then responding to a colleague, and then immediately responding to the manager. This context switching is normal for computers but it's not normal for people, so this is important as you move forward in the world. >> So what I'm hearing is a term, an SAP coin, X-data, experience data. The idea that you need to collect, as much pressure as we're under to transform digitally, the first step is to collect and analyze the data. One of the questions I put towards another analyst was where is this data coming from? I know the data is because people are doing stuff, and there's a trail somewhere, but where do I go first to start as the indicator to collect this data to analyze? >> Well the old school method of doing that would be a survey, or you would observe a worker. Now the actual act of conducting a survey or observing work changes the work process. All right, so the data that you get from that can also be colored or flawed based upon the observer. Citrix experience, their desktop has artificial intelligence built into it. The worker can actually do their task, unbeknownst to them that they are being observed, that the data is being collected with respect to that process. Don't get scared, this is not George Orwell in 1984, though that's been a while, I guess. It's not Big Brother looking at you. The data is anonymized, right? It's not about you, it's about the task that you're completing. So you now have a tool to collect real data and you can continue to collect that data because processes have a life, they change. So you can monitor that, and update and tweak it. >> And an important outcome of that data collection and analysis is delivery, using it to deliver a personalized experience to the user, regardless of generation, how born in the cloud they were or not. >> Absolutely. And now, you're heading back to that cultural aspect. The digital transformation is really cultural transformation. >> Then another aspect, no, output of that, is that you could correlate this X-data with operational data and see where there's human error. So your processes analysis, you champion process analysis, you can say, okay, where are we making the most mistakes, because we're having a human translate something from one screen to another, while we see where this error rate is coming up and now we can automate or modernize this process to improve the overall not only employee experience but customer experience as well. >> Yeah, absolutely. It's important to understand not just the investment that you're making in any process, but the return you're getting from that process. By collecting data, you can determine if value is being delivered not just to the organization, but to your customers. So this ROI, return on investment, is often not just about money. It's about the value of the employee, and you can actually measure that value. Measure what they're doing, measure the return, and drive better environments, better employees, better outcomes based on the data. >> And that's got to elevate up to the C-suite as a business imperative, to understand that ROI, because those are employees that in many facets are involved and connected with those customers who are paying for products and services. So those employees, whether they're in sales or marketing, or finance, or legal, or a contact center, they're critical touchpoints to your customers. If their experience isn't great and they decide to leave, that customer experience, that's a possible brand reputation challenge. >> No, absolutely. And you touched upon touchpoints, right? In the past, you basically knew how your client was going to interact with you. Dissimilarly, you need to understand how your employee interacts with the organization. They're not going to just be in a cube interacting with the IT every day. They may be at home. They may, in the very near future, not today, they may be interacting with Alexa to get your information, or through Alexa with one of your clients and one of your customers. How do you manage that touchpoint? Well, with tools like Citrix, they are actually giving you the ability to normalize data across multiple channels, across multiple touchpoints, so you can make sure you have the same experience, the preferred experience with your clients and customers as well as with your employees. >> Serious impact. Well, Kevin, thank you so much for joining Keith and me on theCUBE this afternoon. >> It was very enjoyable, thank you. >> Good, our pleasure. >> For Keith Townsend, I'm Lisa Martin. You're watching theCUBE live, day one of our coverage of Citrix Synergy 2019. Thanks for watching. (percussive music)

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.