(upbeat music) >> Welcome back to Supercloud 2. This is Dave Vellante. We're here exploring the intersection of data and analytics in the future of cloud and data. In this segment, we're going to look deeper into the life sciences business with Jesse Cugliotta, who leads the Healthcare and Life Sciences industry practice at Snowflake. And Nicholas Nick Taylor, who's the executive director of Informatics at Ionis Pharmaceuticals. Gentlemen, thanks for coming in theCUBE and participating in the program. Really appreciate it. >> Thank you for having us- >> Thanks for having me. >> You're very welcome, okay, we're go really try to look at data sharing as a use case and try to understand what's happening in the healthcare industry generally and specifically, how Nick thinks about sharing data in a governed fashion whether tapping the capabilities of multiple clouds is advantageous long term or presents more challenges than the effort is worth. And to start, Jesse, you lead this industry practice for Snowflake and it's a challenging and vibrant area. It's one that's hyper-focused on data privacy. So the first question is, you know there was a time when healthcare and other regulated industries wouldn't go near the cloud. What are you seeing today in the industry around cloud adoption and specifically multi-cloud adoption? >> Yeah, for years I've heard that healthcare and life sciences has been cloud diverse, but in spite of all of that if you look at a lot of aspects of this industry today, they've been running in the cloud for over 10 years now. Particularly when you look at CRM technologies or HR or HCM, even clinical technologies like EDC or ETMF. And it's interesting that you mentioned multi-cloud as well because this has always been an underlying reality especially within life sciences. This industry grows through acquisition where companies are looking to boost their future development pipeline either by buying up smaller biotechs, they may have like a late or a mid-stage promising candidate. And what typically happens is the larger pharma could then use their commercial muscle and their regulatory experience to move it to approvals and into the market. And I think the last few decades of cheap capital certainly accelerated that trend over the last couple of years. But this typically means that these new combined institutions may have technologies that are running on multiple clouds or multiple cloud strategies in various different regions to your point. And what we've often found is that they're not planning to standardize everything onto a single cloud provider. They're often looking for technologies that embrace this multi-cloud approach and work seamlessly across them. And I think this is a big reason why we, here at Snowflake, we've seen such strong momentum and growth across this industry because healthcare and life science has actually been one of our fastest growing sectors over the last couple of years. And a big part of that is in fact that we run on not only all three major cloud providers, but individual accounts within each and any one of them, they had the ability to communicate and interoperate with one another, like a globally interconnected database. >> Great, thank you for that setup. And so Nick, tell us more about your role and Ionis Pharma please. >> Sure. So I've been at Ionis for around five years now. You know, when when I joined it was, the IT department was pretty small. There wasn't a lot of warehousing, there wasn't a lot of kind of big data there. We saw an opportunity with Snowflake pretty early on as a provider that would be a lot of benefit for us, you know, 'cause we're small, wanted something that was fairly hands off. You know, I remember the days where you had to get a lot of DBAs in to fine tune your databases, make sure everything was running really, really well. The notion that there's, you know, no indexes to tune, right? There's very few knobs and dials, you can turn on Snowflake. That was appealing that, you know, it just kind of worked. So we found a use case to bring the platform in. We basically used it as a logging replacement as a Splunk kind of replacement with a platform called Elysium Analytics as a way to just get it in the door and give us the opportunity to solve a real world use case, but also to help us start to experiment using Snowflake as a platform. It took us a while to A, get the funding to bring it in, but B, build the momentum behind it. But, you know, as we experimented we added more data in there, we ran a few more experiments, we piloted in few more applications, we really saw the power of the platform and now, we are becoming a commercial organization. And with that comes a lot of major datasets. And so, you know, we really see Snowflake as being a very important part of our ecology going forward to help us build out our infrastructure. >> Okay, and you are running, your group runs on Azure, it's kind of mono cloud, single cloud, but others within Ionis are using other clouds, but you're not currently, you know, collaborating in terms of data sharing. And I wonder if you could talk about how your data needs have evolved over the past decade. I know you came from another highly regulated industry in financial services. So what's changed? You sort of touched on this before, you had these, you know, very specialized individuals who were, you know, DBAs, and, you know, could tune databases and the like, so that's evolved, but how has generally your needs evolved? Just kind of make an observation over the last, you know, five or seven years. What have you seen? >> Well, we, I wasn't in a group that did a lot of warehousing. It was more like online trade capture, but, you know, it was very much on-prem. You know, being in the cloud is very much a dirty word back then. I know that's changed since I've left. But in, you know, we had major, major teams of everyone who could do everything, right. As I mentioned in the pharma organization, there's a lot fewer of us. So the data needs there are very different, right? It's, we have a lot of SaaS applications. One of the difficulties with bringing a lot of SaaS applications on board is obviously data integration. So making sure the data is the same between them. But one of the big problems is joining the data across those SaaS applications. So one of the benefits, one of the things that we use Snowflake for is to basically take data out of these SaaS applications and load them into a warehouse so we can do those joins. So we use technologies like Boomi, we use technologies like Fivetran, like DBT to bring this data all into one place and start to kind of join that basically, allow us to do, run experiments, do analysis, basically take better, find better use for our data that was siloed in the past. You mentioned- >> Yeah. And just to add on to Nick's point there. >> Go ahead. >> That's actually something very common that we're seeing across the industry is because a lot of these SaaS applications that you mentioned, Nick, they're with from vendors that are trying to build their own ecosystem in walled garden. And by definition, many of them do not want to integrate with one another. So from a, you know, from a data platform vendor's perspective, we see this as a huge opportunity to help organizations like Ionis and others kind of deal with the challenges that Nick is speaking about because if the individual platform vendors are never going to make that part of their strategy, we see it as a great way to add additional value to these customers. >> Well, this data sharing thing is interesting. There's a lot of walled gardens out there. Oracle is a walled garden, AWS in many ways is a walled garden. You know, Microsoft has its walled garden. You could argue Snowflake is a walled garden. But the, what we're seeing and the whole reason behind the notion of super-cloud is we're creating an abstraction layer where you actually, in this case for this use case, can share data in a governed manner. Let's forget about the cross-cloud for a moment. I'll come back to that, but I wonder, Nick, if you could talk about how you are sharing data, again, Snowflake sort of, it's, I look at Snowflake like the app store, Apple, we're going to control everything, we're going to guarantee with data clean rooms and governance and the standards that we've created within that platform, we're going to make sure that it's safe for you to share data in this highly regulated industry. Are you doing that today? And take us through, you know, the considerations that you have in that regard. >> So it's kind of early days for us in Snowflake in general, but certainly in data sharing, we have a couple of examples. So data marketplace, you know, that's a great invention. It's, I've been a small IT shop again, right? The fact that we are able to just bring down terabyte size datasets straight into our Snowflake and run analytics directly on that is huge, right? The fact that we don't have to FTP these massive files around run jobs that may break, being able to just have that on tap is huge for us. We've recently been talking to one of our CRO feeds- CRO organizations about getting their data feeds in. Historically, this clinical trial data that comes in on an FTP file, we have to process it, take it through the platforms, put it into the warehouse. But one of the CROs that we talked to recently when we were reinvestigate in what data opportunities they have, they were a Snowflake customer and we are, I think, the first production customer they have, have taken that feed. So they're basically exposing their tables of data that historically came in these FTP files directly into our Snowflake instance now. We haven't taken advantage of that. It only actually flipped the switch about three or four weeks ago. But that's pretty big for us again, right? We don't have to worry about maintaining those jobs that take those files in. We don't have to worry about the jobs that take those and shove them on the warehouse. We now have a feed that's directly there that we can use a tool like DBT to push through directly into our model. And then the third avenue that's came up, actually fairly recently as well was genetics data. So genetics data that's highly, highly regulated. We had to be very careful with that. And we had a conversation with Snowflake about the data white rooms practice, and we see that as a pretty interesting opportunity. We are having one organization run genetic analysis being able to send us those genetic datasets, but then there's another organization that's actually has the in quotes "metadata" around that, so age, ethnicity, location, et cetera. And being able to join those two datasets through some kind of mechanism would be really beneficial to the organization. Being able to build a data white room so we can put that genetic data in a secure place, anonymize it, and then share the amalgamated data back out in a way that's able to be joined to the anonymized metadata, that could be pretty huge for us as well. >> Okay, so this is interesting. So you talk about FTP, which was the common way to share data. And so you basically, it's so, I got it now you take it and do whatever you want with it. Now we're talking, Jesse, about sharing the same copy of live data. How common is that use case in your industry? >> It's become very common over the last couple of years. And I think a big part of it is having the right technology to do it effectively. You know, as Nick mentioned, historically, this was done by people sending files around. And the challenge with that approach, of course, while there are multiple challenges, one, every time you send a file around your, by definition creating a copy of the data because you have to pull it out of your system of record, put it into a file, put it on some server where somebody else picks it up. And by definition at that point you've lost governance. So this creates challenges in general hesitation to doing so. It's not that it hasn't happened, but the other challenge with it is that the data's no longer real time. You know, you're working with a copy of data that was as fresh as at the time at that when that was actually extracted. And that creates limitations in terms of how effective this can be. What we're starting to see now with some of our customers is live sharing of information. And there's two aspects of that that are important. One is that you're not actually physically creating the copy and sending it to someone else, you're actually exposing it from where it exists and allowing another consumer to interact with it from their own account that could be in another region, some are running in another cloud. So this concept of super-cloud or cross-cloud could becoming realized here. But the other important aspect of it is that when that other- when that other entity is querying your data, they're seeing it in a real time state. And this is particularly important when you think about use cases like supply chain planning, where you're leveraging data across various different enterprises. If I'm a manufacturer or if I'm a contract manufacturer and I can see the actual inventory positions of my clients, of my distributors, of the levels of consumption at the pharmacy or the hospital that gives me a lot of indication as to how my demand profile is changing over time versus working with a static picture that may have been from three weeks ago. And this has become incredibly important as supply chains are becoming more constrained and the ability to plan accurately has never been more important. >> Yeah. So the race is on to solve these problems. So it start, we started with, hey, okay, cloud, Dave, we're going to simplify database, we're going to put it in the cloud, give virtually infinite resources, separate compute from storage. Okay, check, we got that. Now we've moved into sort of data clean rooms and governance and you've got an ecosystem that's forming around this to make it safer to share data. And then, you know, nirvana, at least near term nirvana is we're going to build data applications and we're going to be able to share live data and then you start to get into monetization. Do you see, Nick, in the near future where I know you've got relationships with, for instance, big pharma like AstraZeneca, do you see a situation where you start sharing data with them? Is that in the near term? Is that more long term? What are the considerations in that regard? >> I mean, it's something we've been thinking about. We haven't actually addressed that yet. Yeah, I could see situations where, you know, some of these big relationships where we do need to share a lot of data, it would be very nice to be able to just flick a switch and share our data assets across to those organizations. But, you know, that's a ways off for us now. We're mainly looking at bringing data in at the moment. >> One of the things that we've seen in financial services in particular, and Jesse, I'd love to get your thoughts on this, is companies like Goldman or Capital One or Nasdaq taking their stack, their software, their tooling actually putting it on the cloud and facing it to their customers and selling that as a new monetization vector as part of their digital or business transformation. Are you seeing that Jesse at all in healthcare or is it happening today or do you see a day when that happens or is healthier or just too scary to do that? >> No, we're seeing the early stages of this as well. And I think it's for some of the reasons we talked about earlier. You know, it's a much more secure way to work with a colleague if you don't have to copy your data and potentially expose it. And some of the reasons that people have historically copied that data is that they needed to leverage some sort of algorithm or application that a third party was providing. So maybe someone was predicting the ideal location and run a clinical trial for this particular rare disease category where there are only so many patients around the world that may actually be candidates for this disease. So you have to pick the ideal location. Well, sending the dataset to do so, you know, would involve a fairly complicated process similar to what Nick was mentioning earlier. If the company who was providing the logic or the algorithm to determine that location could bring that algorithm to you and you run it against your own data, that's a much more ideal and a much safer and more secure way for this industry to actually start to work with some of these partners and vendors. And that's one of the things that we're looking to enable going into this year is that, you know, the whole concept should be bring the logic to your data versus your data to the logic and the underlying sharing mechanisms that we've spoken about are actually what are powering that today. >> And so thank you for that, Jesse. >> Yes, Dave. >> And so Nick- Go ahead please. >> Yeah, if I could add, yeah, if I could add to that, that's something certainly we've been thinking about. In fact, we'd started talking to Snowflake about that a couple of years ago. We saw the power there again of the platform to be able to say, well, could we, we were thinking in more of a data share, but could we share our data out to say an AI/ML vendor, have them do the analytics and then share the data, the results back to us. Now, you know, there's more powerful mechanisms to do that within the Snowflake ecosystem now, but you know, we probably wouldn't need to have onsite AI/ML people, right? Some of that stuff's very sophisticated, expensive resources, hard to find, you know, it's much better for us to find a company that would be able to build those analytics, maintain those analytics for us. And you know, we saw an opportunity to do that a couple years ago and we're kind of excited about the opportunity there that we can just basically do it with a no op, right? We share the data route, we have the analytics done, we get the result back and it's just fairly seamless. >> I mean, I could have a whole another Cube session on this, guys, but I mean, I just did a a session with Andy Thurai, a Constellation research about how difficult it's been for organization to get ROI because they don't have the expertise in house so they want to either outsource it or rely on vendor R&D companies to inject that AI and machine intelligence directly into applications. My follow-up question to you Nick is, when you think about, 'cause Jesse was talking about, you know, let the data basically stay where it is and you know bring the compute to that data. If that data lives on different clouds, and maybe it's not your group, but maybe it's other parts of Ionis or maybe it's your partners like AstraZeneca, or you know, the AI/ML partners and they're potentially on other clouds or that data is on other clouds. Do you see that, again, coming back to super-cloud, do you see it as an advantage to be able to have a consistent experience across those clouds? Or is that just kind of get in the way and make things more complex? What's your take on that, Nick? >> Well, from the vendors, so from the client side, it's kind of seamless with Snowflake for us. So we know for a fact that one of the datasets we have at the moment, Compile, which is a, the large multi terabyte dataset I was talking about. They're on AWS on the East Coast and we are on Azure on the West Coast. And they had to do a few tweaks in the background to make sure the data was pushed over from, but from my point of view, the data just exists, right? So for me, I think it's hugely beneficial that Snowflake supports this kind of infrastructure, right? We don't have to jump through hoops to like, okay, well, we'll download it here and then re-upload it here. They already have the mechanism in the background to do these multi-cloud shares. So it's not important for us internally at the moment. I could see potentially at some point where we start linking across different groups in the organization that do have maybe Amazon or Google Cloud, but certainly within our providers. We know for a fact that they're on different services at the moment and it just works. >> Yeah, and we learned from Benoit Dageville, who came into the studio on August 9th with first Supercloud in 2022 that Snowflake uses a single global instance across regions and across clouds, yeah, whether or not you can query across you know, big regions, it just depends, right? It depends on latency. You might have to make a copy or maybe do some tweaks in the background. But guys, we got to jump, I really appreciate your time. Really thoughtful discussion on the future of data and cloud, specifically within healthcare and pharma. Thank you for your time. >> Thanks- >> Thanks for having us. >> All right, this is Dave Vellante for theCUBE team and my co-host, John Furrier. Keep it right there for more action at Supercloud 2. (upbeat music)

>>Hello everyone. Welcome to the Super Cloud Event preview. I'm John Forry, host of the Cube, and with Dave Valante, host of the popular Super cloud events. This is Super Cloud two preview. I'm joined by industry leader and Cube alumni, Victoria Vigo, vice president of klos Cross Cloud Services at VMware. Vittorio. Great to see you. We're here for the preview of Super Cloud two on January 17th, virtual event, live stage performance, but streamed out to the audience virtually. We're gonna do a preview. Thanks for coming in. >>My pleasure. Always glad to be here. >>It's holiday time. We had the first super cloud on in August prior to VMware, explore North America prior to VMware, explore Europe prior to reinvent. We've been through that, but right now, super Cloud has got momentum. Super Cloud two has got some success. Before we dig into it, let's take a step back and set the table. What is Super Cloud and why is important? Why are people buzzing about it? Why is it a thing? >>Look, we have been in the cloud now for like 10, 15 years and the cloud is going strong and I, I would say that going cloud first was deliberate and strategic in most cases. In some cases the, the developer was going for the path of risk resistance, but in any sizable company, this caused the companies to end up in a multi-cloud world where 85% of the companies out there use two or multiple clouds. And with that comes what we call cloud chaos, because each cloud brings their own management tools, development tools, security. And so that increase the complexity and cost. And so we believe that it's time to usher a new era in cloud computing, which we, you call the super cloud. We call it cross cloud services, which allows our customers to have a single way to build, manage, secure, and access any application across any cloud. Lowering the cost and simplifying the environment. Since >>Dave Ante and I introduced and rift on the concept of Supercloud, as we talked about at reinvent last year, a lot has happened. Supercloud one, it was in August, but prior to that, great momentum in the industry. Great conversation. People are loving it, they're hating it, which means it's got some traction. Berkeley has come on board as with a position paper. They're kind of endorsing it. They call it something different. You call it cross cloud services, whatever it is. It's kind of the same theme we're seeing. And so the industry has recognized something is happening that's different than what Cloud one was or the first generation of cloud. Now we have something different. This Super Cloud two in January. This event has traction with practitioners, customers, big name brands, Sachs, fifth Avenue, Warner, media Financial, mercury Financial, other big names are here. They're leaning in. They're excited. Why the traction in the customer's industry converts over to, to the customer traction. Why is it happening? You, you get a lot of data. >>Well, in, in Super Cloud one, it was a vendor fest, right? But these vendors are smart people that get their vision from where, from the customers. This, this stuff doesn't happen in a vacuum. We all talk to customers and we tend to lean on the early adopters and the early adopters of the cloud are the ones that are telling us, we now are in a place where the complexity is too much. The cost is ballooning. We're going towards slow down potentially in the economy. We need to get better economics out of, of our cloud. And so every single customers I talked to today, or any sizable company as this problem, the developers have gone off, built all these applications, and now the business is coming to the operators and asking, where are my applications? Are they performing? What is the security posture? And how do we do compliance? And so now they're realizing we need to do something about this or it is gonna be unmanageable. >>I wanna go to a clip I pulled out from the, our video data lake and the cube. If we can go to that clip, it's Chuck Whitten Dell at a keynote. He was talking about what he calls multi-cloud by default, not by design. This is a state of the, of the industry. If we're gonna roll that clip, and I wanna get your reaction to that. >>Well, look, customers have woken up with multiple clouds, you know, multiple public clouds. On-premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was it can be, and it should be, it is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they wanna maintain an on-premise cloud. On-premise clouds are not going away. I mentioned edge Cloud, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud, by default we mean that's the state of the world. Today, our goal is to bring multi-cloud by design, as you heard. Yeah, I >>Mean, I, okay, Vittorio, that's, that's the head of Dell Technologies president. He obvious he runs it. Michael Dell's still around, but you know, he's the leader. This is a interesting observation. You know, he's not a customer. We have some customer equips we'll go to as well, but by default it kind of happened not by design. So we're now kind of in a zoom out issue where, okay, I got this environment just landed on me. What, what is the, what's your reaction to that clip of how multi-cloud has become present in, in everyone's on everyone's plate right now to deal with? Yeah, >>I it is, it is multi-cloud by default, I would call it by accident. We, we really got there by accident. I think now it's time to make it a strategic asset because look, we're using multiple cloud for a reason, because all these hyperscaler bring tremendous innovation that we want to leverage. But I strongly believe that in it, especially history repeat itself, right? And so if you look at the history of it, as was always when a new level of obstruction that simplify things, that we got the next level of innovation at the lower cost, you know, from going from c plus plus to Visual basic, going from integrating application at the bits of by layer to SOA and then web services. It's, it's only when we simplify the environment that we can go faster and lower cost. And the multi-cloud is ready for that level of obstruction today. >>You know, you've made some good points. You know, developers went crazy building great apps. Now they got, they gotta roll it out and operationalize it globally. A lot of compliance issues going on. The costs are going up. We got an economic challenge, but also agility with the cloud. So using cloud and or hybrid, you can get better agility. And also moving to the cloud, it's kind of still slow. Okay, so I get that at reinvent this year and at VMware explorer we were observing and we reported that you're seeing a transition to a new kind of ecosystem partner. Ones that aren't just ISVs anymore. You have ISVs, independent software vendors, but you got the emergence of bigger players that just, they got platforms, they have their own ecosystems. So you're seeing ecosystems on top of ecosystems where, you know, MongoDB CEO and the Databricks CEO both told me, we're not an isv, we're a platform built on a cloud. So this new kind of super cloudlike thing is going on. Why should someone pay attention to the super cloud movement? We're on two, we're gonna continue to do these out in the open. Anyone can participate. Why should people pay attention to this? Why should they come to the event? Why is this important? Is this truly an inflection point? And if they do pay attention, what should they pay attention to? >>I would pay attention to two things. If you are customers that are now starting to realize that you have a multi-cloud problem and the costs are getting outta control, look at what the leading vendors are saying, connect the dots with the early adopters and some of the customers that we are gonna have at Super Cloud two, and use those learning to not fall into the same trap. So I, I'll give you an example. I was talking to a Fortune 50 in Europe in my latest trip, and this is an a CIO that is telling me >>We build all these applications and now for compliance reason, the business is coming to me, I don't even know where they are, right? And so what I was telling him, so look, there are other customers that are already there. What did they do? They built a platform engineering team. What is the platform? Engineering team is a, is an operation team that understands how developers build modern applications and lays down the foundation across multiple clouds. So the developers can be developers and do their thing, which is writing code. But now you as a cio, as a, as a, as a governing body, as a security team can have the guardrail. So do you know that these applications are performing at a lower cost and are secure and compliant? >>Patura, you know, it's really encouraging and, and love to get your thoughts on this is one is the general consensus of industry leaders. I talked to like yourself in the round is the old way was soft complexity with more complexity. The cloud demand simplicity, you mentioned abstraction layer. This is our next inflection point. It's gotta be simpler and it's gotta be easy and it's gotta be performant. That's the table stakes of the cloud. What's your thoughts on this next wave of simplicity versus complexity? Because again, abstraction layers take away complexity, they should make it simpler. What's your thoughts? >>Yeah, so I'll give you few examples. One, on the development side and runtime. You, you one would think that Kubernetes will solve all the problems you have Kubernetes everywhere, just look at, but every cloud has a different distribution of Kubernetes, right? So for example, at VMware with tansu, we create a single place that allows you to deploy that any Kubernetes environment. But now you have one place to set your policies. We take care of the differences between this, this system. The second area is management, right? So once you have all everything deployed, how do you get a single object model that tells you where your stuff is and how it's performing, and then apply policies to it as well. So these are two areas and security and so on and so forth. So the idea is that figure out what you can abstract and make common across cloud. Make that simple and put it in one place while always allowing the developers to go underneath and use the differentiated features for innovation. >>Yeah, one of the areas I'm excited, I want to get your thoughts of too is, we haven't talked about this in the past, but it, I'll throw it out there. I think the, the new AI coming out chat, G P T and other things like lens, you see it and see new kinds of AI coming that's gonna be right in the heavy lifting opportunity to make things easier with AI and automation. I think AI will be a big factor in super cloud and, and cross cloud. What's your thoughts? >>Well, the one way to look at AI is, is one of the main, main services that you would want out of a multi-cloud, right? You want eventually, right now Google seems to have an edge, but you know, the competition creates, you know, innovation. So later on you wanna use something from Azure or from or from Oracle or something that, so you want at some point that is gonna be prone every single service in in the cloud is gonna be prone to obstruction and simplification. And I, I'm just excited about to see >>What book, I can't wait for the chat services to write code automatically for us. Well, >>They >>Do, they do. They're doing it now. They do. >>Oh, the other day, somebody, you know that I do this song par this for. So for fun sometimes. And somebody the other day said, ask the AI to write a parody song for multi-cloud. And so I have the lyrics stay >>Tuned. I should do that from my blog post. Hey, write a blog post on this January 17th, Victoria, thanks for coming in, sharing the preview bottom line. Why should people come? Why is it important? What's your final kind of takeaway? Billboard message >>History is repeat itself. It goes to three major inflection points, right? We had the inflection point with the cloud and the people that got left behind, they were not as competitive as the people that got on top o of this wave. The new wave is the super cloud, what we call cross cloud services. So if you are a customer that is experiencing this problem today, tune in to to hear from other customers in, in your same space. If you are behind, tune in to avoid the, the, the, the mistakes and the, the shortfalls of this new wave. And so that you can use multi-cloud to accelerate your business and kick butt in the future. >>All right. Kicking kick your names and kicking butt. Okay, we're here on J January 17th. Super Cloud two. Momentum continues. We'll be super cloud three. There'll be super cloud floor. More and more open conversations. Join the community, join the conversation. It's open. We want more voices. We want more, more industry. We want more customers. It's happening. A lot of momentum. Victoria, thank you for your time. Thank you. Okay. I'm John Farer, host of the Cube. Thanks for watching.

(upbeat music) >> Hey, gang. Welcome back to theCUBE's coverage of Snowflake Summit '22 live on the show floor at Caesar's Forum in Las Vegas. Lisa Martin here with Dave Vellante. Couple of guests joining us to unpack more of what we've been talking about today. George Fraser joins us, the CEO of Fivetran, and Veronika Durgin, the head of data at Saks Fifth Avenue. Guys, welcome to the program. >> Thank you for having us. >> Hello. >> George, talk to us about Fivetran for the audience that may not be super familiar. Talk to us about the company, your vision, your mission, your differentiation, and then maybe the partnership with Snowflake. >> Well, a lot of people in the audience here at Snowflake Summit probably are familiar with Fivetran. We have almost 2000 shared customers with them. So a considerable amount of the data that we're all talking about here, flows through Fivetran. But in brief, what Fivetran is, is we're data pipeline. And that means that we go get all the data of your company in all the places that it lives. So all your tools and systems that you use to run your company. We go get that data and we bring it all together in one place like Snowflake. And that is the first step in doing anything with data is getting it all in one place. >> So you've been considerable amount of shared customers. I think I saw this morning on the slide over 5,900, but you're saying you're already at around 2000 shared customers. Lots of innovation I'm sure, with between both companies, but talk to us about some of the latest developments at Fivetran, in terms of product, in terms of company growth, what's going on? >> Well, one of the biggest things that happened recently with Fivetran is we acquired another data integration company called HVR. And HVR specialty has always been replicating the biggest, baddest enterprise databases like Oracle and SQL Server databases that are enormous, that are run within an inch of their capabilities by their DBAs. And HVR was always known as the best in the business at that scenario. And by bringing that together with Fivetran, we now really have the full spectrum of capabilities. We can replicate all types of data for all sizes of company. And so that's a really exciting development for us and for the industry. >> So Veronika, head of data at Saks, what does that entail? How do you spend your time? What's your purview? >> So the cool thing abouts Saks is a very old company. Saks is the premier luxury e-commerce platform. And we help our Saks Fifth Avenue customers just express themselves through fashion. So we're trying to modernize very old company and we do have the biggest, baddest databases of any flavor you can imagine. So my job is to modernize, to bring us to near real-time data, to make sure data is available to all of our users so they can actually take advantage of it. >> So let's talk about some of those biggest, baddest hair balls that you've, and how you deal with that. So lot of over time, you've built up a lot of data. You've got different data stores. So, what are you doing with that? And what role does Fivetran and Snowflake play in helping you modernize? >> Yeah, Fivetran helps us ingest data from all of those data sources into Snowflake near real-time. It's very important to us. And like one of the examples that I give is within a matter of maybe a few weeks, we were able to get data from over a dozen of different data sources into Snowflake in near real-time. And some of those data sources were not available to our users in the past, and everybody was so excited. And the reason they weren't available is because they require a lot of engineering effort to actually build those data pipelines to manage them and maintain them. >> Lisa: Whoa, sorry. >> That was just a follow up. So, Fivetran is the consolidator of all that data and- >> That's right. >> Snowflake plays that role also. >> We bring it all together, and the place that it is consolidated is Snowflake. And from there you can really do anything with it. And there's really three things you were touching on it that make data integration hard. One is volume, and that's the one that people tend to talk about, just size of data. And that is important, but it's not the only thing. It's also latency. How fresh is the data in the locus of consolidation? Before Fivetran, the state of the art was nightly snapshots, once a day was considered pretty good. And we consider now once a minute pretty good and we're trying to make it even better. And then the last challenge, which people tend not to talk about, it's the dark secret of our industry is just incidental complexity. All of these data sources have a lot of strange behaviors and rules and corner cases. Every data source is a little bit different. And so a lot of what we bring that to the table, is that we've done the work over 10 years. And in the case of HVR, since the 90s', to map out all of these little complexities of all these data sources, that as a user, you don't have to see it. You just connect source, connect destination, and that's it. >> So you don't have to do the M word migrate off of all those databases. You can maybe allow them to dial them down over time, then create new value with using Fivetran and Snowflake. Is that the right way to think about it? >> Well, Fivetran, it's incredibly simple. You just connect it to whatever source, And then the matter of minutes you have a pipeline. And for us, it's in the matter of minutes, for Fivetran, there's hundreds of engineers, we're extending our data engineering team to now Fivetran. And we can pick and choose which tables we want to replicate which fields. And once data lands in Snowflake, now we have data across different sources in one place, in central place. And now we can do all kinds of different things. We can integrate it data together, we can do validations, we can do reconciliations. We now have ability to do point in time historical journey, in the past in transactional system, you don't see that, you only see data that's right now, but now that we replicate everything to Snowflake and Snowflake being so powerful as an analytical platform, we can do, what did it look like two months ago? What did it look like two years ago? >> You've got all that time series data, okay. >> And to address that word you mentioned a moment ago, migrate, this is something people often get confused about. What we're talking about here is not a migration, these source systems are not going away. These databases are the systems powering saks.com and they're staying right there. They're the systems you interact with when you place an order on this site. The purpose of our tool and the whole stack that Veronika has put together, is to serve other workloads in Snowflake that need to have access to all of the data together. >> But if you didn't have Snowflake, you would have to push those other data stores, try to have them do things that they have sometimes a tough time doing. >> Yeah, and you can't run analytical workloads. You cannot do reporting on the transactional database. It's not meant for that. It's supporting capability of an application and it's configured to be optimized for that. So we always had to offload those specific analytical reporting functionality, or machine learning somewhere else, and Snowflake is excellent for that. It's meant for that, yeah. >> I was going to ask you what you were doing before, you just answered that. What was the aha moment for realizing you needed to work with the power of Fivetran and Snowflake? If we look at, you talked about Saks being a legacy history company that's obviously been very successful at transforming to the digital age, but what was that one thing, as the head of the data you felt this is it? >> Great question. I've worked with Fivetran in the past. This is my third company, same with Snowflake. I actually brought Fivetran into two companies at this point. So my first experience with both Fivetran and Snowflake, was this like, this is where I want to be, this is the stack and the tooling, and just the engineering behind it. So as I moved on the next company, that that was, I'm bringing tools with me. So that was part. And the other thing I wanted to mention, when we evaluate tools for a new platform, we look at things in like three dimensions, right? One with cloud first, we want to have cloud native tools, and they have to be modular, but we also don't want to have too many tools. So Fivetran's certainly checks that off. They're first cloud native, and they also have a very long list of connectors. The other thing is for us, it's very important that data engineering effort is spent on actually analyzing data, not building pipelines and supporting infrastructure. In Fivetran, reliable, it's secure, it has various connectors, so it checks off that box as well. And another thing is that we're looking for companies we can partner with. So companies that help us grow and grow with us, we'll look in a company culture, their maturity, how they treat their customers and how they innovate. And again, Fivetran checks off that box as well. >> And I imagine Snowflake does as well, Frank Lutman on stage this morning talked about mission alignment. And it seemed to me like, wow, one of the missions of Snowflake is to align with its customer's missions. It sounds like from the conversations that Dave and I have had today, that it's the same with partners, but it sounds like you have that cultural alignment with Fivetran and Snowflake. >> Oh, absolutely. >> And Fivetran has that, obviously with 2000 shared customers. >> Yeah, I think that, well, not quite there yet, but we're close, (laughs) I think that the most important way that we've always been aligned with our customers is that we've been very clear on what we do and don't do. And that our job is to get the data from here to there, that the data be accurately replicated, which means in practice often joke that it is exactly as messed up as it was in the source. No better and no worse, but we really will accomplish that task. You do not need to worry about that. You can well and fully delegate it to us, but then what you do with the data, we don't claim that we're going to solve that problem for you. That's up to you. And anyone who claims that they're going to solve that problem for you, you should be very skeptical. >> So how do you solve that problem? >> Well, that's where modeling comes in, right? You get data from point A to point B, and it's like bad in, bad out. Like, that's it, and that's where we do those reconciliations, and that's where we model our data. We actually try to understand what our businesses, how our users, how they talk about data, how they talk about business. And that's where data warehouse is important. And in our case, it's data evolve. >> Talk to me a little bit before we wrap here about the benefits to the end user, the consumer. Say I'm on saks.com, I'm looking for a particular item. What is it about this foundation that Saks has built with Fivetran and with Snowflake, that's empowering me as a consumer, to be able to get, find what I want, get the transaction done like that? >> So getting access to, our end goal is to help our customers, right? Make their experience beautiful, luxurious. We want to make sure that what we put in front of you is what you're looking for. So you can actually make that purchase, and you're happy with it. So having that data, having that data coming from various different sources into one place enables us to do that near real-time analytics so we can help you as a customer to find what you're looking for. >> Magic on the back end, delighting customers. >> So the world is still messed up, right? Airlines are out of whack. There's supply imbalances. You've got the situation in Ukraine with oil prices. The Fed missed the mark. So can data solve these problems? If you think about the context of the macro environment, and you bring it down to what you're seeing at Saks, with your relationship with Fivetran and with Snowflake, do you see the light at the end of that confusion tunnel? >> That's such a great question. Very philosophical. I don't think data can solve it. Is the people looking at data and working together that can solve it. >> I think data can help, data can't stop a war. Data can help you forecast supply chain misses and mitigate those problems. So data can help. >> Can be a facilitator. >> Sorry, what? >> Can be a facilitator. >> Yeah, it can be a facilitator of whatever you end up doing with it. Data can be used for good or evil. It's ultimately up to the user. >> It's a tool, right? Do you bring a hammer to a gunfight? No, but t's a tool in the right hands, for the right purpose, it can definitely help. >> So you have this great foundation, you're able to delight customers as especially from a luxury brand perspective. I imagine that luxury customers have high expectations. What's next for Saks from a data perspective? >> Well, we want to first and foremost to modernize our data platform. We want to make sure we actually bring that near real-time data to our customers. We want to make sure data's reliable. That well understood that we do the data engineering and the modeling behind the scenes so that people that are using our data can rely on it. Because it's like, there is bad data is bad data but we want to make sure it's very clear. And what's next? The sky's the limit. >> Can you describe your data teams? Is it highly centralized? What's your philosophy in terms of the architecture of the organization? >> So right now we are starting with a centralized team. It just works for us as we're trying to rebuild our platform, and modernize it. But as we become more mature, we establish our practices, our data governance, our definitions, then I see a future where we like decentralize a little bit and actually each team has their own analytical function, or potentially data engineering function as well. >> That'll be an interesting discussion when you get there. >> That's a hot topic. >> It's one of the hardest problems in building a data team is whether decentralized or decentralized. We're still centralized at Fivetran, but companies now over 1000 people, and we're starting to feel the strain of that. And inevitably, you eventually have to find a way to find scenes and create specialization. >> You just have to be fluid, right? And then go with the company as the company grows and things change. >> Yeah, I've worked with some companies. JPMC is here, they've got a little, I'll call it a skunk works. They're probably under states what they're doing, but they're testing that out. A company like HelloFresh is doing some things 'cause their Hadoop cluster just couldn't scale. So they have to begin to decentralize. It is a hot topic these days. And I'm not sure there's a right or wrong. It's really a situational. But I think in a lot of situations, it's maybe the trend. >> Yeah. >> Yeah, I think centralized versus decentralized technology is a different question than centralized versus decentralized teams. >> Yes. >> They're both valid, but they're very different. And sometimes people conflate them, and that's very dangerous. Because you might want one to be centralized and the other to be decentralized. >> Well, it's true. And I think a lot of folks look at a centralized team and say, "Hey, it's more efficient to have these specialized roles, but at the same time, what's the outcome?" If the outcome can be optimized and it's maybe a little bit more people expensive, or I don't know. And they're in the lines of business where there's data context, that might be a better solution for a company. >> So to truly understand the value of data, you have to specialize in that specific area. So I see people like deep diving into specific vertical or whatever that is, and truly understanding what data they have and how to taken advantage of it. >> Well, all this talk about monetization and building data products, you're there, right? >> Yeah. >> You're on the cusp of that. And so who's going to build those data products? It's going to be somebody in the business. Today they don't "Own the life cycle" of the data. They don't feel responsible for it, but they complain when it's not what they want. And so, I feel as though what Snowflake is doing is actually attacking some of those problems. Not 100% there obviously, but a lot of work to do. >> Great analysts are great navigators of organizations amongst other things. And one of the best things that's happened as part of this evolution from technology like Hadoop to technology like Snowflake is the new stack is a lot simpler. There's a lot less technical knowledge that you need. You still need technical knowledge, but not nearly what you used to. And that has made it accessible to more people. People who bring different skills to the table. And in many cases, those are the skills you really need to deliver value from data is not, do you know the inner workings of HDFS? But do you know how to extract from your constituents in the organization, a precise version of the question that they're trying to ask? >> We really want them spending their time, the technical infrastructure is an operational detail, so you can put your teams on those types of questions, not how do we make it work? And that's what Hadoop was, "Hey, we got it to work." >> And that's something we're obsessed with. We're always trying to hide the technical complexities of the problem of data centralization behind the scenes. Even if it's harder for us, even if it's more expensive for us, we will pay any costs so that you don't have to see it. Because that allows our customers to focus on more high impact. >> Well, this is a case where a technology vendor's R&D is making your life easier. >> Veronika: Easier, right. >> I would presume you'd rather spend money to save time, than spend your time, to save engineering time, to save money. >> That's true. And at the end of the day, hiring three data engineers to do custom work that a tool does, it's actually not saving money. It costs more in the end. But to your point, pulling business people into those data teams gives them ownership, and they feel like they're part of the solution. And it's such a great feeling so that they're excited to contribute, they're excited to help us. So I love where the industry's going like in that direction. >> And of course, that's the theme of the show, the world around data collaborations. Absolutely critical, guys. Thank you so much for joining Dave and me, talking about Fivetran, Snowflake together, what you're doing to empower Saks, to be a data company. I'm going to absolutely have a different perspective next time I shop there. Thanks for joining us. Thank you. >> Dave: Thank you, guys. >> Thank you. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22, from Vegas. Stick around, our next guest joins us momentarily. (upbeat music)

>>from around the globe. It's the Cube covering space and cybersecurity. Symposium 2020 hosted by Cal Poly. Yeah, Lauren, Welcome to the Space and Cybersecurity Symposium 2020 put on by Cal Poly and hosted with Silicon Angle acute here in Palo Alto, California for a virtual conference. Couldn't happen in person this year. I'm John for a year. Host the intersection of space and cybersecurity. I'll see critical topics, great conversations. We got a great guest here to talk about the addressing the cybersecurity workforce gap, and we have a great guest, a feature speaker. Stewart Knox, the undersecretary with California's Labor and Workforce Development Office. Stewart Thanks for joining us today. >>Thank you so much, John. Appreciate your time today and listening to a little bit of our quandaries with making sure that we have the security that's necessary for the state of California and making sure that we have the work force that is necessary for cybersecurity in space. >>Great, I'd love to get started. I got a couple questions for you, but first take a few minutes for an opening statement to set the stage. >>Sure, realizing that in California we lead the nation in much of cybersecurity based on Department of Defense contractors within the Santa California leading the nation with over $160 billion within the industry just here in California alone and having over 800,000 bus workers. Full time employment in the state of California is paramount for us to make sure that we face, um, defense manufacturers approximate 700,000 jobs that are necessary to be filled. There's over 37,000 vacancies that we know of in California, just alone in cybersecurity. And so we look forward to making sure that California Workforce Development Agency is leading the charge to make sure that we have equity in those jobs and that we are also leading in a way that brings good jobs to California and to the people of California, a good education system that is developed in a way that those skills are necessarily met for the for the employers here in California and the nation, >>One of the exciting things about California is obviously look at Silicon Valley, Hewlett Packard in the garage, storied history space. It's been a space state. Many people recognize California. You mentioned defense contractors. It's well rooted with with history, um, just breakthroughs bases, technology companies in California. And now you've got technology. This is the cybersecurity angle. Um, take >>them into >>Gets more commentary to that because that's really notable. And as the workforce changes, these two worlds are coming together, and sometimes they're in the same place. Sometimes they're not. This is super exciting and a new dynamic that's driving opportunities. Could you share, um, some color commentary on that dynamic? >>Absolutely. And you're so correct. I think in California we lead the nation in the way that we developed programs that are companies lead in the nation in so many ways around, uh, cyberspace cybersecurity, Uh, in so many different areas for which in the Silicon Valley is just, uh, such a leader in those companies are good qualified companies to do so. Obviously, one of the places we play a role is to make sure that those companies have a skilled workforce. Andi, also that the security of those, uh, systems are in place for our defense contractors onda For the theater companies, those those outlying entities that are providing such key resource is to those companies are also leading on the cutting edge for the future. Also again realizing that we need to expand our training on skills to make sure that those California companies continue to lead is just, um, a great initiative. And I think through apprenticeship training programs on By looking at our community college systems, I think that we will continue to lead the nation as we move forward. >>You know, we've had many conversations here in this symposium, virtually certainly around. The everyday life of consumer is impacted by space. You know, we get our car service Uber lyft. We have maps. We have all this technology that was born out of defense contracts and r and D that really changed generations and create a lot of great societal value. Okay, now, with space kind of on the next generation is easier to get stuff into space. The security of the systems is now gonna be not only paramount for quality of life, but defending that and the skills are needed in cybersecurity to defend that. And the gap is there. What >>can we >>do to highlight the opportunities for career paths? It used to be the day when you get a mechanical engineering degree or aerospace and you graduated. You go get a job. Not anymore. There's a variety of of of paths career wise. What can we do to highlight this career path? >>Absolutely correct. And I think it starts, you know, k through 12 system on. I know a lot of the work that you know, with this bow and other entities we're doing currently, uh, this is where we need to bring our youth into an age where they're teaching us right as we become older on the uses of technology. But it's also teaching, um, where the levels of those education can take them k through 12. But it's also looking at how the community college system links to that, and then the university system links above and beyond. But it's also engage in our employers. You know, One of the key components, obviously, is the employers player role for which we can start to develop strategies that best meet their needs quickly. I think that's one of the comments we hear the most labor agency is how we don't provide a change as fast as we should, especially in technology. You know, we buy computers today, and they're outdated. Tomorrow it's the same with the technology that's in those computers is that those students are going to be the leaders within that to really develop how those structures are in place. S O. K. Through 12 is probably primary place to start, but also continuing. That passed the K 12 system and I bring up the employers and I bring them up in a way, because many times when we've had conversations with employers around what their skills needs were and how do we develop those better? One of the pieces that of that that I think is really should be recognized that many times they recognized that they wanted a four year degree, potentially or five year, six year degree. But then, when we really looked at the skill sets, someone coming out of the community college system could meet those skill sets. And I think we need to have those conversations to make sure not that they shouldn't be continue their education. They absolutely should. Uh, but how do we get those skill sets built into this into 12 plus the two year plus the four year person? >>You know, I love the democratization of these new skills because again. There's no pattern matching because they weren't around before, right? So you gotta look at the exposure to your point K through 12 exposure. But then there's an exploration piece of whether it's community, college or whatever progression. And sometimes it's nonlinear, right? I mean, people are learning different ways, combining the exposure and the exploration. That's a big topic. Can you share your view on this because this now opens up mawr doors for people choice. You got new avenues. You got online clock and get a cloud computing degree now from Amazon and walk in and help. I could be, you know, security clearance, possibly in in college. So you know you get exposure. Is there certain things you see? Is it early on middle school? And then I'll see the exploration Those air two important concepts. Can you unpack that a little bit exposure and exploration of skills? >>Absolutely. And I think this takes place, you know, not only in in the K 12 because somebody takes place in our community colleges and universities is that that connection with those employers is such a key component that if there's a way we could build in internships where experiences what we call on the job training programs apprenticeship training pre apprenticeship training programs into a design where those students at all levels are getting an exposure to the opportunities within the Space and Cybersecurity Avenue. I think that right there alone will start to solve a problem of having 37 plus 1000 openings at any one time in California. Also, I get that there's there's a burden on employers. Thio do that, and I think that's a piece that we have to acknowledge. And I think that's where education to play a larger role That's a place we had. Labor, Workforce, Development Agency, player role With our apprenticeship training programs are pre apprenticeship training programs. I could go on all day of all of our training programs that we have within the state of California. Many of the list of your partners on this endeavor are partners with Employment Training Panel, which I used to be the director of the Brown administration of um, That program alone does incumbent worker training on DSO. That also is an exposure place where ah worker, maybe, you know, you know, use the old adage of sweeping the floors one day and potentially, you know, running a large portion of the business, you know, within years. But it's that exposure that that employee gets through training programs on band. Acknowledging those skill sets and where their opportunities are, is what's valid and important. I think that's where our students we need to play a larger role in the K 12. That's a really thio Get that pushed out there. >>It's funny here in California you're the robotics clubs in high school or like a varsity sport. You're seeing kids exposed early on with programming. But you know, this whole topic of cybersecurity in space intersection around workforce and the gaps and skills is not just for the young. Certainly the young generations gotta be exposed to the what the careers could be and what the possible jobs and societal impact and contributions what they could be. But also it's people who are already out there. You know, you have retraining re Skilling is plays an important role. I know you guys do a lot of thinking on this is the under secretary. You have to look at this because you know you don't wanna have a label old and antiquated um systems. And then a lot of them are, and they're evolving and they're being modernized by digital transformation. So what does the role of retraining and skill development these programs play? Can you share what you guys are working on in your vision for that? >>Absolutely. That's a great question. And I think that is where we play a large role, obviously in California and with Kobe, 19 is we're faced with today that we've never seen before, at least in my 27 years of running program. Similar Thio, of course, in economic development, we're having such a large number of people displaced currently that it's unprecedented with unemployment rates to where we are. We're really looking at How do we take? And we're also going to see industries not return to the level for which they stood at one point in time. Uh, you know, entertainment industries, restaurants, all the alike, uh, really looking at how do we move people from those jobs that were middle skill jobs, topper skilled jobs? But the pay points maybe weren't great, potentially, and there's an opportunity for us to skill people into jobs that are there today. It may take training, obviously, but we have dollars to do that generally, especially within our K 12 and are que 14 systems and our universities. But we really wanna look at where those skill sets are are at currently. And we want to take people from that point in time where they said today, and try to give them that exposure to your point. Earlier question is, how do we get them exposed to a system for which there are job means that pay well with benefit packages with companies that care about their employees? Because that's what our goal is. >>You know. You know, I don't know if you have some visibility on this or ah opinion, but one observation that I've had and talking to whether it's a commercial or public sector is that with co vid uh, there have been a lot of awareness of the situation. We're adequately prepared. There's, um, readiness. But as everyone kind of deals with it, they're also starting to think about what to do. Post covert as we come out of it, Ah, growth strategy for a company or someone's career, um, people starting to have that on the top of their minds So I have to ask you, Is there anything that you see that they say? Okay, certain areas, maybe not doubling down on other areas. We're gonna double down on because we've seen some best practices on a trajectory of value for coming out of co vid with, you know, well, armed skills or certain things because you because that's what a lot of people are thinking right now. It's probably cyber is I mean, how many jobs are open? So you got well, that that's kind of maybe not something double down on here are areas we see that are working. Can you share your current visibility to that dynamic? >>Absolutely. Another great question. One of the key components that we look at Labor Workforce Development Agency. And so look at industries and growth modes and ones that are in decline boats. Now Kobe has changed that greatly. We were in a growth rate for last 78 years. We saw almost every industry might miss a few. You know that we're all in growth in one way or enough, obviously, that has changed. Our landscape is completely different than we saw 67 months ago. So today we're looking at cybersecurity, obviously with 30 plus 1000 jobs cos we're looking at Defense Department contractor is obviously with federal government contracts. We were looking at the supply chains within those we're looking at. Health care, which has always been one, obviously are large one of our large entities that has has grown over the years. But it's also changed with covered 19. We're looking at the way protective equipment is manufactured in the way that that will continue to grow over time. We're looking at the service industry. I mean, it will come back, but it won't come back the way we've seen it, probably in the past, but where the opportunities that we develop programs that we're making sure that the skill sets of those folks are transferrable to other industries with one of the issues that we face constant labor and were forced moment programs is understanding that over the period of time, especially in today's world again, with technology that people skill sets way, don't see is my Parents Day that you worked at a job for 45 years and you retired out of one job. Potentially, that is, that's been gone for 25 years, but now, at the pace for which we're seeing systems change. This is going to continue to amp up. I will stay youth of today. My 12 year old nephew is in the room next door to me on a classroom right now online. And so you know, there. It's a totally different atmosphere, and he's, you know, enjoying actually being in helping learning from on all online system. I would not have been able to learn that way, but I think we do see through the K Through 12 system where we're moving, um, people's interest will change, and I think that they will start to see things in a different way than we have in the past. They were forced systems. We are an old system been around since the thirties. Some even will say prior to the thirties came out of the Great Depression in some ways, and that system we have to change the way we develop our programs are should not be constant, and it should be an evolving system. >>It's interesting a lot of the conversation between the private and public partnerships and industry. You're seeing an agile mind set where it's a growth mindset. It's also reality based mindset and certainly space kind of forces. This conversation with cyber security of being faster, faster, more relevant, more modern. You mentioned some of those points, and with co vid impact the workforce development, it's certainly going to put a lot of pressure on faster learning. And then you mentioned online learning. This has become a big thing. It's not just putting education online per se. There's new touch points. You know you got APS, you got digital. This digital transformation is also accelerating. How do you guys view the workforce development? Because it's going to be open. It's gonna be evolving. There's new data coming in, and maybe kids don't want to stare at a video conference. Is there some game aspect to it? Is there how do you integrate thes new things that are coming really fast? And it's happening kind of in real time in front of our eyes. So I love to get your thoughts on how you guys see that, because it will certainly impact their ability to compete for jobs and or to itself learn. >>I think one of the key components of California's our innovation right and So I think one of the things that we pride ourselves in California is around that, um that said, that is the piece that I think the Silicon Valley and there's many areas in California that that have done the same, um, or trying to do the same, at least in their economy, is to build in innovation. And I think that's part of the K through 12 system with our with our our state universities and our UCS is to be able to bridge that. I think that you we see that within universities, um, that really instill an innovative approach to teaching but also instill innovation within their students. I'm not sure there yet with our fully with our K 12 system. And I think that's a place that either our community colleges could be a bridge, too, as well. Eso that's one component of workforce development I think that we look at as being a key. A key piece you brought up something that's really interesting to me is when you talk about agile on day, one of the things that even in state government on this, is gonna be shocking to you. But we have not been an agile system, Aziz. Well, I think one of the things that the Newsome administration Governor Newsom's administration has brought is. And when I talk about agile systems, I actually mean agile systems. We've gone from Kobol Systems, which are old and clunky, still operating. But at the same time, we're looking at upgrading all of our systems in a way that even our technology in the state of California should be matching the technology that our great state has within our our state. So, um, there in lies. It's also challenges of finding the qualified staff that we need in the state of California for all of our systems and servers and everything that we have. Um, currently. So you know, not only are we looking at external users, users of labor, workforce development, but we're looking at internal users that the way we redevelop our systems so that we are more agile in two different ways. >>You just got me. I triggered with COBOL. I programmed in the eighties with COBOL is only one credit lab in college. Never touched it again. Thank God. But this. But this >>is the >>benefit of cloud computing. I think this is at the heart, and this is the undertone of the conference and symposium is cloud computing. You can you can actually leverage existing resource is whether there legacy systems because they are running. They're doing a great job, and they do a certain work load extremely well. Doesn't make sense to replace what does a job, but you can integrate it in this. What cloud does this is Opening up? Can mawr more and more capabilities and workloads? This is kind of the space industry is pointing to when they say we need people that can code. And that could solve data problems. Not just a computer scientist, but a large range of people. Creative, um, data, science, everything. How does California's workforce solve the needs of America's space industry? This is because it's a space state. How do you see that? Let your workforce meeting those needs. >>Yeah, I think I think it's an investment. Obviously, it's an investment on our part. It's an investment with our college partners. It's an investment from our K 12 system to make sure that that we are allocating dollars in a way through meeting the demand of industry Onda, we do look at industry specific around there needs. Obviously, there's a large one. We wanna be very receptive and work with our employers and our employee groups to make sure that we need that demand. I think it's putting our money where our mouth is and and designing and working with employer groups to make sure that the training meets their needs. Um, it's also working with our employer groups to make sure that the employees are taken care of. That equity is built within the systems, Um, that we keep people employed in California on their able to afford a home, and they're able to afford a life here in California. But it's also again, and I brought up the innovation component. I think it's building an innovation within systems for which they are employers but are also our incoming employees are incumbent workers. And you brought this up earlier. People that already employed and people that are unemployed currently with the skill set that might match up, is how do we bridge those folks into employment that they maybe have not thought about. We have a whole career network of systems out throughout the city, California with the Americans job Centers of California on day will be working, and they already are working with a lot of dislocated workers on day. One of the key components of that is to really look at how do we, um, take what their current skills that might be and then expose them to a system for which we have 37 plus 1000 job openings to Andi? How do we actually get those books employed? It's paying for potentially through those that local Workforce Innovation Opportunity Act, funding for Americans job centers, um, to pay for some on the job, training it Z to be able to pay for work experiences. It's to be able to pay for internships for students, um, to get that opportunity with our employers and also partner with our employers that they're paying obviously a percentage of that, too. >>You know, one of the things I've observed over my, um, career 54 times around the sun is you know, in the old days when I was in college in school, you had career people have longer jobs, as you mentioned. Not like that anymore. But also I knew someone I'm gonna be in line to get that job, maybe nepotism or things of that nature. Now the jobs have no historical thing or someone worked longer in a job and has more seniority. Ah, >>lot of these >>jobs. Stewart don't HAVA requirements like no one's done them before. So the ability for someone who, um, is jumping in either from any college, there's no riel. It's all level set. It's like complete upside down script here. It's not like, Oh, I went to school. Therefore I get the job you could be Anyone could walk into these careers because the jobs air so new. So it's not where you came from or what school you went to or your nationality or gender. The jobs have been democratized. They're not discriminating against people with skills. So this opens up mawr. How >>do you >>see that? Because this really is an opportunity for this next generation to be more diverse and to be mawr contributed because diversity brings expertise and different perspectives. Your thoughts on that? >>Absolutely. And that was one of the things we welcome. Obviously we want to make sure that that everybody is treated equally and that the employers view everyone as employer employer of choice but an employee of choices. Well, we've also been looking at, as I mentioned before on the COVITZ situation, looking at ways that books that are maybe any stuck in jobs that are don't have a huge career pathway or they don't have a pathway out of poverty. I mean, we have a lot of working for people in the state of California, Um, that may now do to cope and lost their employment. Uh, this, you know, Let's let's turn back to the old, you know? Let's try, eliminate, eliminate, eliminate. How do we take those folks and get them employed into jobs that do have a good career pathway? And it's not about just who you knew or who you might have an in with to get that job. It is based on skills, I think, though that said there we need to have a better way to actually match those jobs up with those employers. And I think those are the long, ongoing conversations with those employer groups to make sure that one that they see those skill sets is valid and important. Um, they're helping design this crew sets with us, eh? So that they do match up and that were quickly matching up those close skills. That so that we're not training people for yesterday skills. >>I think the employer angles super important, but also the educators as well. One of the things that was asked in another question by the gas they they said. She said The real question to ask is, how early do you start exposing the next generation? You mentioned K through 12. Do you have any data or insight into or intuition or best practice of where that insertion point is without exposure? Point is, is that middle school is a elementary, obviously high school. Once you're in high school, you got your training. Wheels are off, you're off to the races. But is there a best practice? What's your thoughts? Stewart On exposure level to these kinds of new cyber and technical careers? >>Sure, absolutely. I I would say kindergarten. We San Bernardino has a program that they've been running for a little bit of time, and they're exposing students K through 12 but really starting in kindergarten. One is the exposure Thio. What a job Looks like Andi actually have. I've gone down to that local area and I've had three opportunity to see you know, second graders in a health care facility, Basically that they have on campus, built in on dear going from one workstation as a second grader, Uh, looking at what those skills would be and what that job would entail from a nurse to a Dr Teoh physician's assistant in really looking at what that is. Um you know, obviously they're not getting the training that the doctor gets, but they are getting the exposure of what that would be. Andi, I think that is amazing. And I think it's the right place to start. Um, it was really interesting because I left. This was pre covet, but I jumped on the plane to come back up north. I was thinking to myself, How do we get this to all school district in California, where we see that opportunity, um, to expose jobs and skill sets to kids throughout the system and develop the skill set so that they do understand that they have an opportunity. >>We're here at Cal Poly Space and Cybersecurity Symposium. We have educators. We have, um, students. We have industry and employers and government together. What's your advice to them all watching and listening about the future of work. Let's work force. What can people do? What do you think you're enabling? What can maybe the private sector help with And what are you trying to do? Can you share your thoughts on that? Because we have a range from the dorm room to the boardroom here at this event. Love to get your thoughts on the workforce development view of this. >>Yeah, absolutely. I think that's the mix. I mean, I think it's going to take industry to lead A in a lot of ways, in terms of understanding what their needs are and what their needs are today and what they will be tomorrow. I think it takes education, toe listen, and to understand and labor and workforce development also listen and understand what those needs will look like. And then how do we move systems? How do we move systems quickly? How do we move systems in a way that meets those needs? How do we, uh, put money into systems where the most need is, but also looking at trends? What is that trend going to look like in two years? What does that train gonna look like in five years. But that's again listening to those employers. Um, it's also the music community based organizations. I think, obviously some of our best students are also linked to CBS. And one way or another, it may be for services. It maybe for, uh, faith based. It may be anything, but I think we also need to bring in the CBS is Well, ah, lot of outreach goes through those systems in conjunction with, but I think that's the key component is to make sure that our employers are heard on. But they sit at the table like you said to the boardroom of understanding, and I think bringing students into that so that they get a true understanding of what that looks like a well, um, is a key piece of this. >>So one of the things I want to bring up with you is maybe a bit more about the research side of it. But, um, John Markoff, who was a former New York Times reporter with author of the book What the Dormouse, said It was a book about the counter culture of the sixties and the computer revolution, and really there was about how government defense spending drove the computer revolution that we now saw with Apple and PC, and then the rest is history in California has really participated. Stanford, uh, Berkeley and the University of California School system and all the education community colleges around it. That moment, the enablement. And now you're seeing space kind of bringing that that are a lot of research coming in and you eat a lot of billionaires putting money in. You got employers playing a role. You have this new focus space systems, cybersecurity, defending and making it open and and not congested and peaceful is going to enable quickly new inflection points for opportunities. E want to get your thoughts on that? Because California is participate in drove these revolutions that created massive value This next wave seems to be coming upon us. >>Yeah, absolutely. And again, Nazis covered again as too much of ah starting point to this. But I think that is also an opportunity to actually, because I think one of the things that we were seeing seven months ago was a skill shortage, and we still see the skills shortage, obviously. But I think a key piece to that is we saw people shortage. Not only was it skills shortage, but we didn't have enough people really to fill positions in addition to and I think that people also felt they were already paying the bills and they were making ends meet and they didn't have the opportunities. Thio get additional skills This again is where we're looking at. You know that our world has changed. It changed in the sixties based on what you're you're just expressing in terms of California leading the way. Let's like California lead the way again in developing a system from which labor, workforce development with our universities are, you know, are amazing universities and community college system and structure of how do we get students back into school? You know, a lot of graduates may already have a degree, but how do they now take a skill so that they already have and develop that further with the idea that they those jobs have changed? Whales have a lot of folks that don't have a degree, and that's okay. But how do we make that connection to a system that may have failed? Ah, lot of our people over the years, um, and our students who didn't make it through the school system. How do we develop in adult training school? How do we develop contract education through our community college system with our employer sets that we developed cohorts within those systems of of workers that have amazing talents and abilities to start to fill these needs? And I think that's the key components of hearing Agency, Labor, Workforce Development Agency. We work with our community. Colleges are UCS in our state universities t develop and figure that piece out, and I think it is our opportunity for the future. >>That's such a great point. I want to call that out This whole opportunity to retrain people that are out there because these air new jobs, I think that's a huge opportunity, and and I hope you keep building and investing in those programs. That's that's really worth calling out. Thank you for doing that. And, yeah, it's a great opportunity. Thes jobs they pay well to cyber security is a good job, and you don't really need to have that classical degree. You can learn pretty quickly if you're smart. So again, great call out there question for you on geography, Um, mentioned co vid we're talking about Covic. Virtualization were virtual with this conference. We couldn't be in person. People are learning virtually, but people are starting to relocate virtually. And so one observation that I have is the space state that California is there space clusters of areas where space people hang out or space spaces and whatnot. Then you got, like, the tech community cybersecurity market. You know, Silicon Valley is a talented in these hubs, and sometimes cyber is not always in the same hubs of space. Maybe Silicon Valley has some space here, Um, and some cyber. But that's not generally the case. This is an opportunity potentially to intersect. What's your thoughts on this? Because this is This is something that we're seeing where your space has historical, you know, geography ease. Now, with borderless communication, the work boat is not so much. You have to move the space area. You know what I'm saying? So okay. What's your thoughts on this? How do you guys look at this? Is on your radar On how you're viewing this this dynamic? >>It's absolute on our radar, Like you said, you know, here we are talking virtually on and, you know, 75% of all of our staff currently in some of our department that 80% of our staff are now virtual. Um you know, seven months ago, uh, we were not were government again being slow move, we quickly transitioned. Obviously, Thio being able to have a tele work capacity. We know employers move probably even quickly, more quickly than we did, but we see that as an opportunity for our rural areas. Are Central Valley are north state um, inland Empire that you're absolutely correct. I mean, if you didn't move to a city or to a location for which these jobs were really housed, um, you didn't have an opportunity like you do today. I think that's a piece that we really need to work with our education partners on of to be able to see how much this has changed. Labor agency absolutely recognizes this. We are investing funding in the Central Valley. We're investing funding in the North State and empire to really look a youth populations of how the new capacity that we have today is gonna be utilized for the future for employers. But we also have to engage our universities around. This is well, but mostly are employers. I know that they're already very well aware. I know that a lot of our large employers with, um, Silicon Valley have already done their doing almost 100% tele work policies. Um, but the affordability toe live in rural areas in California. Also, it enables us to have, ah, way thio make products more affordable is, well, potentially in the future. But we want to keep California businesses healthy and whole in California. Of course, on that's another way we can We can expand and keep California home to our 40 plus million people, >>most to a great, great work. And congratulations for doing such a great job. Keep it up. I gotta ask about the governor. I've been following his career since he's been office. A za political figure. Um, he's progressive. He's cutting edge. He likes toe rock the boat a little bit here and there, but he's also pragmatic. Um, you're starting to see government workers starting to get more of a tech vibe. Um um just curious from your perspective. How does the governor look at? I mean, the old, almost the old guard. But like you know, used to be. You become a lawyer, become a lawmaker Now a tech savvy lawmaker is a premium candidates, a premium person in government, you know, knowing what COBOL is. A start. I mean, these are the things. As we transform and evolve our society, we need thinkers who can figure out which side the streets, self driving cars go on. I mean, who does that? I mean, it's a whole another generation off thinking. How does the Governor how do you see this developing? Because this is the challenge for society. How does California lead? How do you guys talk about the leadership vision of Why California and how will you lead the future? >>Absolutely no governor that I'm aware of that I've been around for 26 27 years of workforce development has led with an innovation background, as this governor has a special around technology and the use of technology. Uh, you know, he's read a book about the use of technology when he was lieutenant governor, and I think it's really important for him that we, as his his staff are also on the leading edge of technology. I brought a badge. I'll systems. Earlier, when I was under the Brown administration, we had moved to where I was at a time employment training panel. We moved to an agile system and deported that one of the first within within the state to do that and coming off of an old legacy system that was an antique. Um, I will say it is challenging. It's challenging on a lot of levels. Mostly the skill sets that are folks have sometimes are not open to a new, agile system to an open source system is also an issue in government. But this governor, absolutely. I mean, he has established three Office of Digital Innovation, which is part of California and department technology, Um, in partnership with and that just shows how much he wants. Thio push our limits to make sure that we are meeting the needs of Californians. But it's also looking at, you know, Silicon Valley being at the heart of our state. How do we best utilize systems that already there? How do we better utilize the talent from those those folks is well, we don't always pay as well as they dio in the state. But we do have great benefit packages. Everybody does eso If anybody's looking for a job, we're always looking for technology. Folks is well on DSO I would say that this governor, absolute leads in terms of making sure that we will be on cutting edge of technology for the nation, >>you know, and, you know, talk about pay. I mean, I know it's expensive to live in some parts of California, but there's a huge young population that wants a mission driven job and serving, um, government for the governments. Awesome. Ah, final parting question for you, Stuart, is, as you look at, um, workforce. Ah, lot of people are passionate about this, and it's, you know, you you can't go anywhere without people saying, You know, we got to do education this way and that way there's an opinion everywhere you go. Cybersecurity is a little bit peaked and focused, but there are people who are paying attention to education. So I have to ask you, what creative ways can people get involved and contribute to workforce development? Whether it's stem underrepresented minorities, people are looking for new, innovative ways to contribute. What advice would you give these people who have the passion to contribute to the next cyber workforce. >>Yeah, I appreciate that question, because I think is one of the key components. But my secretary, Julie Sue, secretary of Labor and Workforce Development Agency, talks about often, and a couple of us always have these conversations around. One is getting people with that passion to work in government one or on. I brought it up community based organizations. I think I think so many times, um, that we didn't work with our CBS to the level of in government we should. This administration is very big on working with CBS and philanthropy groups to make sure that thing engagement those entities are at the highest level. So I would say, You know, students have opportunities. Thio also engage with local CBS and be that mission what their values really drives them towards Andi. That gives them a couple of things to do right. One is to look at what ways that we're helping society in one way or another through the organizations, but it also links them thio their own mission and how they could develop those skills around that. But I think the other piece to that is in a lot of these companies that you are working with and that we work with have their own foundations. So those foundations are amazing. We work with them now, especially in the new administration. More than we ever have, these foundations are really starting to help develop are strategies. My secretary works with a large number of foundations already. Andi, when we do is well in terms of strategy, really looking at, how do we develop young people's attitudes towards the future but also skills towards the future? >>Well, you got a pressure cooker of a job. I know how hard it is. I know you're working hard, appreciate you what you do and and we wish you the best of luck. Thank you for sharing this great insight on workforce development. And you guys working hard. Thank you for what you do. Appreciate it. >>Thank you so much. Thistle's >>three cube coverage and co production of the space and cybersecurity supposed in 2020 Cal Poly. I'm John for with silicon angle dot com and the Cube. Thanks for watching

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>Welcome back to UI path forward three. This is UI pass. Third North American conference. We're here at the Bellagio hotel. You are watching the cube, the leader in live tech coverage. We go out to the events, we extract the signal from the noise, we pick the brains of experts. Kevin crone is here. He's a financial services intelligent automation leader at PWC. Kevin, thanks for coming on the cube Bexar Avenue. You're very welcome. So financial services has always been kind of a leading indicator of technology adoption. I presume that automation is, is no difference, but you know, you're in the New York area, you're belly to belly with the financial services companies, the big whales, what's going on in Fs these days? Sure. >>So as we look across the financial services industry, they were one of the leaders with automation more because the overarching business environment really forced them. As we looked at, um, the regulatory burden that a lot of our banking clients we're under over the past decade kind of post crash that really, um, has kind of forced two things. One, it's limited the amount of um, discretionary spend that they have to spend on really big technology transformation projects. It's also forced a lot of margin pressure and having to think about, uh, differently how they could run their business at a much lower and more effective price points. And so that's, um, driven automation to the top. And we've seen tools like U I path and kind of the broader RPA ecosystem becoming kind of, you know, the right technology at the right time of being able to, um, really kind of embrace that, that, that, that rightsizing agenda and financial services sector. >>Yeah. And furniture at the macro level, they're a little bit out of favor right now you've had this, what we thought was this rising interest rate environment and that's reversed. And so that's not necessarily good for them. So they got to look for other ways to sort of drive the bottom line. So maybe you could talk a little bit about, you know, gen generally where you're seeing automation, um, back office, front office. Think about the maturity curve. What are the leaders doing? What, >>what's the sort of best practice right now for intelligent automation RPA? Sure. So as we looked at intelligent automation right now, I think one of the interesting things, vital services was an early adopter. So a lot of, a lot of the big banks and asset managers and insurance companies really start investing in this, this class of technology four, five, six, seven years ago. And so we're actually seeing the, the early returns from, from those, which is informing how this, you know, this topic goes to other industries. But I think as we look at those returns, we see a couple of major challenges. Um, there's challenges with getting the scaling technology, there's challenges with, so that's interesting. Okay. So the, the ladder changing, the nature of work is, as you're saying, largely automating existing mundane processes, kind of paving the cow path as I sometimes say. >>However, if, if it's a, if it's not the most efficient processy to begin read process to begin with, they need to sort of re look at that and that may be falls into the, to the, to the former category of enterprise life. And so where people investing in boat or they are they just hitting the low hanging fruit where we're seeing an investment in both. And then PWC is used that a while fucks your mission program should have both channels and each channel should be informing the other. So if citizens are coming up with ideas of things that they can automate themselves, that's great, but those should also be contributed into the kind of broader ecosystem. And there may be, um, what's called grander ways to, to, to solve that problem, both from a technology perspective and from a process reengineering perspective. Is there a, is there an automation ex-officio is there a chief who's sort of looking at all this stuff or is it more organic? >>It's, you know, one of the, I think interesting things we've seen and learned from our clients over the past couple of years is the con, you know, we thought there'd be an emergence of a chief automation officer or something like that. But really the automation agenda's owned in so many different places within our clients. And it's not consistent client decline in some cases. It's really a CIO own topic. A lot of cases it's more of a chief operating officer, chief digital officer, chief transformation officer. We're also seeing a push at the chief HR officer level because this is really, you know, there's a, there's a big question straight in terms of thinking about kind of skills and how you equip your workforce with the right digital skills for the future, which is now putting HR at the table for this, which is the place where I think traditionally with big technology transformations, they've never really sat. >>So, in thinking about, um, ROI, you know, you've laid out this sort of bifurcated, you know, paths to vectors the hard sort of end-to-end problems and then the sort of low hanging fruit changing the way to work. I would presume the second one gives you the quick hit, you know, faster break even, but probably lower net present value. Um, and, and so maybe you could talk a little bit about the ROI equation and how people are looking at that. Yeah. It's interesting cause I think yeah, to your point, I think an enterprise led initiative, you're going to want to define a business case and say this is why we're doing it and what we're looking to achieve going down to SIS and let channel, that's a harder thing to do because you don't want to stifle innovation. The organization, one of our views is that the people that sit closest to the business process are the ones that should be coming up the right ideas, if they're given the right upscaling and the right tools at their disposal. >>Um, but you know, it's bottoms up exercise. And so again, going back to the concept of having a kind of an ecosystem with both an enterprise channel and a citizen channel is important because you're at the enterprise level, you're going to need to understand what type of benefits are actually being created at the, you know, at the micro level and figure out two things. One, are there things that, you know, do, have we built enough that we can start to release capacity from organization? Um, or is there something else that if I put in, will allow us to really think about transforming our business? So it's a, it's a lover. It's not that the end solution, right? When I tell people about you that don't know what RPA is, I say it's a lot of back office stuff and it is. Um, but we heard today that from one of the keynotes that, you know, we gotta move from the back office to the, to the front office. >>How much is that happening in financial services and how much of a sort of a holistic end to end strategy are you seeing? I'm sure you guys are promoting that are fans of that because you're going to get a much bigger business impact. It's transformational. But where are we at in the maturity of that? Yeah, it's interesting, right? So we, you know, staying on this theme of the enterprise and citizen light innovation levers, you know, the enterprise. Um, and you know, innovation levers tend to be focused more in the back office, high transaction volume type processes. I think when we look at the citizen led channel and a lot of the ideas that have been coming out in our cotton with our clients are starting to embrace this. They tend to be more front office oriented processes. There's lots of things, especially client servicing or that are tasks that are done are somewhat mundane. >>And um, you know, it's the business case and LOC isn't necessarily back capacity. It's about client experience and customer service. So, you know, you can take the, um, you know, the, the, the wealth advisor that has to log into five different systems to answer a simple client question. That's a, you know, that's a process that being able to actually have an automated way to generate that same thing at their fingertips, um, you know, could be really powerful. And so there's a big push there. I think the interesting part on the, um, going back to your bet, your business case question from before is that, um, you know, the, the business case for a lot of those types of automations, um, it's not just a factor of um, you know, have we built enough that we think that there's benefit, it's also about adoption. So if I build a robot to automate that wealth advisor process that I just noted, if 50 wealth advisors can adopt that rather than one wealth advisor, it's going to be a much greater business case. And that's a much, that's a different way of thinking about business case in the RPA sense. Because most people tend to think, here's a process, this process, I have five people that run this on a day to day basis. Um, and here's, here's my business case. In this case it's, I built something really innovative. If I can get a a hundred people to use this because it's, it takes 10 minutes out of their day, there's real, there's, there's real time there, but it is causing a lot of our clients to think differently. >>So you talk about three things as challenges scaled the business case, which you just talked about and change management. Is that part of the, and they're interrelated? Is that part of the challenge with scale? It is far as the channel. >>I mean just building on the last point around adoption, you know, that what we're doing, what we're talking about here with RPA, I think people that live in the RPA space day to day, this does this almost become second nature. And like, yeah, the technology is not that complicated. This is very basic, but you start going out to the entire organization and especially outside of technology. Um, it's, it's new. And so the change management's really important. Um, and it's important we, we view from two lenses. One is really thinking about how do you, um, upskill your workforce at a minimum so they know what technology is actually out there. It doesn't necessarily mean you're gonna make everyone a bot builder in your organization. But knowing what RPA is and knowing that, Hey, I have some tools to go help solve a given business problem is really important. But, uh, you know, the, the uh, the second point that we think is really important in here is the ability to, um, really think, sorry, really think about the, um, you know, what the longterm impact of kind of, um, you know, the overall organizational model and how that actually adopts to using automation over time. >>And that ties into change management, which is the other thing and people don't like change. Um, the other thing we heard this morning, um, Craig LeClaire Forrester analyst talked about how a lot of robots are idle sitting around ill, you know, then though at the orchestrator. And so I was, I was thinking, well, we're seeing sass models emerge, you know, UI path announced their cloud product and I would expect you're going to see new pricing models as well, kind of usage base pricing, which is kind of generally not how things are priced today. But is that something that customers are pushing for >>or definitely. I mean I think there's, um, there's two, two things we hear from customers in this space. I think as RPA, as a product is developed and you know, I think there was a push, uh, with most, with all the vendors towards kind of what's priced for bot. But the concept of a bot is a somewhat ambiguous concept to a lot of our clients. And what our clients really want is to price and value, right? And understand, um, if I'm building bots that are, you know, covering this part of the organization, I'm appropriately paying for this, um, rather than worry about how much workload did I put onto one bod versus another. I think with, uh, with the mass adoption of cloud and the fact that the RP ecosystems quickly moving from an on prem solution to a cloud based solution, I think a lot of this is just gonna happen naturally. Um, over time. I think the other, I think the other really important part in there is not to just make this a technology question about the kind of the pricing. It's also a question on value delivered and realize the benefits case and can you actually tie what those realized benefits are to what the actual price that's actually going to pay for the software is >>all right. You ready for some curve balls? Sure. Okay. So you're, you know, thought leader you worked for one of the largest consultancies on the planet global scale. You guys do some really great work disruption. We talk about digital transformation, automation obviously plays in there, blockchain, AI, RPA, et cetera. Do you, do you think that banks will lose control of payment systems? >>I'm not sure. I would say the pro, the biggest problems that banks are facing, um, with regards to that isn't necessarily whether they control the payment system or not. I actually think it's how effective they can run the system internally. I mean, I'm a, I'm an automation guy, right? And my goal is to make clients run as efficiently and as effectively as possible. And I look at a lot of the legacy debt that sits within a lot of our clients infrastructure. I think that's the biggest problem to tackle. I think if they don't tackle that and are not successful topics like RPA and automation, it, it's going to create the forces of nature that allow some of the broader disruption to happen. So it's, you know, to me, at least in my mind, it's one of these things that you, you have your agenda in what'd you can control. These are the things that you actually shouldn't be focusing on. So you're set up to compete with some of the big disruptors in the future. >>Yeah, interesting. I mean that's one industry, there's a disruption all around us, but that's one industry along with healthcare and defense that it hasn't been highly disrupted yet because it's very high risk. Not only that they're, you know, they've got very strong relationship with the government. So this, and they're big and they're well funded, but, but it seems like that disruption scenario is coming to financial services. When you talk to people in the industry, they certainly see it, but there's also a lot of complacency. It's like, Hey, we're a big, big Fs. We're doing really well. Um, dots on that. >>Um, you know, there is, you know, when we looked across and I'll just say kind of technology investment in the banking sector, big banks and asset managers, insurance companies are some of the biggest spenders on technology out there. And in your view, look at a lot of the commentary that comes out of analyst calls. There's pretty consistent, um, push a to talk about, um, you know, Becky organization as a technology company or some form of that. And there's also a big push to talk about how much money they're spending. That's great. But we've also, yeah, I think when you, you kind of look under the covers, there's been a lot of historical challenges with um, with implementing big technology projects and things. There's a lot of legacy debt that's been built over the past 25 years and complexity really thinking about this from a front to back perspective. >>Like from the point, you know, taking a, the trading side of a bank, looking at the point of trade entry through post-trade processing through finance processing through kind of every step in the life cycle. It's still run from a technology perspective, probably not as efficient as possible. And I think especially when you get outside the front office area and some of the training areas and look at that. So there's a ton of opportunity for improvement and, and you know, kind of building on the last theme, I think to the extent that technologies like RPA and automation are embraced, it helps think about that problem a little bit differently and gives us a chance to tackle some of these big meaty legacy problems that had been around for a while. If we're successful at this and we can force the ROI to be proved, we can force the change management exercise to happen. I think it sets our clients up for, again, for success to avoid some of these disruptive factors. >>Yeah. So huge opportunity then for a UI path than some of its competitors, you know, penetration wise, adoption wise, what inning are we in? >>Uh, adding to we're, we're in early days. I mean, I think we've seen a ton of interest. It's under the excitement from our clients. But you know, our surveys of, of, of the financial services industry, um, most clients will acknowledge their past the pilot and proof of concept phase and there may be even past the first 10 bought phase, but they're not at scale. Right. And I think until three things happen, I think until we can prove that the technology is being used, um, you know, from an organizational coverage across a much wider swath than it is today. I think when we can prove that there's actually a real demonstrable benefit happening from a, from an organizational operating model perspective, and to the extent that the workforce is actually embracing this and I'm posing it, I think we'll, you know, >>be in a much better position to say, Hey, we're working now getting to ending five or six and, and this, this picture's becoming more complete. But it's still early. A lot of opportunities. Kevin, thanks very much to come into the Q was great to have you. Thank you for having me. Hi, and thank you for watching. We're right back with our next guest right after this short break. You're watching the cube live from UI path forward 2019 at the Bellagio right back.

>> Live from Orlando, Florida its theCUBE covering Enterprise Connect 2019. Brought to you by Five9. >> Hello from Orlando, Lisa Martin with Stu Miniman theCUBE. We are live, day three at Enterprise Connect 2019. We have been in Five9's booth all week and we're very excited to welcome to the program for the first time Harry Moseley the CIO of Zoom Video Communications. Harry thanks so much for joining Stu and me on The CUBE today. >> Lisa, Stu its a pleasure to be here, thank you for having me. >> And you're a hall of famer, you have been inducted into the CIO Magazine's hall of fame and recognized as one of the world's top 100 CIO's be Computer World >> Yes that's right >> So we're in the presence of a VIP >> (chuckles) Well thank you for that it's, as I say its all credit back to the wonderful people that have supported me throughout my career. And I've worked with some amazing people and leaders and, who have supported me and the visions that I've created for their organizations. And so, I understand its about me but it's also about the great teams that I've worked with in my past. I can't make this stuff up, yep. >> Harry, we love talking to CIO's especially one with such a distinguished career as yours 'cause the role of CIO has gone through a lot of changes. IT has gone through a lot of changes. You know we've been doing this program for nine years. Remember reading Nick Carr's IT, does IT matter? And you know, we believe IT matters more than ever Not just IT, the business, the relationship maybe give us a little more of your view point as to the role of the CIO and technology, at a show like this. We hear about the CMO and the business and IT all working together. >> Yeah so its actually, in my opinion, there's never been a better time to be a CIO, irrespective of the company you are in, whether its a tech company like where I'm, you know Zoom Video Communications or any one of the prior companies I worked for, professional services, financial services. But even when you think about it like trucking, You think about trucking as an industry, you think about trucking as a company, its like it was a very sort of brick and mortars? But now its all about digital, right? A friend of mine runs a shipping container company and to think that they load five miles of wagons every day. And so I said to him, "how long does it take to load a wagon on a truck?" "It takes four minutes, and you know what Harry, "we're working that down to three. "And that'll increase our revenue by 20 to 25 percent.' And so its just fantastic. And the pace of change, you know it's just growing exponentially. It's just fascinating, the things that we can actually do today we only dreamed about them a year ago. And you think about it sort of' I can't wait to be back here next year, 'cause we're going to just lift the roof off this place in terms of the capabilities. And so its fantastic, yeah it's just absolutely fantastic. >> So looking at, a lot of us know Zoom for video conferencing and different things like that, but you said something very interesting in your fireside chat this morning that I hadn't thought about, and that is when, either going from audio to video, when you're on a video chat you really can't or shouldn't multi-task. So in terms of capturing peoples attention, enabling meetings to happen maybe more on time, faster, more productive. Thought that was an interesting realization, I thought, you're right. >> It just clicks, it just works. You know mobile, you know when I go back to my you know sort of' going back and again, thank you for the recognition from the key note. But when I go back earlier in my career it's like dialing that number, dialing that ten digit number, misdialing that number, what happened? I got to' hang up, I got to' get a dial tone, I got to' dial the numbers again. Now I'm like two minutes late and I know I'm late more often than I'd like, but when its late because of something like that, that's frustrating. That's really frustrating. And so the notion that you can just click on your mobile device, you can click on your laptop, I have no stress anymore, in joining meetings anywhere. I love telling the story about how I had a client meeting, I was in O'Hare Airport and I joined the client prospect meeting. I joined the prospect meeting on my phone using the free wifi service at O'Hare Airport. Put up my virtual background on my phone I just showed you this Stu, with our logo shared the content off of my phone 18 minutes into this 30 minutes call, the person I was talking to, the CIO for this firm called a halt to the meeting. This is what exactly what happened. Enough, I've heard enough. (announcement in background) >> Keep going. >> Keep going, okay. Enough, I didn't know what enough meant. And so I was a little spooked by that if you will. He goes, "you're on a phone, you're in O'Hare Airport, "you've got a virtual background, "you're sharing content, its all flawless. "Its like this is an amazing experience "that we can't get from all the technology "investment we've done in this space "for our company. "So guys, enough. "We're starting a proof of concept on Monday. "No more discussions about it. "Harry, looking forward to being a business partner." >> Does it get better than that? >> It doesn't get better than that. Its like you know, you hop through security, you get on a plane, and its cruisin' all the way home. >> Yeah I mean Harry, I do have to say, you know disclaimer, we are Zoom customers I'm actually a Zoom admin and its that simplicity that you've built into it is the experience, makes it easy. >> And then when you, and Stu, sorry to interrupt you but I got really excited about this stuff as you can tell. But, and then you look at the enterprise. So you're admin? You get into the enterprise management portal and its like Stu, I had a really bad experience. Oh let me look that up, oh yeah, okay. Where were you? You know, I was in outer Mongolia Ah okay, about five minutes into the call you had some packet loss, its like yeah it wasn't. But it still maintains the connection, right? So you can actually, so our Enterprise Management Portal is awesome. >> Yeah so that actually where I was going with the question, is you know I remember back, I actually worked for Lucent right after they spun out from AT&T. And we had videos talking about pervasive video everywhere, in my home in the business. Feels like we're almost there but still even when I have a team get together my folks that live in Silicon Valley, their connectivity's awful. You know when they have their, and its like oh well my computer or my phone don't have the cycles to be able to run. Maybe we have to turn off some of the video Are we getting there, will 5G solve some of these issues? Will the next generation of phones and computers keep up with it? Because it's, I'm sure you can guess we're big fans of video. It's a lot of what we do. >> Because video is the new voice, right. We like video. If I can only hear you and I can't see you, then when I make a statement I can't see you nodding. If I say something you like, you nod. So we get that concurrency of the experience Again it comes back Stu, where were we a year ago? The capabilities we had, where will we be a year from today? Whether its AI, whether its the power in the device in front of us whether its the network, you know, 5G is becoming a reality. It's going to take some time to get there but you've got sort of great technologies and capabilities, that you know, you look at the introduction of our real-time transcription services. I mean how cool is that? I'm sure there's lots of questions, so lots of people would ask about that real-time transcription in terms of, well what's next? I'm not going to talk about what's next. But as they say in life, watch this space. >> Yeah, just you made some announcements at the show with some partners I actually believe Otter AI is one of the ones you mentioned there. I got a demo of their thing, real time, a little bit of AI built in there. Can you talk about some of those partnerships? >> Yeah so we have great, we love our partnerships right? Whether its on the AI space, with Apple and Siri and Amazon and Otter. We also love our partnerships with Questron and Logitek and HP, and Polly of course. Again its the notion of, we have terrific software. You guys realize that, right? Its terrific software, proprietary QOS proprietary capabilities, its like its a fantastic experience every time on our software. These partners have great technologies too. But they're more on the hardware side, we are software engineers at our core. As Andreson said, I think it was about ten years go, "software is the easing thing in the world "so you take terrific software "you imbed it in terrific hardware "with terrific partners and what happens "is you get exceptional experiences." And that's what we want to deliver to people. So its not about the technology, its about the people. Its about making people happy, making easy, taking stress off the table. You go to the meeting, you light it up, you share the content, you record it, you can watch it later, its just terrific. >> So the people, the experiences you about we've been hearing that thematically for the last three days. As we know as consumers, the consumer behavior is driving so much of this change that has to happen, for companies to not just digitally transform, but to be competitive. We're in Five9's booth and they've mentioned they've got five billion minutes of recorded customer conversations. You guys can record, but its not just about the recording of the voice and the video and the transcription. Tell us about what you're doing to enable the context, so that the data and the recordings have much more value. >> Yeah so , I mean its the notion of being able to sort of rewind and replay. I'll give you another example if I may. Coming out of an office in Palo Alto jumped in the Uber, going back to San Jose for a client meeting. I'm a New Yorker as we talked about a few minutes ago and, I don't know the traffic patterns in Southern, in the Valley. And its about 5:00 o'clock, 5:15. San Jose meetings 5:45. Normally it would be fine, but its rush hour, what do I know about rush hour? I know a lot more now than then. I realize I'm not going to be able to make it on time. Put up the client logo, virtual background on the phone, in the Uber, client gets on the call, Harry where are you? I'm in the back of an Uber. Again, the same sort of experience. Then he asks the question, "well with this recording capability, "can I watch it at 35,000 feet?" Of course you can. And that was it. That was the magic moment for this particular client, because he said "I'm client facing all the time. "I don't get it in time, "I don't always make my management meetings "so I won't have to ask my colleagues what happened "and get their interpretation of the meeting. "I can actually watch the meeting "when I'm at 35,000 feet on a plane, going to Europe." So that's what this is all about. >> Alright, well Harry obviously this space excites you a bunch. Can you bring us back a little bit? This brought you out of retirement and the chase, the space is changing so fast. We come a year from now, what kind of things do we think we'll be talking about, and what's going to keep you excited going forward? >> So lets talk about the first part first and then sort of' break it into two. So yes I had a fantastic career and I retired and so when I met Eric and I met the leadership team at Zoom and I dug into the technology and I understood sort of' A, the culture of the company which is amazing. When I understood the product capability and how this was built as video first, and how we would have this maniacal focus if you will on sort of being a software company at our core. And how it was all about the people. That was sort of a very big part of my decision. So that was one. Two is, look we have a labor shortage right? We can't hire enough people, we can't hire the people, we have more jobs than we have people. So and so, retaining talent is really important. Giving them the technology and the studies that have been done, if you make an investment in the technology, that helps with retention. That helps with profit. It helps with, product innovation. So investment in the people. And the ability to collaborate. It's very hard to work if you don't collaborate, right? It just makes it really, very lumpy if you will. So the ability to collaborate locally, nationally, and globally, and people say, well what's collaborating locally? It's kind of like we can just walk down the corridor. Yeah, well if you're in two different buildings how do you get there? And then it gives us, a foot of snow between you, its makes it really hard. So collaborating locally, nationally, and globally is super important. So you put all that together that was the, what convinced me to say okay you know what, retirement, we're just going to put a pause button on that. And we're going to gave some fun over here. And that really has been, so I've, over a year now and its been absolutely amazing. So yes, big advances. What's in the the future? I think the future, you know there's been a lot of discussion around AI. We hear that its like, all the time. And we've seen from a variety of different providers this week in terms of their, their thoughts around how they're going to leverage AI. Its not about the technology, its about the end of the its about the user experience. And you look at the things that we started to do, we talked about real-time transcriptions a few moments ago, you look at the partnership that we have with Linkedin where you can hover over the name and their Linkinin profile pops up. You're going to see this, I just see this as an exponential change in these abilities. Because you have these building blocks today that you can grow on an exponential basis. So, the world is our oyster, is how I fundamentally think about it. And the art of the possible is now possible, And so lets, I think the future is going to' be absolutely amazing. Who would have, sorry Lisa, who would have thought a year ago, you could get on a plane using facial recognition? Let me just throw that out there. I mean, that's pretty amazing. Who would have thought a year ago that when you rent a car, you can just look at the camera on the way out and you're approved to go? Who would have thought that? >> So with that speed I'm curious to get your take on how Zoom is facilitating adoption. You mentioned some great customers examples where your engagement with them via Zoom Video Conference basically sold the POC in and of itself, with you at an airport >> That's a great questions. >> I guess O'Hare has pretty good wifi. >> What's that? >> O'Hare has pretty good wifi. >> A little choppy but, but it worked. >> It worked. >> Because of our great software, yeah. >> There you go, but in terms of adoption so as customers understand, alright our consumers are so demanding, we have to be able to react, and facilitate collaboration internally and externally. How, what are some of the tools and the techniques that Zoom delivers to enable those guys and gals to go I get it, I'm going to use it, And I'm actually going to actually use it successfully? >> This is a question, I don't know how many clients, CIOs, CTOs, C suite execs I talk to, and they all say, they all ask me similar sorts of questions. Like we're not a video first culture. Its like video, its kind of like we're a phone culture. And then I, so I throw that right back at them and I say and why is that? Because we don't have a good video platform. Aha. Now, when you have good video, when it just works when its easy, when its seamless, when its platform agnostic. IOS, Andriod, Mac, Windows, Linux, VDI, web. When you have this sort of, this platform when you're agnostic to the platform, and its a consistent high quality experience, you use it. So its the notion of, Lisa, it's the notion of would we rather get into a room and, would we rather get into a room and have a face to face meeting? Absolutely. So why would you get on a call and not like to see the people you're talking to. You like to see the people. Why, because its a video first. >> Unless its just one of those meetings that's on my calender and I didn't want to be there and I'm not going to listen. But I totally agree with you Harry. So, another hot button topic that I think we're at the center of here and that I'm sure you have an opinion on. Remote workers. So we watched some really big companies I think really got back in the dialogue a coupla' years ago when Yahoo was like okay, everybody's got to' come in work for us and we've seen some very large public companies that said you need to be in your workforce. and as I said, I'm sure you've got some pretty strong opinions on this >> I don't know what's going on here, quite honestly Stu but its like I think you're reading my brain because these are things I love talking about. So yeah, its. Sorry repeat the question? >> Remote workers. >> Remote workers, yeah. So first of all, I was at an event recently we talked about remote work. We didn't like the term. Its a distributed workforce. >> Yes. Because if you say you're a remote worker its kind like, that doesn't give you that warm feeling of being part of the organization. So we call it, so we said, we should drop calling people remote workers and we should call them a distributed work force. So that's one. Two is, I'm in New york, I'm in Orlando, I'm in Chicago, I'm in Atlanta, I'm in Denver. I'm on planes, I'm in an Uber. I don't feel disconnected at all. Why? Because I can see my colleagues, and its immersive. They share content with me. I'm walking down Park Avenue and I've got my phone and they're sharing content and I'm zooming in and I can see them and I can hear them and I'm giving feedback and I'm marking up on my phone, as I'm walking. So I don't feel, and then when I go to, its fascinating, and then I go to San Jose and I'm walking around the office and I'm seeing people physically. It doesn't feel like I haven't seen them, its really funny. I was in San Jose last week, Wednesday and Thursday in San Jose, took the red-eye back. Hate the red-eye but, I don't like flying during the day, I think it's inefficient, a waste of time. Took the red-eye back, now I'm on calls Friday morning from my office at home with my green screen, Zoom background and everybody's got, it's like I'm talking to the same people I was talking to yesterday but they were in the flesh, now they're on video. It's like Harry where are you, why didn't you come to the room? Well I'm back in New York. It's just just that simple, yep. >> That simple and really it sounds like Harry, what Zoom is delivering is a cultural transformation for some of these newer or older companies who, there is no reason not to be a video culture. We thank you so much for taking some time >> Thank you, thank you >> To stop by theCUBE and chat with Stu and me about all of the exciting things that brought you back into tech. and I'm excited to dial up how I'm using Zoom. >> Well we can take five minutes after this and I can show you some cool tricks >> Wow, from the CIO himself. Harry Moseley, thank you so much for your time. >> Thank you, thank you >> Great to have you on the program. For Stu Miniman, I'm Lisa Martin and you're watching theCUBE (upbeat tune)

(electronic dance music) (Intel Jingle) (ethereal electronic dance music) ♪ Okay ♪ (upbeat techno dance music) ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Yeah everybody get loose yeah ♪ ♪ Yeah ♪ ♪ Ye-yeah yeah ♪ ♪ Yeah yeah ♪ ♪ Everybody everybody yeah ♪ ♪ Whoo whoo ♪ ♪ Whoo whoo ♪ ♪ Whoo yeah ♪ ♪ Everybody get loose whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ >> As a courtesy to the presenters and those around you, please silence all mobile devices, thank you. (electronic dance music) ♪ Everybody get loose ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ (upbeat salsa music) ♪ Ha ha ha ♪ ♪ Ah ♪ ♪ Ha ha ha ♪ ♪ So happy ♪ ♪ Whoo whoo ♪ (female singer scatting) >> Ladies and gentlemen, please take your seats. Our program will begin momentarily. ♪ Hey ♪ (female singer scatting) (male singer scatting) ♪ Hey ♪ ♪ Whoo ♪ (female singer scatting) (electronic dance music) ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ Red don't go ♪ ♪ All hands are in don't go ♪ ♪ In don't go ♪ ♪ Oh red go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are red don't go ♪ ♪ All hands are in red red red red ♪ ♪ All hands are in don't go ♪ ♪ All hands are in red go ♪ >> Ladies and gentlemen, there are available seats. Towards house left, house left there are available seats. If you are please standing, we ask that you please take an available seat. We will begin momentarily, thank you. ♪ Let go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ (upbeat electronic dance music) ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ I live ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Hey ♪ ♪ Yeah ♪ ♪ Oh ♪ ♪ Ah ♪ ♪ Ah ah ah ah ah ah ♪ ♪ Just make me ♪ ♪ Just make me ♪ (bouncy techno music) >> Ladies and gentlemen, once again we ask that you please take the available seats to your left, house left, there are many available seats. If you are standing, please make your way there. The program will begin momentarily, thank you. Good morning! This is Lenovo Transform 2.0! (keyboard clicks) >> Progress. Why do we always talk about it in the future? When will it finally get here? We don't progress when it's ready for us. We need it when we're ready, and we're ready now. Our hospitals and their patients need it now, our businesses and their customers need it now, our cities and their citizens need it now. To deliver intelligent transformation, we need to build it into the products and solutions we make every day. At Lenovo, we're designing the systems to fight disease, power businesses, and help you reach more customers, end-to-end security solutions to protect your data and your companies reputation. We're making IT departments more agile and cost efficient. We're revolutionizing how kids learn with VR. We're designing smart devices and software that transform the way you collaborate, because technology shouldn't just power industries, it should power people. While everybody else is talking about tomorrow, we'll keep building today, because the progress we need can't wait for the future. >> Please welcome to the stage Lenovo's Rod Lappen! (electronic dance music) (audience applauding) >> Alright. Good morning everyone! >> Good morning. >> Ooh, that was pretty good actually, I'll give it one more shot. Good morning everyone! >> Good morning! >> Oh, that's much better! Hope everyone's had a great morning. Welcome very much to the second Lenovo Transform event here in New York. I think when I got up just now on the steps I realized there's probably one thing in common all of us have in this room including myself which is, absolutely no one has a clue what I'm going to say today. So, I'm hoping very much that we get through this thing very quickly and crisply. I love this town, love New York, and you're going to hear us talk a little bit about New York as we get through here, but just before we get started I'm going to ask anyone who's standing up the back, there are plenty of seats down here, and down here on the right hand side, I think he called it house left is the professional way of calling it, but these steps to my right, your left, get up here, let's get you all seated down so that you can actually sit down during the keynote session for us. Last year we had our very first Lenovo Transform. We had about 400 people. It was here in New York, fantastic event, today, over 1,000 people. We have over 62 different technology demonstrations and about 15 breakout sessions, which I'll talk you through a little bit later on as well, so it's a much bigger event. Next year we're definitely going to be shooting for over 2,000 people as Lenovo really transforms and starts to address a lot of the technology that our commercial customers are really looking for. We were however hampered last year by a storm, I don't know if those of you who were with us last year will remember, we had a storm on the evening before Transform last year in New York, and obviously the day that it actually occurred, and we had lots of logistics. Our media people from AMIA were coming in. They took the, the plane was circling around New York for a long time, and Kamran Amini, our General Manager of our Data Center Infrastructure Group, probably one of our largest groups in the Lenovo DCG business, took 17 hours to get from Raleigh, North Carolina to New York, 17 hours, I think it takes seven or eight hours to drive. Took him 17 hours by plane to get here. And then of course this year, we have Florence. And so, obviously the hurricane Florence down there in the Carolinas right now, we tried to help, but still Kamran has made it today. Unfortunately, very tragically, we were hoping he wouldn't, but he's here today to do a big presentation a little bit later on as well. However, I do want to say, obviously, Florence is a very serious tragedy and we have to take it very serious. We got, our headquarters is in Raleigh, North Carolina. While it looks like the hurricane is just missing it's heading a little bit southeast, all of our thoughts and prayers and well wishes are obviously with everyone in the Carolinas on behalf of Lenovo, everyone at our headquarters, everyone throughout the Carolinas, we want to make sure everyone stays safe and out of harm's way. We have a great mixture today in the crowd of all customers, partners, industry analysts, media, as well as our financial analysts from all around the world. There's over 30 countries represented here and people who are here to listen to both YY, Kirk, and Christian Teismann speak today. And so, it's going to be a really really exciting day, and I really appreciate everyone coming in from all around the world. So, a big round of applause for everyone whose come in. (audience applauding) We have a great agenda for you today, and it starts obviously a very consistent format which worked very successful for us last year, and that's obviously our keynote. You'll hear from YY, our CEO, talk a little bit about the vision he has in the industry and how he sees Lenovo's turned the corner and really driving some great strategy to address our customer's needs. Kirk Skaugen, our Executive Vice President of DCG, will be up talking about how we've transformed the DCG business and once again are hitting record growth ratios for our DCG business. And then you'll hear from Christian Teismann, our SVP and General Manager for our commercial business, get up and talk about everything that's going on in our IDG business. There's really exciting stuff going on there and obviously ThinkPad being the cornerstone of that I'm sure he's going to talk to us about a couple surprises in that space as well. Then we've got some great breakout sessions, I mentioned before, 15 breakout sessions, so while this keynote section goes until about 11:30, once we get through that, please go over and explore, and have a look at all of the breakout sessions. We have all of our subject matter experts from both our PC, NBG, and our DCG businesses out to showcase what we're doing as an organization to better address your needs. And then obviously we have the technology pieces that I've also spoken about, 62 different technology displays there arranged from everything IoT, 5G, NFV, everything that's really cool and hot in the industry right now is going to be on display up there, and I really encourage all of you to get up there. So, I'm going to have a quick video to show you from some of the setup yesterday on a couple of the 62 technology displays we've got on up on stage. Okay let's go, so we've got a demonstrations to show you today, one of the greats one here is the one we've done with NC State, a high-performance computing artificial intelligence demonstration of fresh produce. It's about modeling the population growth of the planet, and how we're going to supply water and food as we go forward. Whoo. Oh, that is not an apple. Okay. (woman laughs) Second one over here is really, hey Jonas, how are you? Is really around virtual reality, and how we look at one of the most amazing sites we've got, as an install on our high-performance computing practice here globally. And you can see, obviously, that this is the Barcelona supercomputer, and, where else in New York can you get access to being able to see something like that so easily? Only here at Lenovo Transform. Whoo, okay. (audience applauding) So there's two examples of some of the technology. We're really encouraging everyone in the room after the keynote to flow into that space and really get engaged, and interact with a lot of the technology we've got up there. It seems I need to also do something about my fashion, I've just realized I've worn a vest two days in a row, so I've got to work on that as well. Alright so listen, the last thing on the agenda, we've gone through the breakout sessions and the demo, tonight at four o'clock, there's about 400 of you registered to be on the cruise boat with us, the doors will open behind me. the boat is literally at the pier right behind us. You need to make sure you're on the boat for 4:00 p.m. this evening. Outside of that, I want everyone to have a great time today, really enjoy the experience, make it as experiential as you possibly can, get out there and really get in and touch the technology. There's some really cool AI displays up there for us all to get involved in as well. So ladies and gentlemen, without further adieu, it gives me great pleasure to introduce to you a lover of tennis, as some of you would've heard last year at Lenovo Transform, as well as a lover of technology, Lenovo, and of course, New York City. I am obviously very pleasured to introduce to you Yang Yuanqing, our CEO, as we like to call him, YY. (audience applauding) (upbeat funky music) >> Good morning, everyone. >> Good morning. >> Thank you Rod for that introduction. Welcome to New York City. So, this is the second year in a row we host our Transform event here, because New York is indeed one of the most transformative cities in the world. Last year on this stage, I spoke about the Fourth Industrial Revolution, and our vision around the intelligent transformation, how it would fundamentally change the nature of business and the customer relationships. And why preparing for this transformation is the key for the future of our company. And in the last year I can assure you, we were being very busy doing just that, from searching and bringing global talents around the world to the way we think about every product and every investment we make. I was here in New York just a month ago to announce our fiscal year Q1 earnings, which was a good day for us. I think now the world believes it when we say Lenovo has truly turned the corner to a new phase of growth and a new phase of acceleration in executing the transformation strategy. That's clear to me is that the last few years of a purposeful disruption at Lenovo have led us to a point where we can now claim leadership of the coming intelligent transformation. People often asked me, what is the intelligent transformation? I was saying this way. This is the unlimited potential of the Fourth Industrial Revolution driven by artificial intelligence being realized, ordering a pizza through our speaker, and locking the door with a look, letting your car drive itself back to your home. This indeed reflect the power of AI, but it just the surface of it. The true impact of AI will not only make our homes smarter and offices more efficient, but we are also completely transformed every value chip in every industry. However, to realize these amazing possibilities, we will need a structure built around the key components, and one that touches every part of all our lives. First of all, explosions in new technology always lead to new structures. This has happened many times before. In the early 20th century, thousands of companies provided a telephone service. City streets across the US looked like this, and now bundles of a microscopic fiber running from city to city bring the world closer together. Here's what a driving was like in the US, up until 1950s. Good luck finding your way. (audience laughs) And today, millions of vehicles are organized and routed daily, making the world more efficient. Structure is vital, from fiber cables and the interstate highways, to our cells bounded together to create humans. Thankfully the structure for intelligent transformation has emerged, and it is just as revolutionary. What does this new structure look like? We believe there are three key building blocks, data, computing power, and algorithms. Ever wondered what is it behind intelligent transformation? What is fueling this miracle of human possibility? Data. As the Internet becomes ubiquitous, not only PCs, mobile phones, have come online and been generating data. Today it is the cameras in this room, the climate controls in our offices, or the smart displays in our kitchens at home. The number of smart devices worldwide will reach over 20 billion in 2020, more than double the number in 2017. These devices and the sensors are connected and generating massive amount of data. By 2020, the amount of data generated will be 57 times more than all the grains of sand on Earth. This data will not only make devices smarter, but will also fuel the intelligence of our homes, offices, and entire industries. Then we need engines to turn the fuel into power, and the engine is actually the computing power. Last but not least the advanced algorithms combined with Big Data technology and industry know how will form vertical industrial intelligence and produce valuable insights for every value chain in every industry. When these three building blocks all come together, it will change the world. At Lenovo, we have each of these elements of intelligent transformations in a single place. We have built our business around the new structure of intelligent transformation, especially with mobile and the data center now firmly part of our business. I'm often asked why did you acquire these businesses? Why has a Lenovo gone into so many fields? People ask the same questions of the companies that become the leaders of the information technology revolution, or the third industrial transformation. They were the companies that saw the future and what the future required, and I believe Lenovo is the company today. From largest portfolio of devices in the world, leadership in the data center field, to the algorithm-powered intelligent vertical solutions, and not to mention the strong partnership Lenovo has built over decades. We are the only company that can unify all these essential assets and deliver end to end solutions. Let's look at each part. We now understand the important importance data plays as fuel in intelligent transformation. Hundreds of billions of devices and smart IoTs in the world are generating better and powering the intelligence. Who makes these devices in large volume and variety? Who puts these devices into people's home, offices, manufacturing lines, and in their hands? Lenovo definitely has the front row seats here. We are number one in PCs and tablets. We also produces smart phones, smart speakers, smart displays. AR/VR headsets, as well as commercial IoTs. All of these smart devices, or smart IoTs are linked to each other and to the cloud. In fact, we have more than 20 manufacturing facilities in China, US, Brazil, Japan, India, Mexico, Germany, and more, producing various devices around the clock. We actually make four devices every second, and 37 motherboards every minute. So, this factory located in my hometown, Hu-fi, China, is actually the largest laptop factory in the world, with more than three million square feet. So, this is as big as 42 soccer fields. Our scale and the larger portfolio of devices gives us access to massive amount of data, which very few companies can say. So, why is the ability to scale so critical? Let's look again at our example from before. The early days of telephone, dozens of service providers but only a few companies could survive consolidation and become the leader. The same was true for the third Industrial Revolution. Only a few companies could scale, only a few could survive to lead. Now the building blocks of the next revolution are locking into place. The (mumbles) will go to those who can operate at the scale. So, who could foresee the total integration of cloud, network, and the device, need to deliver intelligent transformation. Lenovo is that company. We are ready to scale. Next, our computing power. Computing power is provided in two ways. On one hand, the modern supercomputers are providing the brute force to quickly analyze the massive data like never before. On the other hand the cloud computing data centers with the server storage networking capabilities, and any computing IoT's, gateways, and miniservers are making computing available everywhere. Did you know, Lenovo is number one provider of super computers worldwide? 170 of the top 500 supercomputers, run on Lenovo. We hold 89 World Records in key workloads. We are number one in x86 server reliability for five years running, according to ITIC. a respected provider of industry research. We are also the fastest growing provider of hyperscale public cloud, hyper-converged and aggressively growing in edge computing. cur-ges target, we are expand on this point soon. And finally to run these individual nodes into our symphony, we must transform the data and utilize the computing power with advanced algorithms. Manufactured, industry maintenance, healthcare, education, retail, and more, so many industries are on the edge of intelligent transformation to improve efficiency and provide the better products and services. We are creating advanced algorithms and the big data tools combined with industry know-how to provide intelligent vertical solutions for several industries. In fact, we studied at Lenovo first. Our IT and research teams partnered with our global supply chain to develop an AI that improved our demand forecasting accuracy. Beyond managing our own supply chain we have offered our deep learning supply focused solution to other manufacturing companies to improve their efficiency. In the best case, we have improved the demand, focused the accuracy by 30 points to nearly 90 percent, for Baosteel, the largest of steel manufacturer in China, covering the world as well. Led by Lenovo research, we launched the industry-leading commercial ready AR headset, DaystAR, partnering with companies like the ones in this room. This technology is being used to revolutionize the way companies service utility, and even our jet engines. Using our workstations, servers, and award-winning imaging processing algorithms, we have partnered with hospitals to process complex CT scan data in minutes. So, this enable the doctors to more successfully detect the tumors, and it increases the success rate of cancer diagnosis all around the world. We are also piloting our smart IoT driven warehouse solution with one of the world's largest retail companies to greatly improve the efficiency. So, the opportunities are endless. This is where Lenovo will truly shine. When we combine the industry know-how of our customers with our end-to-end technology offerings, our intelligent vertical solutions like this are growing, which Kirk and Christian will share more. Now, what will drive this transformation even faster? The speed at which our networks operate, specifically 5G. You may know that Lenovo just launched the first-ever 5G smartphone, our Moto Z3, with the new 5G Moto model. We are partnering with multiple major network providers like Verizon, China Mobile. With the 5G model scheduled to ship early next year, we will be the first company to provide a 5G mobile experience to any users, customers. This is amazing innovation. You don't have to buy a new phone, just the 5G clip on. What can I say, except wow. (audience laughs) 5G is 10 times the fast faster than 4G. Its download speed will transform how people engage with the world, driverless car, new types of smart wearables, gaming, home security, industrial intelligence, all will be transformed. Finally, accelerating with partners, as ready as we are at Lenovo, we need partners to unlock our full potential, partners here to create with us the edge of the intelligent transformation. The opportunities of intelligent transformation are too profound, the scale is too vast. No company can drive it alone fully. We are eager to collaborate with all partners that can help bring our vision to life. We are dedicated to open partnerships, dedicated to cross-border collaboration, unify the standards, share the advantage, and market the synergies. We partner with the biggest names in the industry, Intel, Microsoft, AMD, Qualcomm, Google, Amazon, and Disney. We also find and partner with the smaller innovators as well. We're building the ultimate partner experience, open, shared, collaborative, diverse. So, everything is in place for intelligent transformation on a global scale. Smart devices are everywhere, the infrastructure is in place, networks are accelerating, and the industries demand to be more intelligent, and Lenovo is at the center of it all. We are helping to drive change with the hundreds of companies, companies just like yours, every day. We are your partner for intelligent transformation. Transformation never stops. This is what you will hear from Kirk, including details about Lenovo NetApp global partnership we just announced this morning. We've made the investments in every single aspect of the technology. We have the end-to-end resources to meet your end-to-end needs. As you attend the breakout session this afternoon, I hope you see for yourself how much Lenovo has transformed as a company this past year, and how we truly are delivering a future of intelligent transformation. Now, let me invite to the stage Kirk Skaugen, our president of Data Center growth to tell you about the exciting transformation happening in the global Data C enter market. Thank you. (audience applauding) (upbeat music) >> Well, good morning. >> Good morning. >> Good morning! >> Good morning! >> Excellent, well, I'm pleased to be here this morning to talk about how we're transforming the Data Center and taking you as our customers through your own intelligent transformation journey. Last year I stood up here at Transform 1.0, and we were proud to announce the largest Data Center portfolio in Lenovo's history, so I thought I'd start today and talk about the portfolio and the progress that we've made over the last year, and the strategies that we have going forward in phase 2.0 of Lenovo's transformation to be one of the largest data center companies in the world. We had an audacious vision that we talked about last year, and that is to be the most trusted data center provider in the world, empowering customers through the new IT, intelligent transformation. And now as the world's largest supercomputer provider, giving something back to humanity, is very important this week with the hurricanes now hitting North Carolina's coast, but we take this most trusted aspect very seriously, whether it's delivering the highest quality products on time to you as customers with the highest levels of security, or whether it's how we partner with our channel partners and our suppliers each and every day. You know we're in a unique world where we're going from hundreds of millions of PCs, and then over the next 25 years to hundred billions of connected devices, so each and every one of you is going through this intelligent transformation journey, and in many aspects were very early in that cycle. And we're going to talk today about our role as the largest supercomputer provider, and how we're solving humanity's greatest challenges. Last year we talked about two special milestones, the 25th anniversary of ThinkPad, but also the 25th anniversary of Lenovo with our IBM heritage in x86 computing. I joined the workforce in 1992 out of college, and the IBM first personal server was launching at the same time with an OS2 operating system and a free mouse when you bought the server as a marketing campaign. (audience laughing) But what I want to be very clear today, is that the innovation engine is alive and well at Lenovo, and it's really built on the culture that we're building as a company. All of these awards at the bottom are things that we earned over the last year at Lenovo. As a Fortune now 240 company, larger than companies like Nike, or AMEX, or Coca-Cola. The one I'm probably most proud of is Forbes first list of the top 2,000 globally regarded companies. This was something where 15,000 respondents in 60 countries voted based on ethics, trustworthiness, social conduct, company as an employer, and the overall company performance, and Lenovo was ranked number 27 of 2000 companies by our peer group, but we also now one of-- (audience applauding) But we also got a perfect score in the LGBTQ Equality Index, exemplifying the diversity internally. We're number 82 in the top working companies for mothers, top working companies for fathers, top 100 companies for sustainability. If you saw that factory, it's filled with solar panels on the top of that. And now again, one of the top global brands in the world. So, innovation is built on a customer foundation of trust. We also said last year that we'd be crossing an amazing milestone. So we did, over the last 12 months ship our 20 millionth x86 server. So, thank you very much to our customers for this milestone. (audience applauding) So, let me recap some of the transformation elements that have happened over the last year. Last year I talked about a lot of brand confusion, because we had the ThinkServer brand from the legacy Lenovo, the System x, from IBM, we had acquired a number of networking companies, like BLADE Network Technologies, et cetera, et cetera. Over the last year we've been ramping based on two brand structures, ThinkAgile for next generation IT, and all of our software-defined infrastructure products and ThinkSystem as the world's highest performance, highest reliable x86 server brand, but for servers, for storage, and for networking. We have transformed every single aspect of the customer experience. A year and a half ago, we had four different global channel programs around the world. Typically we're about twice the mix to our channel partners of any of our competitors, so this was really important to fix. We now have a single global Channel program, and have technically certified over 11,000 partners to be technical experts on our product line to deliver better solutions to our customer base. Gardner recently recognized Lenovo as the 26th ranked supply chain in the world. And, that's a pretty big honor, when you're up there with Amazon and Walmart and others, but in tech, we now are in the top five supply chains. You saw the factory network from YY, and today we'll be talking about product shipping in more than 160 countries, and I know there's people here that I've met already this morning, from India, from South Africa, from Brazil and China. We announced new Premier Support services, enabling you to go directly to local language support in nine languages in 49 countries in the world, going directly to a native speaker level three support engineer. And today we have more than 10,000 support specialists supporting our products in over 160 countries. We've delivered three times the number of engineered solutions to deliver a solutions orientation, whether it's on HANA, or SQL Server, or Oracle, et cetera, and we've completely reengaged our system integrator channel. Last year we had the CIO of DXE on stage, and here we're talking about more than 175 percent growth through our system integrator channel in the last year alone as we've brought that back and really built strong relationships there. So, thank you very much for amazing work here on the customer experience. (audience applauding) We also transformed our leadership. We thought it was extremely important with a focus on diversity, to have diverse talent from the legacy IBM, the legacy Lenovo, but also outside the industry. We made about 19 executive changes in the DCG group. This is the most senior leadership team within DCG, all which are newly on board, either from our outside competitors mainly over the last year. About 50 percent of our executives were now hired internally, 50 percent externally, and 31 percent of those new executives are diverse, representing the diversity of our global customer base and gender. So welcome, and most of them you're going to be able to meet over here in the breakout sessions later today. (audience applauding) But some things haven't changed, they're just keeping getting better within Lenovo. So, last year I got up and said we were committed with the new ThinkSystem brand to be a world performance leader. You're going to see that we're sponsoring Ducati for MotoGP. You saw the Ferrari out there with Formula One. That's not a surprise. We want the Lenovo ThinkSystem and ThinkAgile brands to be synonymous with world record performance. So in the last year we've gone from 39 to 89 world records, and partners like Intel would tell you, we now have four times the number of world record workloads on Lenovo hardware than any other server company on the planet today, with more than 89 world records across HPC, Java, database, transaction processing, et cetera. And we're proud to have just brought on Doug Fisher from Intel Corporation who had about 10-17,000 people on any given year working for him in workload optimizations across all of our software. It's just another testament to the leadership team we're bringing in to keep focusing on world-class performance software and solutions. We also per ITIC, are the number one now in x86 server reliability five years running. So, this is a survey where CIOs are in a blind survey asked to submit their reliability of their uptime on their x86 server equipment over the last 365 days. And you can see from 2016 to 2017 the downtime, there was over four hours as noted by the 750 CXOs in more than 20 countries is about one percent for the Lenovo products, and is getting worse generation from generation as we went from Broadwell to Pearlie. So we're taking our reliability, which was really paramount in the IBM System X heritage, and ensuring that we don't just recognize high performance but we recognize the highest level of reliability for mission-critical workloads. And what that translates into is that we at once again have been ranked number one in customer satisfaction from you our customers in 19 of 22 attributes, in North America in 18 of 22. This is a survey by TVR across hundreds of customers of us and our top competitors. This is the ninth consecutive study that we've been ranked number one in customer satisfaction, so we're taking this extremely seriously, and in fact YY now has increased the compensation of every single Lenovo employee. Up to 40 percent of their compensation bonus this year is going to be based on customer metrics like quality, order to ship, and things of this nature. So, we're really putting every employee focused on customer centricity this year. So, the summary on Transform 1.0 is that every aspect of what you knew about Lenovo's data center group has transformed, from the culture to the branding to dedicated sales and marketing, supply chain and quality groups, to a worldwide channel program and certifications, to new system integrator relationships, and to the new leadership team. So, rather than me just talk about it, I thought I'd share a quick video about what we've done over the last year, if you could run the video please. Turn around for a second. (epic music) (audience applauds) Okay. So, thank you to all our customers that allowed us to publicly display their logos in that video. So, what that means for you as investors, and for the investor community out there is, that our customers have responded, that this year Gardner just published that we are the fastest growing server company in the top 10, with 39 percent growth quarter-on-quarter, and 49 percent growth year-on-year. If you look at the progress we've made since the transformation the last three quarters publicly, we've grown 17 percent, then 44 percent, then 68 percent year on year in revenue, and I can tell you this quarter I'm as confident as ever in the financials around the DCG group, and it hasn't been in one area. You're going to see breakout sessions from hyperscale, software-defined, and flash, which are all growing more than a 100 percent year-on-year, supercomputing which we'll talk about shortly, now number one, and then ultimately from profitability, delivering five consecutive quarters of pre-tax profit increase, so I think, thank you very much to the customer base who's been working with us through this transformation journey. So, you're here to really hear what's next on 2.0, and that's what I'm excited to talk about today. Last year I came up with an audacious goal that we would become the largest supercomputer company on the planet by 2020, and this graph represents since the acquisition of the IBM System x business how far we were behind being the number one supercomputer. When we started we were 182 positions behind, even with the acquisition for example of SGI from HP, we've now accomplished our goal actually two years ahead of time. We're now the largest supercomputer company in the world. About one in every four supercomputers, 117 on the list, are now Lenovo computers, and you saw in the video where the universities are said, but I think what I'm most proud of is when your customers rank you as the best. So the awards at the bottom here, are actually Readers Choice from the last International Supercomputing Show where the scientific researchers on these computers ranked their vendors, and we were actually rated the number one server technology in supercomputing with our ThinkSystem SD530, and the number one storage technology with our ThinkSystem DSS-G, but more importantly what we're doing with the technology. You're going to see we won best in life sciences, best in data analytics, and best in collaboration as well, so you're going to see all of that in our breakout sessions. As you saw in the video now, 17 of the top 25 research institutions in the world are now running Lenovo supercomputers. And again coming from Raleigh and watching that hurricane come across the Atlantic, there are eight supercomputers crunching all of those models you see from Germany to Malaysia to Canada, and we're happy to have a SciNet from University of Toronto here with us in our breakout session to talk about what they're doing on climate modeling as well. But we're not stopping there. We just announced our new Neptune warm water cooling technology, which won the International Supercomputing Vendor Showdown, the first time we've won that best of show in 25 years, and we've now installed this. We're building out LRZ in Germany, the first ever warm water cooling in Peking University, at the India Space Propulsion Laboratory, at the Malaysian Weather and Meteorological Society, at Uninett, at the largest supercomputer in Norway, T-Systems, University of Birmingham. This is truly amazing technology where we're actually using water to cool the machine to deliver a significantly more energy-efficient computer. Super important, when we're looking at global warming and some of the electric bills can be millions of dollars just for one computer, and could actually power a small city just with the technology from the computer. We've built AI centers now in Morrisville, Stuttgart, Taipei, and Beijing, where customers can bring their AI workloads in with experts from Intel, from Nvidia, from our FPGA partners, to work on their workloads, and how they can best implement artificial intelligence. And we also this year launched LICO which is Lenovo Intelligent Compute Orchestrator software, and it's a software solution that simplifies the management and use of distributed clusters in both HPC and AI model development. So, what it enables you to do is take a single cluster, and run both HPC and AI workloads on it simultaneously, delivering better TCO for your environment, so check out LICO as well. A lot of the customers here and Wall Street are very excited and using it already. And we talked about solving humanity's greatest challenges. In the breakout session, you're going to have a virtual reality experience where you're going to be able to walk through what as was just ranked the world's most beautiful data center, the Barcelona Supercomputer. So, you can actually walk through one of the largest supercomputers in the world from Barcelona. You can see the work we're doing with NC State where we're going to have to grow the food supply of the world by 50 percent, and there's not enough fresh water in the world in the right places to actually make all those crops grow between now and 2055, so you're going to see the progression of how they're mapping the entire globe and the water around the world, how to build out the crop population over time using AI. You're going to see our work with Vestas is this largest supercomputer provider in the wind turbine areas, how they're working on wind energy, and then with University College London, how they're working on some of the toughest particle physics calculations in the world. So again, lots of opportunity here. Take advantage of it in the breakout sessions. Okay, let me transition to hyperscale. So in hyperscale now, we have completely transformed our business model. We are now powering six of the top 10 hyperscalers in the world, which is a significant difference from where we were two years ago. And the reason we're doing that, is we've coined a term called ODM+. We believe that hyperscalers want more procurement power than an ODM, and Lenovo is doing about $18 billion of procurement a year. They want a broader global supply chain that they can get from a local system integrator. We're more than 160 countries around the world, but they want the same world-class quality and reliability like they get from an MNC. So, what we're doing now is instead of just taking off the shelf motherboards from somewhere, we're starting with a blank sheet of paper, we're working with the customer base on customized SKUs and you can see we already are developing 33 custom solutions for the largest hyperscalers in the world. And then we're not just running notebooks through this factory where YY said, we're running 37 notebook boards a minute, we're now putting in tens and tens and tens of thousands of server board capacity per month into this same factory, so absolutely we can compete with the most aggressive ODM's in the world, but it's not just putting these things in in the motherboard side, we're also building out these systems all around the world, India, Brazil, Hungary, Mexico, China. This is an example of a new hyperscale customer we've had this last year, 34,000 servers we delivered in the first six months. The next 34,000 servers we delivered in 68 days. The next 34,000 servers we delivered in 35 days, with more than 99 percent on-time delivery to 35 data centers in 14 countries as diverse as South Africa, India, China, Brazil, et cetera. And I'm really ashamed to say it was 99.3, because we did have a forklift driver who rammed their forklift right through the middle of the one of the server racks. (audience laughing) At JFK Airport that we had to respond to, but I think this gives you a perspective of what it is to be a top five global supply chain and technology. So last year, I said we would invest significantly in IP, in joint ventures, and M and A to compete in software defined, in networking, and in storage, so I wanted to give you an update on that as well. Our newest software-defined partnership is with Cloudistics, enabling a fully composable cloud infrastructure. It's an exclusive agreement, you can see them here. I think Nag, our founder, is going to be here today, with a significant Lenovo investment in the company. So, this new ThinkAgile CP series delivers the simplicity of the public cloud, on-premise with exceptional support and a marketplace of essential enterprise applications all with a single click deployment. So simply put, we're delivering a private cloud with a premium experience. It's simple in that you need no specialists to deploy it. An IT generalist can set it up and manage it. It's agile in that you can provision dozens of workloads in minutes, and it's transformative in that you get all of the goodness of public cloud on-prem in a private cloud to unlock opportunity for use. So, we're extremely excited about the ThinkAgile CP series that's now shipping into the marketplace. Beyond that we're aggressively ramping, and we're either doubling, tripling, or quadrupling our market share as customers move from traditional server technology to software-defined technology. With Nutanix we've been public, growing about more than 150 percent year-on-year, with Nutanix as their fastest growing Nutanix partner, but today I want to set another audacious goal. I believe we cannot just be Nutanix's fastest growing partner but we can become their largest partner within two years. On Microsoft, we are already four times our market share on Azure stack of our traditional business. We were the first to launch our ThinkAgile on Broadwell and on Skylake with the Azure Stack Infrastructure. And on VMware we're about twice our market segment share. We were the first to deliver an Intel-optimized Optane-certified VSAN node. And with Optane technology, we're delivering 50 percent more VM density than any competitive SSD system in the marketplace, about 10 times lower latency, four times the performance of any SSD system out there, and Lenovo's first to market on that. And at VMworld you saw CEO Pat Gelsinger of VMware talked about project dimension, which is Edge as a service, and we're the only OEM beyond the Dell family that is participating today in project dimension. Beyond that you're going to see a number of other partnerships we have. I'm excited that we have the city of Bogota Columbia here, an eight million person city, where we announced a 3,000 camera video surveillance solution last month. With pivot three you're going to see city of Bogota in our breakout sessions. You're going to see a new partnership with Veeam around backup that's launching today. You're going to see partnerships with scale computing in IoT and hyper-converged infrastructure working on some of the largest retailers in the world. So again, everything out in the breakout session. Transitioning to storage and data management, it's been a great year for Lenovo, more than a 100 percent growth year-on-year, 2X market growth in flash arrays. IDC just reported 30 percent growth in storage, number one in price performance in the world and the best HPC storage product in the top 500 with our ThinkSystem DSS G, so strong coverage, but I'm excited today to announce for Transform 2.0 that Lenovo is launching the largest data management and storage portfolio in our 25-year data center history. (audience applauding) So a year ago, the largest server portfolio, becoming the largest fastest growing server OEM, today the largest storage portfolio, but as you saw this morning we're not doing it alone. Today Lenovo and NetApp, two global powerhouses are joining forces to deliver a multi-billion dollar global alliance in data management and storage to help customers through their intelligent transformation. As the fastest growing worldwide server leader and one of the fastest growing flash array and data management companies in the world, we're going to deliver more choice to customers than ever before, global scale that's never been seen, supply chain efficiencies, and rapidly accelerating innovation and solutions. So, let me unwrap this a little bit for you and talk about what we're announcing today. First, it's the largest portfolio in our history. You're going to see not just storage solutions launching today but a set of solution recipes from NetApp that are going to make Lenovo server and NetApp or Lenovo storage work better together. The announcement enables Lenovo to go from covering 15 percent of the global storage market to more than 90 percent of the global storage market and distribute these products in more than 160 countries around the world. So we're launching today, 10 new storage platforms, the ThinkSystem DE and ThinkSystem DM platforms. They're going to be centrally managed, so the same XClarity management that you've been using for server, you can now use across all of your storage platforms as well, and it'll be supported by the same 10,000 plus service personnel that are giving outstanding customer support to you today on the server side. And we didn't come up with this in the last month or the last quarter. We're announcing availability in ordering today and shipments tomorrow of the first products in this portfolio, so we're excited today that it's not just a future announcement but something you as customers can take advantage of immediately. (audience applauding) The second part of the announcement is we are announcing a joint venture in China. Not only will this be a multi-billion dollar global partnership, but Lenovo will be a 51 percent owner, NetApp a 49 percent owner of a new joint venture in China with the goal of becoming in the top three storage companies in the largest data and storage market in the world. We will deliver our R and D in China for China, pooling our IP and resources together, and delivering a single route to market through a complementary channel, not just in China but worldwide. And in the future I just want to tell everyone this is phase one. There is so much exciting stuff. We're going to be on the stage over the next year talking to you about around integrated solutions, next-generation technologies, and further synergies and collaborations. So, rather than just have me talk about it, I'd like to welcome to the stage our new partner NetApp and Brad Anderson who's the senior vice president and general manager of NetApp Cloud Infrastructure. (upbeat music) (audience applauding) >> Thank You Kirk. >> So Brad, we've known each other a long time. It's an exciting day. I'm going to give you the stage and allow you to say NetApp's perspective on this announcement. >> Very good, thank you very much, Kirk. Kirk and I go back to I think 1994, so hey good morning and welcome. My name is Brad Anderson. I manage the Cloud Infrastructure Group at NetApp, and I am honored and privileged to be here at Lenovo Transform, particularly today on today's announcement. Now, you've heard a lot about digital transformation about how companies have to transform their IT to compete in today's global environment. And today's announcement with the partnership between NetApp and Lenovo is what that's all about. This is the joining of two global leaders bringing innovative technology in a simplified solution to help customers modernize their IT and accelerate their global digital transformations. Drawing on the strengths of both companies, Lenovo's high performance compute world-class supply chain, and NetApp's hybrid cloud data management, hybrid flash and all flash storage solutions and products. And both companies providing our customers with the global scale for them to be able to meet their transformation goals. At NetApp, we're very excited. This is a quote from George Kurian our CEO. George spent all day yesterday with YY and Kirk, and would have been here today if it hadn't been also our shareholders meeting in California, but I want to just convey how excited we are for all across NetApp with this partnership. This is a partnership between two companies with tremendous market momentum. Kirk took you through all the amazing results that Lenovo has accomplished, number one in supercomputing, number one in performance, number one in x86 reliability, number one in x86 customers sat, number five in supply chain, really impressive and congratulations. Like Lenovo, NetApp is also on a transformation journey, from a storage company to the data authority in hybrid cloud, and we've seen some pretty impressive momentum as well. Just last week we became number one in all flash arrays worldwide, catching EMC and Dell, and we plan to keep on going by them, as we help customers modernize their their data centers with cloud connected flash. We have strategic partnerships with the largest hyperscalers to provide cloud native data services around the globe and we are having success helping our customers build their own private clouds with just, with a new disruptive hyper-converged technology that allows them to operate just like hyperscalers. These three initiatives has fueled NetApp's transformation, and has enabled our customers to change the world with data. And oh by the way, it has also fueled us to have meet or have beaten Wall Street's expectations for nine quarters in a row. These are two companies with tremendous market momentum. We are also building this partnership for long term success. We think about this as phase one and there are two important components to phase one. Kirk took you through them but let me just review them. Part one, the establishment of a multi-year commitment and a collaboration agreement to offer Lenovo branded flash products globally, and as Kurt said in 160 countries. Part two, the formation of a joint venture in PRC, People's Republic of China, that will provide long term commitment, joint product development, and increase go-to-market investment to meet the unique needs to China. Both companies will put in storage technologies and storage expertise to form an independent JV that establishes a data management company in China for China. And while we can dream about what phase two looks like, our entire focus is on making phase one incredibly successful and I'm pleased to repeat what Kirk, is that the first products are orderable and shippable this week in 160 different countries, and you will see our two companies focusing on the here and now. On our joint go to market strategy, you'll see us working together to drive strategic alignment, focused execution, strong governance, and realistic expectations and milestones. And it starts with the success of our customers and our channel partners is job one. Enabling customers to modernize their legacy IT with complete data center solutions, ensuring that our customers get the best from both companies, new offerings the fuel business success, efficiencies to reinvest in game-changing initiatives, and new solutions for new mission-critical applications like data analytics, IoT, artificial intelligence, and machine learning. Channel partners are also top of mind for both our two companies. We are committed to the success of our existing and our future channel partners. For NetApp channel partners, it is new pathways to new segments and to new customers. For Lenovo's channel partners, it is the competitive weapons that now allows you to compete and more importantly win against Dell, EMC, and HP. And the good news for both companies is that our channel partner ecosystem is highly complementary with minimal overlap. Today is the first day of a very exciting partnership, of a partnership that will better serve our customers today and will provide new opportunities to both our companies and to our partners, new products to our customers globally and in China. I am personally very excited. I will be on the board of the JV. And so, I look forward to working with you, partnering with you and serving you as we go forward, and with that, I'd like to invite Kirk back up. (audience applauding) >> Thank you. >> Thank you. >> Well, thank you, Brad. I think it's an exciting overview, and these products will be manufactured in China, in Mexico, in Hungary, and around the world, enabling this amazing supply chain we talked about to deliver in over 160 countries. So thank you Brad, thank you George, for the amazing partnership. So again, that's not all. In Transform 2.0, last year, we talked about the joint ventures that were coming. I want to give you a sneak peek at what you should expect at future Lenovo events around the world. We have this Transform in Beijing in a couple weeks. We'll then be repeating this in 20 different locations roughly around the world over the next year, and I'm excited probably more than ever about what else is coming. Let's talk about Telco 5G and network function virtualization. Today, Motorola phones are certified on 46 global networks. We launched the world's first 5G upgradable phone here in the United States with Verizon. Lenovo DCG sells to 58 telecommunication providers around the world. At Mobile World Congress in Barcelona and Shanghai, you saw China Telecom and China Mobile in the Lenovo booth, China Telecom showing a video broadband remote access server, a VBRAS, with video streaming demonstrations with 2x less jitter than they had seen before. You saw China Mobile with a virtual remote access network, a VRAN, with greater than 10 times the throughput and 10x lower latency running on Lenovo. And this year, we'll be launching a new NFV company, a software company in China for China to drive the entire NFV stack, delivering not just hardware solutions, but software solutions, and we've recently hired a new CEO. You're going to hear more about that over the next several quarters. Very exciting as we try to drive new economics into the networks to deliver these 20 billion devices. We're going to need new economics that I think Lenovo can uniquely deliver. The second on IoT and edge, we've integrated on the device side into our intelligent devices group. With everything that's going to consume electricity computes and communicates, Lenovo is in a unique position on the device side to take advantage of the communications from Motorola and being one of the largest device companies in the world. But this year, we're also going to roll out a comprehensive set of edge gateways and ruggedized industrial servers and edge servers and ISP appliances for the edge and for IoT. So look for that as well. And then lastly, as a service, you're going to see Lenovo delivering hardware as a service, device as a service, infrastructure as a service, software as a service, and hardware as a service, not just as a glorified leasing contract, but with IP, we've developed true flexible metering capability that enables you to scale up and scale down freely and paying strictly based on usage, and we'll be having those announcements within this fiscal year. So Transform 2.0, lots to talk about, NetApp the big news of the day, but a lot more to come over the next year from the Data Center group. So in summary, I'm excited that we have a lot of customers that are going to be on stage with us that you saw in the video. Lots of testimonials so that you can talk to colleagues of yourself. Alamos Gold from Canada, a Canadian gold producer, Caligo for data optimization and privacy, SciNet, the largest supercomputer we've ever put into North America, and the largest in Canada at the University of Toronto will be here talking about climate change. City of Bogota again with our hyper-converged solutions around smart city putting in 3,000 cameras for criminal detection, license plate detection, et cetera, and then more from a channel mid market perspective, Jerry's Foods, which is from my home state of Wisconsin, and Minnesota which has about 57 stores in the specialty foods market, and how they're leveraging our IoT solutions as well. So again, about five times the number of demos that we had last year. So in summary, first and foremost to the customers, thank you for your business. It's been a great journey and I think we're on a tremendous role. You saw from last year, we're trying to build credibility with you. After the largest server portfolio, we're now the fastest-growing server OEM per Gardner, number one in performance, number one in reliability, number one in customer satisfaction, number one in supercomputing. Today, the largest storage portfolio in our history, with the goal of becoming the fastest growing storage company in the world, top three in China, multibillion-dollar collaboration with NetApp. And the transformation is going to continue with new edge gateways, edge servers, NFV solutions, telecommunications infrastructure, and hardware as a service with dynamic metering. So thank you for your time. I've looked forward to meeting many of you over the next day. We appreciate your business, and with that, I'd like to bring up Rod Lappen to introduce our next speaker. Rod? (audience applauding) >> Thanks, boss, well done. Alright ladies and gentlemen. No real secret there. I think we've heard why I might talk about the fourth Industrial Revolution in data and exactly what's going on with that. You've heard Kirk with some amazing announcements, obviously now with our NetApp partnership, talk about 5G, NFV, cloud, artificial intelligence, I think we've hit just about all the key hot topics. It's with great pleasure that I now bring up on stage Mr. Christian Teismann, our senior vice president and general manager of commercial business for both our PCs and our IoT business, so Christian Teismann. (techno music) Here, take that. >> Thank you. I think I'll need that. >> Okay, Christian, so obviously just before we get down, you and I last year, we had a bit of a chat about being in New York. >> Exports. >> You were an expat in New York for a long time. >> That's true. >> And now, you've moved from New York. You're in Munich? >> Yep. >> How does that feel? >> Well Munich is a wonderful city, and it's a great place to live and raise kids, but you know there's no place in the world like New York. >> Right. >> And I miss it a lot, quite frankly. >> So what exactly do you miss in New York? >> Well there's a lot of things in New York that are unique, but I know you spent some time in Japan, but I still believe the best sushi in the world is still in New York City. (all laughing) >> I will beg to differ. I will beg to differ. I think Mr. Guchi-san from Softbank is here somewhere. He will get up an argue very quickly that Japan definitely has better sushi than New York. But obviously you know, it's a very very special place, and I have had sushi here, it's been fantastic. What about Munich? Anything else that you like in Munich? >> Well I mean in Munich, we have pork knuckles. >> Pork knuckles. (Christian laughing) Very similar sushi. >> What is also very fantastic, but we have the real, the real Oktoberfest in Munich, and it starts next week, mid-September, and I think it's unique in the world. So it's very special as well. >> Oktoberfest. >> Yes. >> Unfortunately, I'm not going this year, 'cause you didn't invite me, but-- (audience chuckling) How about, I think you've got a bit of a secret in relation to Oktoberfest, probably not in Munich, however. >> It's a secret, yes, but-- >> Are you going to share? >> Well I mean-- >> See how I'm putting you on the spot? >> In the 10 years, while living here in New York, I was a regular visitor of the Oktoberfest at the Lower East Side in Avenue C at Zum Schneider, where I actually met my wife, and she's German. >> Very good. So, how about a big round of applause? (audience applauding) Not so much for Christian, but more I think, obviously for his wife, who obviously had been drinking and consequently ended up with you. (all laughing) See you later, mate. >> That's the beauty about Oktoberfest, but yes. So first of all, good morning to everybody, and great to be back here in New York for a second Transform event. New York clearly is the melting pot of the world in terms of culture, nations, but also business professionals from all kind of different industries, and having this event here in New York City I believe is manifesting what we are trying to do here at Lenovo, is transform every aspect of our business and helping our customers on the journey of intelligent transformation. Last year, in our transformation on the device business, I talked about how the PC is transforming to personalized computing, and we've made a lot of progress in that journey over the last 12 months. One major change that we have made is we combined all our device business under one roof. So basically PCs, smart devices, and smart phones are now under the roof and under the intelligent device group. But from my perspective makes a lot of sense, because at the end of the day, all devices connect in the modern world into the cloud and are operating in a seamless way. But we are also moving from a device business what is mainly a hardware focus historically, more and more also into a solutions business, and I will give you during my speech a little bit of a sense of what we are trying to do, as we are trying to bring all these components closer together, and specifically also with our strengths on the data center side really build end-to-end customer solution. Ultimately, what we want to do is make our business, our customer's businesses faster, safer, and ultimately smarter as well. So I want to look a little bit back, because I really believe it's important to understand what's going on today on the device side. Many of us have still grown up with phones with terminals, ultimately getting their first desktop, their first laptop, their first mobile phone, and ultimately smartphone. Emails and internet improved our speed, how we could operate together, but still we were defined by linear technology advances. Today, the world has changed completely. Technology itself is not a limiting factor anymore. It is how we use technology going forward. The Internet is pervasive, and we are not yet there that we are always connected, but we are nearly always connected, and we are moving to the stage, that everything is getting connected all the time. Sharing experiences is the most driving force in our behavior. In our private life, sharing pictures, videos constantly, real-time around the world, with our friends and with our family, and you see the same behavior actually happening in the business life as well. Collaboration is the number-one topic if it comes down to workplace, and video and instant messaging, things that are coming from the consumer side are dominating the way we are operating in the commercial business as well. Most important beside technology, that a new generation of workforce has completely changed the way we are working. As the famous workforce the first generation of Millennials that have now fully entered in the global workforce, and the next generation, it's called Generation Z, is already starting to enter the global workforce. By 2025, 75 percent of the world's workforce will be composed out of two of these generations. Why is this so important? These two generations have been growing up using state-of-the-art IT technology during their private life, during their education, school and study, and are taking these learnings and taking these behaviors in the commercial workspace. And this is the number one force of change that we are seeing in the moment. Diverse workforces are driving this change in the IT spectrum, and for years in many of our customers' focus was their customer focus. Customer experience also in Lenovo is the most important thing, but we've realized that our own human capital is equally valuable in our customer relationships, and employee experience is becoming a very important thing for many of our customers, and equally for Lenovo as well. As you have heard YY, as we heard from YY, Lenovo is focused on intelligent transformation. What that means for us in the intelligent device business is ultimately starting with putting intelligence in all of our devices, smartify every single one of our devices, adding value to our customers, traditionally IT departments, but also focusing on their end users and building products that make their end users more productive. And as a world leader in commercial devices with more than 33 percent market share, we can solve problems been even better than any other company in the world. So, let's talk about transformation of productivity first. We are in a device-led world. Everything we do is connected. There's more interaction with devices than ever, but also with spaces who are increasingly becoming smart and intelligent. YY said it, by 2020 we have more than 20 billion connected devices in the world, and it will grow exponentially from there on. And users have unique personal choices for technology, and that's very important to recognize, and we call this concept a digital wardrobe. And it means that every single end-user in the commercial business is composing his personal wardrobe on an ongoing basis and is reconfiguring it based on the work he's doing and based where he's going and based what task he is doing. I would ask all of you to put out all the devices you're carrying in your pockets and in your bags. You will see a lot of you are using phones, tablets, laptops, but also cameras and even smartwatches. They're all different, but they have one underlying technology that is bringing it all together. Recognizing digital wardrobe dynamics is a core factor for us to put all the devices under one roof in IDG, one business group that is dedicated to end-user solutions across mobile, PC, but also software services and imaging, to emerging technologies like AR, VR, IoT, and ultimately a AI as well. A couple of years back there was a big debate around bring-your-own-device, what was called consumerization. Today consumerization does not exist anymore, because consumerization has happened into every single device we build in our commercial business. End users and commercial customers today do expect superior display performance, superior audio, microphone, voice, and touch quality, and have it all connected and working seamlessly together in an ease of use space. We are already deep in the journey of personalized computing today. But the center point of it has been for the last 25 years, the mobile PC, that we have perfected over the last 25 years, and has been the undisputed leader in mobility computing. We believe in the commercial business, the ThinkPad is still the core device of a digital wardrobe, and we continue to drive the success of the ThinkPad in the marketplace. We've sold more than 140 million over the last 26 years, and even last year we exceeded nearly 11 million units. That is about 21 ThinkPads per minute, or one Thinkpad every three seconds that we are shipping out in the market. It's the number one commercial PC in the world. It has gotten countless awards but we felt last year after Transform we need to build a step further, in really tailoring the ThinkPad towards the need of the future. So, we announced a new line of X1 Carbon and Yoga at CES the Consumer Electronics Show. And the reason is not we want to sell to consumer, but that we do recognize that a lot of CIOs and IT decision makers need to understand what consumers are really doing in terms of technology to make them successful. So, let's take a look at the video. (suspenseful music) >> When you're the number one business laptop of all time, your only competition is yourself. (wall shattering) And, that's different. Different, like resisting heat, ice, dust, and spills. Different, like sharper, brighter OLA display. The trackpoint that reinvented controls, and a carbon fiber roll cage to protect what's inside, built by an engineering and design team, doing the impossible for the last 25 years. This is the number one business laptop of all time, but it's not a laptop. It's a ThinkPad. (audience applauding) >> Thank you very much. And we are very proud that Lenovo ThinkPad has been selected as the best laptop in the world in the second year in a row. I think it's a wonderful tribute to what our engineers have been done on this one. And users do want awesome displays. They want the best possible audio, voice, and touch control, but some users they want more. What they want is super power, and I'm really proud to announce our newest member of the X1 family, and that's the X1 extreme. It's exceptionally featured. It has six core I9 intel chipset, the highest performance you get in the commercial space. It has Nvidia XTX graphic, it is a 4K UHD display with HDR with Dolby vision and Dolby Atmos Audio, two terabyte in SSD, so it is really the absolute Ferrari in terms of building high performance commercial computer. Of course it has touch and voice, but it is one thing. It has so much performance that it serves also a purpose that is not typical for commercial, and I know there's a lot of secret gamers also here in this room. So you see, by really bringing technology together in the commercial space, you're creating productivity solutions of one of a kind. But there's another category of products from a productivity perspective that is incredibly important in our commercial business, and that is the workstation business . Clearly workstations are very specifically designed computers for very advanced high-performance workloads, serving designers, architects, researchers, developers, or data analysts. And power and performance is not just about the performance itself. It has to be tailored towards the specific use case, and traditionally these products have a similar size, like a server. They are running on Intel Xeon technology, and they are equally complex to manufacture. We have now created a new category as the ultra mobile workstation, and I'm very proud that we can announce here the lightest mobile workstation in the industry. It is so powerful that it really can run AI and big data analysis. And with this performance you can go really close where you need this power, to the sensors, into the cars, or into the manufacturing places where you not only wannna read the sensors but get real-time analytics out of these sensors. To build a machine like this one you need customers who are really challenging you to the limit. and we're very happy that we had a customer who went on this journey with us, and ultimately jointly with us created this product. So, let's take a look at the video. (suspenseful music) >> My world involves pathfinding both the hardware needs to the various work sites throughout the company, and then finding an appropriate model of desktop, laptop, or workstation to match those needs. My first impressions when I first seen the ThinkPad P1 was I didn't actually believe that we could get everything that I was asked for inside something as small and light in comparison to other mobile workstations. That was one of the I can't believe this is real sort of moments for me. (engine roars) >> Well, it's better than general when you're going around in the wind tunnel, which isn't alway easy, and going on a track is not necessarily the best bet, so having a lightweight very powerful laptop is extremely useful. It can take a Xeon processor, which can support ECC from when we try to load a full car, and when we're analyzing live simulation results. through and RCFT post processor or example. It needs a pretty powerful machine. >> It's come a long way to be able to deliver this. I hate to use the word game changer, but it is that for us. >> Aston Martin has got a lot of different projects going. There's some pretty exciting projects and a pretty versatile range coming out. Having Lenovo as a partner is certainly going to ensure that future. (engine roars) (audience applauds) >> So, don't you think the Aston Martin design and the ThinkPad design fit very well together? (audience laughs) So if Q, would get a new laptop, I think you would get a ThinkPad X P1. So, I want to switch gears a little bit, and go into something in terms of productivity that is not necessarily on top of the mind or every end user but I believe it's on top of the mind of every C-level executive and of every CEO. Security is the number one threat in terms of potential risk in your business and the cost of cybersecurity is estimated by 2020 around six trillion dollars. That's more than the GDP of Japan and we've seen a significant amount of data breach incidents already this years. Now, they're threatening to take companies out of business and that are threatening companies to lose a huge amount of sensitive customer data or internal data. At Lenovo, we are taking security very, very seriously, and we run a very deep analysis, around our own security capabilities in the products that we are building. And we are announcing today a new brand under the Think umbrella that is called ThinkShield. Our goal is to build the world's most secure PC, and ultimately the most secure devices in the industry. And when we looked at this end-to-end, there is no silver bullet around security. You have to go through every aspect where security breaches can potentially happen. That is why we have changed the whole organization, how we look at security in our device business, and really have it grouped under one complete ecosystem of solutions, Security is always something where you constantly are getting challenged with the next potential breach the next potential technology flaw. As we keep innovating and as we keep integrating, a lot of our partners' software and hardware components into our products. So for us, it's really very important that we partner with companies like Intel, Microsoft, Coronet, Absolute, and many others to really as an example to drive full encryption on all the data seamlessly, to have multi-factor authentication to protect your users' identity, to protect you in unsecured Wi-Fi locations, or even simple things like innovation on the device itself, to and an example protect the camera, against usage with a little thing like a thinkShutter that you can shut off the camera. SO what I want to show you here, is this is the full portfolio of ThinkShield that we are announcing today. This is clearly not something I can even read to you today, but I believe it shows you the breadth of security management that we are announcing today. There are four key pillars in managing security end-to-end. The first one is your data, and this has a lot of aspects around the hardware and the software itself. The second is identity. The third is the security around online, and ultimately the device itself. So, there is a breakout on security and ThinkShield today, available in the afternoon, and encourage you to really take a deeper look at this one. The first pillar around productivity was the device, and around the device. The second major pillar that we are seeing in terms of intelligent transformation is the workspace itself. Employees of a new generation have a very different habit how they work. They split their time between travel, working remotely but if they do come in the office, they expect a very different office environment than what they've seen in the past in cubicles or small offices. They come into the office to collaborate, and they want to create ideas, and they really work in cross-functional teams, and they want to do it instantly. And what we've seen is there is a huge amount of investment that companies are doing today in reconfiguring real estate reconfiguring offices. And most of these kind of things are moving to a digital platform. And what we are doing, is we want to build an entire set of solutions that are just focused on making the workspace more productive for remote workforce, and to create technology that allow people to work anywhere and connect instantly. And the core of this is that we need to be, the productivity of the employee as high as possible, and make it for him as easy as possible to use these kind of technologies. Last year in Transform, I announced that we will enter the smart office space. By the end of last year, we brought the first product into the market. It's called the Hub 500. It's already deployed in thousands of our customers, and it's uniquely focused on Microsoft Skype for Business, and making meeting instantly happen. And the product is very successful in the market. What we are announcing today is the next generation of this product, what is the Hub 700, what has a fantastic audio quality. It has far few microphones, and it is usable in small office environment, as well as in major conference rooms, but the most important part of this new announcement is that we are also announcing a software platform, and this software platform allows you to run multiple video conferencing software solutions on the same platform. Many of you may have standardized for one software solution or for another one, but as you are moving in a world of collaborating instantly with partners, customers, suppliers, you always will face multiple software standards in your company, and Lenovo is uniquely positioned but providing a middleware platform for the device to really enable multiple of these UX interfaces. And there's more to come and we will add additional UX interfaces on an ongoing base, based on our customer requirements. But this software does not only help to create a better experience and a higher productivity in the conference room or the huddle room itself. It really will allow you ultimately to manage all your conference rooms in the company in one instance. And you can run AI technologies around how to increase productivity utilization of your entire conference room ecosystem in your company. You will see a lot more devices coming from the node in this space, around intelligent screens, cameras, and so on, and so on. The idea is really that Lenovo will become a core provider in the whole movement into the smart office space. But it's great if you have hardware and software that is really supporting the approach of modern IT, but one component that Kirk also mentioned is absolutely critical, that we are providing this to you in an as a service approach. Get it what you want, when you need it, and pay it in the amount that you're really using it. And within UIT there is also I think a new philosophy around IT management, where you're much more focused on the value that you are consuming instead of investing into technology. We are launched as a service two years back and we already have a significant number of customers running PC as a service, but we believe as a service will stretch far more than just the PC device. It will go into categories like smart office. It might go even into categories like phone, and it will definitely go also in categories like storage and server in terms of capacity management. I want to highlight three offerings that we are also displaying today that are sort of building blocks in terms of how we really run as a service. The first one is that we collaborated intensively over the last year with Microsoft to be the launch pilot for their Autopilot offering, basically deploying images easily in the same approach like you would deploy a new phone on the network. The purpose really is to make new imaging and enabling new PC as seamless as it's used to be in the phone industry, and we have a complete set of offerings, and already a significant number customers have deployed Autopilot with Lenovo. The second major offering is Premier Support, like in the in the server business, where Premier Support is absolutely critical to run critical infrastructure, we see a lot of our customers do want to have Premier Support for their end users, so they can be back into work basically instantly, and that you have the highest possible instant repair on every single device. And then finally we have a significant amount of time invested into understanding how the software as a service really can get into one philosophy. And many of you already are consuming software as a service in many different contracts from many different vendors, but what we've created is one platform that really can manage this all together. All these things are the foundation for a device as a service offering that really can manage this end-to-end. So, implementing an intelligent workplace can be really a daunting prospect depending on where you're starting from, and how big your company ultimately is. But how do you manage the transformation of technology workspace if you're present in 50 or more countries and you run an infrastructure for more than 100,000 people? Michelin, famous for their tires, infamous for their Michelin star restaurant rating, especially in New York, and instantly recognizable by the Michelin Man, has just doing that. Please welcome with me Damon McIntyre from Michelin to talk to us about the challenges and transforming collaboration and productivity. (audience applauding) (electronic dance music) Thank you, David. >> Thank you, thank you very much. >> We on? >> So, how do you feel here? >> Well good, I want to thank you first of all for your partnership and the devices you create that helped us design, manufacture, and distribute the best tire in the world, okay? I just had to say it and put out there, alright. And I was wondering, were those Michelin tires on that Aston Martin? >> I'm pretty sure there is no other tire that would fit to that. >> Yeah, no, thank you, thank you again, and thank you for the introduction. >> So, when we talk about the transformation happening really in the workplace, the most tangible transformation that you actually see is the drastic change that companies are doing physically. They're breaking down walls. They're removing cubes, and they're moving to flexible layouts, new desks, new huddle rooms, open spaces, but the underlying technology for that is clearly not so visible very often. So, tell us about Michelin's strategy, and the technology you are deploying to really enable this corporation. >> So we, so let me give a little bit a history about the company to understand the daunting tasks that we had before us. So we have over 114,000 people in the company under 170 nationalities, okay? If you go to the corporate office in France, it's Clermont. It's about 3,000 executives and directors, and what have you in the marketing, sales, all the way up to the chain of the global CIO, right? Inside of the Americas, we merged in Americas about three years ago. Now we have the Americas zone. There's about 28,000 employees across the Americas, so it's really, it's really hard in a lot of cases. You start looking at the different areas that you lose time, and you lose you know, your productivity and what have you, so there, it's when we looked at different aspects of how we were going to manage the meeting rooms, right? because we have opened up our areas of workspace, our CIO, CEOs in our zones will no longer have an office. They'll sit out in front of everybody else and mingle with the crowd. So, how do you take those spaces that were originally used by an individual but now turn them into like meeting rooms? So, we went through a large process, and looked at the Hub 500, and that really met our needs, because at the end of the day what we noticed was, it was it was just it just worked, okay? We've just added it to the catalog, so we're going to be deploying it very soon, and I just want to again point that I know everybody struggles with this, and if you look at all the minutes that you lose in starting up a meeting, and we know you know what I'm talking about when I say this, it equates to many many many dollars, okay? And so at the end the day, this product helps us to be more efficient in starting up the meeting, and more productive during the meeting. >> Okay, it's very good to hear. Another major trend we are seeing in IT departments is taking a more hands-off approach to hardware. We're seeing new technologies enable IT to create a more efficient model, how IT gets hardware in the hands of end-users, and how they are ultimately supporting themselves. So what's your strategy around the lifecycle management of the devices? >> So yeah you mentioned, again, we'll go back to the 114,000 employees in the company, right? You imagine looking at all the devices we use. I'm not going to get into the number of devices we have, but we have a set number that we use, and we have to go through a process of deploying these devices, which we right now service our own image. We build our images, we service them through our help desk and all that process, and we go through it. If you imagine deploying 25,000 PCs in a year, okay? The time and the daunting task that's behind all that, you can probably add up to 20 or 30 people just full-time doing that, okay? So, with partnering with Lenovo and their excellent technology, their technical teams, and putting together the whole process of how we do imaging, it now lifts that burden off of our folks, and it shifts it into a more automated process through the cloud, okay? And, it's with the Autopilot on the end of the project, we'll have Autopilot fully engaged, but what I really appreciate is how Lenovo really, really kind of got with us, and partnered with us for the whole process. I mean it wasn't just a partner between Michelin and Lenovo. Microsoft was also partnered during that whole process, and it really was a good project that we put together, and we hope to have something in a full production mode next year for sure. >> So, David thank you very, very much to be here with us on stage. What I really want to say, customers like you, who are always challenging us on every single aspect of our capabilities really do make the big difference for us to get better every single day and we really appreciate the partnership. >> Yeah, and I would like to say this is that I am, I'm doing what he's exactly said he just said. I am challenging Lenovo to show us how we can innovate in our work space with your devices, right? That's a challenge, and it's going to be starting up next year for sure. We've done some in the past, but I'm really going to challenge you, and my whole aspect about how to do that is bring you into our workspace. Show you how we make how we go through the process of making tires and all that process, and how we distribute those tires, so you can brainstorm, come back to the table and say, here's a device that can do exactly what you're doing right now, better, more efficient, and save money, so thank you. >> Thank you very much, David. (audience applauding) Well it's sometimes really refreshing to get a very challenging customers feedback. And you know, we will continue to grow this business together, and I'm very confident that your challenge will ultimately help to make our products even more seamless together. So, as we now covered productivity and how we are really improving our devices itself, and the transformation around the workplace, there is one pillar left I want to talk about, and that's really, how do we make businesses smarter than ever? What that really means is, that we are on a journey on trying to understand our customer's business, deeper than ever, understanding our customer's processes even better than ever, and trying to understand how we can help our customers to become more competitive by injecting state-of-the-art technology in this intelligent transformation process, into core processes. But this cannot be done without talking about a fundamental and that is the journey towards 5G. I really believe that 5G is changing everything the way we are operating devices today, because they will be connected in a way like it has never done before. YY talked about you know, 20 times 10 times the amount of performance. There are other studies that talk about even 200 times the performance, how you can use these devices. What it will lead to ultimately is that we will build devices that will be always connected to the cloud. And, we are preparing for this, and Kirk already talked about, and how many operators in the world we already present with our Moto phones, with how many Telcos we are working already on the backend, and we are working on the device side on integrating 5G basically into every single one of our product in the future. One of the areas that will benefit hugely from always connected is the world of virtual reality and augmented reality. And I'm going to pick here one example, and that is that we have created a commercial VR solution for classrooms and education, and basically using consumer type of product like our Mirage Solo with Daydream and put a solution around this one that enables teachers and schools to use these products in the classroom experience. So, students now can have immersive learning. They can studying sciences. They can look at environmental issues. They can exploring their careers, or they can even taking a tour in the next college they're going to go after this one. And no matter what grade level, this is how people will continue to learn in the future. It's quite a departure from the old world of textbooks. In our area that we are looking is IoT, And as YY already elaborated, we are clearly learning from our own processes around how we improve our supply chain and manufacturing and how we improve also retail experience and warehousing, and we are working with some of the largest companies in the world on pilots, on deploying IoT solutions to make their businesses, their processes, and their businesses, you know, more competitive, and some of them you can see in the demo environment. Lenovo itself already is managing 55 million devices in an IoT fashion connecting to our own cloud, and constantly improving the experience by learning from the behavior of these devices in an IoT way, and we are collecting significant amount of data to really improve the performance of these systems and our future generations of products on a ongoing base. We have a very strong partnership with a company called ADLINK from Taiwan that is one of the leading manufacturers of manufacturing PC and hardened devices to create solutions on the IoT platform. The next area that we are very actively investing in is commercial augmented reality. I believe augmented reality has by far more opportunity in commercial than virtual reality, because it has the potential to ultimately improve every single business process of commercial customers. Imagine in the future how complex surgeries can be simplified by basically having real-time augmented reality information about the surgery, by having people connecting into a virtual surgery, and supporting the surgery around the world. Visit a furniture store in the future and see how this furniture looks in your home instantly. Doing some maintenance on some devices yourself by just calling the company and getting an online manual into an augmented reality device. Lenovo is exploring all kinds of possibilities, and you will see a solution very soon from Lenovo. Early when we talked about smart office, I talked about the importance of creating a software platform that really run all these use cases for a smart office. We are creating a similar platform for augmented reality where companies can develop and run all their argumented reality use cases. So you will see that early in 2019 we will announce an augmented reality device, as well as an augmented reality platform. So, I know you're very interested on what exactly we are rolling out, so we will have a first prototype view available there. It's still a codename project on the horizon, and we will announce it ultimately in 2019, but I think it's good for you to take a look what we are doing here. So, I just wanted to give you a peek on what we are working beyond smart office and the device productivity in terms of really how we make businesses smarter. It's really about increasing productivity, providing you the most secure solutions, increase workplace collaboration, increase IT efficiency, using new computing devices and software and services to make business smarter in the future. There's no other company that will enable to offer what we do in commercial. No company has the breadth of commercial devices, software solutions, and the same data center capabilities, and no other company can do more for your intelligent transformation than Lenovo. Thank you very much. (audience applauding) >> Thanks mate, give me that. I need that. Alright, ladies and gentlemen, we are done. So firstly, I've got a couple of little housekeeping pieces at the end of this and then we can go straight into going and experiencing some of the technology we've got on the left-hand side of the room here. So, I want to thank Christian obviously. Christian, awesome as always, some great announcements there. I love the P1. I actually like the Aston Martin a little bit better, but I'll take either if you want to give me one for free. I'll take it. We heard from YY obviously about the industry and how the the fourth Industrial Revolution is impacting us all from a digital transformation perspective, and obviously Kirk on DCG, the great NetApp announcement, which is going to be really exciting, actually that Twitter and some of the social media panels are absolutely going crazy, so it's good to see that the industry is really taking some impact. Some of the publications are really great, so thank you for the media who are obviously in the room publishing right no. But now, I really want to say it's all of your turn. So, all of you up the back there who are having coffee, it's your turn now. I want everyone who's sitting down here after this event move into there, and really take advantage of the 15 breakouts that we've got set there. There are four breakout sessions from a time perspective. I want to try and get you all out there at least to use up three of them and use your fourth one to get out and actually experience some of the technology. So, you've got four breakout sessions. A lot of the breakout sessions are actually done twice. If you have not downloaded the app, please download the app so you can actually see what time things are going on and make sure you're registering correctly. There's a lot of great experience of stuff out there for you to go do. I've got one quick video to show you on some of the technology we've got and then we're about to close. Alright, here we are acting crazy. Now, you can see obviously, artificial intelligence machine learning in the browser. God, I hate that dance, I'm not a Millenial at all. It's effectively going to be implemented by healthcare. I want you to come around and test that out. Look at these two guys. This looks like a Lenovo management meeting to be honest with you. These two guys are actually concentrating, using their brain power to race each others in cars. You got to come past and give that a try. Give that a try obviously. Fantastic event here, lots of technology for you to experience, and great partners that have been involved as well. And so, from a Lenovo perspective, we've had some great alliance partners contribute, including obviously our number one partner, Intel, who's been a really big loyal contributor to us, and been a real part of our success here at Transform. Excellent, so please, you've just seen a little bit of tech out there that you can go and play with. I really want you, I mean go put on those black things, like Scott Hawkins our chief marketing officer from Lenovo's DCG business was doing and racing around this little car with his concentration not using his hands. He said it's really good actually, but as soon as someone comes up to speak to him, his car stops, so you got to try and do better. You got to try and prove if you can multitask or not. Get up there and concentrate and talk at the same time. 62 different breakouts up there. I'm not going to go into too much detai, but you can see we've got a very, very unusual numbering system, 18 to 18.8. I think over here we've got a 4849. There's a 4114. And then up here we've got a 46.1 and a 46.2. So, you need the decoder ring to be able to understand it. Get over there have a lot of fun. Remember the boat leaves today at 4:00 o'clock, right behind us at the pier right behind us here. There's 400 of us registered. Go onto the app and let us know if there's more people coming. It's going to be a great event out there on the Hudson River. Ladies and gentlemen that is the end of your keynote. I want to thank you all for being patient and thank all of our speakers today. Have a great have a great day, thank you very much. 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(upbeat music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studio this morning for a CUBE Conversation talking about content marketing, attention economy, a lot of really interesting topics that should be top of mind for marketers, that we're in very interesting times on the B2C side and even more, I think, on the B2B side. So we're excited to have Randy Wootton, he's the CEO of Percolate. Randy, great to see you. >> Thanks very much for having me. A real pleasure to be here. >> Absolutely, so for those who aren't familiar, give us kind of the quick and dirty on Percolate. >> Percolate has been around for about seven years. It started as a social media marketing platform. So helping people, helping brands, build their brands on the social landscape, and integrating campaigns to deploy across the different social channels. Over the last couple of years, it's been moving more into the space called content marketing, which is really an interesting new area that marketers are coming to terms with. How do you put together content and orchestrate it across all the different channels. >> And it's interesting, a lot of vocabulary on the website around experiences and content not a lot about products. So how should marketers think and how does experience and content ultimately map back to the products and services you're trying to sell. >> Well, I do think that's a great point. And the distinction between modern brands, who are trying to create relationships with their consumers, rather than pushing products, especially if you're B2B, or technology pushing speeds and feeds. Instead, you are trying to figure out what is going to enable you to create a brand that consumers pull through versus getting pushed at. And so I think the idea around content marketing is that in some ways advertising isn't working anymore. People aren't paying attention to display ads, they're not clicking, they aren't processing the information. But, they are still buying. So the idea for marketers is, how do you get the appropriate content at the right time, to the right person, in their purchase journey. >> Right, and there's so many different examples of people doing new things. There's more conversations kind of, of the persona of the company, of the purpose, purpose driven things, really trying to appeal to their younger employees as well as a younger customer. You have just crazy off the wall things, which never fail to entertain. Like Geico, who seems to break every rule of advertising by having a different theme every time you see a Geico ad. So people are trying humor, they're trying theater, they're trying a lot of things to get through because the tough thing today is getting people's attention. >> I think so, and they talk a lot about the attention economy. That we live in a world of exponential fragmentation. All the information that we are processing across all these different devices. And a brand trying to break through, there's a couple of challenges, one is you have to create a really authentic voice, one that resonates with who you are and how you show up. And then, I think the second point is you recognize that you are co-building the brand with the consumers. It's no longer you build the Super Bowl ad and transmit it on T.V., and people experience your brand. You have this whole unfolding experience in real time. You've seen some of the airlines, for example, that have struggled with the social media downside of brand building. And so how do control, not control, but engage with consumers in a way that feels very authentic and it continues to build a relationship with your consumers. >> Yeah, it's interesting, a lot of things have changed. The other thing that has changed now is that you can have a direct relationship with that consumer whether you want it or not, via social media touches, maybe you were before, that was hidden through your distribution, or you didn't have that, that direct connect. So, you know, being able to respond to this kind of micro-segmentation, it's one thing to talk about micro-segmentation on the marketing side, it's a whole different thing with that one individual, with the relatively loud voice, is screaming "Hey, I need help." >> That's right, and I think there are a couple of things on that point. One is, I've been in technology for 20 years. I've been at Microsoft, I was at Salesforce, I was at AdReady, Avenue A, and Quantive. And now, Rocket Fuel before I came to Percolate. And I've always been wrestling with two dimensions of the digital marketing challenge. One is around consumer identity, and really understanding who the consumer is, and where they've been and what they've done. The second piece is around the context. That is, where they are in the moment, and which device they're on. And so, those are two dimensions of the triangle. The third is the content, or in advertising it's the creative. And that's always been the constraint. You never have enough creative to be able to really deliver on the promise of personalization, of getting the right message to the right person at the right time. And that now is the blockade. That now is the bottleneck, and that now is what brands are really trying to come to terms with. Is how do we create enough content so that you can create a compelling experience for each person, and then if there's someone who is engaging in a very loud voice, how do you know, and how do you engage to sort of address that, but not loose connections with all your other consumers. >> Right, it's interesting, you bring up something, in some of the research, in micro-moments. And in the old days, I controlled all of the information, you had to come for me for the information, and it was a very different world. And now, as you said, the information is out there, there's too much information. Who's my trusted conduit for the information. So by the time they actually get to me, or I'm going to try to leverage these micro-moments, it's not about, necessarily direct information exchange. What are some of these kind of micro-moments, and how are they game changers? >> Well, I think the fact that we can make decisions in near real time. And when I was at Rocket Fuel, we were making decisions in less than 20 milliseconds, processing something like 200 billion bid transactions a day, and so I just think people are not yet aware of the amount, the volume and the velocity of data that is being processed each and every day. And, to make decisions about specific moments. So the two moments I give as examples are: One, I'm sitting at home watching the Oakland Raiders with my two boys, I'm back on the couch and we're watching the game, and Disney makes an advertisement. I'm probably open to a Disney advertisement with my boys next to me, who are probably getting an advertisement at the same time by Disney. I'm a very different person in that moment, or that micro-moment than when I'm commuting in from Oakland to San Francisco on BART, reading the New York Times. I'm not open to a Disney ad at that moment, because I'm concentrating on work, I'm concentrating on the commute. And I think the thing that brands are coming to terms with is, how much am I willing to pay to engage with me sitting on the couch versus me sitting on BART. And that is where the real value comes from, is understanding which moments are the valuable ones. >> So there's so much we can learn from Ad Tech. And I don't think Ad Tech gets enough kind of credit for operating these really large, really hyper speed, really sophisticated marketplaces that are serving up I don't even know how many billions of transactions per unit time. A lot of activity going on. So, you've been in that world for a while. As you've seen them shift from kind of people driving, and buyers driving to more automation, what are some of the lessons learned, and what should learn more from a B2B side from this automated marketplace. >> Well, a couple of things, one is the machines are not our enemies, they are there to enable or enhance our capabilities. Though I do think it's going to require people to re-think work, specifically at agencies, in terms of, you don't need people to do media mixed modeling on the front end in Excel files, instead, you need capacity on the back end after the data has come out, and to really understand the insights. So there is some re-training or re-skilling that's needed. But, the machines make us smarter. It's not artificial intelligence, it's augmented intelligence. I think for B2B in particular what you're finding is, all the research shows that B2B purchasers spend something like 70 or 80% of their time in making the purchase decision before they even engage with the sales rep. And as a B2B company ourselves, we know how expensive our field reps are. And so to make sure that they are engaging with people at the right time, understanding the information that they would have had, before our sales cycle starts, super important. And I think that goes back to the content orchestration, or content marketing revolution that we are seeing now. And, you know, I that there is, when you think about it, most marketers today, use PowerPoint and Excel to have their marketing calendar and run their campaigns. And we're the only function left where you don't have an automated system, like a sales force for marketers, or a service now for marketers. Where a chief of marketing or a SVP of marketing, has, on their phone the tool of record, they system of record that they want to be able to oversee the campaigns. >> Right, although on the other hand, you're using super sophisticated A/B testing across multiple, multiple data sets, rather than doing that purchase price, right. You can test for colors, and fonts, and locations. And it's so different than trying to figure out the answer, make the investment, blast the answer, than this kind of DevOps way, test, test, test, test, test, adjust, test, test, test, test, adjust. >> You're absolutely right, and that's what, at Rocket Fuel, and any real AI powered system, they're using artificial intelligence as the higher order, underneath that you have different categories, like neural networks, deep learning and machine learning. We were using a logistic regression analysis. And we were running algorithms 27 models a day, every single day, that would test multiple features. So it wasn't just A/B testing, it was multi variant analysis happening in real time. Again, the volume and velocity of data is beyond human comprehension, and you need the machine learning to help you make sense of all that data. Otherwise, you just get overwhelmed, and you drown in the data. >> Right, so I want to talk a little bit about PNG. >> I know they're close and dear to your heart. In the old days, but more recently, I just want to bring up, they obviously wield a ton of power in the advertising spin campaign. And they recently tried to bring a little bit more discipline and said, hey we want tighter controls, tighter reporting, more independent third party reporting. There's this interesting thing going now where before, you know, you went for a big in, 'causethen you timed it by some conversion rating you had customers at the end. But now people it seems like, are so focused on the in kind of forgetting necessarily about the conversion because you can drive promoted campaigns in the social media, that now there's the specter of hmm, are we really getting, where we're getting. So again, the PNG, and the consumer side, are really leading kind of this next revolution of audit control and really closer monitoring to what's happening in these automated ad marketplaces. >> Well, I think what you're finding is, there's digital transformation happening across all functions, all industries. And, I think that in the media space in particular, you're also having an agency business model transformation. And what they used to provide for brands has to change as you move forward. PNG has really been driving that. PNG because of how much money they spend on media, has the biggest stick in the fight, and they've brought a lot of accountability. Mark Pritchard, in particular, has laid down these gauntlets the last couple of years, in terms of saying, I want more accountability, more visibility. Part of the challenge with the digital ecosystem is the propensity for fraud and lack of transparency, 'cause things are moving so quickly. So, the fact, that on one side the machines are working really well for ya, on the other side it's hard to audit it. But PNG is really bringing that level of discipline there. I think the thing that PNG is also doing really well, is they're really starting to re-think about how CPG brands can create relationships with their consumers and customers, much like we were talking about before. Primarily, before, CPG brands would work through distributors and retailers, and not really have a relationship with the end consumer. But now as they've started to build up their first party profiles, through clubs and loyalty programs, they're starting to better understand, the soccer mom. But it's not just the soccer mom, it's the soccer mom in Oakland at 4 o'clock in the afternoon, as she goes to Starbucks, when she's picked up her kids from school. All of those are features that better help PNG understand who that person is, in that context, and what's the appropriate engagement to create a compelling experience. That's really hard to do at the individual level. And when you think about the myriad of brands, that PNG has, they have to coordinate their stories and conversations across all of those brands, to drive market share. >> Yeah, it's a really interesting transformation, as we were talking earlier, I used to joke always, that we should have the underground railroad, from Cincinnati to Silicone Valley to get good product managers, right. 'Cause back in the day you still were doing PRD's and MRD's and those companies have been data driven for a long time and work with massive shares and small shifts in market percentages. But, as you said, they now, they're having to transform still data driven, but it's a completely different set of data, and much more direct set of data from the people that actually consume our products. >> And it's been a long journey, I remember when I was at Microsoft, gosh this would have been back in 2004 or 2005, we were working with PNG and they brought their brands to Microsoft. And we did digital immersions for them, to help them understand how they could engage consumers across the entire Microsoft network, and that would include X-Box, Hotmail at the time, MSN, and the brands were just coming to terms with what their digital strategy was and how they would work with Portal versus how they would work with other digital touchpoints. And I think that has just continued to evolve, with the rise of Facebook, with the rise of Twitter, and how do brands maintain relationships in that context, is something that every brand manager of today is having to do. My father, I think we were chatting a little earlier, started his career in 1968 as a brand manager for PNG. And, I remember him telling the stories about how the disciplined approach to brand building, and the structure and the framework hasn't changed, the execution has, over the last 50 years. >> So, just to bring it full circle before we close out, there's always a segment of marketing that's driven to just get me leads, right, give me leads, I need barcode scans at the conference et cetera. And then there's always been kind of the category of kind of thought leadership. Which isn't necessarily tied directly back to some campaign, but we want to be upfront, and show that we're a leading brand. Content marketing is kind of in-between, so, how much content marketing lead towards kind of thought leadership, how much lead kind of towards, actually lead conversions that I can track, and how much of it is something completely different. >> That's a great question, I think this is where people are trying to come to terms, what is content, long form, short form video. I think of content as being applied across all three dimensions of marketing. One is thought leadership, number two is demand gen, and number three is actualization or enablement in a B2B for your sales folks. And how do you have the right set of content along each of those dimensions. And I don't think they're necessarily, I fundamentally think the marketing funnel is broken. It's not you jump in at the top, and you go all the way to the bottom and you buy. You have this sort of webbed touch of experiences. So the idea is, going back to our earlier conversation, is, who is that consumer, what do you know about him, what is the context, and what's the appropriate form of content for them, where they are in their own buyer journey. So, a UGC video on YouTube may be okay for one consumer in a specific moment, but a short form video may be better for someone else, and a white paper may be better. And I think that people don't necessarily go down the funnel and purchase because they click on a search ad, they instead may be looking at a white paper at the end of the purchase, and so the big challenge, is the attribution of value, and that's one of the things that we're looking at Percolate. Is almost around thinking about it as content insight. Which content is working for whom. 'Cause right now you don't know, and I think the really interesting thing is you have a lot of people producing a lot of content. And, they don't know if it's working. And I think when we talk to marketers, that we hear their teams are producing content, and they want to know, they don't want to create content that doesn't work. They just want a better understanding of what's working, and that's the last challenge in the digital marketing transformation to solve. >> And how do you measure it? >> How do you measure, how do you define it? And categorize it, so that's one of the challenges, we were chatting a little bit before, about what you guys are doing at CUBE, and your clipper technology and how you're able to dis-aggregate videos, to these component pieces, or what in an AI world, you'd call features, that then can be loaded as unstructured data, and you can apply AI against it and really come up with interesting insights. So I think there's, as much as I say, the transformation of the internet has been huge, AI is going to transform our world more than we even can conceive of today. And I think content eventually will be impacted materially by AI. >> I still can't help but think of the original marketing quote, I've wasted half of my marketing budget, I'm just not sure which half. But, really it's not so much the waste as we have to continue to find better ways to measure the impact of all these kind of disparate non-traditional funnel things. >> I think you're right, I think it was Wanamaker who said that. I think your point is spot on, it's something we've always wrestled with, and as you move more into the branding media, they struggle more with the accountability. That's one of the reasons why direct response in the internet has been such a great mechanism, is because it's data based, you can show results. The challenge there is the attribution. But I think as we get into video, and you can get to digital video assets, and you can break it down into its component pieces, and all the different dimensions, all of that's fair game for better understanding what's working. >> Randy, really enjoyed the conversation, and thanks for taking a minute out of your busy day. >> My pleasure, always enjoy it. >> Alright, he's Randy, I'm Jeff, you're watching theCUBE from Palo Alto Studios, thanks for watching. (digital music)

(upbeat techno music) >> Narrator: Live, from San Jose, it's theCUBE! Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to Big Data SV, everybody. My name is Dave Vellante and this is theCUBE, the leader in live tech coverage. You know, this is our 10th big data event. When we first started covering big data, back in 2010, it was Hadoop, and everything was a batch job. About four or five years ago, everybody started talking about real time and the ability to affect outcomes before you lose the customer. Lewis Kaneshiro was here. He's the CEO of Streamlio and he's joined by Karthik Ramasamy who's the chief product officer. They're both co-founders. Gentlemen, welcome to theCUBE. My first question is, why did you start this company? >> Sure, we came together around a vision that enterprises need to access the value around fast data. And so as you mentioned, enterprises are moving out of the slow data era, and looking for a fast data value to their data, to really deliver that back to their users or their use cases. And so, coming together around that idea of real time action what we did was we realized that enterprises can't all access this data with projects right now that are not meant to work together, that are very difficult, perhaps, to stitch together. So what we did was create an intelligent platform for fast data that's really accessible to enterprises of all sizes. What we do is we unify the core components to access fast data, which is messaging, compute and stream storage, accessing the best of breed open-source technology that's really open-source out of Twitter and Yahoo! >> It's a good thing I was going to ask why does the world need to know there are, you know, streaming platforms, but Lewis kind of touched on it, 'cause it's too hard. It's too complicated, so you guys are trying to simplify all that. >> Yep, the reason mainly we wanted to simplify it because, based on all our experiences at Twitter and Yahoo! one of the key aspects was to to simplify it so that it's conceivable by regular enterprise because Twitter and Yahoo! kind of our position can afford the talent and the expertise in order to do this real time platforms. But when it goes to normal enterprises, they don't have access to the expertise and the cost benefits that they might have to reincur. So, because of that we wanted to use these open-source projects, the Twitter and the Yahoo!'s provider, combine them, and make sure that you have a simple, easy, drag and drop kind of interface, so that it's easily conceivable for any enterprise. Essentially, what we are trying to do is reduce the (mumbles) for enterprises for real time, for all enterprises. >> Dave: Yeah, enterprises will pay up... >> Yes. >> For a solution. The companies that you used to work for, they all gladly throw engineering at the problem. >> Yeah. >> Sure. >> To save time, but most organizations, they don't have the resources and so. Okay, so how does it, would it work prior to Streamlio? Maybe take us through sort of how a company would attack this problem, the complexities of what they have to deal with, and what life is like with you guys. >> So, current state of the world is it's fragmented solution, today. So the state of the world is where you take multiple pieces of different projects and you'd assemble them together in formats so that you can do (mumbles) right? So the reason why people end up doing is each of these big data projects that people use was the same for completely different purpose. Like messaging is one, and compute is another one, and third one is storage one. So, essentially what we have done as company is to simplify this aspect by integrating this well-known, best-of-the-breed projects called, for messaging we use something called Apache Poser, for compute we use something called Apache Krem, from Twitter, and similarly for storage, for real time storage, we use something called Apache Bookkeeper, so and to unify them, so that, under the hoods, it may be three systems, but, as a user, when you are using it, it serves or functions as a single system. So you install the system, and ingest your data, express your computation, and get the results out, in one single system. >> So you've unified or converged these functions. If I understand it correctly, we talking off camera a little bit, the team, Lewis, that you've assembled actually developed a lot of these, or hugely committed to these open-source projects, right? >> Absolutely, co-creators of each of the projects and what that allows us to do is to really integrate, at a deep level, each project. For example, Pulsar is actually a pub/sub system that is built on Bookkeeper, and Bookkeeper, in our minds, is a pure list best-of-breed stream storage solution. So, fast and durable storage. That storage is also used in Apache Heron to store State. So, as you can see, enterprises, rather than stitching together multiple different solutions for queuing, streaming, compute, and storage, now have one option that they can install in a very small cluster, and operationally it's very simple to scale up. We simply add nodes if you get data spikes. And what this allows is enterprises to access new and exciting use cases that really weren't possible before. For example, machine learning model deployment to real time. So I'm a data scientist and what I found is in data science, you spend a lot of time training models in batch mode. It's a legacy type of approach, but once the model is trained, you want to put that model into production in real time so that you can deliver that value back to a user in real time. Let's call it under two second SLA. So, that has been a great use case for Streamlio because we are a ready made intelligent platform for fast data, for MLai deployment. >> And the use cases are typically stateful and your persisting data, is that right? >> Yes, use cases, it can be used for stateless use cases also, but the key advantage that we bring to a table is stateful storage. And since we ship along with the storage (mumbles) stateful storage becomes much easier because of the fact that it can be used to store a real intermediate state of the computation or it can be used for the staging (mumbles) data when it spills over from what the memory is it's automatically stored to disk or you can even in the data for as long as you want so that you can unlock the value later after the data has been processed for the fast data. You can access the lazy data later, in time. >> So give us the run-down on the company, funding, you know, VCs, head count. Give us the basics. >> Sure, we raise Series A from Lightspeed Venture Partners, lead by John Vrionis and Sudip Chakrabarti. We've raised seven and a half million and emerged from stealth back in August. That allowed us to ramp up our team to 17, now, mainly engineers, in order to really have a very solid product, but we launched post rev, prelaunch and some of our customers are really looking at geo replication across multiple data centers and so active, active geo replication is an open-source feature in Apache Pulsar, and that's been a huge draw, compared to some other solutions that are out there. As you can see, this theme of simplifying architecture is where Streamlio sits, so unifying, queuing and streaming allows us to replace a number of different legacy systems. So that's been one avenue to help growth. The other, obviously is on the compute piece. As enterprises are finding new and exciting use cases to deliver back to their users, the compute piece needs to scale up and down. We also announce Pulsar Functions, which is stream-native compute that allows very simple function computation in native Python and Java, so you spin out the Apache Python cluster or Streamlio platform, and you simply have compute functionality. That allows us to access edge use cases, so IOT is a huge, kind of exciting POC's for us right now where we have connected car examples that don't need heavyweight schedule or deployment at the edge. It's Pulsar Pulsar functions. What that allows us to do are things like fraud detection, anomaly detection at the edge, model deployment at the edge, interpolation, observability, and alerts. >> And, so how do you charge for this? Is it usage based. >> Sure. What we found is enterprise are more comfortable on a per node basis, simply because we have the ambition to really scale up and help enterprises really use Streamlio as their fast data platform across the entire enterprise. We found that having a per data charge rate actually would limit that growth, and so per node and shared architecture. So, we took an early investment in optimizing around Kubernetes. And so, as enterprises are adopting Kubernetes, we are the most simple installation on Kubernetes, so on-prem, multicloud, at the edge. >> I love it, so I mean for years we've just been talking about the complexity headwinds in this big data space. We certainly saw that with Hadoop. You know, Spark was designed to certainly solve some of those problems, but. Sounds like you're doing some really good work to take that further. Lewis and Karthik, thank you so much for coming on theCUBE. I really appreciate it. >> Thanks for having us, Dave. >> All right, thank you for watching. We're here at Big Data SV, live from San Jose. We'll be right back. (techno music)

>> Voiceover: Live from New York it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. (upbeat techno music) >> Welcome back to New York City as theCUBE continues our coverage here at IBM's Machine Learning Everywhere: Build Your Ladder to AI. Along with Dave Vellante, I'm John Walls. We're now joined by Vitaly Tsivan who is Executive Vice President at AMC Networks. And Vitaly, thanks for joining us here this morning. >> Thank you. >> I don't know how this interview is going to go, frankly. Because we've got a die-hard Yankee fan in our guest, and a Red Sox fans who bleeds Red Sox Nation. Can you guys get along for about 15 minutes? >> Dave: Maybe about 15. >> I'm glad there's a bit of space between us. >> Dave: It's given us the off-season and the Yankees have done so well. I'll be humble. Okay? (John laughs) We'll wait and see. >> All right. Just in case, I'm ready to jump in if we have to separate here. But it is good to have you here with us this morning. Thanks for making the time. First off, talk about AMC Networks a little bit. So, five U.S. networks. You said multiple international networks and great presence there. But you've had to make this transition to becoming a data company, in essence. You have content and you're making this merger in the data. How has that gone for you? And how have you done that? >> First of all, you make me happy when you say that AMC Networks have made a transition to be a data company. So, we haven't. We are using data to help our primary business, which is obviously broadcasting our content to our viewers. But yes, we use data to help to tune our business, to follow the lead that viewers are giving us. As you can imagine, in the last so many years, viewers have actually dictating how they want to watch. Whether it's streaming video rather than just turning their satellite boxes or TV boxes on, and pretty much dictating what content they want to watch. So, we have to follow, we have to adjust and be at the cutting edge all for our business. And this is where data come into play. >> How did you get there? You must have done a lot of testing, right? I mean, I remember when binge watching didn't even exist, and then all of a sudden now everybody drops 10 episodes at once. Was that a lot of A-B testing? Just analyzing data? How does a company like yours come to that realization? Or is it just, wow, the competition is doing it, we should too. Explain how -- >> Vitaly: Interesting. So, when I speak to executives, I always tell them that business intelligence and data analytics for any company is almost like an iceberg. So, you can actually see the top of it, and you enjoy it very much but there's so much underwater. So, that's what you're referring to which is that in order to be able to deliver that premium thing that's the tip of the iceberg is that we have to have state of the art data management platforms. We have to curate our own first by data. We have to acquire meaningful third party data. We have to mingle it all together. We have to employ optimization predictive algorithms on top of that. We have to employ statistics, and arm business with data-driven decisions. And then it all comes to fruition. >> Now, your company's been around for awhile. You've got an application -- You're a developer. You're an application development executive. So, you've sort of made your personal journey. I'm curious as to how the company made its journey. How did you close that gap between the data platforms that we all know, the Googles, the Facebooks, etc., which data is the central part of their organization, to where you used to be? Which probably was building, looking back doing a lot of business intelligence, decision support, and a lot of sort of asynchronous activities. How did you get from there to where you are today? >> Makes sense. So, I've been with AMC Networks for four years. Prior to that I'd been with Disney, ABC, ESPN four, six years, doing roughly the same thing. So, number one, we're utilizing ever rapidly changing technologies to get us to the right place. Number two is during those four years with AMC, we've employed various tactics. Some of them are called data democratization. So, that's actually not only get the right data sources not only process them correctly, but actually arm everyone in the company with immediate, easy access to this data. Because the entire business, data business, is all about insights. So, the insights -- And if you think of the business, if you for a minute separate business and business intelligence, then business doesn't want to know too much about business intelligence. What they want insights on a silver plate that will tell them what to do next. Now, that's the hardest thing, you can imagine, right? And so the search and drive for those insights has to come from every business person in the organization. Now, obviously, you don't expect them to build their own statistical algorithms and see the results in employee and machine learning. But if you arm them with that data at the tip of their fingers, they'll make many better decisions on a daily basis which means that they're actually coming up with their own small insights. So, there are small insights, big insights, and they're all extremely valuable. >> A big part of that is cultural as well, that mindset. Many companies that I work with, they're data is very siloed. I don't know if that was the case with your firm, maybe less prior to your joining. I'd be curious as to how you've achieved that cultural mindset shift. Cause a lot of times, people try to keep their own data. They don't want to share it. They want to keep it in a silo, gain political power. How did you address that? >> Vitaly: Absolutely. One of my conversations with the president, we were discussing the fact that if we were to go make recordings of how people talk about data in their organization today and go back in time and show them what they will be doing three years from now, they would be shocked. They wouldn't believe that. So, absolutely. So, culturally, educationally, bringing everyone into the place where they can understand data. They can take advantage of the data. It's an undertaking. But we are successful in doing that. >> Help me out here. Maybe I just have never acquired a little translation here, or simplification. So, you think about AMC. You've got programming. You've got your line up. I come on, I click, I go, I watch a movie and I enjoy it or watch my program, whatever. So, now in this new world of viewer habits changing, my behaviors are changing. What have you done? What have you looked for in terms of data and telling you about me that has now allowed you to modify your business and adapt to that. So, I mean, health data shouldn't drive that on a day to day basis in terms of how I access your programming. >> So, good example to that would be something we called TV everywhere. So, you said it yourself, obviously users or viewers are used to watching television as when the shows were provided via television. So, with new technologies, with streaming opportunities, today, they want to watch when they want to watch, and what they want to watch. So, one of the ways we accommodate them with that is that we don't just television, so we are on every available platform today and we are allowing viewers to watch our content on demand, digitally, when they want to watch it. So, that is one of the ways how we are reacting to it. And so, that puts us in the position as one of the B to C type of businesses, where we're now speaking directly to our consumers not via just the television. So, we're broadcasting, their watching which means that we understand how they watch and we try to react accordingly to that. Which is something that Netflix is bragging about is that they know the patterns, they actually kind of promote their business so we on that business too. >> Can you describe your innovation formula, if you will? How do you go about innovating? Obviously, there's data, there's technology. Presumably, there's infrastructure that scales. You have to be able to scale and have massive speed and infrastructure that heals itself. All those other things. But what's your innovation formula? How would you describe it? So, informally simple. It starts with business. I'm fortunate that business has desire to innovate. So, formulating goals is something that drives us to respond to it. So, we don't just walk around the thing, and look around and say, "Let's innovate." So, we follow the business goals with innovation. A good example is when we promote our shows. So, the major portion of our marketing campaigns falls on our own air. So, we promote our shows to our AMC viewers or WE tv viewers. When we do that, we try to optimize our campaigns to the highest level possible, to get the most out of ROI out of that. And so, we've succeeded and we managed today to get about 30% ROI on that and either just do better with our promotional campaigns or reallocate that time for other businesses. >> You were saying that after the first question, or during responding to the first question, about you saying we're really not ... We're a content company still. And we have incorporated data, but you really aren't, Dave and I have talked about this a lot, everybody's a data company now, in a way. Because you have to be. Cause you've got this hugely competitive landscape that you're operating in, right? In terms of getting more odd calls. >> That's right. >> So, it's got to be no longer just a part of what you do or a section of what you do. It's got to be embedded in what you do. Does it not? Oh, it absolutely is. I still think that it's a bit premature to call AMC Networks a data company. But to a degree, every company today is a data company. And with the culture change over the years, if I used to solicit requests and go about implementing them, today it's more of a prioritization of work because every department in the company got educated to the degree that they all want to get better. And they all want those insights from the data. They want their parts of the business to be improved. And we're venturing into new businesses. And it's quite a bit in demand. >> So, is it your aspiration to become a data company? Or is it more data-driven sort of TV network? How would you sort of view that? >> I'd like to say data-driven TV network. Of course. >> Dave: Okay. >> It's more in tune with reality. >> And so, talk about aligning with the business goals. That's kind of your starting point. You were talking earlier about a gut feel. We were joking about baseball. Moneyball for business. So, you're a data person. The data doesn't lie, etc. But insights sometimes are hard. They don't just pop out. Is that true? Do you see that changing as the time to insight, from insight to decision going to compress? What do you see there? >> The search for insights will never stop. And the more dense we are in that journey the better we are going to be as a company. The data business is so much depends on technologies. So, that when technologies matures, and we manage to employ them in a timely basis, so we simply get better from that. So, good example is machine learning. There are a ton of optimizations, optimization algorithms, forecasting algorithms that we put in place. So, for awhile it was a pinnacle of our deliveries. Now, with machine learning maturing today. We are able or trying to be in tune with the audience that is changing their behavior. So, the patterns that we would be looking for manually in the past, machine is now looking for those patterns. So, that's the perfect example for our strength to catch up with the reality. What I'm hoping for, and that's where the future is, is that one day we won't be just reacting utilizing machine learning to the change in patterns in behavior. We are actually going to be ahead of those patterns and anticipate those changes to come, and react properly. >> I was going to say, yeah, what is the next step? Because you said that you are reacting. >> Vitaly: I was ahead of your question. >> Yeah, you were. (laughter) So, I'm going to go ahead and re-ask it. >> Dave: Data guy. (laughter) >> But you've got to get to that next step of not just anticipating but almost creating, right, in your way. Creating new opportunities, creating news data to develop these insights into almost shaping viewer behavior, right? >> Vitaly: Totally. So, like I said, optimization is one avenue that we pursue and continue to pursue. Forecasting is another. But I'm talking about true predictability. I mean, something goes beyond just to say how our show will do. Even beyond, which show would do better. >> John: Can you do that? Even to the point and say these are the elements that have been successful for this genre and for this size of audience, and therefore as we develop programming, whether it's in script and casting, whatever. I mean, take it all the way down to that micro-level to developing almost these ideals, these optimal programs that are going to be better received by your audience. >> Look, it's not a big secret. Every company that is in the content business is trying to get as many The Walking Deads as they can in their portfolio. Is there a direct path to success? Probably not, otherwise everyone would have been-- >> John: Over do it. >> Yeah, would be doing that. But yeah, so those are the most critical and difficult insights to get ahold of and we're working toward that. >> Are you finding that your predictive capabilities are getting meaningfully better? Maybe you could talk about that a little bit in terms of predicting those types of successes. Or is it still a lot of trial and error? >> I'd like to say they are meaningfully better. (laughter) Look, we do, there are obviously interesting findings. There are sometimes setbacks and we learn from it, and we move forward. >> Okay, as good as the weather or better? Or worse? (laughs) >> Depends on the morning and the season. (laughter) >> Vitaly, how have your success or have your success measurements changed as we enter this world of digital and machine learning and artificial intelligence? And if so, how? >> Well, they become more and more challenging and complex. Like, I gave an example for data democratization. It was such an interesting and telling company-wide initiative. And at the time, it felt as a true achievement when everybody get access to their data on their desktops and laptops. When we look back now a few years, it was a walk in the park to achieve. So, the more complex data and objectives we set in front of ourselves, the more educated people in the company become, the more challenging it is to deliver and take the next step. And we strive to do that. >> I wonder if I can ask you a question from a developers perspective. You obviously understand the developer mindset. We were talking to Dennis earlier. He's like, "Yeah, you know, it's really the data scientists that are loving the data, taking a bath in it. The data engineers and so forth." And I was kind of pushing on that saying, "Well, but eventually the developers have to be data-oriented. Data is the new development kit. What's your take? I mean, granted the 10 million Java developers most of them are not focused on the data per se. Will that change? Is that changing? >> So, first of all, I want separate the classical IT that you just referred to, which are developers. Because this discipline has been well established whether it's Waterfall or Agile. So, every company has those departments and they serve companies well. Business intelligence is a different animal. So, most of the work, if not all of the work we do is more of an R&D type of work. It is impossible to say, in three months I'll arrive with the model that will transform this business. So, we're driving there. That's the major distinction between the two. Is it the right path for some of the data-oriented developers to move on from, let's say, IT disciplines and into BI disciplines? I would highly encourage that because the job is so much more challenging, so interesting. There's very little routine as we said. It's actually challenge, challenge, and challenge. And, you know, you look at the news the way I do, and you see that data scientists becomes the number one desired job in America. I hope that there will be more and more people in that space because as every other department was struggling to find good people, right people for the space, and even within that space, you have as you mentioned, data engineers. You have data scientists or statisticians. And now it's maturing to the point that you have people who are above and beyond that. Those who actually can envision models not to execute on them. >> Are you investigating blockchain and playing around with that at all? Is there an application in your business? >> It hasn't matured fully yet in our hands but we're looking into it. >> And the reason I ask is that there seems to me that blockchain developers are data-oriented. And those two worlds, in my view, are coming together. But it's earlier days. >> Look, I mean, we are in R&D space. And like I said, we don't know exactly, we can't fully commit to a delivery. But it's always a balance between being practical and dreaming. So, if I were to say, you know, let me jump into a blockchain right now and be ahead of the game. Maybe. But then my commitments are going to be sort of farther ahead and I'm trying to be pragmatic. >> Before we let you go, I got to give you 30 seconds on your Yankees. How do you feel about the season coming up? >> As for with every season, I'm super-excited. And I can't wait until the season starts. >> We're always excited when pitchers and catchers show up. >> That's right. (laughter) >> If I were a Yankee fan, I'd be excited too. I must admit. >> Nobody's lost a game. >> That's right. >> Vitaly, thank you for being with us here. We appreciate it. And continued success at AMC Networks. Thank you for having me. >> Back with more on theCUBE right after this. (upbeat techno music)

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

>> Narrator: Live from Washington D.C. it's theCUBE, covering .conf2017. Brought to you by Splunk. (techno music) >> Well welcome back here on theCUBE as we continue our coverage at .conf2017. Splunks get together here in the nation's capital, Washington D.C. We are live here on theCUBE along with Dave Vellante. I'm John Walls. Glad to have you with us here for two days of coverage. We're joined now by Team Dell EMC I guess you could say. Colin Gallagher, who's the Senior Director of VxRail Product Marketing. Colin, good to see you, sir. >> Likewise. >> And Cory Minton, many time Cuber. Colin, you're a Cuber, as well. Principle Engineer, Data Analytical Leader at Dell EMC, and BigDataBeard.com, right? >> Yes, sir. >> Alright, and just in case, you have a special session going on. They're going to be handing these out a little bit later. So, I'm going to let you know that I'm prepared >> Cory: I love that, that's perfect. >> With you and your many legions of fans, allow me to join the club. >> That's awesome. Well welcome, we're so glad to have you. You've got a big data beard. You don't have to have a beard to talk big data at Dell EMC, but it certainly is not frowned upon if you do. >> John: Alright, well this would be the only way I'd ever grow one. >> There you go. >> I can promise you that. >> Looks good on you. >> I like the color, though, too. Anyway, they'll be handing these out at the special session. That'll be a lot of fun. Fellows, big announcement last week where you've got a marriage of sorts with Splunk technology and what Dell EMC is offering on VxRail. Tell us a little bit about that. Ready Systems is how you're branding this new offer. >> So we announced our Ready Systems for Splunk. These are turnkey offerings of Dell EMC technology pre-certified and pre-validated with Splunk and pre-sized. So we give you the option to buy from us both your Splunk solution and the underlying infrastructure that's been certified and validated in a wide variety of flavors based on top of VxRail, based on top of VxRack, based on top of some of our other storage products, as well, that gives you a full turnkey implementation for Splunk. So as Splunk is moving from the land of the hoodies and the experimenters to more mainstream running the business, these are the solutions that IT professionals can trust from both brands that IT professionals (mumbles). >> So you're both a Splunk reseller and a seller of infrastructure, is that right? >> Indeed. So we actually, we joined Splunk in a partnership as a strategic alliance partner a little over a year ago. And that gave us the opportunity to act as a reseller for Splunk. And we've recently gone through a rationalization of their catalog, so we actually have now an expanded offering. So, customers have more choice with us in terms of the offers that we provide from Splunk. And then part of our alliance relationship is that not only are we a reseller, but because of our relationship they now commit engineering and resources to us to help validate our solutions. So we actually work hand in hand with their partner engineering team to make sure that the solutions that we're designing from an infrastructure perspective at least meet or exceed the hardware requirements that Splunk wants to see their platform run on top of. >> Dave: Okay, cool. So you're a data guy. >> Indeed. >> You've been watching the evolution of things like Hadoop. When I look at the way in which customers deal with Hadoop, you know, ingest, you know, clean or transform, analyze, etc., etc., operationalize, there seem to be a lot of parallels between what goes on in that big data world and then the Splunk world, although Splunk is a package, it seems to be an integrated system. What are the similarities? What are the differences? And, what are the requirements for infrastructure? >> I think that the ecosystems, like you said, it's open source versus a commercial platform with a specific objective. And if you look at Splunk's deployment and their development over the years they've really started going from what was really a Google search for log, as Doug talked about today in the kickoff, to really being a robust analytics platform. So I think there's a lot of parallels in terms of technology. We're still ... It's designed to do many of the same things, which is I need to ingest data into somewhere, I need to make sense of it. So, we index it or do some sort of curation process to where then I can ask questions of it. And whether you choose to go the open source route, which is a very popular route, or you choose to go a commercial platform like Splunk, it really depends on your underlying call it ethos, right? It's that fundamental buy versus build, right? For somebody to achieve some of the business outcomes of like deploying a security event and information management tool like Splunk can do, to do that in open source may require some development, some integration of disparate open source platforms. I think Splunk is really good about focusing specifically on the business outcome that they're trying to drive and speeding their customers' time to value with that specific outcome in mind, whereas I think the open source community, like the Hadoop community, I think it offers maybe some ability to do some things that Splunk maybe wouldn't be interested in, things like rich media analytics, things that aren't good for Splunk indexing. >> Are there unique attributes of a data rich workload that you've accommodated that's maybe different from a traditional enterprise workload, and what are those? >> Yeah, so at the end of the day any application is going to have specific bottlenecks, right? One of the basis of performance engineering is move the bottleneck, right? In enterprise applications we had this evolution of originally they were kind of deployed in a server, and then we saw virtualization and shared storage really come in vogue for a number of years. And that's true in these applications, these data rich applications, as well. I think what we're starting to see is that regardless of what the workload is, whether it's a traditional business application like Oracle, SAP, or Microsoft or it's a data application like Splunk, anytime it becomes critical to the operation of a business organizations have to start to do things that we've done to every enterprise IT app in the past, which is we align it to our strategy. Is it highly available? Is it redundant? Is it built on hardware that we can be confident in that's going to be up and running when we need it? So I think from a performance and an engineering perspective, we treat each workload special, right? So we look at what Splunk requirements are and we understand that their requirements may be slightly different than running SAP or Oracle, and that's why we build the bespoke systems like our Ready System for Splunk specifically, right? It's not a catch all that hey it works for everything. It is a specifically designed platform to run Splunk exceptionally well. >> So Colin, a lot of the data practitioners that I talk to at this show and other data oriented shows like, "Ah, infrastructure. "I don't care about infrastructure." Why should they care about infrastructure? Why does infrastructure matter, and what are the things that they should know? >> Infrastructure does matter. I mean infrastructure, if youre infrastructure isn't there, if your infrastructure isn't highly available, as Cory said, if it lets you down in the middle of something, your business is going to shut down, right? Any user can say, "Talk about what happened "the last time you had a data center event, "and how long were you offline, "and what did that really mean for your business? "What's the cost of downtime for you?" And everything we build at an application level and a software level really rests on an infrastructure foundation, right? Infrastructure is the foundation of your data center and the foundation of your IT, and so infrastructure does matter in the sense that, as Cory said, as you build mission critical platforms on it the infrastructure needs to be highly reliable, highly available, and trusted, and that's what we really focus on bringing. And as applications like Splunk evolve more into that mainstream world, they need to be built on that mission critical, reliable, managed infrastructure, right? It's one thing for infrastructure development, and this kind of happens in the history of IT, as well. It happened in client server back in the day. You know, new applications ... Even the web environment I remember a company was running, one of my clients was running a web server under their secretary's desk, and she was administering in half time. You would never have a large company doing that. >> They'd be back up (mumbles). Before you leave. >> As it becomes more important it becomes more central, but also it becomes more important to centrally manage those, right? I'm a 15 year storage veteran, for good or for worse, and what we really sell in storage is selling centralized management of that storage. That's the value that we bring from centralized infrastructure versus a bunch of servers that are sitting distributed around the environment under someone's desk is that centralized management, the ability to share the resources across them, the ability to take one down while the others keep running, shift that workload over and shift it back. And that's what we can do with our Ready Systems. We can bring that level of shared management, shared performance management, to the Splunk world. >> I'll tell you, one of the things that we talked about, we talked about in a number of sessions this week, is application owners, specifically the folks that are here at this conference, need to understand that when they decide to make changes at the application level, whether they like infrastructure or they think it's valuable or not, what they need to understand is that there are impacts, and that if you look at the exciting things that were announced today around Enterprise Security updates, right? Enterprise Security is an interesting app from Splunk, but if a customer goes from just having Splunk Enterprise to running Enterprise Security as a premium application, there's significant downstream impacts on infrastructure that if the application team doesn't account for they can basically put themselves in a corner from a performance and a capacity perspective that can cause serious problems and slow down the business outcome that they're trying to achieve because they didn't think about the infrastructure impacts. >> Well, and what they want really is they want infrastructure that they can code, right? And we talked about this at VMworld we were talking about off camera that cloud model, bringing that cloud model to your data as oppose to trying to force your business into the cloud. So what about Ready Systems mimics that cloud model? Is it a cloud like infrastructure? Wondering if you could talk-- >> Yeah, I think it's that cloud like experience. Because we know we're in a multi cloud world, right? Cloud is not a place, cloud is an operating model, right? And so I think that the Ready Systems specifically provides a couple of things that are that cloud like experience, which is simple ordering and configuration and consumption that is aligned to the application, right? So we actually align the sizing of the system to the license size and the expected experience that this one customer would have so they get that very curated bespoke system that's designed specifically for them, but in a very easy to consume fashion that's also validated by the software vendor, in this case Splunk, that they say, "These are known good configurations "that you will be successful with." So we give customers that comfort that, "Hey, this is a proven way "to deploy this application successfully, "and you don't have to go through "a significant architecture design concept "to get to that cloud like experience." Then you layer in the fact that what makes up the Ready System, which is it is a platform powered by, in the VxRail case powered by VMware, right, ESX and vSAN, which obviously if you look at any of the cloud providers everything is virtualized at the end of the day for the most part, or at least most of the environments are. And so we give, and VMware has been focused on that for years and years of giving that cloud like experience to their customers. >> You talk about, you mentioned selling, sort of reseller, you've got this partnership growing, you're a customer. So, you have all these hats, right, and connections with Splunk. What does that do for you you think just in general? What kind of value do you put on that having these multiple perspectives to how they operate whether it's in your environment or what you're doing for your customers using their insights? >> Yeah, I think at the end of the day we're here to make it simpler for customers. So if we do the work, and we invest the time and energy and resources in this partnership, and we go do the validation, we do the joint engineering, we do the joint certification, that's work that customers don't have to do, and that's value that we can deliver to them that whatever reason they buy Splunk for whatever workload or business outcome they're trying to achieve, we accelerate it. That's one of the biggest values, right? And then you look at who do they interact with in the field? Well, it's engineers from our awesome presales team from around the world that we've actually trained in Splunk. So we have now north of 25 folks that have Splunk SE certifications that are actually Dell EMC employees that are out working with Splunk customers to build platforms and achieve that value very, very quickly. And then them understanding that, "Oh, by the way, Dell EMC is also a user of Splunk, "a great customer of Splunk "and a number of interesting use cases "that we're actually replatforming now "and drinking our own Kool Aid so to say," that I think it just lends credibility to it. And that's a lot of the reason why we've made the investments in being part of this awesome show, but also in doing things like providing the applications. So we actually have four apps in Splunkbase that are available to monitor Dell EMC platforms using Splunk. So I think customers just get a wholistic experience that they've got a technology partner that wants to see them be successful deploying Splunk. >> I wonder if we could talk about stacks, because I've heard Chad Sack-edge talk about stack wars, tongue and cheek, but his point is that customers have to make bets. You've heard him talk about this. You've got the cloud stacks, whether it's Azure or AWS or Google. Obviously VMware has a prominent stack, maybe the most prominent stack. And there's still the open source, whether it's Hadoop or OpenStack. Should we be thinking about the Splunk stack? Is that emerging as a stack, or is it a combination of Splunk and these other? >> You know, we actually had that conversation today with some of the partner engineering team, and I don't know that I would today. I think Splunk continues to be, it's its own application in many cases. And I actually think that a lot of what Splunk is about is actually making sure that those stacks all work. So there was even announcements made today about a new app. So they have a new app for Pivotal Cloud Foundry, right? So if you think about stacks for application development, if you're going to hit push on a new application you're going to need to monitor it. Splunk is one of those things that persistent. The data is persistent. You want to keep large amounts of data for long periods of time so that you can build your models, understand what's really going on in the background, but then you need that real time reporting of, "Hey, if I hit push on a Cloud Foundry app "and all of a sudden I have an impact "to the service that's underlying it "because there's some microservice that gets broken, "if I don't have that monitoring platform "that can tell me that and correlate that event "and give me the guidance to not only alert against it "but actually go investigate it and act against it, "I'm in trouble." The stacks, I think many of them have their own monitoring capabilities, but I think Splunk has proven it that they are invested in being the monitoring and the data fabric that I think is wanting to help all the stacks be successful. So I don't necessarily put it in the stack. And I kind of don't put Hadoop in its own stack, either, because I think at the end of the day Hadoop needs a stack for deployment models. So you may see it go from a physical construct of being, a bit trying to be its own software that controls the underlying hardware, but I think you're seeing abstraction layers happen everywhere. They're containerizing Hadoop now. Virtualization of Hadoop is legit. Most of the big cloud providers talk about the decoupling of compute from storage in Hadoop for persistent and transient clusters. So I think the stacks will be interesting for application development, and applications like Splunk will be one of two things. They'll either consume one of those stacks for deployment or they'll be a standalone monitoring tool that makes us successful. >> So you don't see in the near term anyway Splunk becoming an application development platform the way that a lot of the-- >> Cory: They may have visions of it. That's not, yeah. >> They haven't laid that out there. It's something that we've been bounding around here. >> Yeah, I think it's interesting. Again, I think it goes back to .. Because the flexibility in what you can do with Splunk. I mean we've developed some of our own applications to help monitor Dell EMC storage platforms, and that's, it's interesting. But in terms of building what we'd I guess we'd consider like traditional seven factor app development, I don't know that it provides it. >> Yeah, well it's interesting because, I'm noodling here, Doug Merritt said, "Hey, we think we're going to be the next five billion, "10 billion, 20 billion dollar ecosystem slash company," and so you start to wonder, "Okay, how does that TAM grow to that point? That's one avenue that we considered. I want to talk about the anatomy of a transaction and how that's evolved. Colin, you mentioned Client Server, and you think about data rich applications going from sort of systems of record and the transactions associated with that. And while there were many going to Client Server and HTTP, and then now mobile apps really escalated that. And now with containers, with microservices, the amount of data and the complexity of transactions is greater and greater and greater. As a technologist, I wonder if you could sort of add some color to that. >> Yeah, I think as we kind of go down a path of application stacks are interesting, but at the end of the day we're still delivering a service, right? At the end of the day it's always about how do I deliver service, whether it's a business service, it's a mobile application, which is a service where I could get closer to my customer, I could transact business with them on a different model, I think all of it ... Because everything has gone digital, everything we do is digital, you're seeing more and more machines get created, there's more and more IP addressed devices out there on the planet that are creating data, and this machine generated data deluge that we're under right now it ain't slowing down, right? And so as we create these additional devices, somebody has got to make sense of this stuff. And if you listen to a lot of the analysts they talk about machine data is the most target rich in terms of business value, and it's their fastest growing. And it's now at a scale because we've now created so many devices that are creating their own logs, creating their own transactional data, right, there's just not that many tools that out of the box make it simple to collect the data, search the data, and derive value from it in the way that Splunk does. You can get to a lot of the things that Splunk can deliver from an outcome other ways with other platforms, but the simplicity and the ability to do it with a platform that out of the box does it and has a vibrant community of folks that will help you get there, it's a pretty big deal. So I think it's, you know, it's interesting. I don't know, like under the covers microservices are certainly interesting. They're still services. They're just smaller and packaged slightly differently and shared in a different way. >> And a lot more of them. >> Yeah, and scaled differently, right? And I totally get that, but at the end of the day we're still from a Splunk perspective and from a data perspective, we've still got to make sense of all of it. >> Right, well, I think the difference is just the amount of data. You talked about kind of new computing models, serverless sort of, stateless, IoT coming into play. It's just the data curve is reshaping. >> Well, it's not just the amount of data, it's the number of sources. The data is exploding, but also, as Cory mentioned, it's exploding because it's coming from so many places. Your refrigerator can generate data for you now, right? Every single ... Everything that generates Internet, anything doing anything now really has a microprocessor in it. I don't know if you guys saw my escape room at VMworld. There were 12 microprocessors running this escape room. So one of the things we played about doing was bring it here and trying to Splunk the escape room to actually see real time what the data was doing. And we weren't able to ship it back from Barcelona in time, but it would've been interesting to see, because you can see just the centers that are in that room real time and being able to correlate all that. And that's the value of Splunk is being able to pull that from those disparate sources altogether and give you those analytics. >> Yeah, it's funny you talk about an IoT use case. So we've got these... Our partner, who's a joint partner of both Dell EMC and Splunk, we actually have these Misfit devices that are activity trackers. And we're actually-- >> Misfit device? >> Misfit. Yeah, it's a brand. >> John: Love it. >> It's fitting, I think. But we have these devices that we gave away to a number of the attendees here, and we actually asked them if they're willing to participate. They can actually use the app on your phone to grab the data. And by simply going to a website they can allow us to pull the data from their device about their activity, about their sleep. And so we actually have in our booth and in Arrow's booth we're Splunking Conf and it's called How Happy is Conf? And so you can actually see Splunk running, and by the way, it's running in Arrow's lab. It's running on top of Dell EMC infrastructure designed for Splunk. You can actually see us Splunking how happy conf attendees are. And we're measuring happiness by their sleep. How much sleep-- >> John: Sleep quality and-- >> The exercise, the number of steps, right? So we have a little battle going between-- >> Is more sleep or less sleep happy? >> Are consumption behaviors also tracked on that? I just want to know. I'm curious. >> It's voluntary. You'd have to provide that. >> Alright, because that's another measure of happiness. >> It certainly is. But it's just a great use case where we talk about IoT and the number of sources of data that Splunk as a platform ... It's very, very simple to deploy that platform, have a web service that's able to pull that data from an API from a platform that's not ours, right, but bring that data into our environment, use Splunk to ingest and index that data, then actually create some interesting dashboards. It's a real world use case, right? Now, how much people really want to (mumbles) Splunk health devices we'll determine, but in the IoT context it's an absolute analog for what a lot of organizations are trying to do. >> Interesting, good stuff. Gentlemen, thanks for being with us. We appreciate that. Cory, it's probably not the real deal, but as close as I'm going to go. Good luck with your session. We appreciate the time to both of you, and you and your Misfit. Back with more here on theCUBE coming up in just a bit here in Washington D.C. (techno music)

>> Announcer: Live from Washington, D.C., it's theCUBE covering AWS Public Sector Summit 2017. Brought to you by Amazon Web Services and it's partner ecosystem. >> Welcome back, live here on theCUBE along with John Furrier, I'm John Walls. Welcome to AWS Public Sector Summit 2017. Again, live from Washington, D.C., your nation's capital, our nation's capital. With us now is our host for the week, puts on one heck of a show, I'm want to tell you, 10,000 strong here, jammed into the Washington Convention Center, Theresa Carlson from World Wide Public Sector. Nice to have you here, Theresa. >> Hi, good afternoon. >> Thanks for joining us. >> Love theCUBE and thank you for being here with us today. >> Absolutely. >> All week in fact. >> It's been great, it really has. Let's just talk about the show first off. Way back, six years ago, we could probably get everybody there jammed into our little area here, just about I think. >> Pretty much. >> Hard to do today. >> That's right. >> How do you feel about when you've seen this kind of growth not only of the show, but in your sector in general? >> I think at AWS we're humbled and excited and, on a personal level because I was sort of given the charge of go create this Public Sector business world-wide, I'm blown away, I pinch myself every time because you did hear my story. The first event, we had about 50 people in the basement of some hotel. And then, we're like, okay. And today, 10,000 people. Last year we had it at the Marriott Wardman Park and we shut down Connecticut Avenue so we knew we needed to make a change. (laughing) But it's great, this is really about our customers and partners. This is really for them. It's for them to make connections, share, and the whole theme of this is superheroes and they are our superheroes. >> One of the heroes you had on the stage today, John Edwards from the CIA, one of your poster-children if you will for great success and that kind of collaboration, said something to the effect of quote, "The best decision we ever made at the CIA "was engaging with AWS in that partnership." When you hear something like that from such a treasured partner, you got to feel pretty good. >> You just have to drop the microphone, boom, and you're sort of done. They are doing amazing work and their innovation levels are really leading, I would say, in the US Public Sector for sure and also, not just in US Public Sector but around the world. Their efforts of what they're doing and the scale and reach at which they're doing it so that's pretty cool. >> John, you've talked about the CIA moment, I'd like to hear the story, share with Theresa. >> Oh, you're going to steal my thunder here? >> No, I'm setting you up. That's what a good partner does. It's all yours. >> Well, John, we've talked multiple times already so I'll say it for the third time. The shot heard around the cloud was my definition of seminal moment, in big mega-trends there's always a moment. It was when Obama tweeted, Twitter grew, plane landing on the Hudson, there's always a seminal moment in major trends that make or break companies. For you guys, it was the CIA. Since then, it's just been a massive growth for you guys. That deal was interesting because it validated Shadow IT, validated the cloud, and it also unseated IBM, the behemoth sales organization that owned the account. In a way, a lot of things lined up. Take us through what's happened then, and since then to now. >> Well, you saw between yesterday at Werner Vogels' keynote and my keynote this morning, just the breadth and depth of the type of customers we have. Everything from the UK government, GCHQ, the Department of Justice with the IT in the UK, to the centers for Medicare for HHS, to amazing educational companies, Cal. Polytech., Australian Tax Office. That's just the breadth and depth of the type of customers we have and all of their stories were impactful, every story is impactful in their own way and across whatever sector they have. That really just tells you that the type of workloads that people are running has evolved because I remember in the early days, when you and I first talked, we talked about what are the kind of workloads and we were talking a little bit about website hosting. That's, of course, really evolved into things like machine learning, artificial intelligence, a massive scale of applications. >> Five or six years ago when we first chatted at re:Invent, it's interesting 'cause now this is the size of re:Invent what it was then so you're on a same trajectory from a show size. Again, validation to the growth in Public Sector. But I was complimenting you on our opening today, saying that you're tenacious because we've talked early days, it was a slog in the early days to get going in the cloud, you were knocking on a lot of doors, convincing people, hey, the future's going to look his way and I don't want to say they slammed the proverbial door in your face but it was more of, woah, they don't believe the cloud is ever going to happen for the government. Share some of those stories because now, looking back, obviously the world has changed. >> It has and, in fact, it's changed in many aspects of it, from policy makers, which I think would be great for you all to have on here sometime to get their perspective on cloud, but policy makers who are now thinking about, we just had a new modernization of IT mandate come out in the US Federal Government where they're going to give millions and millions of dollars toward the modernization of IT for US Government agencies which is going to be huge. That's the first time that's ever happened. To an executive order around cyber-security which is pretty much mandated to look at cloud and how you use it. You're seeing thing like that to even how grants are given where it used to be an old-school model of hardware only to now use cloud. Those ideas and aspects of how individuals are using IT but also just the procurements that are coming out. The buying vehicles that you're seeing come out of government, almost all of them have cloud now. >> John and I were talking about D.C. and the political climate. Obviously, we always talk about it on my show, comment on that. But, interesting, theCUBE, we could do damage here in D.C.. So much target-rich environment for content but more than ever, to me, is the tech scene here is really intrinsically different. For example, this is not a shiny new toy kind of trend, it is a fundamental transformation of the business model. What's interesting to me is, again, since the CIA shot heard around the cloud moment, you've seen a real shift in operating model. So the question I have for you, Theresa, if you can comment on this is: how has that changed? How has the procuring of technology changed? How has he human side of it changed? Because people want to do a good job, they're just on minicomputers and mainframes from the old days with small incremental improvement over the years in IT but now to a fundamental, agile, there's going to be more apps, more action. >> You said something really important just a moment ago, this is a different kind of group than you'll get in Silicon Valley and it is but it's very enterprise. Everybody you see here, every project they work on, we're talking DoD, the enterprise of enterprises. They have really challenging and tough problems to solve every day. How that's changed, in the old days here in government, they know how to write acquisitions for a missile or a tank or something really big in IT. What's changing is their ability to write acquisitions for agile IT, things like cloud utility based models, moving fast, flywheel approach to IT acquisitions. That's what's changing, that kind of acquisition model. Also, you're seeing the system integrator community here change. Where they were, what I call, body shops to do a lot of these projects, they're having to evolve their IT skills, they're getting much more certified in areas of AWS, at the system admin to certified solution architects at the highest level, to really roll these projects out. So training, education, the type of acquisition, and how they're doing it. >> What happened in terms of paradigm shift, mindset? Something had to happen 'cause you brought a vision to the table but somebody had to buy it. Usually, when we talk about legacy systems, it was a legacy mindset too, resistant, reluctant, cautious, all those things. >> Theresa: Well, everything gets thrown out. >> What happened? Where did it tip the other way? Where did it go? >> I think, over time, it's different parts of the government but culture is the hardest thing to, always, change. Other elements of any changes, you get there, but culture is fundamentally the hardest thing. You're seeing that. You've always heard us say, you can't fight gravity, and cloud is the new normal. That's for the whole culture. People are like, I cannot do my project anymore without the use of cloud computing. >> We also have a saying, you can't fight fashion either, and sometimes being in fashion is what the trends are going on. So I got to ask you, what is the fashion statement in cloud these days with your customers? Is it, you mentioned there, moving much down in the workload, is it multi-cloud? Is it analytics? Where's the fashionable, cool action right now? >> I think, here, right now, the cool thing that people really are talking about are artificial intelligence and machine learning, how they take advantage of that. You heard a lot about recognition yesterday, Poly and Lex, these new tools how they are so differentiating anything that they can possibly develop quickly. It's those kind of tools that really we're hearing and of course, IOT for state and local is a big deal. >> I got to ask you the hard question, I always ask Andy a hard question too, if he's watching, you're going to get this one probably at re:Invent. Amazon is a devops culture, you ship code fast and you make all these updates and it's moving very, very fast. One of the things that you guys have done well, but I still think you need some work to do in terms of critical analysis, is getting the releases out that are on public cloud into the GovCloud. You guys have shortened that down to less than a year on most things. You got the east region now rolled out so full disaster recovery but government has always been lagging behind most commercial. How are you guys shrinking that window? When do you see the day when push button commercial, GovCloud are all lockstep and pushing code to both clouds? >> We could do that today but there's a couple of big differentiators that are important for the GovCloud. That is it requires US citizenship, which as you know, we've talked about the challenges of technology and skills. That's just out there, right? At Amazon Web Services, we're a very diverse company, a group of individuals that do our coding and development, and not all of them are US citizens. So for these two clouds, you have to be a US citizen so that is an inhibitor. >> In terms of developers? In terms of building the product? >> Not building but the management aspect. Because of their design, we have multiple individuals managing multiple clouds, right? Now, with us, it's about getting that scale going, that flywheel for us. >> So now it's going to be managed in the USA versus made in the USA with everything as a service. >> Yeah, it is. For us, it's about making sure, number one, we can roll them out, but secondly, we do not want to roll services into those clouds unless they are critical. We are moving a lot faster, we rolled in a lot more services, and the other cool thing is we're starting to do some unique things for our GovCloud regions which, maybe the next time, we can talk a little bit more about those things. >> Final question for me, and let John jump in, the CIA has got this devops factory thing, I want you to talk about it because I think it points to the trend that's encouraging to me at least 'cause I'm skeptical on government, as you know. But this is a full transformation shift on how they do development. Talk about these 4000 developers that got rid of their development workstations, are now doing cloud, and the question is, who else is doing it? Is this a trend that you see happening across other agencies? >> The reason that's really important, I know you know, in the old-school model, you waited forever to provision anything, even just to do development, and you heard John talk about that. That's what he meant on this sort of workstation, this long period of time it took for them to do any kind of development. Now, what they do is they just use any move they have and they go and they provision the cloud like that. Then, they can also not just do that, they can create armies of cores or Amazon machine images so they have super-repeatable tools. Think about that. When you have these super-repeatable tools sitting in the cloud, that you can just pull down these machine images and begin to create both code and development and build off those building blocks, you move so much faster than you did in the past. So that's sort of a big trend, I would say they're definitely leading it. But other key groups are NASA, HHS, Department of Justice. Those are some of the key, big groups that we're seeing really do a lot changes in their dev. >> I got to ask you about the-- >> Oh, I have to say DHS, also DHS on customs and border patrols, they're doing the same, really innovators. >> One of the things that's happening which I'm intrigued by is the whole digital transformation in our culture, right, society. Certainly, the Federal Government wants to take care of the civil liberties of the citizens. So it's not a privacy question, it's more about where smart cities is going. We're starting to see, I call, the digital parks, if you will, where you're starting to see a digital park go into Yosemite and camping out and using pristine resources and enjoying them. There's a demand for citizens to democratize resources available to them, supercomputing or datasets, what's your philosophy on that? What is Amazon doing to facilitate and accelerate the citizen's value of technology so it can be in the hands of anyone? >> I love that question because I'll tell you, at the heart of our business is what we call citizen service, paving the way for disruptive innovation, making the world a better place. That's through citizen's services and they're access. For us, we have multiple things. Everything from our dataset program, where we fund multiple datasets that we put up on the cloud and let everybody take advantage of them, from the individual student to the researcher, for no fee. >> John F.: You pick up the cost on that? >> We do, we fund, we put those datasets in completely, we allow them to go and explore and use. The only time they would ever pay is if they go off and start creating their own systems. The most highly curated datasets up there right now are pretty much on AWS. You heard me talk about the earth, through AWS Earth that we have that shows the earth. We have weather datasets, cancer datasets, we're working with so many groups, genomic, phenotypes, genomes of rice, the rice genome that we've done. >> So this is something that you see that you're behind, >> Oh, completely. >> you're passionate about and will continue to do? >> Because you never know when that individual student or small community school is out there and they can access tools that they never could've accessed before. The training and education, that creativity of the mind, we need to open that up to everybody and we fundamentally believe that cloud is a huge opportunity for that. You heard me tell the 1000 genomes story in the past of where took that cancer dataset or that genome dataset from NIH, put it into AWS for the first time, the first week we put it up we had 3200 new researchers crowdsource on that dataset. That was the first time, that I know of, that anyone had put up a major dataset for researchers. >> And the scale, certainly, is a great resource. And smart cities is an interesting area. I want to get your thoughts on your relationship with Intel. They have 5G coming out, they have a full network transformation, you're going to have autonomous vehicles out there, you're going to have all kinds of digital. How are you guys planning on powering the cloud and what's the role that Intel will play with you guys in the relationship? >> Of course, serverless computing comes into play significantly in areas like that because you want to create efficiencies, even in the cloud, we're all about that. People have always said, oh, AWS won't do that 'cause that's disrupting themselves. We're okay with disrupting ourselves if it's the right thing. We also don't want to hog resourcing of these tools that aren't necessary. So when it comes to devices like that and IOT, you need very efficient computing and you need tools that allow that efficient computing to both scale but not over-resource things. You'll see us continue to have models like that around IOT, or lambda, or serverless computing and how we access and make sure that those resources are used appropriately. >> We're almost out of time so I'd like to shift over if we can. Really impressed with the NGO work, the non-profit work as well and your work in the education space. Just talk about the nuance, differences between working with those particular constituents in the customer base, what you've learned and the kind of work you're providing in those silos right now. >> They are amazing, they are so frugal with their resources and it makes you hungry to really want to go out and help their mission because what you will find when you go meet with a lot of these not-for-profits, they are doing some of the most amazing work that even many people have really not heard of and they're being so frugal with how they resource and drive IT. There's a program called Feed the World and I met the developer of this and it's like two people. They've fed millions of people around the world with like three developers and creating an app and doing great work. To everything from like the American Heart Association that has a mission, literally, of stopping heart disease which is our number one killer around the world. When you meet them and you see the things they're doing and how they are using cloud computing to change and forward their mission. You heard us talk about human trafficking, it's a horrible, misunderstood environment out there that more of us need to be informed on and help with but computing can be a complete differentiator for them, cloud computing. We give millions of dollars of grants away, not just give away, we help them. We help them with the technical resourcing, how they're efficient, and we work really hard to try to help forward their mission and get the word out. It's humbling and it's really nice to feel that you're not only doing things for big governments but you also can help that individual not-for-profit that has a mission that's really important to not only them but groups in the world. >> It's a different level of citizen service, right? I mean, ocean conservancy this morning, talking about that and tidal change. >> What's the biggest thing that, in your mind, personal question, obviously you've been through from the beginning to now, a lot more growth ahead of you. I'm speculating that AWS Public Sector, although you won't disclose the numbers, I'll find a number out there. It's big, you guys could run the table and take a big share, similar to what you've done with startup and now enterprise market. Do you have a pinch-me moment where you go, where are we? Where are you on that spectrum of self-awareness of what's actually happening to you and this world and your team? In Public Sector, we operate just like all of AWS and all of Amazon. We really have treated this business like a startup and I create new teams just like everybody else does. I make them frugal and small and I say go do this. I will tell you, I don't even think about it because we are just scratching the surface, we are just getting going, and today we have customers in 155 countries and I have employees in about 25 countries now. Seven years ago, that was not the case. When you're moving that fast, you know that you're just getting going and that you have so much more that you can do to help your customers and create a partner ecosystem. It's a mission for us, it really is a mission and my team and myself are really excited, out there every day working to support our customers, to really grow and get them moving faster. We sort of keep pushing them to go faster. We have a long way to go and maybe ask me five years from now, we'll see. >> How about next year? We'll come back, we'll ask you again next year. >> Yeah, maybe I'll know more next year. >> John W.: Theresa, thank you for the time, very generous with your time. I know you have a big schedule over the course of this week so thank you for being here with us once again on theCUBE. >> Thank you. >> Many time CUBE alum, Theresa Carlson from AWS. Back with more here from the AWS Public Sector Summit 2017, Washington, D.C. right after this. (electronic music)

>> Narrator: Covering Data Works Summit Europe 2017 brought to you by Horton Works. (uptempo, energetic music) >> Okay, welcome back everyone. We're here live in Germany at Munich for DataWorks Summit 2017, formerly Hadoop Summit. I'm John Furrier, my co-host Dave Vellante. Our next guest is Mike Merritt-Holmes, is senior Vice President of Global Services Strategy at Think Big, a Teradata company, formerly the co-founder of the Big Data Partnership merged in with Think Big and Teradata. Mike, welcome to The Cube. >> Mike: Thanks for having me. >> Great having an entrepreneur on, you're the co-founder, which means you've got that entrepreneurial blood, and I got to ask you, you know, you're in the big data space, you got to be pretty pumped by all the hype right now around AI because that certainly gives a lot of that extra, extra steroid of recognition. People love AI it gives a face to it, and certainly IOT is booming as well, Internet of Things, but big data's cruising along. >> I mean it's a great place to be. The train is certainly going very, very quickly right now. But the thing for us is, we've been doing data science and AI and trying to build business outcomes, and value for businesses for a long time. It's just great now to see this really, the data science and AI both were really starting to take effect and so companies are starting to understand it and really starting to really want to embrace it which is amazing. >> It's inspirational too, I mean I have a bunch of kids in my family, some are in college and some are in high school, even the younger generation are getting jazzed up on just software, right, but the big data stuffs been cruising along now. It's been a good, decade now of really solid DevOps culture, cloud now accelerating, but now the customers are forcing the vendors to be very deliberate in delivering great product, because the demand (chuckling) for real time, the demand for more stuff, is at an all time high. Can you elaborate your thoughts on, your reaction to what customers are doing, because they're the ones driving everyone, not to create friction, to create simplicity. >> Yeah, and you know, our customers are global organizations, trying to leverage this kind of technology, and they are, you know, doing an awesome amount of stuff right now to try to move them from, effectively, a step change in their business, whether it's, kind of, shipping companies doing preventive asset maintenance, or whether it's retailers looking to target customers in a more personalized way, or really understand who their customers are, where they come from, they're leveraging all those technologies, and really what they're doing is pushing the boundaries of all of them, and putting more demands on all of the vendors in the space to say, we want to do this quicker, faster, but more easily as well. >> And then the things that you're talking about, I want to get your thoughts on, because this is the conversation that you're having with customers, I want to extract is, have those kind of data-driven mindset questions, have come out the hype of the Hadoob. So, I mean we've been on a hype cycle for awhile, but now its back to reality. Where are we with the customer conversations, and, from your stand point, what are they working on? I mean, is it mostly IT conversation? Is it a frontoffice conversation? Is it a blend of both? Because, you know, data science kind of threads both sides of the fence there. >> Yeah, I mean certainly you can't do big data without IT being involved, but since the start, I mean, we've always been engaged with the business, it's always been about business outcome, because you bring data into a platform, you provide all this data science capability, but unless you actually find ROI from that, then there's no point, because you want to be moving the business forward, so it's always been about business engagement, but part of that has always been also about helping them to change their mindset. I don't want a report, I want to understand why you look at that report and what's the thing you're looking for, so we can start to identify that for you quicker. >> What's the coolest conversation you've been in, over the past year? >> Uh, I mean, I can't go into too much details, but I've had some amazing conversations with companies like Lego, for instance, they're an awesome company to work with. But when you start to see some of the things we're doing, we're doing some amazing object recognition with deep-learning in Japan. We're doing some ford analytics in the Nordics with deep-learning, we're doing some amazing stuff that's really pushing the boundaries, and when you start to put those deep-learning aspects into real world applications, and you start to see, customers clambering over to want to be part of that, it's a really exciting place to be. >> Let me just double-click on that for a second, because a lot of, the question I get a lot on The Cube, and certainly off-camera is, I want to do deep-learning, I want to do AI, I love machine learning, I hear, oh, it's finally coming to reality so people see it forming. How do they get started, what are some of the best practices of getting involved in deep-learning? Is it using open-source, obviously, is one avenue, but what advice would you give customers? >> From a deep-learning perspective, so I think first of all, I mean, a lot of the greatest deep-learning technologies, run open-source, as you rightly said, but I think actually there's a lot of tutorials and stuff on there, but really what you need is someone who has done it before, who knows where the pitfalls are, but also know when to use the right technology at the right time, and also to know around some of the aspects about whether using a deep-learning methodology is going to be the right approach for your business problem. Because a lot of companies are, like, we want to use this deep-learning thing, its amazing, but actually its not appropriate, necessarily, for the use case you're trying to draw from. >> It's the classic holy grail, where is it, if you don't know what you're looking for, it's hard to know when to apply it. >> And also, you've got to have enough data to utilize those methods as well, so. >> You hear a lot about the technical complexity associated with Hadoop specifically, but just ol' big data generally. I wonder if you could address that, in terms of what you're seeing, how people are dealing with that technical complexity but what other headwinds are there, in terms of adopting these new capabilities. >> Yeah, absolutely, so one of the challenges that we still see is that customers are struggling to leverage value from their platform, and normally that's because of the technical complexities. So we really, we introduced to the open-source world last month Kaylo, something you can download free of charge. It's completely open-source on the Apache license, and that really was about making it easier for customers to start to leverage the data on the platform, to self-serve injection onto that, and for data scientists to wrangle the data better. So, I think there's a real push right now about that next level up, if you like, in the technology stack to start to enable non-technical users to start to do interesting things on the platform directly, rather than asking someone to do it for them. And that, you know, we've had technologies in the PI space like Tableau, and, obviously, the (mumbling) did a data-warehouse solutions on Teradata that have been giving customers something, before and previously, but actually now they're asking for more, not just that, but more as well. And that's where we are starting to see the increases. >> So that's sort of operationalizing analytics as an example, what are some of the business complexities and challenges of actually doing that? >> That's a very good question, because, I think, when you find out great insight, and you go, wow you've built this algorithm, I've seen things I've never seen before, then the business wants to have that always on they want to know that it's that insight all the time is it changing, is it going up, is it going down do I need to change my business decisions? And doing that and making that operational means, not only just deploying it but also monitoring those models, being able to keep them up to date regularly, understanding whether those things are still accurate or not, because you don't want to be making business decisions, on algorithms that are now a bit stale. So, actually operationalizing it, is about building out an entire capability that's keeping these things accurate, online, and, therefore, there's still a bit of work to do, I think, actually in the marketplace still, around building out an operational capability. >> So you kind of got bottom-up, top-down. Bottom-up is the you know the Hadoop experiments, and then top-down is CXO saying we need to do big data. Have those two constituencies come together now, who's driving the bus? Are they aligned or is it still, sort of, a mess organizationally? >> Yeah, I mean, generally, in the organization, there's someone playing the Chief Data Officer, whether they have that as a title or a roll, ultimately someone is in charge of generating value from the data they have in the organization. But they can't do that with IT, and I think where we've seen companies struggle is where they've driven it from the bottom-up, and where they succeed is where they drive it from the top-down, because by driving it from the top-down, you really align what you're doing with the business and strategy that you have. So, the company strategy, and what you're trying to achieve, but ultimately, they both need to meet in the middle, and you can't do one without the other. >> And one of our practitioner friends, who's describing this situation in our office in Palo Alto, a couple of weeks ago. he said, you know, the challenge we have as an organization is, you've got top people saying alright, we're moving. And they start moving, the train goes, and then you've got kind of middle management, sort of behind them, and then you got the doers that are far behind, and aligning those is a huge challenge for this particular organization. How do you recommend organizations to address that alignment challenge, does Think Big have capabilities to help them through that, or is that, sort of, you got to call Accenture? >> In essence, our reason for being is to help with those kind of things, and, you know, whether it's right from the start, so, oh, my God, my Chief Data Officer or my CEO is saying we need to be doing this thing right now, come on, let's get on with it, and we help them to understand what does that mean, what are the use cases, how, where's the value going to come from, what's that architecting to look like, or whether its helping them to build out capability, in terms of data science or building out the cluster itself, and then managing that and providing training for staff. Our whole reason for being is supporting that transformation as a business, from, oh, my God, what do I do about this thing, to, I'm fully embracing it, I know what's going on, I'm enabling my business, and I'm completely comfortable with that world. >> There was a lot talk three, or four or five years ago, about the ROI of so-called big data initiatives, not being really, you know, there were edge cases which were huge ROI, but there was a lot of talk about not a lot of return. My question is, has that, first question, has that changed, are you starting to see much bigger phone numbers coming back where the executives are saying yeah, lets double down on this. >> Definitely, I'm definitely seeing that. I mean, I think it's fair to say that companies are a bit nervous about reporting their ROI around this stuff, in some cases, so there's more ROI out there than you necessarily see out in the public place, but-- >> Why is that? Because they don't want to expose to the competition, or they don't want to front run their earnings, or whatever it is? >> They're trying to get a competitive edge. The minute you start saying, we're doing this, their competitors have an opportunity to catch up. >> John: Very secretive. >> Yeah and I think, it's not necessarily about what they're doing, it's about keeping the edge over their customers, really, over their competitors. So, but what we're seeing is that many customers are getting a lot of ROI more recently because they're able to execute better, rather than being struggling with the IT problems, and even just recently, for instance, we had a customer of ours, the CEO phones us up and says, you know what, we've got this problem with our sales. We don't really know why this is going down, you know, in this country, in this part of the world, it's going up, in this country, it's going down, we don't know why, and that's making us very nervous. Could you come in and just get the data together, work out why it's happening, so that we can understand what it is. And we came in, and within weeks, we were able to give them a very good insight into exactly why that is, and they changed their strategy, moving forward, for the next year, to focus on addressing that problem, and that's really amazing ROI for a company to be able to get that insight. Now, we're working with them to operationalize that, so that particular insight is always available to them, and that's an example of how companies are now starting to see that ROI come through, and a lot of it is about being able to articulate the right business question, rather than trying to worry about reports. What is the business question I'm trying to solve or answer, and that's when you can start to see the ROI come through. >> Can you talk about the customer orientation when they get to that insight, because you mentioned earlier that they got used to the reports, and you mentioned visualization, Tableau, they become table states, once you get addicted to the visualization, you want to extract more insights so the pressure seems to be getting more insight. So, two questions, process gap around what they need to do process-wise, and then just organizational behavior. Are they there mentally, what are some of the criteria in your mind, in your experiments, with customers around the processes that they go through, and then organizational mindset. >> Yeah, so what I would say is, first of all, from an organizational mindset perspective, it's very important to start educating, not just the analysis team, but the entire business on what this whole machine-learning, big data thing is all about, and how to ask the right questions. So, really starting to think about the opportunities you have to move your business forward, rather than what you already know, and think forward rather than retrospective. So, the other thing we often have to teach people, as well, is that this isn't about what you can get from the data warehouse, or replacing your data warehouse or anything like that. It's about answering the right questions, with the right tools, and here is a whole set of tools that allow you to answer different questions that you couldn't before, so leverage them. So, that's very important, and so that mindset requires time actually, to transform business into that mindset, and a lot of commitment from the business to make that happen. >> So, mindset first, and then you look at the process, then you get to the product. >> Yep, so, and basically, once you have that mindset, you need to set up an engine that's going to run, and start to drive the ROI out, and the engine includes, you know, your technical folk, but also your business users, and that engine will then start to build up momentum. The momentum builds more interest, and, overtime, you start to get your entire business into using these tools. >> It kind of makes sense, just kind of riffing in real time here, so the product-gap conversation should probably come after you lay that out first, right? >> Totally, yeah, I mean, you don't choose a product before you know what you need to do with it. So, but actually often companies don't know what they need to do with it, because they've got the wrong mindset in the first place. And so part of the road map stuff that we do, that we have a road map offering, is about changing that mindset, and helping them to get through that first stage, where we start to put, articulate the right use cases, and that really is driving a lot of value for our customers. Because they start from the right place-- >> Sometimes we hear stories, like the product kind of gives them a blind spot, because they tend to go into, with a product mindset first, and that kind of gives them some baggage, if you will. >> Well, yeah, because you end up with a situation, where you go, you get a product in, and then you say what can we do with it. Or, in fact, what happens is the vendor will say, these are the things you could do, and they give you use cases. >> It constrains things, forecloses tons of opportunities, because you're stuck within a product mindset. >> Yeah, exactly that, and you're not, you don't want to be constrained. And that's why open-source, and the kind of ecosystem that we have within the big data space is so powerful, because there's so many different tools for different things but don't choose your tool until you know what you're trying to achieve. >> I have a market question, maybe you just give us opinion, caveat, if you like, it's sort of a global, macro view. When we started first looking at the big data market, we noticed right away the dominant portion of revenue was coming from services. Hardware was commodity, so, you know, maybe sort of less than you would, obviously, in a mainframe world, and open-source software has a smaller contribution, so services dominated, and, frankly, has continued to dominate, since the early days. Do you see that changing, or do you think those percentages, if you will, will stay relatively constant? >> Well, I think it will change over time, but not in the near future, for sure, there's too much advancement in the technology landscape for that to stop, so if you had a set of tools that weren't really evolving, becoming very mature, and that's what tools you had, ultimately, the skill sets around them start to grow, and it becomes much easier to develop stuff, and then companies start to build out industry- or solutions-specific stuff on top, and it makes it very easy to build products. When you have an ecosystem that's evolving, growing with the speed it is, you're constantly trying to keep up with that technology, and, therefore, services have to play an awful big part in making sure that you are using the right technology, at the right time, and so, for the near future, for certain, that won't change. >> Complexity is your friend. >> Yeah, absolutely. Well, you know, we live in a complex world, but we live and breathe this stuff, so what's complex to some is not to us, and that's why we add value, I guess. >> Mike Merritt-Holmes here inside The Cube with Teradata Think Big. Thanks for spending the time sharing your insights. >> Thank you for having me. >> Understand the organizational mindset, identify the process, then figure out the products. That's the insight here on The Cube, more coverage of Data Works Summit 2017, here in Germany after this short break. (upbeat electronic music)