(vibrant electronic music) >> Welcome back to SuperCloud2. You know, this event, and the Super Cloud initiative in general, it's an open industry-wide collaboration. Last August at SuperCloud22, we really honed in on the definition, which of course we've published. And there's this shared doc, which folks are still adding to and refining, in fact, just recently, Dr. Nelu Mihai added some critical points that really advanced some of the community's initial principles, and today at SuperCloud2, we're digging further into the topic with input from real world practitioners, and we're exploring that intersection of data, data mesh, and cloud, and importantly, the realities and challenges of deploying technology to drive new business capability, and I'm pleased to welcome Ash Naseer to the program. He's a Senior Director of Data Engineering at Warner Bros. Discovery. Ash, great to see you again, thanks so much for taking time with us. >> It's great to be back, these conversations are always very fun. >> I was so excited when we met last spring, I guess, so before we get started I wanted to play a clip from that conversation, it was June, it was at the Snowflake Summit in Las Vegas. And it's a comment that you made about your company but also data mesh. Guys, roll the clip. >> Yeah, so, when people think of Warner Bros., you always think of the movie studio. But we're more than that, right, I mean, you think of HBO, you think of TNT, you think of CNN. We have 30 plus brands in our portfolio, and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company, so that CNN can work at their own pace, you know, when there's election season, they can ingest their own data. And they don't have to bump up against, as an example, HBO, if Game of Thrones is goin' on. >> So-- Okay, so that's pretty interesting, so you've got these sort of different groups that have different data requirements inside of your organization. Now data mesh, it's a relatively new concept, so you're kind of ahead of the curve. So Ash, my question is, when you think about getting value from data, and how that's changed over the past decade, you've had pre-Hadoop, Hadoop, what do you see that's changed, now you got the cloud coming in, what's changed? What had to be sort of fixed? What's working now, and where do you see it going? >> Yeah, so I feel like in the last decade, we've gone through quite a maturity curve. I actually like to say that we're in the golden age of data, because the tools and technology in the data space, particularly and then broadly in the cloud, they allow us to do things that we couldn't do way back when, like you suggested, back in the Hadoop era or even before that. So there's certainly a lot of maturity, and a lot of technology that has come about. So in terms of the good, bad, and ugly, so let me kind of start with the good, right? In terms of bringing value from the data, I really feel like we're in this place where the folks that are charged with unlocking that value from the data, they're actually spending the majority of their time actually doing that. And what do I mean by that? If you think about it, 10 years ago, the data scientist was the person that was going to sort of solve all of the data problems in a company. But what happened was, companies asked these data scientists to come in and do a multitude of things. And what these data scientists found out was, they were spending most of their time on, really, data wrangling, and less on actually getting the value out of the data. And in the last decade or so, I feel like we've made the shift, and we realize that data engineering, data management, data governance, those are as important practices as data science, which is sort of getting the value out of the data. And so what that has done is, it has freed up the data scientist and the business analyst and the data analyst, and the BI expert, to really focus on how to get value out of the data, and spend less time wrangling data. So I really think that that's the good. In terms of the bad, I feel like, there's a lot of legacy data platforms out there, and I feel like there's going to be a time where we'll be in that hybrid mode. And then the ugly, I feel like, with all the data and all the technology, creates another problem of itself. Because most companies don't have arms around their data, and making sure that they know who's using the data, what they're using for, and how can the company leverage the collective intelligence. That is a bigger problem to solve today than 10 years ago. And that's where technologies like the data mesh come in. >> Yeah, so when I think of data mesh, and I say, you're an early practitioner of data mesh, you mentioned legacy technology, so the concept of data mesh is inclusive. In theory anyway, you're supposed to be including the legacy technologies. Whether it's a data lake or data warehouse or Oracle or Snowflake or whatever it is. And when you think about Jamak Dagani's principles, it's domain-centric ownership, data as product. And that creates challenges around self-serve infrastructure and automated governance, and then when you start to combine these different technologies. You got legacy, you got cloud. Everything's different. And so you have to figure out how to deal with that, so my question is, how have you dealt with that, and what role has the cloud played in solving those problems, in particular, that self-serve infrastructure, and that automated governance, and where are we in terms of solving that problem from a practitioner's standpoint? >> Yeah, I always like to say that data is a team sport, and we should sort of think of it as such, and that's, I feel like, the key of the data mesh concept, is treating it as a team sport. A lot of people ask me, they're like, "Oh hey, Ash, I've heard about this thing called data mesh. "Where can I buy one?" or, "what's the technology that I use to get a data mesh? And the reality is that there isn't one technology, you can't really buy a data mesh. It's really a way of life, it's how organizations decide to approach data, like I said, back to a team sport analogy, making sure that everyone has the seat on the table, making sure that we embrace the fact that we have a lot of data, we have a lot of data problems to solve. And the way we'll be successful is to make everyone inclusive. You know, you think about the old days, Data silos or shadow IT, some might call it. That's been around for decades. And what hasn't changed was this notion that, hey, everything needs to be sort of managed centrally. But with the cloud and with the technologies that we have today, we have the right technology and the tooling to democratize that data, and democratize not only just the access, but also sort of building building blocks and sort of taking building blocks which are relevant to your product or your business. And adding to the overall data mesh. We've got all that technology. The challenge is for us to really embrace it, and make sure that we implement it from an organizational standpoint. >> So, thinking about super cloud, there's a layer that lives above the clouds and adds value. And you think about your brands you got 30 brands, you mentioned shadow IT. If, let's say, one of those brands, HBO or TNT, whatever. They want to go, "Hey, we really like Google's analytics tools," and they maybe go off and build something, I don't know if that's even allowed, maybe it's not. But then you build this data mesh. My question is around multi-cloud, cross cloud, super cloud if you will. Is that a advantage for you as a practitioner, or does that just make things more complicated? >> I really love the idea of a multi-cloud. I think it's great, I think that it should have been the norm, not the exception, I feel like people talk about it as if it's the exception. That should have been the case. I will say, though, I feel like multi-cloud should evolve organically, so back to your point about some of these different brands, and, you know, different brands or different business units. Or even in a merger and acquisitions situation, where two different companies or multiple different companies come together with different technology stacks. You know, I feel like that's an organic evolution, and making sure that we use the concepts and the technologies around the multi-cloud to bring everyone together. That's where we need to be, and again, it talks to the fact that each of those business units and each of those groups have their own unique needs, and we need to make sure that we embrace that and we enable that, rather than stifling everything. Now where I have a little bit of a challenge with the multi-cloud is when technology leaders try to build it by design. So there's a notion there that, "Hey, you need to sort of diversify "and don't put all your eggs in one basket." And so we need to have this multi-cloud thing. I feel like that is just sort of creating more complexity where it doesn't need to be, we can all sort of simplify our lives, but where it evolves organically, absolutely, I think that's the right way to go. >> But, so Ash, if it evolves organically don't you need some kind of cloud interpreter, to create a common experience across clouds, does that exist today? What are your thoughts on that? >> There is a lot of technology that exists today, and that helps go between these different clouds, a lot of these sort of cloud agnostic technologies that you talked about, the Snowflakes and the Databricks and so forth of the world, they operate in multiple clouds, they operate in multiple regions, within a given cloud and multiple clouds. So they span all of that, and they have the tools and technology, so, I feel like the tooling is there. There does need to be more of an evolution around the tooling and I think the market's need are going to dictate that, I feel like the market is there, they're asking for it, so, there's definitely going to be that evolution, but the technology is there, I think just making sure that we embrace that and we sort of embrace that as a challenge and not try to sort of shut all of that down and box everything into one. >> What's the biggest challenge, is it governance or security? Or is it more like you're saying, adoption, cultural? >> I think it's a combination of cultural as well as governance. And so, the cultural side I've talked about, right, just making sure that we give these different teams a seat at the table, and they actually bring that technology into the mix. And we use the modern tools and technologies to make sure that everybody sort of plays nice together. That is definitely, we have ways to go there. But then, in terms of governance, that is another big problem that most companies are just starting to wrestle with. Because like I said, I mean, the data silos and shadow IT, that's been around there, right? The only difference is that we're now sort of bringing everything together in a cloud environment, the collective organization has access to that. And now we just realized, oh we have quite a data problem at our hands, so how do we sort of organize this data, make sure that the quality is there, the trust is there. When people look at that data, a lot of those questions are now coming to the forefront because everything is sort of so transparent with the cloud, right? And so I feel like, again, putting in the right processes, and the right tooling to address that is going to be critical in the next years to come. >> Is sharing data across clouds, something that is valuable to you, or even within a single cloud, being able to share data. And my question is, not just within your organization, but even outside your organization, is that something that has sort of hit your radar or is it mature or is that something that really would add value to your business? >> Data sharing is huge, and again, this is another one of those things which isn't new. You know, I remember back in the '90s, when we had to share data externally, with our partners or our vendors, they used to physically send us stacks of these tapes, or physical media on some truck. And we've evolved since then, right, I mean, it went from that to sharing files online and so forth. But data sharing as a concept and as a concept which is now very frictionless, through these different technologies that we have today, that is very new. And that is something, like I said, it's always been going on. But that needs to be really embraced more as well. We as a company heavily leverage data sharing between our own different brands and business units, that helps us make that data mesh, so that when CNN, as an example, builds their own data model based on election data and the kinds of data that they need, compare that with other data in the rest of the company, sports, entertainment, and so forth and so on. Everyone has their unique data, but that data sharing capability brings it together wherever there is a need. So you think about having a Tiger Woods documentary, as an example, on HBO Max and making sure that you reach the audiences that are interested in golf and interested in sports and so forth, right? That all comes through the magic of data sharing, so, it's really critical, internally, for us. And then externally as well, because just understanding how our products are doing on our partners' networks and different distribution channels, that's important, and then just understanding how our consumers are consuming it off properties, right, I mean, we have brands that transcend just the screen, right? We have a lot of physical merchandise that you can buy in the store. So again, understanding who's buying the Batman action figures after the Batman movie was released, that's another critical insight. So it all gets enabled through data sharing, and something we rely heavily on. >> So I wanted to get your perspective on this. So I feel like the nirvana of data mesh is if I want to use Google BigQuery, an Oracle database, or a Microsoft database, or Snowflake, Databricks, Amazon, whatever. That that's a node on the mesh. And in the perfect world, you can share that data, it can be governed, I don't think we're quite there today, so. But within a platform, maybe it's within Google or within Amazon or within Snowflake or Databricks. If you're in that world, maybe even Oracle. You actually can do some levels of data sharing, maybe greater with some than others. Do you mandate as an organization that you have to use this particular data platform, or are you saying "Hey, we are architecting a data mesh for the future "where we believe the technology will support that," or maybe you've invented some technology that supports that today, can you help us understand that? >> Yeah, I always feel like mandate is a strong area, and it breeds the shadow IT and the data silos. So we don't mandate, we do make sure that there's a consistent set of governance rules, policies, and tooling that's there, so that everyone is on the same page. However, at the same time our focus is really operating in a federated way, that's been our solution, right? Is to make sure that we work within a common set of tooling, which may be different technologies, which in some cases may be different clouds. Although we're not that multi-cloud. So what we're trying to do is making sure that everyone who has that technology already built, as long as it sort of follows certain standards, it's modern, it has the capabilities that will eventually allow us to be successful and eventually allow for that data sharing, amongst those different nodes, as you put it. As long as that's the case, and as long as there's a governance layer, a master governance layer, where we know where all that data is and who has access to what and we can sort of be really confident about the quality of the data, as long as that case, our approach to that is really that federated approach. >> Sorry, did I hear you correctly, you're not multi-cloud today? >> Yeah, that's correct. There are certain spots where we use that, but by and large, we rely on a particular cloud, and that's just been, like I said, it's been the evolution, it was our evolution. We decided early on to focus on a single cloud, and that's the direction we've been going in. >> So, do you want to go to a multi-cloud, or, you mentioned organic before, if a business unit wants to go there, as long as they're adhering to those standards that you put out, maybe recommendations, that that's okay? I guess my question is, does that bring benefit to your business that you'd like to tap, or do you feel like it's not necessary? >> I'll go back to the point of, if it happens organically, we're going to be open about it. Obviously we'll have to look at every situations, not all clouds are created equal as well, so there's a number of different considerations. But by and large, when it happens organically, the key is time to value, right? How do you quickly bring those technologies in, as long as you could share the data, they're interconnected, they're secured, they're governed, we are confident on the quality, as long as those principles are met, we could definitely go in that direction. But by and large, we're sort of evolving in a singular direction, but even within a singular cloud, we're a global company. And we have audiences around the world, so making sure that even within a single cloud, those different regions interoperate as one, that's a bigger challenge that we're having to solve as well. >> Last question is kind of to the future of data and cloud and how it's going to evolve, do you see a day when companies like yours are increasingly going to be offering data, their software, services, and becoming more of a technology company, sort of pointing your tooling and your proprietary knowledge at the external world, as an opportunity, as a business opportunity? >> That's a very interesting concept, and I know companies have done that, and some of them have been extremely successful, I mean, Amazon is the biggest example that comes to mind, right-- >> Yeah. >> When they launched AWS, something that they had that expertise they had internally, and they offered it to the world as a product. But by and large, I think it's going to be far and few between, especially, it's going to be focused on companies that have technology as their DNA, or almost like in the technology sector, building technology. Most other companies have different markets that they are addressing. And in my opinion, a lot of these companies, what they're trying to do is really focus on the problems that we can solve for ourselves, I think there are more problems than we have people and expertise. So my guess is that most large companies, they're going to focus on solving their own problems. A few, like I said, more tech-focused companies, that would want to be in that business, would probably branch out, but by and large, I think companies will continue to focus on serving their customers and serving their own business. >> Alright, Ash, we're going to leave it there, Ash Naseer. Thank you so much for your perspectives, it was great to see you, I'm sure we'll see you face-to-face later on this year. >> This is great, thank you for having me. >> Ah, you're welcome, alright. Keep it right there for more great content from SuperCloud2. We'll be right back. (gentle percussive music)

hybrid work is the new reality according to the most recent survey data from enterprise technology research cios expect that 65 of their employees will work either as fully remote or in a hybrid model splitting time between remote and in office remote of course can be anywhere it could be home it could be at the beach overseas literally anywhere there's internet so it's no surprise that these same technology executives cite security as their number one priority well ahead of other critical technology initiatives including collaboration software cloud computing and analytics which round out the top four in the etr survey now as we've reported securing endpoints was important prior to the pandemic but the explosion in the past two plus years of remote work and corollary device usage has made the problem even more acute and let's face it managing sprawling i.t assets has always been a pain patch management for example has been a nagging concern for practitioners and with ransomware attacks on the rise it's critical that security teams harden it assets throughout their life cycle staying current and constantly staying on top of vulnerabilities within the threat surface welcome to this special program on the cube enable and secure the everywhere workplace brought to you by ivanti in this program we highlight key partnerships between avanti and its ecosystem to address critical problems faced by technology and security teams in our first segment we explore a collaboration between avanti and sentinel one where the two companies are teaming to simplify patch management my name is dave vellante and i'll be your host today and with me are nayaki nayar who's the president and chief product officer at avanti and nick warner president and security of the security group at sentinel one welcome naki and nick and hackie good to have you back in the cube great to see you guys thank you thank you dave uh really good to be back on cube uh i'm a veteran of cube so thank you for having us and um look forward to a great discussion today yeah you better thanks okay hey good nick nick good to have you on as well what do we need to know about this partnership please so uh if you look at uh we are super excited about this partnership nick thank you for joining us on this session today um when you look at ivanti ivanti has been a leader in two big segments uh we are a leader in unified endpoint management with the acquisition of mobileye now we have a holistic end-to-end management of all devices be it windows linux mac ios you name it right so we have that seamless single pane of glass to manage all devices but in addition to that we are also a leader in risk-based patch management um dave that's what we are very excited about this partnership with the with central one where now we can combine the strength we have in the risk-based patch management with central one's xdr platform and truly help address what i call the need of the hour with our customers for them to be able to detect uh vulnerabilities and being able to remediate them proactively remediate them right so that's what we are super excited about this partnership and nick would love to hand it over to you to talk about uh the partnership and the journey ahead of us thanks and you know from center one's perspective we see autonomous vulnerability assessment and remediation as really necessary given the evolution uh in the sophistication the volume and the ferocity of threats out there and what's really key is being able to remediate risks and machine speed and also identify vulnerability exposure in real time and you know if you look traditionally at uh vulnerability scanning and patch management they've really always been two separate things and when things are separate they take time between the two coordination communication what we're looking to do with our singularity xdr platform is holistically deliver one unified solution that can identify threats identify vulnerabilities and automatically and autonomously leverage patch management to much better protect our customers so maybe maybe that's why patch management is such a challenge for many organizations because as you described nick it's sort of a siloed from security and those worlds are coming together but maybe you guys could address the specific problems that you're trying to solve with this collaboration yeah so if you look at uh just in a holistic level uh dave today cyber crime is at catastrophic heights right and this is not just a cio or a cso issue this is a board issue every organization every enterprise is addressing this at the board level and when you double click on it one of the challenges that we have heard from our customers over and over again is the complexity and the manual processes that are in place for remediation or patching all their operating systems their applications their third party apps and that is where it's very very time consuming very complex very cumbersome and the question is how do we help them automate it right how do we help them remove those manual processes and autonomously intermediate right so which is where this partnership between ivanti and central one helps organizations to bring this autonomous nature to bring those proactive predictive capabilities to detect an issue prioritize that issue based on risk-based prioritization is what we call it and autonomously remediate that issue right so that's where uh this partnership really really uh helps our customers address the the top concerns they have in cyber crime or cyber security got it so prioritization automation nick maybe you could address what are the keys i mean you got to map vulnerabilities to software updates how do you make sure that your the patches there's not a big lag between your patch and and the known vulnerabilities and you've got this diverse set of you know i.t portfolio assets how do you manage all that it's a great question and i and i think really the number one uh issue around this topic is that security teams and it teams are facing a really daunting task of identifying all the time every day all the vulnerabilities in their ecosystem and the biggest problem with this is how do they get context and priority and i think what people have come to realize through the years of dealing with with patch management uh and vulnerability scanning is that patching without the context of what the possible impact or priority of that risk is really comes down to busy work and i think what's so important in a totally interconnected world with attacks happening at machine speed is being able to take that precious asset that we call time and make sure you properly prioritize that how we're doing it from sentinel one singularity xdr perspective is by leveraging autonomous threat information and being able to layer that against vulnerability information to properly view through that lens the highest priority threats and vulnerabilities that you need to patch and then using our single agent technology be able to autonomously remediate and patch those vulnerabilities whether or not it's on a mac a pc server a cloud workload and the beauty of our solution is it gives you proper clarity so you can see the impact of vulnerabilities each and every day in your environment and know that you're doing the right thing in the right order got it okay so the context gives you the risks profile allows you to prioritize and then of course you can you know remediate what else should we know about this this joint solution uh in terms of you know what it is how i engage any other detail on how it addresses the the problem specifically yeah so it's all about race against the time um uh dave when it's how we help our customers uh detect the vulnerability prioritize it and remediate it the attackers are able to weaponize those vulnerabilities and and have an attack right so it's really it's how we help our customers be a lot more proactive and predictive address those vulnerabilities versus um before the attackers really get access to it right so that's where our joint solution in fact i always say whatever edr with this edr or mdr or xdr the r portion of that r is very one he comes in our neurons for patch management or what we call neurons but risk based patch management combined with um central ones xdr is where we truly uh bring the combined solutions to to to life right so the r is where ivanti really plays a big part in uh in the joint solution yeah absolutely the response i mean people i think all agree you're going to get infiltrated that's how you respond to it you know the thing about this topic is when you make a business case a lot of times you'll go to the cfo and say hey if we don't do this we're going to be in big trouble and so it's this fear factor and i get that it's super important but but are there other measurements of success that that you you can share in other words how are customers going to determine the value of this joint solution so it's a mean time to repair let me go nick and then i'm sure you have your uh metrics and how you're measuring the success it's about how we can detect an issue and repair that issue it's reducing that mean time to repair as much as possible and making it as real-time as possible for our customers right that's where the true outcome through success and the metric that customers can track measure and continuously improve on nick you want to add to that for sure yeah you know you make some great great points niaki and what what i would add is um what sentinel one singularity platform is known for is automated and autonomous detection prevention and response and remediation across threats and if you look traditionally at patch management or vulnerability assessment they're typically deployed and run in point-of-time solutions what i mean by that is that they're scans and re-scans the way that advanced edr solutions and xdr solutions such as single one singularity platform work is we're constantly recording everything that's happening on all of your systems in real time and so what we do is literally eliminate the window of opportunity between a patch being uh needed a vulnerability being discovered and you knowing that you have that need for that vulnerability to be patched in your environment you don't have to wait for that 12 or 24-hour window to scan for vulnerabilities you will immediately know it in your network you'll also know the security implications of that vulnerability so you know when and how to prioritize and then furthermore you can take autonomous hatching measures against that so at the end of the day the name of the game in security is time and it's about reducing that window of opportunity for the adversaries for the threat actors and this is a epic leap forward in in doing that for our customers and that capability nick is a function of your powerful agent or is it architecture where's that come from that's a great question it's it's a combination of a couple of things the first is our agent technology which performs constant monitoring on every system every behavior every process running on all your systems live and in real time so this is not a batch process that that kicks up once a day this is always running in the background so the moment a new application is installed the moment a new application version is deployed we know about it we record it instantaneously so if you think about that and layer against getting best in class vulnerability information from a partner like avanti and then also being able to deploy patch management against that you can start to see how you're applying that in real time in your environment and the last thing i i'd like to add is because we're watching everything and then layering it against thread intel and context using our proprietary machine learning technology that that idea of being able to prioritize and escalate is critical because if you talk to security providers there's a couple different uh challenges that they're facing and i would say the top two are alert fatigue and then also human human power limitations and so no security team has enough people on their team and no security teams have an absence of alerts and so the fact that we can prioritize alerts surface the ones that are the most important give context to that and also save them precious hours of their personnel's time by being able to do this autonomously and automatically we're really killing two birds with one stone that's great there's the business case right there you just laid out some other things that we can measure right it all comes back to the data doesn't it we got to go but i'll give you the last word yeah i mean we are super excited about this partnership uh like nick said uh we believe in how we can help our customers discover all the assets we have they have um manage those assets but a big chunk of it is how we help them secure it right secure uh their devices the applications the data that's on those devices the end points and being able to provide an experience a service experience at the end of the day so that end users don't have to worry about securing you don't have to think about security it should be embedded it should be autonomous and it should be contactually personalized right so uh that's the journey we are on and uh thank you nick for this great partnership and look forward to a great journey ahead of us thank you yeah thanks to both of you nick appreciate it okay keep it right there after this quick break we're gonna be back to look at how ivanti is working with other partners to simplify and harden the anywhere workplace you're watching the cube your leader in enterprise and emerging tech coverage [Music] you

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit '22 live from Caesar's Forum in Las Vegas. I'm Lisa Martin, my cohost Dave Vellante, we've been here the last day and a half unpacking a lot of news, a lot of announcements, talking with customers and partners, and we have another great session coming for you next. We've got a customer and a partner talking tech and data mash. Please welcome Mitesh Shah, VP in market strategy at Elation. >> Great to be here. >> and Ash Naseer great, to have you, senior director of data engineering at Warner Brothers Discovery. Welcome guys. >> Thank you for having me. >> It's great to be back in person and to be able to really get to see and feel and touch this technology, isn't it? >> Yeah, it is. I mean two years or so. Yeah. Great to feel the energy in the conference center. >> Yeah. >> Snowflake was virtual, I think for two years and now it's great to kind of see the excitement firsthand. So it's wonderful. >> Th excitement, but also the boom and the number of customers and partners and people attending. They were saying the first, or the summit in 2019 had about 1900 attendees. And this is around 10,000. So a huge jump in a short time period. Talk a little bit about the Elation-Snowflake partnership and probably some of the acceleration that you guys have been experiencing as a Snowflake partner. >> Yeah. As a snowflake partner. I mean, Snowflake is an investor of us in Elation early last year, and we've been a partner for, for longer than that. And good news. We have been awarded Snowflake partner of the year for data governance, just earlier this week. And that's in fact, our second year in a row for winning that award. So, great news on that front as well. >> Repeat, congratulations. >> Repeat. Absolutely. And we're going to hope to make it a three-peat as well. And we've also been awarded industry competency badges in five different industries, those being financial services, healthcare, retail technology, and Median Telcom. >> Excellent. Okay. Going to right get into it. Data mesh. You guys actually have a data mesh and you've presented at the conference. So, take us back to the beginning. Why did you decide that you needed to implement something like data mesh? What was the impetus? >> Yeah. So when people think of Warner brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of HBO, you think of TNT, you think of CNN, we have 30 plus brands in our portfolio and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against as an example, HBO, if Game of Thrones is going on. >> So, okay. So the, the impetus was to serve those lines of business better. Actually, given that you've got these different brands, it was probably easier than most companies. Cause if you're, let's say you're a big financial services company, and now you have to decide who owns what. CNN owns its own data products, HBO. Now, do they decide within those different brands, how to distribute even further? Or is it really, how deep have you gone in that decentralization? >> That's a great question. It's a very close partnership, because there are a number of data sets, which are used by all the brands, right? You think about people browsing websites, right? You know, CNN has a website, Warner brothers has a website. So for us to ingest that data for each of the brands to ingest that data separately, that means five different ways of doing things and you know, a big environment, right? So that is where our team comes into play. We ingest a lot of the common data sets, but like I said, any unique data sets, data sets regarding theatrical as an example, you know, Warner brothers does it themselves, you know, for streaming, HBO Max, does it themselves. So we kind of operate in partnership. >> So do you have a centralized data team and also decentralized data teams, right? >> That's right. >> So I love this conversation because that was heresy 10 years ago, five years ago, even, cause that's inefficient. But you've, I presume you've found that it's actually more productive in terms of the business output, explain that dynamic. >> You know, you bring up such a good point. So I, you know, I consider myself as one of the dinosaurs who started like 20 plus years ago in this industry. And back then, we were all taught to think of the data warehouse as like a monolithic thing. And the reason for that is the technology wasn't there. The technology didn't catch up. Now, 20 years later, the technology is way ahead, right? But like, our mindset's still the same because we think of data warehouses and data platforms still as a monolithic thing. But if you really sort of remove that sort of mental barrier, if you will, and if you start thinking about, well, how do I sort of, you know, federate everything and make sure that you let folks who are building, or are closest to the customer or are building their products, let them own that data and have a partnership. The results have been amazing. And if we were only sort of doing it as a centralized team, we would not be able to do a 10th of what we do today. So it's that massive scale in, in our company as well. >> And I should have clarified, when we talk about data mesh are we talking about the implementing in practice, the octagon sort of framework, or is this sort of your own sort of terminology? >> Well, so the interesting part is four years ago, we didn't have- >> It didn't exist. >> Yeah. It didn't exist. And, and so we, our principle was very simple, right? When we started out, we said, we want to make sure that our brands are able to operate independently with some oversight and guidance from our technology teams, right? That's what we set out to do. We did that with Snowflake by design because Snowflake allows us to, you know, separate those, those brands into different accounts. So that was done by design. And then the, the magic, I think, is the Snowflake data sharing where, which allows us to sort of bring data in here once, and then share it with whoever needs it. So think about HBO Max. On HBO Max, You not only have HBO Max content, but content from CNN, from Cartoon Network, from Warner Brothers, right? All the movies, right? So to see how The Batman movie did in theaters and then on streaming, you don't need, you know, Warner brothers doesn't need to ingest the same streaming data. HBO Max does it. HBO Max shares it with Warner brothers, you know, store once, share many times, and everyone works at their own pace. >> So they're building data products. Those data products are discoverable APIs, I presume, or I guess maybe just, I guess the Snowflake cloud, but very importantly, they're governed. And that's correct, where Elation comes in? >> That's precisely where Elation comes in, is where sort of this central flexible foundation for data governance. You know, you mentioned data mesh. I think what's interesting is that it's really an answer to the bottlenecks created by centralized IT, right? There's this notion of decentralizing that the data engineers and making the data domain owners, the people that know the data the best, have them be in control of publishing the data to the data consumers. There are other popular concepts actually happening right now, as we speak, around modern data stack. Around data fabric that are also in many ways underpinned by this notion of decentralization, right? These are concepts that are underpinned by decentralization and as the pendulum swings, sort of between decentralization and centralization, as we go back and forth in the world of IT and data, there are certain constants that need to be centralized over time. And one of those I believe is very much a centralized platform for data governance. And that's certainly, I think where we come in. Would love to hear more about how you use Elation. >> Yeah. So, I mean, elation helps us sort of, as you guys say, sort of, map, the treasure map of the data, right? So for consumers to find where their data is, that's where Elation helps us. It helps us with the data cataloging, you know, storing all the metadata and, you know, users can go in, they can sort of find, you know, the data that they need and they can also find how others are using data. So it's, there's a little bit of a crowdsourcing aspect that Elation helps us to do whereby you know, you can see, okay, my peer in the other group, well, that's how they use this piece of data. So I'm not going to spend hours trying to figure this out. You're going to use the query that they use. So yeah. >> So you have a master catalog, I presume. And then each of the brands has their own sub catalogs, is that correct? >> Well, for the most part, we have that master catalog and then the brands sort of use it, you know, separately themselves. The key here is all that catalog, that catalog isn't maintained by a centralized group as well, right? It's again, maintained by the individual teams and not only in the individual teams, but the folks that are responsible for the data, right? So I talked about the concept of crowdsourcing, whoever sort of puts the data in, has to make sure that they update the catalog and make sure that the definitions are there and everything sort of in line. >> So HBO, CNN, and each have their own, sort of access to their catalog, but they feed into the master catalog. Is that the right way to think about it? >> Yeah. >> Okay. And they have their own virtual data warehouses, right? They have ownership over that? They can spin 'em up, spin 'em down as they see fit? Right? And they're governed. >> They're governed. And what's interesting is it's not just governed, right? Governance is a, is a big word. It's a bit nebulous, but what's really being enabled here is this notion of self-service as well, right? There's two big sort of rockets that need to happen at the same time in any given organization. There's this notion that you want to put trustworthy data in the hands of data consumers, while at the same time mitigating risk. And that's precisely what Elation does. >> So I want to clarify this for the audience. So there's four principles of database. This came after you guys did it. And I wonder how it aligns. Domain ownership, give data, as you were saying to the, to the domain owners who have context, data as product, you guys are building data products, and that creates two problems. How do you give people self-service infrastructure and how do you automate governance? So the first two, great. But then it creates these other problems. Does that align with your philosophy? Where's alignment? What's different? >> Yeah. Data products is exactly where we're going. And that sort of, that domain based design, that's really key as well. In our business, you think about who the customer is, as an example, right? Depending on who you ask, it's going to be, the answer might be different, you know, to the movie business, it's probably going to be the person who watches a movie in a theater. To the streaming business, to HBO Max, it's the streamer, right? To others, someone watching live CNN on their TV, right? There's yet another group. Think about all the franchising we do. So you see Batman action figures and T-shirts, and Warner brothers branded stuff in stores, that's yet another business unit. But at the end of the day, it's not a different person, it's you and me, right? We do all these things. So the domain concept, make sure that you ingest data and you bring data relevant to the context, however, not sort of making it so stringent where it cannot integrate, and then you integrate it at a higher level to create that 360. >> And it's discoverable. So the point is, I don't have to go tap Ash on the shoulder, say, how do I get this data? Is it governed? Do I have access to it? Give me the rules of it. Just, I go grab it, right? And the system computationally automates whether or not I have access to it. And it's, as you say, self-service. >> In this case, exactly right. It enables people to just search for data and know that when they find the data, whether it's trustworthy or not, through trust flags, and the like, it's doing both of those things at the same time. >> How is it an enabler of solving some of the big challenges that the media and entertainment industry is going through? We've seen so much change the last couple of years. The rising consumer expectations aren't going to go back down. They're only going to come up. We want you to serve us up content that's relevant, that's personalized, that makes sense. I'd love to understand from your perspective, Mitesh, from an industry challenges perspective, how does this technology help customers like Warner Brothers Discovery, meet business customers, where they are and reduce the volume on those challenges? >> It's a great question. And as I mentioned earlier, we had five industry competency badges that were awarded to us by Snowflake. And one of those four, Median Telcom. And the reason for that is we're helping media companies understand their audiences better, and ultimately serve up better experiences for their audiences. But we've got Ash right here that can tell us how that's happening in practice. >> Yeah, tell us. >> So I'll share a story. I always like to tell stories, right? Once once upon a time before we had Elation in place, it was like, who you knew was how you got access to the data. So if I knew you and I knew you had access to a certain kind of data and your access to the right kind of data was based on the network you had at the company- >> I had to trust you. >> Yeah. >> I might not want to give up my data. >> That's it. And so that's where Elation sort of helps us democratize it, but, you know, puts the governance and controls, right? There are certain sensitive things as well, such as viewership, such as subscriber accounts, which are very important. So making sure that the right people have access to it, that's the other problem that Elation helps us solve. >> That's precisely part of our integration with Snowflake in particular, being able to define and manage policies within Elation. Saying, you know, certain people should have access to certain rows, doing column level masking. And having those policies actually enforced at the Snowflake data layer is precisely part of our value product. >> And that's automated. >> And all that's automated. Exactly. >> Right. So I don't have to think about it. I don't have to go through the tap on their shoulder. What has been the impact, Ash, on data quality as you've pushed it down into the domains? >> That's a great question. So it has definitely improved, but data quality is a very interesting subject, because back to my example of, you know, when we started doing things, we, you know, the centralized IT team always said, well, it has to be like this, Right? And if it doesn't fit in this, then it's bad quality. Well, sometimes context changes. Businesses change, right? You have to be able to react to it quickly. So making sure that a lot of that quality is managed at the decentralized level, at the place where you have that business context, that ensures you have the most up to date quality. We're talking about media industry changing so quickly. I mean, would we have thought three years ago that people would watch a lot of these major movies on streaming services? But here's the reality, right? You have to react and, you know, having it at that level just helps you react faster. >> So data, if I play that back, data quality is not a static framework. It's flexible based on the business context and the business owners can make those adjustments, cause they own the data. >> That's it. That's exactly it. >> That's awesome. Wow. That's amazing progress that you guys have made. >> In quality, if I could just add, it also just changes depending on where you are in your data pipeline stage, right? Data, quality data observability, this is a very fast evolving space at the moment, and if I look to my left right now, I bet you I can probably see a half-dozen quality observability vendors right now. And so given that and given the fact that Elation still is sort of a central hub to find trustworthy data, we've actually announced an open data quality initiative, allowing for best-of-breed data quality vendors to integrate with the platform. So whoever they are, whatever tool folks want to use, they can use that particular tool of choice. >> And this all runs in the cloud, or is it a hybrid sort of? >> Everything is in the cloud. We're all in the cloud. And you know, again, helps us go faster. >> Let me ask you a question. I could go on forever in this topic. One of the concepts that was put forth is whether it's a Snowflake data warehouse or a data bricks, data lake, or an Oracle data warehouse, they should all be inclusive. They should just be a node on the mesh. Like, wow, that sounds good. But I haven't seen it yet. Right? I'm guessing that Snowflake and Elation enable all the self-serve, all this automated governance, and that including those other items, it's got to be a one-off at this point in time. Do you ever see you expanding that scope or is it better off to just kind of leave it into the, the Snowflake data cloud? >> It's a good question. You know, I feel like where we're at today, especially in terms of sort of technology giving us so many options, I don't think there's a one size fits all. Right? Even though we are very heavily invested in Snowflake and we use Snowflake consistently across the organization, but you could, theoretically, could have an architecture that blends those two, right? Have different types of data platforms like a teradata or an Oracle and sort of bring it all together today. We have the technology, you know, that and all sorts of things that can make sure that you query on different databases. So I don't think the technology is the problem, I think it's the organizational mindset. I think that that's what gets in the way. >> Oh, interesting. So I was going to ask you, will hybrid tables help you solve that problem? And, maybe not, what you're saying, it's the organization that owns the Oracle database saying, Hey, we have our system. It processes, it works, you know, go away. >> Yeah. Well, you know, hybrid tables I think, is a great sort of next step in Snowflake's evolution. I think it's, in my opinion, I, think it's a game changer, but yeah. I mean, they can still exist. You could do hybrid tables right on Snowflake, or you could, you know, you could kind of coexist as well. >> Yeah. But, do you have a thought on this? >> Yeah, I do. I mean, we're always going to live in a time where you've got data distributed in throughout the organization and around the globe. And that could be even if you're all in on Snowflake, you could have data in Snowflake here, you could have data in Snowflake in EMEA and Europe somewhere. It could be anywhere. By the same token you might be using. Every organization is using on-premises systems. They have data, they naturally have data everywhere. And so, you know, this one solution to this is really centralizing, as I mentioned, not just governance, but also metadata about all of the data in your organization so that you can enable people to search and find and discover trustworthy data no matter where it is in your organization. >> Yeah. That's a great point. I mean, if you have the data about the data, then you can, you can treat these independent nodes. That's just that. Right? And maybe there's some advantages of putting it all in the Snowflake cloud, but to your point, organizationally, that's just not feasible. The whole, unfortunately, sorry, Snowflake, all the world's data is not going to go into Snowflake, but they play a key role in accelerating, what I'm hearing, your vision of data mesh. >> Yeah, absolutely. I think going forward in the future, we have to start thinking about data platforms as just one place where you sort of dump all the data. That's where the mesh concept comes in. It is going to be a mesh. It's going to be distributed and organizations have to be okay with that. And they have to embrace the tools. I mean, you know, Facebook developed a tool called Presto many years ago that that helps them solve exactly the same problem. So I think the technology is there. I think the organizational mindset needs to evolve. >> Yeah. Definitely. >> Culture. Culture is one of the hardest things to change. >> Exactly. >> Guys, this was a masterclass in data mesh, I think. Thank you so much for coming on talking. >> We appreciate it. Thank you so much. >> Of course. What Elation is doing with Snowflake and with Warner Brothers Discovery, Keep that content coming. I got a lot of stuff I got to catch up on watching. >> Sounds good. Thank you for having us. >> Thanks guys. >> Thanks, you guys. >> For Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22. We'll be back after a short break. (upbeat music)

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit '22 live from Caesar's Forum in Las Vegas. I'm Lisa Martin, my cohost Dave Vellante, we've been here the last day and a half unpacking a lot of news, a lot of announcements, talking with customers and partners, and we have another great session coming for you next. We've got a customer and a partner talking tech and data mash. Please welcome Mitesh Shah, VP in market strategy at Elation. >> Great to be here. >> and Ash Naseer great, to have you, senior director of data engineering at Warner Brothers Discovery. Welcome guys. >> Thank you for having me. >> It's great to be back in person and to be able to really get to see and feel and touch this technology, isn't it? >> Yeah, it is. I mean two years or so. Yeah. Great to feel the energy in the conference center. >> Yeah. >> Snowflake was virtual, I think for two years and now it's great to kind of see the excitement firsthand. So it's wonderful. >> Th excitement, but also the boom and the number of customers and partners and people attending. They were saying the first, or the summit in 2019 had about 1900 attendees. And this is around 10,000. So a huge jump in a short time period. Talk a little bit about the Elation-Snowflake partnership and probably some of the acceleration that you guys have been experiencing as a Snowflake partner. >> Yeah. As a snowflake partner. I mean, Snowflake is an investor of us in Elation early last year, and we've been a partner for, for longer than that. And good news. We have been awarded Snowflake partner of the year for data governance, just earlier this week. And that's in fact, our second year in a row for winning that award. So, great news on that front as well. >> Repeat, congratulations. >> Repeat. Absolutely. And we're going to hope to make it a three-peat as well. And we've also been awarded industry competency badges in five different industries, those being financial services, healthcare, retail technology, and Median Telcom. >> Excellent. Okay. Going to right get into it. Data mesh. You guys actually have a data mesh and you've presented at the conference. So, take us back to the beginning. Why did you decide that you needed to implement something like data mesh? What was the impetus? >> Yeah. So when people think of Warner brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of HBO, you think of TNT, you think of CNN, we have 30 plus brands in our portfolio and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against as an example, HBO, if Game of Thrones is going on. >> So, okay. So the, the impetus was to serve those lines of business better. Actually, given that you've got these different brands, it was probably easier than most companies. Cause if you're, let's say you're a big financial services company, and now you have to decide who owns what. CNN owns its own data products, HBO. Now, do they decide within those different brands, how to distribute even further? Or is it really, how deep have you gone in that decentralization? >> That's a great question. It's a very close partnership, because there are a number of data sets, which are used by all the brands, right? You think about people browsing websites, right? You know, CNN has a website, Warner brothers has a website. So for us to ingest that data for each of the brands to ingest that data separately, that means five different ways of doing things and you know, a big environment, right? So that is where our team comes into play. We ingest a lot of the common data sets, but like I said, any unique data sets, data sets regarding theatrical as an example, you know, Warner brothers does it themselves, you know, for streaming, HBO Max, does it themselves. So we kind of operate in partnership. >> So do you have a centralized data team and also decentralized data teams, right? >> That's right. >> So I love this conversation because that was heresy 10 years ago, five years ago, even, cause that's inefficient. But you've, I presume you've found that it's actually more productive in terms of the business output, explain that dynamic. >> You know, you bring up such a good point. So I, you know, I consider myself as one of the dinosaurs who started like 20 plus years ago in this industry. And back then, we were all taught to think of the data warehouse as like a monolithic thing. And the reason for that is the technology wasn't there. The technology didn't catch up. Now, 20 years later, the technology is way ahead, right? But like, our mindset's still the same because we think of data warehouses and data platforms still as a monolithic thing. But if you really sort of remove that sort of mental barrier, if you will, and if you start thinking about, well, how do I sort of, you know, federate everything and make sure that you let folks who are building, or are closest to the customer or are building their products, let them own that data and have a partnership. The results have been amazing. And if we were only sort of doing it as a centralized team, we would not be able to do a 10th of what we do today. So it's that massive scale in, in our company as well. >> And I should have clarified, when we talk about data mesh are we talking about the implementing in practice, the octagon sort of framework, or is this sort of your own sort of terminology? >> Well, so the interesting part is four years ago, we didn't have- >> It didn't exist. >> Yeah. It didn't exist. And, and so we, our principle was very simple, right? When we started out, we said, we want to make sure that our brands are able to operate independently with some oversight and guidance from our technology teams, right? That's what we set out to do. We did that with Snowflake by design because Snowflake allows us to, you know, separate those, those brands into different accounts. So that was done by design. And then the, the magic, I think, is the Snowflake data sharing where, which allows us to sort of bring data in here once, and then share it with whoever needs it. So think about HBO Max. On HBO Max, You not only have HBO Max content, but content from CNN, from Cartoon Network, from Warner Brothers, right? All the movies, right? So to see how The Batman movie did in theaters and then on streaming, you don't need, you know, Warner brothers doesn't need to ingest the same streaming data. HBO Max does it. HBO Max shares it with Warner brothers, you know, store once, share many times, and everyone works at their own pace. >> So they're building data products. Those data products are discoverable APIs, I presume, or I guess maybe just, I guess the Snowflake cloud, but very importantly, they're governed. And that's correct, where Elation comes in? >> That's precisely where Elation comes in, is where sort of this central flexible foundation for data governance. You know, you mentioned data mesh. I think what's interesting is that it's really an answer to the bottlenecks created by centralized IT, right? There's this notion of decentralizing that the data engineers and making the data domain owners, the people that know the data the best, have them be in control of publishing the data to the data consumers. There are other popular concepts actually happening right now, as we speak, around modern data stack. Around data fabric that are also in many ways underpinned by this notion of decentralization, right? These are concepts that are underpinned by decentralization and as the pendulum swings, sort of between decentralization and centralization, as we go back and forth in the world of IT and data, there are certain constants that need to be centralized over time. And one of those I believe is very much a centralized platform for data governance. And that's certainly, I think where we come in. Would love to hear more about how you use Elation. >> Yeah. So, I mean, elation helps us sort of, as you guys say, sort of, map, the treasure map of the data, right? So for consumers to find where their data is, that's where Elation helps us. It helps us with the data cataloging, you know, storing all the metadata and, you know, users can go in, they can sort of find, you know, the data that they need and they can also find how others are using data. So it's, there's a little bit of a crowdsourcing aspect that Elation helps us to do whereby you know, you can see, okay, my peer in the other group, well, that's how they use this piece of data. So I'm not going to spend hours trying to figure this out. You're going to use the query that they use. So yeah. >> So you have a master catalog, I presume. And then each of the brands has their own sub catalogs, is that correct? >> Well, for the most part, we have that master catalog and then the brands sort of use it, you know, separately themselves. The key here is all that catalog, that catalog isn't maintained by a centralized group as well, right? It's again, maintained by the individual teams and not only in the individual teams, but the folks that are responsible for the data, right? So I talked about the concept of crowdsourcing, whoever sort of puts the data in, has to make sure that they update the catalog and make sure that the definitions are there and everything sort of in line. >> So HBO, CNN, and each have their own, sort of access to their catalog, but they feed into the master catalog. Is that the right way to think about it? >> Yeah. >> Okay. And they have their own virtual data warehouses, right? They have ownership over that? They can spin 'em up, spin 'em down as they see fit? Right? And they're governed. >> They're governed. And what's interesting is it's not just governed, right? Governance is a, is a big word. It's a bit nebulous, but what's really being enabled here is this notion of self-service as well, right? There's two big sort of rockets that need to happen at the same time in any given organization. There's this notion that you want to put trustworthy data in the hands of data consumers, while at the same time mitigating risk. And that's precisely what Elation does. >> So I want to clarify this for the audience. So there's four principles of database. This came after you guys did it. And I wonder how it aligns. Domain ownership, give data, as you were saying to the, to the domain owners who have context, data as product, you guys are building data products, and that creates two problems. How do you give people self-service infrastructure and how do you automate governance? So the first two, great. But then it creates these other problems. Does that align with your philosophy? Where's alignment? What's different? >> Yeah. Data products is exactly where we're going. And that sort of, that domain based design, that's really key as well. In our business, you think about who the customer is, as an example, right? Depending on who you ask, it's going to be, the answer might be different, you know, to the movie business, it's probably going to be the person who watches a movie in a theater. To the streaming business, to HBO Max, it's the streamer, right? To others, someone watching live CNN on their TV, right? There's yet another group. Think about all the franchising we do. So you see Batman action figures and T-shirts, and Warner brothers branded stuff in stores, that's yet another business unit. But at the end of the day, it's not a different person, it's you and me, right? We do all these things. So the domain concept, make sure that you ingest data and you bring data relevant to the context, however, not sort of making it so stringent where it cannot integrate, and then you integrate it at a higher level to create that 360. >> And it's discoverable. So the point is, I don't have to go tap Ash on the shoulder, say, how do I get this data? Is it governed? Do I have access to it? Give me the rules of it. Just, I go grab it, right? And the system computationally automates whether or not I have access to it. And it's, as you say, self-service. >> In this case, exactly right. It enables people to just search for data and know that when they find the data, whether it's trustworthy or not, through trust flags, and the like, it's doing both of those things at the same time. >> How is it an enabler of solving some of the big challenges that the media and entertainment industry is going through? We've seen so much change the last couple of years. The rising consumer expectations aren't going to go back down. They're only going to come up. We want you to serve us up content that's relevant, that's personalized, that makes sense. I'd love to understand from your perspective, Mitesh, from an industry challenges perspective, how does this technology help customers like Warner Brothers Discovery, meet business customers, where they are and reduce the volume on those challenges? >> It's a great question. And as I mentioned earlier, we had five industry competency badges that were awarded to us by Snowflake. And one of those four, Median Telcom. And the reason for that is we're helping media companies understand their audiences better, and ultimately serve up better experiences for their audiences. But we've got Ash right here that can tell us how that's happening in practice. >> Yeah, tell us. >> So I'll share a story. I always like to tell stories, right? Once once upon a time before we had Elation in place, it was like, who you knew was how you got access to the data. So if I knew you and I knew you had access to a certain kind of data and your access to the right kind of data was based on the network you had at the company- >> I had to trust you. >> Yeah. >> I might not want to give up my data. >> That's it. And so that's where Elation sort of helps us democratize it, but, you know, puts the governance and controls, right? There are certain sensitive things as well, such as viewership, such as subscriber accounts, which are very important. So making sure that the right people have access to it, that's the other problem that Elation helps us solve. >> That's precisely part of our integration with Snowflake in particular, being able to define and manage policies within Elation. Saying, you know, certain people should have access to certain rows, doing column level masking. And having those policies actually enforced at the Snowflake data layer is precisely part of our value product. >> And that's automated. >> And all that's automated. Exactly. >> Right. So I don't have to think about it. I don't have to go through the tap on their shoulder. What has been the impact, Ash, on data quality as you've pushed it down into the domains? >> That's a great question. So it has definitely improved, but data quality is a very interesting subject, because back to my example of, you know, when we started doing things, we, you know, the centralized IT team always said, well, it has to be like this, Right? And if it doesn't fit in this, then it's bad quality. Well, sometimes context changes. Businesses change, right? You have to be able to react to it quickly. So making sure that a lot of that quality is managed at the decentralized level, at the place where you have that business context, that ensures you have the most up to date quality. We're talking about media industry changing so quickly. I mean, would we have thought three years ago that people would watch a lot of these major movies on streaming services? But here's the reality, right? You have to react and, you know, having it at that level just helps you react faster. >> So data, if I play that back, data quality is not a static framework. It's flexible based on the business context and the business owners can make those adjustments, cause they own the data. >> That's it. That's exactly it. >> That's awesome. Wow. That's amazing progress that you guys have made. >> In quality, if I could just add, it also just changes depending on where you are in your data pipeline stage, right? Data, quality data observability, this is a very fast evolving space at the moment, and if I look to my left right now, I bet you I can probably see a half-dozen quality observability vendors right now. And so given that and given the fact that Elation still is sort of a central hub to find trustworthy data, we've actually announced an open data quality initiative, allowing for best-of-breed data quality vendors to integrate with the platform. So whoever they are, whatever tool folks want to use, they can use that particular tool of choice. >> And this all runs in the cloud, or is it a hybrid sort of? >> Everything is in the cloud. We're all in the cloud. And you know, again, helps us go faster. >> Let me ask you a question. I could go on forever in this topic. One of the concepts that was put forth is whether it's a Snowflake data warehouse or a data bricks, data lake, or an Oracle data warehouse, they should all be inclusive. They should just be a node on the mesh. Like, wow, that sounds good. But I haven't seen it yet. Right? I'm guessing that Snowflake and Elation enable all the self-serve, all this automated governance, and that including those other items, it's got to be a one-off at this point in time. Do you ever see you expanding that scope or is it better off to just kind of leave it into the, the Snowflake data cloud? >> It's a good question. You know, I feel like where we're at today, especially in terms of sort of technology giving us so many options, I don't think there's a one size fits all. Right? Even though we are very heavily invested in Snowflake and we use Snowflake consistently across the organization, but you could, theoretically, could have an architecture that blends those two, right? Have different types of data platforms like a teradata or an Oracle and sort of bring it all together today. We have the technology, you know, that and all sorts of things that can make sure that you query on different databases. So I don't think the technology is the problem, I think it's the organizational mindset. I think that that's what gets in the way. >> Oh, interesting. So I was going to ask you, will hybrid tables help you solve that problem? And, maybe not, what you're saying, it's the organization that owns the Oracle database saying, Hey, we have our system. It processes, it works, you know, go away. >> Yeah. Well, you know, hybrid tables I think, is a great sort of next step in Snowflake's evolution. I think it's, in my opinion, I, think it's a game changer, but yeah. I mean, they can still exist. You could do hybrid tables right on Snowflake, or you could, you know, you could kind of coexist as well. >> Yeah. But, do you have a thought on this? >> Yeah, I do. I mean, we're always going to live in a time where you've got data distributed in throughout the organization and around the globe. And that could be even if you're all in on Snowflake, you could have data in Snowflake here, you could have data in Snowflake in EMEA and Europe somewhere. It could be anywhere. By the same token you might be using. Every organization is using on-premises systems. They have data, they naturally have data everywhere. And so, you know, this one solution to this is really centralizing, as I mentioned, not just governance, but also metadata about all of the data in your organization so that you can enable people to search and find and discover trustworthy data no matter where it is in your organization. >> Yeah. That's a great point. I mean, if you have the data about the data, then you can, you can treat these independent nodes. That's just that. Right? And maybe there's some advantages of putting it all in the Snowflake cloud, but to your point, organizationally, that's just not feasible. The whole, unfortunately, sorry, Snowflake, all the world's data is not going to go into Snowflake, but they play a key role in accelerating, what I'm hearing, your vision of data mesh. >> Yeah, absolutely. I think going forward in the future, we have to start thinking about data platforms as just one place where you sort of dump all the data. That's where the mesh concept comes in. It is going to be a mesh. It's going to be distributed and organizations have to be okay with that. And they have to embrace the tools. I mean, you know, Facebook developed a tool called Presto many years ago that that helps them solve exactly the same problem. So I think the technology is there. I think the organizational mindset needs to evolve. >> Yeah. Definitely. >> Culture. Culture is one of the hardest things to change. >> Exactly. >> Guys, this was a masterclass in data mesh, I think. Thank you so much for coming on talking. >> We appreciate it. Thank you so much. >> Of course. What Elation is doing with Snowflake and with Warner Brothers Discovery, Keep that content coming. I got a lot of stuff I got to catch up on watching. >> Sounds good. Thank you for having us. >> Thanks guys. >> Thanks, you guys. >> For Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22. We'll be back after a short break. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is "Breaking Analysis" with Dave Vellante. >> If it weren't for Pat Gelsinger, Intel's future would be a disaster. Even with his clear vision, fantastic leadership, deep technical and business acumen, and amazing positivity, the company's future is in serious jeopardy. It's the same story we've been telling for years. Volume is king in the semiconductor industry, and Intel no longer is the volume leader. Despite Intel's efforts to change that dynamic With several recent moves, including making another go at its Foundry business, the company is years away from reversing its lagging position relative to today's leading foundries and design shops. Intel's best chance to survive as a leader in our view, will come from a combination of a massive market, continued supply constraints, government money, and luck, perhaps in the form of a deal with apple in the midterm. Hello, and welcome to this week's "Wikibon CUBE Insights, Powered by ETR." In this "Breaking Analysis," we'll update you on our latest assessment of Intel's competitive position and unpack nuggets from the company's February investor conference. Let's go back in history a bit and review what we said in the early 2010s. If you've followed this program, you know that our David Floyer sounded the alarm for Intel as far back as 2012, the year after PC volumes peaked. Yes, they've ticked up a bit in the past couple of years but they pale in comparison to the volumes that the ARM ecosystem is producing. The world has changed from people entering data into machines, and now it's machines that are driving all the data. Data volumes in Web 1.0 were largely driven by keystrokes and clicks. Web 3.0 is going to be driven by machines entering data into sensors, cameras. Other edge devices are going to drive enormous data volumes and processing power to boot. Every windmill, every factory device, every consumer device, every car, will require processing at the edge to run AI, facial recognition, inference, and data intensive workloads. And the volume of this space compared to PCs and even the iPhone itself is about to be dwarfed with an explosion of devices. Intel is not well positioned for this new world in our view. Intel has to catch up on the process, Intel has to catch up on architecture, Intel has to play catch up on security, Intel has to play catch up on volume. The ARM ecosystem has cumulatively shipped 200 billion chips to date, and is shipping 10x Intel's wafer volume. Intel has to have an architecture that accommodates much more diversity. And while it's working on that, it's years behind. All that said, Pat Gelsinger is doing everything he can and more to close the gap. Here's a partial list of the moves that Pat is making. A year ago, he announced IDM 2.0, a new integrated device manufacturing strategy that opened up its world to partners for manufacturing and other innovation. Intel has restructured, reorganized, and many executives have boomeranged back in, many previous Intel execs. They understand the business and have a deep passion to help the company regain its prominence. As part of the IDM 2.0 announcement, Intel created, recreated if you will, a Foundry division and recently acquired Tower Semiconductor an Israeli firm, that is going to help it in that mission. It's opening up partnerships with alternative processor manufacturers and designers. And the company has announced major investments in CAPEX to build out Foundry capacity. Intel is going to spin out Mobileye, a company it had acquired for 15 billion in 2017. Or does it try and get a $50 billion valuation? Mobileye is about $1.4 billion in revenue, and is likely going to be worth more around 25 to 30 billion, we'll see. But Intel is going to maybe get $10 billion in cash from that, that spin out that IPO and it can use that to fund more FABS and more equipment. Intel is leveraging its 19,000 software engineers to move up the stack and sell more subscriptions and high margin software. He got to sell what he got. And finally Pat is playing politics beautifully. Announcing for example, FAB investments in Ohio, which he dubbed Silicon Heartland. Brilliant! Again, there's no doubt that Pat is moving fast and doing the right things. Here's Pat at his investor event in a T-shirt that says, "torrid, bringing back the torrid pace and discipline that Intel is used to." And on the right is Pat at the State of the Union address, looking sharp in shirt and tie and suit. And he has said, "a bet on Intel is a hedge against geopolitical instability in the world." That's just so good. To that statement, he showed this chart at his investor meeting. Basically it shows that whereas semiconductor manufacturing capacity has gone from 80% of the world's volume to 20%, he wants to get it back to 50% by 2030, and reset supply chains in a market that has become important as oil. Again, just brilliant positioning and pushing all the right hot buttons. And here's a slide underscoring that commitment, showing manufacturing facilities around the world with new capacity coming online in the next few years in Ohio and the EU. Mentioning the CHIPS Act in his presentation in The US and Europe as part of a public private partnership, no doubt, he's going to need all the help he can get. Now, we couldn't resist the chart on the left here shows wafer starts and transistor capacity growth. For Intel, overtime speaks to its volume aspirations. But we couldn't help notice that the shape of the curve is somewhat misleading because it shows a two-year (mumbles) and then widens the aperture to three years to make the curve look steeper. Fun with numbers. Okay, maybe a little nitpick, but these are some of the telling nuggets we pulled from the investor day, and they're important. Another nitpick is in our view, wafers would be a better measure of volume than transistors. It's like a company saying we shipped 20% more exabytes or MIPS this year than last year. Of course you did, and your revenue shrank. Anyway, Pat went through a detailed analysis of the various Intel businesses and promised mid to high double digit growth by 2026, half of which will come from Intel's traditional PC they center in network edge businesses and the rest from advanced graphics HPC, Mobileye and Foundry. Okay, that sounds pretty good. But it has to be taken into context that the balance of the semiconductor industry, yeah, this would be a pretty competitive growth rate, in our view, especially for a 70 plus billion dollar company. So kudos to Pat for sticking his neck out on this one. But again, the promise is several years away, at least four years away. Now we want to focus on Foundry because that's the only way Intel is going to get back into the volume game and the volume necessary for the company to compete. Pat built this slide showing the baby blue for today's Foundry business just under a billion dollars and adding in another $1.5 billion for Tower Semiconductor, the Israeli firm that it just acquired. So a few billion dollars in the near term future for the Foundry business. And then by 2026, this really fuzzy blue bar. Now remember, TSM is the new volume leader, and is a $50 billion company growing. So there's definitely a market there that it can go after. And adding in ARM processors to the mix, and, you know, opening up and partnering with the ecosystems out there can only help volume if Intel can win that business, which you know, it should be able to, given the likelihood of long term supply constraints. But we remain skeptical. This is another chart Pat showed, which makes the case that Foundry and IDM 2.0 will allow expensive assets to have a longer useful life. Okay, that's cool. It will also solve the cumulative output problem highlighted in the bottom right. We've talked at length about Wright's Law. That is, for every cumulative doubling of units manufactured, cost will fall by a constant percentage. You know, let's say around 15% in semiconductor world, which is vitally important to accommodate next generation chips, which are always more expensive at the start of the cycle. So you need that 15% cost buffer to jump curves and make any money. So let's unpack this a bit. You know, does this chart at the bottom right address our Wright's Law concerns, i.e. that Intel can't take advantage of Wright's Law because it can't double cumulative output fast enough? Now note the decline in wafer starts and then the slight uptick, and then the flattening. It's hard to tell what years we're talking about here. Intel is not going to share the sausage making because it's probably not pretty, But you can see on the bottom left, the flattening of the cumulative output curve in IDM 1.0 otherwise known as the death spiral. Okay, back to the power of Wright's Law. Now, assume for a second that wafer density doesn't grow. It does, but just work with us for a second. Let's say you produce 50 million units per year, just making a number up. That gets you cumulative output to $100 million in, sorry, 100 million units in the second year to take you two years to get to that 100 million. So in other words, it takes two years to lower your manufacturing cost by, let's say, roughly 15%. Now, assuming you can get wafer volumes to be flat, which that chart showed, with good yields, you're at 150 now in year three, 200 in year four, 250 in year five, 300 in year six, now, that's four years before you can take advantage of Wright's Law. You keep going at that flat wafer start, and that simplifying assumption we made at the start and 50 million units a year, and well, you get to the point. You get the point, it's now eight years before you can get the Wright's Law to kick in, and you know, by then you're cooked. But now you can grow the density of transistors on a chip, right? Yes, of course. So let's come back to Moore's Law. The graphic on the left says that all the growth is in the new stuff. Totally agree with that. Huge term that Pat presented. Now he also said that until we exhaust the periodic table of elements, Moore's Law is alive and well, and Intel is the steward of Moore's Law. Okay, that's cool. The chart on the right shows Intel going from 100 billion transistors today to a trillion by 2030. Hold that thought. So Intel is assuming that we'll keep up with Moore's Law, meaning a doubling of transistors every let's say two years, and I believe it. So bring that back to Wright's Law, in the previous chart, it means with IDM 2.0, Intel can get back to enjoying the benefits of Wright's Law every two years, let's say, versus IDM 1.0 where they were failing to keep up. Okay, so Intel is saved, yeah? Well, let's bring into this discussion one of our favorite examples, Apple's M1 ARM-based chip. The M1 Ultra is a new architecture. And you can see the stats here, 114 billion transistors on a five nanometer process and all the other stats. The M1 Ultra has two chips. They're bonded together. And Apple put an interposer between the two chips. An interposer is a pathway that allows electrical signals to pass through it onto another chip. It's a super fast connection. You can see 2.5 terabytes per second. But the brilliance is the two chips act as a single chip. So you don't have to change the software at all. The way Intel's architecture works is it takes two different chips on a substrate, and then each has its own memory. The memory is not shared. Apple shares the memory for the CPU, the NPU, the GPU. All of it is shared, meaning it needs no change in software unlike Intel. Now Intel is working on a new architecture, but Apple and others are way ahead. Now let's make this really straightforward. The original Apple M1 had 16 billion transistors per chip. And you could see in that diagram, the recently launched M1 Ultra has $114 billion per chip. Now if you take into account the size of the chips, which are increasing, and the increase in the number of transistors per chip, that transistor density, that's a factor of around 6x growth in transistor density per chip in 18 months. Remember Intel, assuming the results in the two previous charts that we showed, assuming they were achievable, is running at 2x every two years, versus 6x for the competition. And AMD and Nvidia are close to that as well because they can take advantage of TSM's learning curve. So in the previous chart with Moore's Law, alive and well, Intel gets to a trillion transistors by 2030. The Apple ARM and Nvidia ecosystems will arrive at that point years ahead of Intel. That means lower costs and significantly better competitive advantage. Okay, so where does that leave Intel? The story is really not resonating with investors and hasn't for a while. On February 18th, the day after its investor meeting, the stock was off. It's rebound a little bit but investors are, you know, they're probably prudent to wait unless they have really a long term view. And you can see Intel's performance relative to some of the major competitors. You know, Pat talked about five nodes in for years. He made a big deal out of that, and he shared proof points with Alder Lake and Meteor Lake and other nodes, but Intel just delayed granite rapids last month that pushed it out from 2023 to 2024. And it told investors that we're going to have to boost spending to turn this ship around, which is absolutely the case. And that delay in chips I feel like the first disappointment won't be the last. But as we've said many times, it's very difficult, actually, it's impossible to quickly catch up in semiconductors, and Intel will never catch up without volume. So we'll leave you by iterating our scenario that could save Intel, and that's if its Foundry business can eventually win back Apple to supercharge its volume story. It's going to be tough to wrestle that business away from TSM especially as TSM is setting up shop in Arizona, with US manufacturing that's going to placate The US government. But look, maybe the government cuts a deal with Apple, says, hey, maybe we'll back off with the DOJ and FTC and as part of the CHIPS Act, you'll have to throw some business at Intel. Would that be enough when combined with other Foundry opportunities Intel could theoretically produce? Maybe. But from this vantage point, it's very unlikely Intel will gain back its true number one leadership position. If it were really paranoid back when David Floyer sounded the alarm 10 years ago, yeah, that might have made a pretty big difference. But honestly, the best we can hope for is Intel's strategy and execution allows it to get competitive volumes by the end of the decade, and this national treasure survives to fight for its leadership position in the 2030s. Because it would take a miracle for that to happen in the 2020s. Okay, that's it for today. Thanks to David Floyer for his contributions to this research. Always a pleasure working with David. Stephanie Chan helps me do much of the background research for "Breaking Analysis," and works with our CUBE editorial team. Kristen Martin and Cheryl Knight to get the word out. And thanks to SiliconANGLE's editor in chief Rob Hof, who comes up with a lot of the great titles that we have for "Breaking Analysis" and gets the word out to the SiliconANGLE audience. Thanks, guys. Great teamwork. Remember, these episodes are all available as podcast wherever you listen. Just search "Breaking Analysis Podcast." You'll want to check out ETR's website @etr.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You could always get in touch with me on email, david.vellante@siliconangle.com or DM me @dvellante, and comment on my LinkedIn posts. This is Dave Vellante for "theCUBE Insights, Powered by ETR." Have a great week. Stay safe, be well, and we'll see you next time. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from the cube in ETR. This is Breaking Analysis with Dave Vellante. >> On Friday, December 10th, Oracle announced a strong earnings beat and raise, on the strength of its licensed business, and slightly better than expected cloud performance. The stock was up sharply on the day and closed up nearly 16% surpassing 280 billion in market value. Oracle's success is due largely to its execution, of a highly differentiated strategy, that has really evolved over the past decade or more, deeply integrating its hardware and software, heavily investing in next generation cloud, creating a homogeneous experience across its application portfolio, and becoming the number one platform. Number one for the world's most mission critical applications. Now, while investors piled into the stock, skeptics will point to the beat being weighed toward licensed revenue and likely keep one finger on the sell button until they're convinced Oracle's cloud momentum, is more consistent and predictable. Hello and welcome to this week's Wikibond CUBE insights powered by ETR. In this breaking analysis, we'll review Oracle's most recent quarter, and pull in some ETR survey data, to frame the company's cloud business, the momentum of fusion ERP, where the company is winning and some gaps and opportunities that we see. The numbers this quarter was strong, particularly top line growth. Here are a few highlights. Oracle's revenues that grew 6% year on year that's in constant currency, surpassed $10 billion for the quarter. Oracle's non-gap operating margins, were an impressive 47%. Safra Catz has always said cloud is more profitable business and it's really starting to show in the income statement. Operating cash and free cash flow were 10.3 billion and 7.1 billion respectively, for the past four quarters, and would have been higher, if not for charges largely related to litigation expenses tied to the hiring of Mark Hurd, which the company said would not repeat in the future quarters. And you can see in this chart how Oracle breaks down its business, which is kind of a mishmash of items they lump into so-called the cloud. The largest piece of the revenue pie is cloud services, and licensed support, which in reading 10Ks, you'll find statements like the following; licensed support revenues are our largest revenue stream and include product upgrades, and maintenance releases and patches, as well as technical support assistance and statements like the following; cloud and licensed revenue, include the sale of cloud services, cloud licenses and on-premises licenses, which typically represent perpetual software licenses purchased by customers, for use in both cloud, and on-premises, IT environments. And cloud license and on-prem license revenues primarily represent amounts earned from granting customers perpetual licenses to use our database middleware application in industry specific products, which our customers use for cloud-based, on-premise and other IT environments. So you tell me, "is that cloud? I don't know." In the early days of Oracle cloud, the company used to break out, IaaS, PaaS and SaaS revenue separately, but it changed its mind, which really makes it difficult to determine what's happening in true cloud. Look I have no problem including same same hardware software control plane, et cetera. The hybrid if it's on-prem in a true hybrid environment like exadata cloud@customer or AWS outposts. But you have to question what's really cloud in these numbers. And Larry in the earnings call mentioned that Salesforce licenses the Oracle database, to run its cloud and Oracle doesn't count that in its cloud number, rather it counts it in license revenue, but as you can see it varies that into a line item that starts with the word cloud. So I guess I would say that Oracle's reporting is maybe somewhat better than IBM's cloud reporting, which is the worst, but I can't really say what is and isn't cloud, in these numbers. Nonetheless, Oracle is getting it done for investors. Here's a chart comparing the five-year performance of Oracle to some of its legacy peers. We excluded Microsoft because it skews the numbers. Microsoft would really crush all these names including Oracle. But look at Oracle. It's wedged in between the performance of the NASDAQ and the S&P 500, it's up over 160% in that five-year timeframe, well ahead of SAP which is up 59% in that time, and way ahead of the dismal -22% performance of IBM. Well, it's a shame. The tech tide is rising, it's lifting all boats but, IBM has unfortunately not been able to capitalize. That's a story for another day. As a market watcher, you can't help but love Larry Ellison. I only met him once at an IDC conference in Paris where I got to interview Scott McNealy, CEO at the time. Ellison is great for analysts because, he's not afraid to talk about the competition. He'll brag, he'll insult, he'll explain, and he'll pitch his stories. Now on the earnings call last night, he went off. Educating the analyst community, on the upside in the fusion ERP business, making the case that because only a thousand of the 7,500 legacy on-prem ERP customers from Oracle, JD Edwards and PeopleSoft have moved Oracle's fusion cloud ERP, and he predicted that Oracle's cloud ERP business will surpass 20 billion in five years. In fact, he said it's going to bigger than that. He slammed the hybrid cloud washing. You can see one of the quotes here in this chart, that's going on when companies have customers running in the cloud and they claim whatever they have on premise hybrid, he called that ridiculous. I would agree. And then he took an opportunity to slam the hyperscale cloud vendors, citing a telco customer that said Oracle's cloud never goes down, and of course, he chose the same week, that AWS had a major outage. And so to these points, I would say that Oracle really was the first tech company, to announce a true hybrid cloud strategy, where you have an entirely identical experience on prem and in the cloud. This was announced with cloud@customer, two years, before AWS announced outposts. Now it probably took Oracle two years to get it working as advertised, but they were first. And to the second point, this is where Oracle differentiates itself. Oracle is number one for mission critical applications. No other vendor really can come close to Oracle in this regard. And I would say that Oracle is recent quarterly performance to a large extent, is due to this differentiated approach. Over the past 10 years, we've talked to hundreds literally. Hundreds and hundreds of Oracle customers. And while they may not always like the tactics and licensing policies of Oracle in their contracting, they will tell you, that business case for investing and staying with Oracle are very strong. And yes, a big part of that is lock-in but R&D investments innovation and a keen sense of market direction, are just as important to these customers. When you're chairman and founder is a technologist and also the CTO, and has the cash on hand to invest, the results are a highly competitive story. Now that's not to say Oracle is not without its challenges. That's not to say Oracle is without its challenges. Those who follow this program know that when it comes to ETR survey data, the story is not always pretty for Oracle. So let's take a look. This chart shows the breakdown of ETR is net score methodology, Net score measures spending momentum and works ETR. Each quarter asks customers, are you adding in the platform, That's the lime green. Increasing spend by 6% or more, that's the fourth green. Is you're spending E+ or minus 5%, that's the gray. You're spending climbing by 6%, that's the pinkish. Or are you leaving the platform, that's the bright red retiring. You subtract the reds from the greens, and that yields a net score, which an Oracle's overall case, is an uninspiring -4%. This is one of the anomalies in the ETR dataset. The net score doesn't track absolute actual levels, of spending the dollars. Remember, as the leader in mission critical workloads, Oracle commands a premium price. And so what happens here is the gray, is still spending a large amount of money, enough to offset the declines, and the greens are spending more than they would on other platforms because Oracle could command higher prices. And so that's how Oracle is able to grow its overall revenue by 6% for example, whereas the ETR methodology, doesn't capture that trend. So you have to dig into the data a bit deeper. We're not going to go too deep today, but let's take a look at how some of Oracle's businesses are performing relative to its competitors. This is a popular view that we like to share. It shows net score or spending momentum on the vertical axis, and market share. Market share is a measure of pervasiveness in the survey. Think of it as mentioned share. That's on the x-axis. And we've broken down and circled Oracle overall, Oracle on prem, which is declining on the vertical axis, Oracle fusion and NetSuite, which are much higher than Oracle overall. And in the case of fusion, much closer to that 40% magic red horizontal line, remember anything above that line, we consider to be elevated. Now we've added SAP overall which has, momentum comparable to fusion in the survey, using this methodology and IBM, which is in between fusion and Oracle, overall on the y-axis. Oracle as you can see on the horizontal axis, has a larger presence than any of these firms that are below the 40% line. Now, above that 40% line, you see companies with a smaller presence in the survey like Workday, salesforce.com, pretty big presence still, Google cloud also, and Snowflake. Smaller presence but much much higher net score than anybody else on this chart. And AWS and Microsoft overall with both a strong presence, and impressive momentum, especially for their respective sizes. Now that view that we just showed you excluded on purpose Oracle specific cloud offering. So let's now take a look at that relative to other cloud providers. This chart shows the same XY view, but it cuts the data by cloud only. And you can see Oracle while still well below the 40% line, has a net score of +15 compared to a -4 overall that we showed you earlier. So here we see two key points. One, despite the convoluted reporting that we talked about earlier, the ETR data supports that Oracle's cloud business has significantly more momentum than Oracle's overall average momentum. And two, while Oracle is smaller and doesn't have the growth of the hyperscale giants, it's cloud is performing noticeably better than IBM's within the ETR survey data. Now a key point Ellison emphasized on the earnings call, was the importance of ERP, and the work that Oracle has done in this space. It lives by this notion of a cloud first mentality. It builds stuff for the cloud and then, would bring it on-prem. And it's been attracting new customers according to the company. He said Oracle has 8,500 fusion ERP customers, and 28,000 NetSuite customers in the cloud. And unlike Microsoft, it hasn't migrated its on-prem install base, to the cloud yet. Meaning these are largely new customers. Now this chart isolates fusion and NetSuite, within a sector ETR calls GPP. The very giant, public and private companies. And this is a bellwether of spending in the ETR dataset. They've gone back and it correlates to performance. So think large public companies, the biggest ones, and also privates big privates like Mars or Cargo or Fidelity. The chart shows the net score breakdown over time for fusion and NetSuite going back to 2019. And you can see, a big uptick as shown in the blue line from the October, 2020 survey. So Oracle has done a good job building and now marketing its cloud ERP to these important customers. Now, the last thing we want to show you is Oracle's performance within industry sectors. On the earnings call, Oracle said that it had a very strong momentum for fusion in financial services and healthcare. And this chart shows the net score for fusion, across each industry sector that ETR tracks, for three survey points. October, 2020, that's the gray bars, July 21, that's the blue bars and October, 2021, the yellow bars. So look it confirms Oracles assertions across the board that they're seeing fusion perform very well including the two verticals that are called out healthcare and banking slash financial services. Now the big question is where does Oracle go from here? Oracle has had a history of looking like it's going to break out, only to hit some bumps in the road. And so investors are likely going to remain a bit cautious and take profits off the table along the way. But since the Barron's article came out, we reported on that earlier this year in February, declaring Oracle a cloud giant, the stock is up more than 50% of course. 16 of those points were from Friday's move upward, but still, Oracle's highly differentiated strategy of integrating hardware and software together, investing in a modern cloud platform and selectively offering services that cater to the hardcore mission critical buyer, these have served the company, its customers and investors as well. From a cloud standpoint, we'd like to see Oracle be more inclusive, and aggressively expand its marketplace and its ecosystem. This would provide both greater optionality for customers, and further establish Oracle as a major cloud player. Indeed, one of the hallmarks of both AWS and Azure is the momentum being created, by their respective ecosystems. As well, we'd like to see more clear confirmation that Oracle's performance is being driven by its investments in technology IE cloud, same same hybrid, and industry features these modern investments, versus a legacy licensed cycles. We are generally encouraged and are reminded, of years ago when Sam Palmisano, he was retiring and leaving as the CEO of IBM. At the time, HP under the direction ironically of Mark Hurd, was the now company, Palmisano was asked, "do you worry about HP?" And he said in fact, "I don't worry about HP. I worry about Oracle because Oracle invests in R&D." And that statement has proven present. What do you think? Has Oracle hit the next inflection point? Let me know. Don't forget these episodes they're all available as podcasts wherever you listen, all you do is search it. Breaking Analysis podcast, check out ETR website at etr.plus. We also publish a full report every week on wikibon.com and siliconANGLE.com. You can get in touch with me on email David.vellante@siliconangle.com, you can DM me @dvellante on Twitter or, comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights. Powered by ETR. Have a great week everybody. Stay safe, be well, and we'll see you next time. (upbeat music)

>>Edge computing is predict, projected to be a multi-trillion dollar business. You know, it's hard to really pinpoint the size of this market. Let alone fathom the potential of bringing software, compute, storage, AI, and automation to the edge and connecting all that to clouds and on-prem systems. But what, you know, what is the edge? Is it factories? Is it oil rigs, airplanes, windmills, shipping containers, buildings, homes, race cars. Well, yes and so much more. And what about the data for decades? We've talked about the data explosion. I mean, it's mind boggling, but guess what, we're gonna look back in 10 years and laugh. What we thought was a lot of data in 2020, perhaps the best way to think about edge is not as a place, but when is the most logical opportunity to process the data and maybe it's the first opportunity to do so where it can be decrypted and analyzed at very low latencies that that defines the edge. And so by locating compute as close as possible to the sources of data, to reduce latency and maximize your ability to get insights and return them to users quickly, maybe that's where the value lies. Hello everyone. And welcome to this cube conversation. My name is Dave Vellante and with me to noodle on these topics is Omar Assad, VP, and GM of primary storage and data management services at HPE. Hello, Omer. Welcome to the program. >>Hey Steve. Thank you so much. Pleasure to be here. >>Yeah. Great to see you again. So how do you see the edge in the broader market shaping up? >>Uh, David? I think that's a super important, important question. I think your ideas are quite aligned with how we think about it. Uh, I personally think, you know, as enterprises are accelerating their sort of digitization and asset collection and data collection, uh, they're typically, especially in a distributed enterprise, they're trying to get to their customers. They're trying to minimize the latency to their customers. So especially if you look across industries manufacturing, which is distributed factories all over the place, they are going through a lot of factory transformations where they're digitizing their factories. That means a lot more data is being now being generated within their factories. A lot of robot automation is going on that requires a lot of compute power to go out to those particular factories, which is going to generate their data out there. We've got insurance companies, banks that are creating and interviewing and gathering more customers out at the edge for that. >>They need a lot more distributed processing out at the edge. What this is requiring is what we've seen is across analysts. A common consensus is that more than 50% of an enterprise is data, especially if they operate globally around the world is going to be generated out at the edge. What does that mean? More data is new data is generated at the edge, but needs to be stored. It needs to be processed data. What is not required needs to be thrown away or classified as not important. And then it needs to be moved for Dr. Purposes either to a central data center or just to another site. So overall in order to give the best possible experience for manufacturing, retail, uh, you know, especially in distributed enterprises, people are generating more and more data centric assets out at the edge. And that's what we see in the industry. >>Yeah. We're definitely aligned on that. There's some great points. And so now, okay. You think about all this diversity, what's the right architecture for these deploying multi-site deployments, robo edge. How do you look at that? >>Oh, excellent question. So now it's sort of, you know, obviously you want every customer that we talk to wants SimpliVity, uh, in, in, and, and, and, and no pun intended because SimpliVity is reasoned with a simplistic edge centric architecture, right? So because let's, let's take a few examples. You've got large global retailers, uh, they have hundreds of global retail stores around the world that is generating data that is producing data. Then you've got insurance companies, then you've got banks. So when you look at a distributed enterprise, how do you deploy in a very simple and easy to deploy manner, easy to lifecycle, easy to mobilize and easy to lifecycle equipment out at the edge. What are some of the challenges that these customers deal with these customers? You don't want to send a lot of ID staff out there because that adds costs. You don't want to have islands of data and islands of storage and promote sites, because that adds a lot of States outside of the data center that needs to be protected. >>And then last but not the least, how do you push lifecycle based applications, new applications out at the edge in a very simple to deploy better. And how do you protect all this data at the edge? So the right architecture in my opinion, needs to be extremely simple to deploy. So storage, compute and networking, uh, out towards the edge in a hyperconverged environment. So that's, we agree upon that. It's a very simple to deploy model, but then comes, how do you deploy applications on top of that? How do you manage these applications on top of that? How do you back up these applications back towards the data center, all of this keeping in mind that it has to be as zero touch as possible. We at HBS believe that it needs to be extremely simple. Just give me two cables, a network cable, a power cable, tied it up, connected to the network, push it state from the data center and back up at state from the ed back into the data center. Extremely simple. >>It's gotta be simple because you've got so many challenges. You've got physics that you have to deal your latency to deal with. You got RPO and RTO. What happens if something goes wrong, you've gotta be able to recover quickly. So, so that's great. Thank you for that. Now you guys have hard news. W what is new from HPE in this space >>From a, from a, from a, from a deployment perspective, you know, HPE SimpliVity is just gaining like it's exploding, like crazy, especially as distributed enterprises adopt it as it's standardized edge architecture, right? It's an HCI box has got stories, computer networking, all in one. But now what we have done is not only you can deploy applications all from your standard V-Center interface, from a data center, what have you have now added is the ability to backup to the cloud, right? From the edge. You can also back up all the way back to your core data center. All of the backup policies are fully automated and implemented in the, in the distributed file system. That is the heart and soul of, of the SimpliVity installation. In addition to that, the customers now do not have to buy any third-party software into backup is fully integrated in the architecture and it's van efficient. >>In addition to that, now you can backup straight to the client. You can backup to a central, uh, high-end backup repository, which is in your data center. And last but not least, we have a lot of customers that are pushing the limit in their application transformation. So not only do we previously were, were one-on-one them leaving VMware deployments out at the edge sites. Now revolver also added both stateful and stateless container orchestration, as well as data protection capabilities for containerized applications out at the edge. So we have a lot, we have a lot of customers that are now deploying containers, rapid manufacturing containers to process data out at remote sites. And that allows us to not only protect those stateful applications, but back them up, back into the central data center. >>I saw in that chart, it was a light on no egress fees. That's a pain point for a lot of CEOs that I talked to. They grit their teeth at those entities. So, so you can't comment on that or >>Excellent, excellent question. I'm so glad you brought that up and sort of at that point, uh, uh, pick that up. So, uh, along with SimpliVity, you know, we have the whole green Lake as a service offering as well. Right? So what that means, Dave, is that we can literally provide our customers edge as a service. And when you compliment that with, with Aruba wired wireless infrastructure, that goes at the edge, the hyperconverged infrastructure, as part of SimpliVity, that goes at the edge, you know, one of the things that was missing with cloud backups is the every time you backup to the cloud, which is a great thing, by the way, anytime you restore from the cloud, there is that breastfeed, right? So as a result of that, as part of the GreenLake offering, we have cloud backup service natively now offered as part of HPE, which is included in your HPE SimpliVity edge as a service offering. So now not only can you backup into the cloud from your edge sites, but you can also restore back without any egress fees from HBS data protection service. Either you can restore it back onto your data center, you can restore it back towards the edge site and because the infrastructure is so easy to deploy centrally lifecycle manage, it's very mobile. So if you want to deploy and recover to a different site, you could also do that. >>Nice. Hey, uh, can you, Omar, can you double click a little bit on some of the use cases that customers are choosing SimpliVity for, particularly at the edge, and maybe talk about why they're choosing HPE? >>What are the major use cases that we see? Dave is obviously, uh, easy to deploy and easy to manage in a standardized form factor, right? A lot of these customers, like for example, we have large retailer across the us with hundreds of stores across us. Right now you cannot send service staff to each of these stores. These data centers are their data center is essentially just a closet for these guys, right? So now how do you have a standardized deployment? So standardized deployment from the data center, which you can literally push out and you can connect a network cable and a power cable, and you're up and running, and then automated backup elimination of backup and state and BR from the edge sites and into the data center. So that's one of the big use cases to rapidly deploy new stores, bring them up in a standardized configuration, both from a hardware and a software perspective, and the ability to backup and recover that instantly. >>That's one large use case. The second use case that we see actually refers to a comment that you made in your opener. Dave was where a lot of these customers are generating a lot of the data at the edge. This is robotics automation that is going to up in manufacturing sites. These is racing teams that are out at the edge of doing post-processing of their cars data. Uh, at the same time, there is disaster recovery use cases where you have, uh, you know, campsites and local, uh, you know, uh, agencies that go out there for humanity's benefit. And they move from one site to the other. It's a very, very mobile architecture that they need. So those, those are just a few cases where we were deployed. There was a lot of data collection, and there's a lot of mobility involved in these environments. So you need to be quick to set up quick, to up quick, to recover, and essentially you're up to your next, next move. >>You seem pretty pumped up about this, uh, this new innovation and why not. >>It is, it is, uh, you know, especially because, you know, it is, it has been taught through with edge in mind and edge has to be mobile. It has to be simple. And especially as, you know, we have lived through this pandemic, which, which I hope we see the tail end of it in at least 2021, or at least 2022. They, you know, one of the most common use cases that we saw, and this was an accidental discovery. A lot of the retail sites could not go out to service their stores because, you know, mobility is limited in these, in these strange times that we live in. So from a central center, you're able to deploy applications, you're able to recover applications. And, and a lot of our customers said, Hey, I don't have enough space in my data center to back up. Do you have another option? So then we rolled out this update release to SimpliVity verse from the edge site. You can now directly back up to our backup service, which is offered on a consumption basis to the customers, and they can recover that anywhere they want. >>Fantastic Omer, thanks so much for coming on the program today. >>It's a pleasure, Dave. Thank you. >>All right. Awesome to see you. Now, let's hear from red bull racing and HPE customer, that's actually using SimpliVity at the edge. Countdown really begins when the checkered flag drops on a Sunday. It's always about this race to manufacture >>The next designs to make it more adapt to the next circuit to run those. Of course, if we can't manufacture the next component in time, all that will be wasted. >>Okay. We're back with Matt kudu, who is the CIO of red bull racing? Matt, it's good to see you again. >>Great to say, >>Hey, we're going to dig into a real-world example of using data at the edge and in near real time to gain insights that really lead to competitive advantage. But, but first Matt, tell us a little bit about red bull racing and your role there. >>Sure. So I'm the CIO at red bull racing and that red bull race. And we're based in Milton Keynes in the UK. And the main job job for us is to design a race car, to manufacture the race car, and then to race it around the world. So as CIO, we need to develop the ITT group needs to develop the applications is the design, manufacturing racing. We also need to supply all the underlying infrastructure and also manage security. So it's really interesting environment. That's all about speed. So this season we have 23 races and we need to tear the car apart and rebuild it to a unique configuration for every individual race. And we're also designing and making components targeted for races. So 20 a movable deadlines, um, this big evolving prototype to manage with our car. Um, but we're also improving all of our tools and methods and software that we use to design and make and race the car. >>So we have a big can do attitude of the company around continuous improvement. And the expectations are that we continuously make the car faster. That we're, that we're winning races, that we improve our methods in the factory and our tools. And, um, so for, I take it's really unique and that we can be part of that journey and provide a better service. It's also a big challenge to provide that service and to give the business the agility, agility, and needs. So my job is, is really to make sure we have the right staff, the right partners, the right technical platforms. So we can live up to expectations >>That tear down and rebuild for 23 races. Is that because each track has its own unique signature that you have to tune to, or are there other factors involved there? >>Yeah, exactly. Every track has a different shape. Some have lots of strengths. Some have lots of curves and lots are in between. Um, the track surface is very different and the impact that has some tires, um, the temperature and the climate is very different. Some are hilly, some, a big curves that affect the dynamics of the power. So all that in order to win, you need to micromanage everything and optimize it for any given race track. >>Talk about some of the key drivers in your business and some of the key apps that give you a competitive advantage to help you win races. >>Yeah. So in our business, everything is all about speed. So the car obviously needs to be fast, but also all of our business operations needed to be fast. We need to be able to design a car and it's all done in the virtual world, but the, the virtual simulations and designs need to correlate to what happens in the real world. So all of that requires a lot of expertise to develop the simulation is the algorithms and have all the underlying infrastructure that runs it quickly and reliably. Um, in manufacturing, um, we have cost caps and financial controls by regulation. We need to be super efficient and control material and resources. So ERP and MES systems are running and helping us do that. And at the race track itself in speed, we have hundreds of decisions to make on a Friday and Saturday as we're fine tuning the final configuration of the car. And here again, we rely on simulations and analytics to help do that. And then during the race, we have split seconds, literally seconds to alter our race strategy if an event happens. So if there's an accident, um, and the safety car comes out, or the weather changes, we revise our tactics and we're running Monte Carlo for example. And he is an experienced engineers with simulations to make a data-driven decision and hopefully a better one and faster than our competitors, all of that needs it. Um, so work at a very high level. >>It's interesting. I mean, as a lay person, historically we know when I think about technology and car racing, of course, I think about the mechanical aspects of a self-propelled vehicle, the electronics and the light, but not necessarily the data, but the data's always been there. Hasn't it? I mean, maybe in the form of like tribal knowledge, if somebody who knows the track and where the Hills are and experience and gut feel, but today you're digitizing it and you're, you're processing it and close to real time. >>It's amazing. I think exactly right. Yeah. The car's instrumented with sensors, we post-process at Virgin, um, video, um, image analysis, and we're looking at our car, our competitor's car. So there's a huge amount of, um, very complicated models that we're using to optimize our performance and to continuously improve our car. Yeah. The data and the applications that can leverage it are really key. Um, and that's a critical success factor for us. >>So let's talk about your data center at the track, if you will. I mean, if I can call it that paint a picture for us, what does that look like? >>So we have to send, um, a lot of equipment to the track at the edge. Um, and even though we have really a great wide area network linked back to the factory and there's cloud resources, a lot of the trucks are very old. You don't have hardened infrastructure, don't have ducks that protect cabling, for example, and you could lose connectivity to remote locations. So the applications we need to operate the car and to make really critical decisions, all that needs to be at the edge where the car operates. So historically we had three racks of equipment, like a safe infrastructure, um, and it was really hard to manage, um, to make changes. It was too flexible. Um, there were multiple panes of glass, um, and, um, and it was too slow. It didn't run her applications quickly. Um, it was also too heavy and took up too much space when you're cramped into a garage with lots of environmental constraints. >>So we, um, we'd, we'd introduced hyperconvergence into the factory and seen a lot of great benefits. And when we came time to refresh our infrastructure at the track, we stepped back and said, there's a lot smarter way of operating. We can get rid of all the slow and flexible, expensive legacy and introduce hyperconvergence. And we saw really excellent benefits for doing that. Um, we saw a three X speed up for a lot of our applications. So I'm here where we're post-processing data, and we have to make decisions about race strategy. Time is of the essence in a three X reduction in processing time really matters. Um, we also, um, were able to go from three racks of equipment down to two racks of equipment and the storage efficiency of the HPE SimpliVity platform with 20 to one ratios allowed us to eliminate a rack. And that actually saved a hundred thousand dollars a year in freight costs by shipping less equipment, um, things like backup, um, mistakes happen. >>Sometimes the user makes a mistake. So for example, a race engineer could load the wrong data map into one of our simulations. And we could restore that VDI through SimpliVity backup at 90 seconds. And this makes sure it enables engineers to focus on the car to make better decisions without having downtime. And we sent them to, I take guys to every race they're managing 60 users, a really diverse environment, juggling a lot of balls and having a simple management platform like HPE SimpliVity gives us, allows them to be very effective and to work quickly. So all of those benefits were a huge step forward relative to the legacy infrastructure that we used to run at the edge. >>Yeah. So you had the nice Petri dish and the factory. So it sounds like your, your goals, obviously your number one KPI is speed to help shave seconds time, but also costs just the simplicity of setting up the infrastructure. >>Yeah. It's speed. Speed, speed. So we want applications absolutely fly, you know, get to actionable results quicker, um, get answers from our simulations quicker. The other area that speed's really critical is, um, our applications are also evolving prototypes, and we're always, the models are getting bigger. The simulations are getting bigger and they need more and more resource and being able to spin up resource and provision things without being a bottleneck is a big challenge in SimpliVity. It gives us the means of doing that. >>So did you consider any other options or was it because you had the factory knowledge? It was HCI was, you know, very clearly the option. What did you look at? >>Yeah, so, um, we have over five years of experience in the factory and we eliminated all of our legacy, um, um, infrastructure five years ago. And the benefits I've described, um, at the track, we saw that in the factory, um, at the track we have a three-year operational life cycle for our equipment. When into 2017 was the last year we had legacy as we were building for 2018. It was obvious that hyper-converged was the right technology to introduce. And we'd had years of experience in the factory already. And the benefits that we see with hyper-converged actually mattered even more at the edge because our operations are so much more pressurized time has even more of the essence. And so speeding everything up at the really pointy end of our business was really critical. It was an obvious choice. >>Why, why SimpliVity? What why'd you choose HPE SimpliVity? >>Yeah. So when we first heard about hyperconverged way back in the, in the factory, um, we had, um, a legacy infrastructure, overly complicated, too slow, too inflexible, too expensive. And we stepped back and said, there has to be a smarter way of operating. We went out and challenged our technology partners. We learned about hyperconvergence within enough, the hype, um, was real or not. So we underwent some PLCs and benchmarking and, and the, the PLCs were really impressive. And, and all these, you know, speed and agility benefits, we saw an HP for our use cases was the clear winner in the benchmarks. So based on that, we made an initial investment in the factory. Uh, we moved about 150 VMs in the 150 VDI into it. Um, and then as, as we've seen all the benefits we've successfully invested, and we now have, um, an estate to the factory of about 800 VMs and about 400 VDI. So it's been a great platform and it's allowed us to really push boundaries and, and give the business, um, the service that expects. >>So w was that with the time in which you were able to go from data to insight to recommendation or, or edict, uh, was that compressed, you kind of indicated that, but >>So we, we all telemetry from the car and we post-process it, and that reprocessing time really it's very time consuming. And, um, you know, we went from nine, eight minutes for some of the simulations down to just two minutes. So we saw big, big reductions in time and all, ultimately that meant an engineer could understand what the car was during a practice session, recommend a tweak to the configuration or setup of it, and just get more actionable insight quicker. And it ultimately helps get a better car quicker. >>Such a great example. How are you guys feeling about the season, Matt? What's the team's sentiment? >>Yeah, I think we're optimistic. Um, we w we, um, uh, we have a new driver >>Lineup. Uh, we have, um, max for stopping his carries on with the team and Sergio joins the team. So we're really excited about this year and, uh, we want to go and win races. Great, Matt, good luck this season and going forward and thanks so much for coming back in the cube. Really appreciate it. And it's my pleasure. Great talking to you again. Okay. Now we're going to bring back Omer for quick summary. So keep it real >>Without having solutions from HB, we can't drive those five senses, CFD aerodynamics that would undermine the simulations being software defined. We can bring new apps into play. If we can bring new them's storage, networking, all of that can be highly advises is a hugely beneficial partnership for us. We're able to be at the cutting edge of technology in a highly stressed environment. That is no bigger challenge than the formula. >>Okay. We're back with Omar. Hey, what did you think about that interview with Matt? >>Great. Uh, I have to tell you I'm a big formula one fan, and they are one of my favorite customers. Uh, so, you know, obviously, uh, one of the biggest use cases as you saw for red bull racing is Trackside deployments. There are now 22 races in a season. These guys are jumping from one city to the next, they've got to pack up, move to the next city, set up, set up the infrastructure very, very quickly and average formula. One car is running the thousand plus sensors on that is generating a ton of data on track side that needs to be collected very quickly. It needs to be processed very quickly, and then sometimes believe it or not, snapshots of this data needs to be sent to the red bull back factory back at the data center. What does this all need? It needs reliability. >>It needs compute power in a very short form factor. And it needs agility quick to set up quick, to go quick, to recover. And then in post processing, they need to have CPU density so they can pack more VMs out at the edge to be able to do that processing now. And we accomplished that for, for the red bull racing guys in basically two are you have two SimpliVity nodes that are running track side and moving with them from one, one race to the next race, to the next race. And every time those SimpliVity nodes connect up to the data center collector to a satellite, they're backing up back to their data center. They're sending snapshots of data back to the data center, essentially making their job a whole lot easier, where they can focus on racing and not on troubleshooting virtual machines, >>Red bull racing and HPE SimpliVity. Great example. It's agile, it's it's cost efficient, and it shows a real impact. Thank you very much. I really appreciate those summary comments. Thank you, Dave. Really appreciate it. All right. And thank you for watching. This is Dave Volante. >>You.

>>Hello. Nice to meet you. My name is yes. Um Escuela. I'm a professor in a university in Japan. So today I want to introduce my research. That title is a really time emotional detection using e g with riel time knowing the reduction. First of all, I want to introduce myself. My major is system identification and signal processing for large removed and by American signal process for owner off them. A common technique. It's most magical. Modern by you creation using this opportunity identification method. So today topic it's e easy modern by the Barriers Council with heavy notes. We call this technique the concept moody. Now what is a concept? I mean, the concept is Japanese world because studies are first in Japan. So consider is similar to emotion and sensibility, but quite different. The commercial nous sensibility is innate ability. The concert is acquired after birth, so concept is similar to how to be So we focus on this can see using the brain signals. As for the brain Sina, there is ah, many way to know the brain. For example, the optical leading X c T m i m e g e g optical topography um, function and my by using these devices, we have three areas off research, for example, like neural engineering area for obligation, including new market neuroscience area for understanding the mechanism a medically oil area for treatment. So but it's very important to use, depending on the purpose. So what did they can be obtained? Uh, in the case of e g, we can see the activity of neurons that scalp the case of in years so we can attain the river off oxygen bar Pratt The case off natural and safe Alagem we can see the activity of new uh, that contact is neck case off position. Martian topography. We can get activity off reception by the contact list. If we use that, I we can measure the amount of blood by the contractors. These devices are showing these figures. So our motivation is to get the concept question using their model by system identification where it's not removed on. The second motivation is to theorize that's simple and small cancer X election using the each information when we use the ever my the large scale and the expensive on binding. So it is unuseful. So we focus on the EEG because the e g iss Moscow inexpensive a non binding on to use. So we focus on the energy. So e g is actually a potential from the major from the scalp that detective data is translated to the pregnancy domain. And if you can see domain that their point to 44. We call the data death of it 4 to 6. We called a cedar with on 17. 14 were called the Alfa Hour and 14 to 26. We called a better work in a conventional method we want if we want use the cats a deep sleep, we use that death of it in a case of light sleep we used a secretive and so but this is just only the sensible method. So we cannot use that for all the film Actuary accuracies under the 20%. So we need to define the situation original. So recall this technique council modeling. So these are the block diagram Kansi the concept What? So this field this part eyes for the noise, this part for the mathematical model. So we calculate this transfer function like this. This is a discrete time water, and, uh, this time, uh, is continuous time model. So then we really right this part Thio Discrete time water. So we cull Create, uh, this part us like this This'll first part on the second part is calculated by the party application so we can get this the argumentative model. So then that we were right this part by using that the transfer function transport formation. So we right this argument ID model like this. So the off about the inverse and better off the inverse is the point as this equation. So each the coefficient is corrugated by this equation on. But then we calculate a way too busy with beaver by using this because of a least squares algorithm. So we call this identification method the self joining identification method. Um, that this is an example of stories modeling. The first of all, we decide we gather the data like a story. It's moving. So we move the small beans, try to trade at 41 hour. So last 10 minutes we used as stories and we measure that culture soul for sliced levin Onda. We associate the egg and we measure the 8000 data. Uh, in 17 years we? Yeah, that's a 17 years. So in the case, off the simple, easy universes that there are many simply devices in the world like this so many of them the There we calculate the signal nodes. Lazio, The signal means the medical easy system on the each device made it sn Lazio. And we investigate 58 kinds off devices on almost off All devices are noise devices. So I'm also asked about to various parts more device that best. So my answer is anything. Our skill is, you know, processing on def. With love. Data can be obtained from the device. No, but what device? He may use the same result commission. Our novelty is level Signal processing on our system is structured by 17 years Data for one situation. So the my answer is what? Anything. So we applied this system to Arial product. We call this product concern Analyzer. In a concept analyzer, you can see the concept that right the our time a concept dinner influence Solis sickness concentration on like so that we combine that this can't say analyzer And the camera system We made the euro system your account so pretty show it this is in Eureka. Well, this is, uh, e g system and we can get can say by using the iPhone on the, uh, we combine the camera system by the iPhone camera and if the cancer is higher than the 6% 60% so automatically recorded like this. Mhm. So every time we wear the e g devices, we can see the no awareness, the constant way. That's so finally we combine the each off cancer. So like that this movie, so we can see the thes one days. Can't say the movie s Oh, this is a miracle. On the next example, it's neuro marketing using a constant analyzer. So this is a but we don't know what is the number one point. So then we analyze the deeds CME by using concert analyzer so we can get the rial time concept then that we can see the one by one situation like this. So this is the interest level and we can see the high interest like this. So the recorded a moment automatically on the next one is really application. The productive design. Ah, >>Japanese professor has come up with a new technology she claims can read minds, she says. The brainwave analysis system will help businesses better understand their customers, needs workers at a major restaurant chain or testing a menu item that is being developed. This device measures brain waves from the frontal lobes of people who try the product. An application analyzes five feelings how much they like something and their interest, concentration, stress and sleepiness. >>The >>new menu item is a cheese souffle topped with kiwi, orange and other fruit. The APP checks the reaction of a person who sees the souffle for the first time. Please open your eyes. When she sees the souffle, the like and interest feelings surge on the ground. This proves the desert is visually appealing. Now please try it. After the first bite, the like level goes up to 60. That shows she likes how the dessert tastes. After another bite, the like level reaches 80. She really enjoys the taste of the souffle. It scores high in terms of both looks and taste, but there's an unexpected problem. When she tries to scoop up the fruit, the stress level soars to 90. I didn't know where to put the spoon. I felt it was a little difficult to eat. It turned out it was difficult to scoop up the fruit with a small spoon. So people at the restaurant chain are thinking of serving this a flavor with a fork instead. Green well. How could be the difference with the device? We can measure emotional changes in minute detail in real time. This is a printing and design firm in Tokyo. >>It >>designs direct mail and credit card application forms. The company is using the brainwave analyzing system to improve the layout of its products. The idea is to make them easier to read during this test, The subject wears an eye tracking device to record where she's looking. In addition to the brainwave analyzing device, her eye movements are shown by the red dots on the screen. Stress levels are indicated on the graph on the left. Please fill out the form. This is a credit card application form. Right after she turns her eyes to this section, her stress levels shoots up. It was difficult to read as each line contained 60 characters, so they decided to divide the section in two, cutting the length of the lines by half 15 a Hong Kong. This system is very useful for us. We can offer differentiated service to our clients by providing science based solutions. The brain wave analyzed. >>Okay, uh, now the we construct a concert detection like this. Like this. Like concentration, interest sickness stories contain, like comfortable, uncomfortable. I'm present the rats emotion, deadly addictive case lighting, comfort, satisfaction and the achievement. So finally we conquer more presentation. So in this presentation, we introduce the our such we construct the council question Onda we demonstrate that c street signal processing and we apply the proposed method to Arial product. Uh, we named the constant riser. So this is the first in the world, that's all. Thank you so much.

>> Hello everyone. My name is Zoltan Toroczkai. I am from University of Notre Dame, Physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with the Redefine Lab and Yoshian collaborators on the topics of this work. So today I'll briefly talk about, our attempt to understand, the fundamental limits of analog, continuous-time computing at least from the point of view of Boolean Satisfiability problem-solving using ordinary differential equations. But I think the issues that we raise during this occasion actually apply to other approaches, analog approaches as well, until to other problems as well. I think everyone here, knows what Boolean Satisfiability problems are. You have N Boolean variables, you have M clauses. Each a disjunction of K literals. Literal is a variable or it's negation. And the goal is to find an assignment to the variable such that all the clauses are true. This is a decision type problem from the NP class, which means you can check in polynomial time for satisfiability of any assignment. And the 3-SAT is NP-complete with K, 3 or larger, which means an efficient 3-SAT solver, (clears throat) implies an efficient solver for all the problems in the NP clause because all the problems in the NP clause can be reduced in polynomial time to 3-SAT. As a matter of fact you can, reduce the NP-complete problems into each other. You can go from 3-SAT to Set Packing or to Maximum Independent Set which is the set packing in graph theoretic notions or terms, to the ising graph SAT problem decision version. This is useful when you are comparing different approaches or working on different kinds of problems. When not all the clauses can be satisfied, you're looking at the optimization version of SAT, called Max-SAT and the goal here is to find the assignment that satisfies the maximum number of clauses, and this is from the NP-hard class. In terms of applications, if we had an efficient SAT solver, or NP-complete problem solver, it would literally, positively influence thousands of problems in applications in industry and science. I'm not going to read this. But this of course gives us some motivation, to work on these kind of problems. Now, our approach to SAT solving, involves embedding the problem in a continuous space, and you use all these to do that. So instead of working zeros and ones, we work with minus one and plus ones, and if we allow the corresponding variables, to change continuously between the two bounds, we formulate the problem with the help of a Clause Matrix. If, if a clause does not contain a variable or its negation, the corresponding matrix element is zero. If it contains the variable in positive form it's one. If it contains the variable in negated form, it's negative one. And now we use this to formulate these products, called clause violation functions, one for every clause, which rarely continues between zero and one and beyond zero if and only if the clause itself is true. Then we form... We define, also define the dynamics, search dynamics in this and the M-dimensional hypercube, where the search happens and if there exists solutions they're sitting in some of the corners of this hypercube. So we define this energy, potential or landscape function as shown here in a way that it, this is zero if and only if all the clauses, all the Kms are zero. All the clauses are satisfied, keeping these auxiliary variables, Ams always positive. And therefore what we do here is a dynamics that is essentially a gradient descent on this potential energy landscape. If you are to keep all the Ams constant then it would get stuck in some local minimum. However what do you do here is, we couple it with the dynamics. We couple it with the clause violation functions as shown here. And if you didn't have these Am here, just had just the Kms, for example, you have essentially, both case you have a positive feedback. You have a decreasing variable, but in that case you'll still get stuck, would still behave... We'll still find solutions better than the constant version or still would get stuck. Only when we put here this Am, which makes them dynamics in this variable exponential like, only then it keeps searching until it finds a solution. And there's a reason for that, that I'm not going to talk about here, but essentially boils down to performing a gradient descent on a globally time-varying landscape. And, and, and this is what works. Now, I'm going to talk about the good or bad, and maybe the ugly. This is, this is... What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any solution in it, then the number of trajectories in it, the case exponentially quickly and the decay rate is a characteristic, invariant characteristic of the dynamics itself with the dynamical systems called the escape rate. The inverse of that is the timescale in which you find solutions by this dynamical system. And you can see here some trajectories, they are curved because it's, it's not linear but it's transiently curved to give, if there are solutions of course, we could see eventually, it does lead to the solutions. Now, in terms of performance, here what you show, for a bunch of, constraint densities, defined by, M over N, the ratio between clauses to variables, for random SAT problems, is random 3-SAT problems. And they, they, as, as function of N, and we look at, monitor the wall time, the wall clock time, and it, it behaves quite well, it behaves as a, as a polynomialy, until you actually hit, or reach the set on set transition, where the hardest problems are found. But what's more interesting is if you monitor the continuous-time t, the performance in terms of the analog continuous-time t, because that seems to be a polynomial. And the way we show that, is we can see the random K-SAT or random 3-SAT for a fixed constraint density. And we here, what you show here is at the, right at the threshold where it's really hard. And, (clears throat) we monitor the fraction of problems that we have not been able to solve it. We select thousands of problems at that cost rate ratio and we solve them with our algorithm, and we monitor the fraction of problems that have not yet been solved by continuous-time t. And these, as you see these decays exponentially in different decay rates for different system sizes and in this spot shows that this decay rate behaves polynomialy. or actually as a power law. So if you combine these two, you find that the time needed to solve all problems, except maybe appeared fraction of them, scales polynomialy with problem size. So you have polynomial continuous-time complexity. And this is also true, for other types of very hard constraints of the SAT problem such as exact color, because you can always transform them into 3-SAT as we discussed before, Ramsay coloring and, and on these problems, even algorithms like a survey propagation wheel will fail. But this doesn't mean that P equals NP because what you have, first of all, if you were to implement these equations in a device, whose behavior is described by these ODEs, then of course, t the continuous-time variable, becomes a physical wall clock time. And that would be polynomialy scaling but you have other variables, auxiliary variables, which fluctuate in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost algorithm. But this is some kind of trade between time and energy while I know how to generate energy or I don't know how to generate time but I know how to generate energy so it could be useful. But there's other issues as well, especially if you're trying to do this on a digital machine, but also happens, problems happen, appear, other problems appear on in physical devices as well as we discuss later. So if you implement these in GPU, you can, then you can get an order of two magnitude speedup, and you can also modify this, to solve Max-SAT problems quite efficiently, we are competitive with the best, heuristics solvers, this is all the problems in 2016, Max-SAT competition. So, so this, this, this is definitely, this is like a good approach, but there's of course, interesting limitations, I would say interesting, because it kind of makes you think about what it needs and how you can explore this, these observations in understanding better analog continuous-time complexity. If you monitor the discrete number, the number of discrete steps, done by the Runge Kutta integrator, and you solve this on a digital machine. You're using some kind of integrator, and, you know, using the same approach, but now you measure the number of problems you haven't solved, by a given number of discrete steps taken by the integrator. You find out, you have exponential discrete-time complexity. And of course, this is a problem. And if you look closely, what happens, even though the analog mathematical trajectory, that's the red curve here, if you monitor what happens in discrete time, the integrator fluctuates very little. So this is like you know, third or four digits precision, but fluctuates like crazy. So it really is like the integration freezes out, and this is because of the phenomenon of stiffness that I'll talk a little bit, more about a little bit later. So you know, it may look like an integration issue on your digital machines that you could improve and you could definitely improve, but actually the issue is bigger than that. It's, it's deeper than that because on a digital machine there is no time energy conversion. So the auxiliary variables are efficiently represented in a digital machine, so there's no exponential fluctuating current or voltage in your computer when you do this. So if e is not equal NP, then the exponential time complexity or exponential cost complexity has to hit you somewhere. And this is how. But you know one would be tempted to think maybe, this wouldn't be an issue in a analog device, and to some extent is true. Analog devices can be orders of magnitude faster, but they also suffer from their own problems because P not equal NP affects that clause of followers as well. So, indeed if you look at other systems, like Coherent Ising Machine with Measurement-Feedback, or Polariton Condensate Graphs or Oscillator Networks, they all hinge on some kind of, our ability to control real variables with arbitrarily high precision, and Oscillator Networks, you want to read out arbitrarily close frequencies. In case of CIMs, we require identical analog amplitudes which is hard to keep and they kind of fluctuate away from one another, shift away from one another, And, and if you control that, of course, then you can control the performance. So, actually one can ask if whether or not this is a universal bottleneck, and it seems so, as I will argue next. We can recall a fundamental result by A. Schönhage, Graham Schönhage from 1978 who says that, it's a purely computer science proof, that, "If you are able to compute, "the addition, multiplication, division "of real variables with infinite precision then, "you could solve NP-complete problems in polynomial time." He doesn't actually propose a solid work, he just shows mathematically that this will be the case. Now, of course, in real world, you have loss of precision. So the next question is, "How does that affect the computation of our problems?" This is what we are after. Loss of precision means information loss or entropy production. So what we are really looking at, the relationship between hardness and cost of computing of a problem. (clears throat) And according to Sean Harget, there is this left branch, which in principle could be polynomial time, but the question, whether or not this is achievable, that is not achievable, but something more achievable that's on the right-hand side. You know, there's always going to be some information loss, some entropy generation that could keep you away from, possibly from polynomial time. So this is what we'd like to understand. And this information loss, the source of this is not just noise, as, as I will argue in any physical system, but it's also of algorithmic nature. So that is a questionable area or, or approach, but Schönhage's result is purely theoretical, no actual solver is proposed. So we can ask, you know, just theoretically, out of curiosity, "Would in principle be such solvers?" Because he's not proposing a solver. In such properties in principle, if you were to look mathematically, precisely what that solver does, would have the right properties. And I argue, yes, I don't have a mathematical proof but I have some arguments that this would be the case. And this is the case for actually our sitdia solver, that if you could calculate, it's subjectivity in a loss this way, then it would be... Would solve NP-complete problems in polynomial continuous-time. Now, as a matter of fact, this is a bit more difficult question because time in all these can be re-scaled however you want. So what Bournez says, that you actually have to measure the length of the trajectory which is an invariant of the dynamical system or the property of the dynamical system, not of it's parametrization. And we did that. So Shubha Kharel my student did that, by first improving on the stiffness of the problem of the integrations using the implicit solvers and some smart tricks, such that you actually are closer to the actual trajectory and using the same approach to know, what fraction of problems you can solve. We did not give a length of the trajectory, you find that it is polynomialy scaling with the problem size. So we have polynomial scale complexity. That means that our solver is both poly-length, and as it is defined, it's also poly-time analog solver. But if you look at as a discrete algorithm, which will measure the discrete steps on a digital machine, it is an exponential solver, and the reason is because of all this stiffness. So every integrator has to truncate, digitize and truncate the equations. And what it has to do is to keep the integration within this so-called Stimpy TD gen for, for that scheme. And you have to keep this product within Eigenvalues of the Jacobian and the step size within this region, if you use explicit methods, you want to stay within this region. But what happens, that some of the eigenvalues grow fast for stiff problems, and then you're, you're forced to reduce that t, so the product stays in this bounded domain, which means that now you have to, we are forced to take smaller and smaller time steps, so you're, you're freezing out the integration and what I will show you, that's the case. Now you can move to implicit solvers, which is a new trick, in this case, your stability domain is actually on the outside, but what happens in this case, is some of the eigenvalues of the Jacobian, also for this instant start to move to zero, as they are moving to zero, they are going to enter this instability region. So your solver is going to try to keep it out, so it's going to increase the delta t, but if you increase that t, you increase the truncation errors, so you get randomized in the large search space. So it's, it's really not, not willing to work out. Now, one can sort of, introduce a theory or a language to discuss computational, analog computational complexity, using the language from dynamical systems theory. But basically I don't have time to go into this but you have for hard problems, the chaotic object the chaotic saddle in the middle of the search space somewhere, and that dictates how the dynamics happens and invariant properties of the dynamics, of course, of that saddle is what determines performance and many things. So an important measure that we find that, is also helpful in describing, this analog complexity is the so-called Kolmogorov or metric entropy. And basically what this does in an intuitive way, is to describe the rate at which the uncertainty, containing the insignificant digits of a trajectory in the back, they flow towards the significant ones, as you lose information because of errors being, grown or, or or, or developed into larger errors in an exponential, at an exponential rate because you have positive Lyapunov exponents. But this is an invariant property. It's the property of the set of all these, not how you compute them. And it's really the intrinsic rate of accuracy loss of a dynamical system. As I said that you have in such a high dimensional dynamical system, you have positive and negative Lyapunov exponents, as many as the total is the dimension of the space and user dimension, the number of unstable manufactured dimensions and assets now more stable many forms dimensions. And there's an interesting and I think important Pesin equality, equality called the Pesin equality, that connects the information theoretic, as per the rate of information loss with the geometric data each trajectory separate minus cut part which is the escape rate that I already talked about. Now, one can actually prove a simple theorem strike back of the calculation. The idea here is that, you know the rate at which the largest rate at which the closely started trajectory, separate from one another. So now you can say that, that is fine, as long as my trajectory finds the solution, before the trajectory separate too quickly. In that case, I can have the hope, that if I start from some region of the face space, several closely started trajectories, they kind of go into the same solution over time and that's, that's, that's this upper bound of this limit. And it is really showing that it has to be... It's an exponentially smaller number, but it depends on the N, dependence of the exponent right here, which combines information loss rate and the solution time performance. So these, if these exponent here or there, has a large independence, so even a linear independence, then you really have to start trajectories, exponentially closer to one another, in order to end up in the same order. So this is sort of like the, the direction that you are going into, and this formulation is applicable to, to all dynamical systems, deterministic dynamical systems. (clears throat) And I think we can expand this further because the, there is a way of getting the expression for the escape rates in terms of N the number of variables from cycle expansions, that I don't have time to talk about, but it's kind of like a program that you can try to pursue. And this is it. So uh, uh... The conclusions, I think are self-explanatory. I think there is a lot of future in, in analog continuous-time computing. They can be efficient by orders of magnitude than digital ones in solving NP-hard problems, because first of all, many of the systems lack of von Neumann bottleneck, there's parallelism involved and you can also have a larger spectrum of continuous-time dynamical algorithms than discrete ones. And, and, you know, but we also have to be mindful of what are the possibilities, what are the limits? And one, one open question, if any important open question is you know, "What are these limits? "Is there some kind of no-go theorem that tells you that, "you can never perform better than this limit "or, or that limit?" And I think that's, that's the exciting part to, to derive these, these limits and to get to an understanding about what's possible in this, in this area. Thank you.

(electronic upbeat theme music) >> Announcer: Live from Las Vegas, it's theCUBE covering NetApp Insight 2018. Brought to you by NetApp. >> Welcome back to theCUBE. We are live at NetApp Insight 2018. I'm Lisa Martin with Stu Miniman, and we're please to welcome for the first time to theCUBE, Cindy Warner, SVP of Worldwide Service and Support at NetApp. Cindy, it's great to have you here. >> Thank you. I'm thrilled to be here. >> So this morning's keynote, talked a lot about transformation. Transformation of NetApp. Transformation that your customers need to execute to be competitive, to be successful. Tell us about customer transformation that you're seeing as the leader of service and support. >> Sure, our customers want outcome plain and simple. They are buying solutions that lead to outcome. So in the service and support area, the conversations we're having with our customers now is all about outcome. What can we do to ensure their outcome. To ensure their transformation. To ensure they can provide the services to their customers that they're looking to provide, or new revenue streams, or what have you. But it's really all about outcome and that's awesome because they don't care what's behind the curtain. They don't care if it's this box or that box. They care about outcomes. So that's a really big transformation for us. >> Yeah Cindy, one of the big challenges that used to be, okay, I got a box. I know exactly where it is. I know exactly, you know, who set it up and all the configuration. Now it's like wait. It's a multi-hybrid cloud world. >> Cindy: Right. >> And I got software spanning all of these environments and my data is all over the place. That has to have a huge ripple effect on the services and support. Walk us through a little bit about what that looks like. >> Yeah, I would tell you the number one thing in our world, if you really think about it, is data sovereignty. Because where's my data, you know. If I were a CTO or CIO, I'd wake up in the morning and go, where's my data. Right because, and we're managing that data for a lot of clients. And so it's really all about where's my data, and making sure that the sovereignty of the data is suppose to be in a certain place. It's suppose to be protected in a certain way. We work with a lot of regulated environments. So think healthcare, right. Think, you know, even automotive to some extend. All that IOT data, who's touching that data? That's personal data. So as, you know, the futurist talked about this morning, the ethical side of data for services and support is really intriguing to us actually. >> What's the conversation like, Cindy, with your enterprise legacy customers who weren't born in the cloud? How are you helping them kind of embrace the change that they have to go through? >> Yeah, I think the number one thing is to not be persuade into thinking it's all cloud, right. It's not everything is not made for the cloud. Especially if it wasn't born on the cloud. The pathway to the cloud could be very difficult, and maybe not even prudent. So we're doing a lot of assessments for our clients to decide what workloads belong in the cloud, and helping them to understand, it isn't all cloud. It's some cloud and it's hybrid cloud, and so it's this wonderful Lego cube that we build for them. >> NetApp has done quite a few acquisitions, you know, in the last couple of years. How does that impact what you're doing? Think about everything from the Gubernatis pieces and what's happening in AI. Talk about some of those challenges and opportunities. >> Sure, I mean, I would tell you something like Green Cloud that we did last year. When we look at managing those workloads, and helping to build up that Rubik's cube, right. Of piece parts and what that overall orchestration and architectural looks like in the future. Something like a Green Cloud helps us to orchestrate that. It helps us to manage that and really, that management plane for our clients is really where the heartburn is. It's taking look and seeing that entire data landscape and managing and orchestrating that. And the movement of all that data. That's the biggie. >> You know, follow up question. When I think about NetApp, NetApp was heavily involved in helping to really fix storage in a virtualized environment. >> Cindy: Sure. >> Lots of us have, you know, the wounds, the memories of, you know, over a decade of kind of fighting through that. What is FCS's role in kind of the cloud native this next wave? >> Yeah I, you know, I think it's the overall integration. Our team now is really fixated on where do we go with the overall integration of legacy and the cloud native stuff that clients are building. And grand it, the cloud native stuff gives competitive differentiation. Gives speed, gives scale. Really great stuff. But you can't leave the other stuff behind, right So for us, integration and how that integration is going to work through APIs or otherwise, is really a huge fixation in services and support. >> So NetApp has grown a lot. Done a lot of transformation. Talk about some of the changes to your customers' segmentation and how you're using that information and that segmentation to really deliver differentiated services. 'Cause let's face it, customers have a lot of choice. >> Right, and that's a key word for us actually. We say that the tag line, and for services and support we're looking for value based differentiated services that deliver outcomes. Big mouth. All I know, and I have no marketing chops, as you can tell, but the truth be told, when we look at our Global 1000 customer, they want high touch. And in some cases, no touch. And they want to get the information, solve problems really quickly without having to go, L1, L2 all through the tiers. And so we're piloting programs that are proactive predictive. And that are very high touch to ensure that they can solve their problems quite quickly. Either on their own or through the right person instead of going through some of that typical pathways to support. >> Alright, Cindy, I love you. You're going to help us decode some of this marketing discussion. So, hashtag data driven is something we're seeing at the show. >> Cindy: Right. >> Help connect for us, you know, how are customers being data driven as they look at their future in the cloud and beyond. >> Well, when I think of data driven, I think of new services. That to me means new services. And looking at the correlation, if you may say. Give you, you know, a start here. So the gentleman that had the DNA and Gene-Up data, right, in the keynote. If we can take that data and correlated to somebody's overall health history and see the transition, right. See as your blood pressure is going up. Or see as, you know, certain changes and doubts are happening in your health profile. Overall holistically, you can I think see the train before it hits you. Right, you can see a stroke coming. And that would be the most beautiful thing. Is to see stuff before it hits you. Same with the car manufacturer. If they see a pattern of brakes that are going out, Marry Barra probably never wants to sit in front of the Senate again, right. So we can see those patterns before a massive recall has to happen. So that's data driven to me. It's either new goods and services or seeing a train before it hits you. >> Cindy, I know this is a short segment, but we want to thank you so much for stopping by. I'm going to give you a CUBE sticker because you are now officially an alumni. >> I'll feel CUBED forever more. >> Excellent. CUBED forever more. That's a new hashtag. We want to thank you for sharing your perspective from a services and support standpoint because those are critical services >> Thank you. >> For customers needs. >> And we want to thank you for watching this segment. I'm Lisa Martin with Stu Miniman. You're watching theCUBE live from NetApp Insight 2018. (electronic upbeat theme music)

>>Okay. And welcome back to the cubes live coverage here in San Francisco, California for 80 us summit 2022 Amazon web services summit 2020 New York city is coming up in the summer will be there. Check us out the cube.net. Our next guest here is Jim long. The CEO of dig also known as local. BTV a very interesting AWS customer doing some really progressive things around video and, uh, challenging the status quo in code cutting and all kinds of broadcast models. Jim, welcome to the cube. Great to see you. >>Thank you, John. Great to be here. Okay. >>So first of all, before we get into some of the disrupt option, take a minute to explain what is dig and local BTV. >>Uh, dig is all about, uh, providing, uh, edge video networking for broadcast television, basically modernizing local television and hopefully extending it to hyper local content like high schools and community government and community channels and things like that. So essentially free bringing, using the internet as an antenna to bring broadcast television to your phone, your laptop you're connected TVs. >>So if I understand it correctly, if I UN and I look at the, the materials of your site, you basically go into each market, Metro areas like New York Philly bay area, grab the tee signal out of the air. >>Yep. >>Local TV, and then open that up to everyone. Who's got, um, an >>Correct. And, uh, what, we've, where we're essentially building a hybrid network with AWS. Uh, I like to say we got all the smart and account stuff, you know, in the cloud at AWS. And we have all the dumb, fast stuff in the actual TV market. We have servers and transcoding there we work with, uh, of course, um, uh, AWS on that centrally as well. But basically that hybrid cloud allows us to be the fastest simplest and lowest cost way to get a local video. Any type could be an antenna or an IP stream to a local house. So we're, so are the local pickup and delivery people. We're not building a brand, we're not building content. We're delivering the local content to the local views. You >>Like the pipes. >>We are, we're essentially an infrastructure company. Um, we're right at that wonderful intersection of the, uh, the infrastructure and the content where I always like to play. >>I like, I love the store. I think the cost of that nature, how you're using Amazon, it's really impressive. Um, what are some of the cool things you're doing on AWS that you think's notable? >>Well, of course the, the standard issue stuff where you want to store all your data in the cloud. Right? So we, uh, and we use a quick site to, to get to that. And obviously we're using S3 and we're using media tailor, which we really like, which is cuz we first actual company on the planet. I believe that's inserting digital ads, impression based ads into local broadcast streams. So that's, that's fun because the advertisers, they like the fact that they could still do traditional TV buys and they could spice it up with digital impressions based, but ads on us. Yeah. And, and we're adding to it a real fun thing called clip it, which is user clipping. It's an app that's been running on AWS for years. It's had over half a million plays in social media. Yeah. We're combining those together and, and AWS makes it very simple to do that. >>Well, I've been using your app on my Firestick and uh, download local BTV on the app store. Um, I gotta say the calendar's awesome. And the performance is 10 times better than, than some of the other streaming apps because the other performance they crash all the time. The calendar's weird. So congratulations. Clearly you're running the cloud technology. I gotta ask you what's going on in the market? Netflix missed their earnings. The stock was down big time. Um, obviously competition what's up going on with Netflix? >>Well, what's, it's a big shift. >>What does it mean for the streaming market? >>Well, what it means is, is, is a consumer choice. It's really the golden age of consumer choice. Uh, originally back when I was a kid, it was all antenna TV. We didn't even have DBRS right. And then, uh, the cable companies and the satellite companies, the phone companies came in and took over and all of a sudden everyone started paying for TV for just linear TV. Right? And then the next thing, you know, streaming comes around, uh, Netflix shows up for, for VOD or, or SVOD, they call it cuz it's payt TV and uh, and the whole, uh, that ecosystem starts to melt down. And now you have a consumer choice market where you can pay, pay for VAD or pay for, for linear. And everyone does linear and everyone does VAD or you can use free TV. Now we correctly guessed that free TV was gonna have a huge comeback. You know, know what is it about free even obviously gen Z smarter than us boomers. They love free too. Uh, targeted advertising makes the ads less, uh, painful or less of a distraction. Uh, so we knew that free ad supported TV was gonna happen. Lots of stuff happened. And then, then the, uh, major media companies started doing their own subscription apps. Right? They're all cool. >>We like paramount plus >>Paramount plus Disney pluses, PN peacock, uh, time Warner's doing something. I mean, it's all cool, but you know, people only have so much of a big pocketbook. So what it's doing is pay TV has now become much more complicated, but also you, you know, you gotta trade off. So you saw it with Netflix, right? Yeah. Netflix is suffering from there's too much pay TV. So where are you gonna put your money on Comcast? On YouTube TV paramount plus Netflix. >>Yeah. I mean, I love the free thing. I gotta bring up something. I wanna get your reaction to a company called low cast went under, they got sued out of their deal. They were the free TV. Are you guys have issues like them? What's the cast most people don't know got was, was >>Doing same. So we started before low cast and we're uh, what we would call a permissions based system, legal system. The broadcast Mar industry, uh, is, uh, is the wild wild west. I mean, I like to say antenna TV is a direct to consumer. The antenna is a direct to consumer device and it's controlled by the channel. People it's not controlled by a platform like Comcast, right? It's not controlled by a stick. >>When you say channel, do you mean like CBS or >>Yeah, CBS or the local Korean religious cooking channel or, uh, Spanish channels or local independent to television, which is really a national treasure for us. The United States really should be making sure that local content, local channels, uh, do well local businesses, you know, with targeted advertising, Janes nail salon can, can now advertise just in San Jose and not the entire San Francisco TV market. Um, so you ha you have, have all that going on and we recognize, you know, that, that local content, but you have to have permission from the channel stuff. It's not easy because you got channels on stations. You have syndicators, it's hard to keep track of. And sometimes you, you, uh, you, you know, you have to shift things around, but, uh, low cast, uh, like another kind before it just went hog wild, illegal, trying to use a loophole, uh, didn't quite work out for 'em and, uh, >>You see, they have put out of business by the networks, the names, the big names. Yes. Content people, >>Correct. I mean the big, the big guys, but I mean, because they weren't following the rules, um, >>The rules, meaning license, the content, right. >>Well correct. Or yes, >>Basically they, they were stealing the content in the eyes of the, >>Well, there is, there is, it is a little of, a bit of a gray area between the FCC and the copyright laws that Congress made. So, um, there are people certainly out there that think there is a path there, low cast, didn't find it. We're not trying to find it. Uh, we just want to get all the free TV, uh, the bottom line. And you've seen fast channels explode recently, Pluto, uh, Samsung TV. >>And what does that all mean? >>Well, what it means is people love free TV and the best free TV out there is your local TV. So putting that on the internet and those comp, but the media companies, they have trouble with this new stuff. What's, >>What's your >>They're overthinking it. What's >>Some of this CBS, NBC, all these big guys. >>Well, those guys have a little less trouble than the people that actually, uh, they're affiliates, right? So there's 210 TV markets and the, uh, your major networks, you know, they have their own stations. And in a bit, you know, in about 39% of the population, which is about 15 to 20, is it >>Cultural or is a system system problem? >>No, it's a, it's a problem of all the, the media companies are just having trouble moving towards the new technology and, and they're, I think they're siloing it. >>So why not? You gonna let 'em die. Are you trying to do deals with em? >>Oh no, no, absolutely. For us, if we don't make money, unless stations make money, we want local TV to, to flourish. It is local TV is Neilson, just report yesterday, you know, uh, that, uh, local TV is growing. We're taking advantage of that. And I think the station groups are having a little trouble realizing that they have the original, fast channels before Pluto, before Tubi did it in movies. And, and, and what >>Are people understanding in the, in the industry? I know NA's coming up a show. Yeah, >>That's right. >>National associated of broadcasters. What's going on in that industry right now. And you're, if you get to put it down the top three problems that are opportunities to be solved, what would they be? >>Well, I think, you know, I think the, the, the, the last, the, the best one that's left is what we're doing. I have to say it, uh, I think it's worth billions. >>You free TV over the air free and stream >>O TV. Oh yeah. Over the air TV that also works with the internet, right. Public internet connected to public television stations so that everybody, including homeless people, et cetera, that, you know, they don't have a TV, they don't have an antenna, they can't afford comp. They got an >>IPhone though. >>They an iPhone. For sure. And, and so it's, it's, uh, it's a wonderful thing. It's, you know, our national broadcasting and I don't think the station groups or the major networks are taking advantage of it they're as much as they should. Yeah. And, and I don't think, you know, obviously NBC and CBS with their new apps, they're sort of done with that. They did mergers, they got, they got the virtual pay guys. I mean, YouTube TV off the ground, the only thing left is suck another shitload of good, uh, eyeballs and, and advertising. >>Well, I mean, yeah, I think that, that, and what you said earlier around subscription fatigue, I mean, nobody wants to have 20 subscriptions. >>Well, that brings up a whole new other war. That's going on that, thank goodness. We're not part of it's the platforms versus the cable companies. Right. Versus whatever. Right. Everyone's trying to be your open garden or your closed garden. They're trying to get your subscriptions in bundle self bundling it's. But I mean, it's wonderful for consumers, if you can navigate through it. Uh, we wanna, we think we'll have one of the gems in any of that everyone's want local TV. And so we'll supply that we're already doing that. We're supplying it to a couple companies, uh, free cast as a company, uh, app, a universal streaming, you know, manager, your all, all your, uh, streaming, a streaming aggregation, put your paid stuff in, put your free stuff in. They do that. And, and as, as does Roku try trying to do that fire TV, Xfinity's trying to do it. So it's all, it's a new war for the platform and hopefully we'll be on everyone. >>Well, you've been in this industry for a long time, you know, the streaming market, you know, the TV market. Um, so it's, it's good. I think it's a new battle, the shift's happening. Um, what should people know about dig local? BTV what are some of your goals for the next year or two? What are you trying to do? >>Well, what we're really trying to do is make sure that local, uh, local television thrives so that it can support wider communities. It could support hyper local content. So if you're, if you're, and we love the old paradigm and channel change, right? Forget, you know, every other app has all these boxes going by on different rows and stuff. And, and yeah, you can search and find stuff, but there's nothing like just changing channels, whether a commercial's on or, or you, you wanna see what else is on. You know, you're gonna go from local television and maybe all of a sudden, you'll see the local high school play over on another part of the, of the spectrum. And, and what we're trying to do is get those communities together. And the local high school people come over and find the local, you know, uh, Spanish, uh, Nova channel or something like that. >>So local is the new hot. >>It is. Absolutely. And by the way, it's where this high CPMs are gonna go. And the more targeted you get >>Ad revenue, >>I mean, that's for us is, is, is our number one, re we have a number of revenue streams, but targeted ads are really great for local, right? And, and so we're, we're gonna make an announce. We've >>Lost that we've lost that local, I've seen local things that local Palo Alto paper, for instance, just shut down this local sports high school coverage, our youth sports, because they don't budget, right? There's no TV community channels, like some Comcast throwaway channel. Um, we lost, we, we lo we're losing >>Local. No, I think that's a real national shame. And so I think if we can strengthen local television, I think it'll strengthen all local media. So we expect to help local radio and local newspapers. That's a bigger part of the vision. Uh, but I it's gonna happen. There's >>An education angle here too. >>There is an education angle because the bottom line is you can use linear television as a way to augment. Uh, we have a really exciting project going on in New York, uh, uh, with, uh, some of the housing, uh, projects, uh, in Harlem and, and, and the Bronx, uh, their I idea is to have the, the homework channel and they can, and literally when you have a, and both swiping and everything you can have, I mean, literally you can have a hundred schools that, that have things well, >>We know zoom schooling sucks. I mean, that didn't work. So I think you're gonna see a lot of augmentation, right. >>Amazon. >>I was just talking to some people here, AI training, machine learning, training, all here could be online in linear format. >>Yeah. And exactly. And then I think about the linear format is it's discovery television, and you can also, um, you know, you can also record it. Yeah. Right. If you see a program and you want to record it, you sit >>Record. So final minute we have left. I want to just get your thoughts on this one thing and, and ask your question. Are you looking for content? Are you, I outreach at the content providers who, >>Well, we're, we're PRI our primary mission is to get more channel local channels on which really means station groups and independence. We have a number, I mean, basically 50% of the channels in any market. When we move into it are like, this is a no-brainer. I want more eyeballs. We're Nielsen, uh, RA, uh, rated mean we support. And so we, >>How many markets are you in right now? >>We're in 21 now. And we hope to be in, uh, over 50 by the end of the year, covering more than half the United States. >>So, all right, Jim, thanks for coming on the queue. Really appreciate it. >>My pleasure. Good luck >>Recognition. Very disruptive disrupting media, um, combination of over the air TV, local with I internet. Obviously we love that with a cube. We want a cube channel anywhere possible. I'm John furry host of the queue here at AWS summit. Highing all the big trends and technologies in cloud and media back with more coverage after this short break,

>> Narrator: From Sand Hill Road, in the heart of Silicon Valley, it's the CUBE. Presenting, The People First Network, insights from entrepreneurs and tech leaders. >> Hello everyone welcome to this special CUBE conversation, I'm John Furrier, host of theCUBE. We're here at Mayfield Fund, on Sand Hill Road and Menlo Park. As part of Mayfield's People First Network, co-creation with SiliconANGLE and theCUBE and Mayfield. Next guest, Beth Devin, Managing Director of Innovation Network and Emerging Technologies at Citi Ventures. Thanks for coming on. >> Thanks for having me. >> Hey, thanks for coming in. We're here for the Mayfield fiftieth anniversary, where they're featuring luminaries like yourself, and we're talking about conversations around how the world's changing and the opportunities and the challenges can be met, and how you can share some of your best practices. Talk about what your role is at Citi Ventures and what your focus is. >> Sure, sure, and boy howdy, has it been changing. It's hard to keep up with. I've been at Citi Ventures about two years and one of the reasons I joined was to stand up an Emerging Technology practice. Citi Ventures does a lot of work in corporate venture investing. We tend to be strategic investors, for start up companies that are aligned with the strategy of Citi, as well as our client. We serve probably, eighty percent of the Fortune Five Hundred companies in the world. But we also are a really important part of the innovation ecosystem at Citi. Which is looking at how to drive culture change, broaden mindset, and really, enlist our employees to be part of the innovation process. So, we have an internal incubator, we have a Shark Tank-like process we call Discover Ten X. And what I really bring to the table with my team is monitoring, and learning about, and digesting technology that's not quite ready for commercialization but we think it might be disruptive in a good or challenging way for the bank or our clients. We try to educate and provide content that's helpful to our executives, and just the employee body at large. >> I want to get into a LinkedIn post you wrote, called the Tech Whisperer, which I love. >> Thank you. >> You're there to identify new things to help people understand what that is. But that's not what you've done. You've actually implemented technology. So, on the other side of the coin, in your career. Tell us about some of the things you've done in your career, because you've been a practitioner. >> Beth: Yeah. >> and now you're identifying trends and technologies, before you were on the other side of the table. >> That's right, and sometimes I'll tell you, I have that itch. I miss the operator role, sometimes. Yeah, you know, I feel so fortunate I sort of stumbled on computer science early when I was going to school. And, the first, I'd say twenty years of my career, were working in enterprise I.T, which at that time I couldn't even have made that distinction, like why do you have to say enterprise I.T. I was a software developer, and I was then a DBA, and I even did assembler language programing. So way back when, I think I was so fortunate to fall in to software engineering. It's like problem solving, or puzzle making, and you with your own brain and sort of typing can figure out these problems. Then over the years I became more of a manager and a leader, and sort of about a reputation for being somebody you could put on any hard problem and I'd figure a way out. You know tell me where we're trying to go it looks knotty, like not a fun project, and I would tackle that. And then I'd say, I had some experience working in lots of different industries. Which really gave me an appreciation, for you know, at the end of the day, we can all debate the role that technology plays in companies. But industries, whether it's health care or media, or financial services. There's a lot of the same challenges that we have. So I worked at Turner Broadcasting before it was acquired, you know by Time Warner and AOL. And I learned about media. And then I had a fantastic time working at Charles Schwab. That was my first big Financial Services role when it came back to the bay area. I worked at Art.Com, it was a need converse company, the first company I worked at where I was in charge of all the technology. We had no brick and mortar, and if the technology wasn't working, we weren't earning revenue, in fact, not only that, we were really making customers angry. I also had a role at a start up, where I was the third person to join the company, and we had a great CEO who had a vision, but it was on paper. And we hadn't really figured out how to build this. I was very proud to assemble a team, get an office, and have a product launch in a year. >> So you're a builder, you're a doer, an assembler, key coding, hexadecimal cord dumps back in the day. >> Way back when. We didn't even have monitors. I'll tell ya, it was a long time ago. >> Glory days, huh? Back when we didn't have shoes on. You know, technology. But what a change. >> Huge change. >> The variety of backgrounds you have, The LinkedIn, the Charles Schwab, I think was during the growth years. >> And the downturn, so we got both sides. >> Both sides of that coin, but again, the technologies were evolving. >> Yes. >> To serve that kind of high frequency customer base. >> Beth: That's right. >> With databases changing, internet getting faster. >> It has. >> Jeff: More people getting online. >> We were early adopters, I'll tell you. I still will tell people, Charles Schwab is one of the best experiences I have, even though at the end I was part of the layoff process. I was there almost seven years, and I watched, we had crazy times in the internet boom. Going in 98, 99, 2000, I can't even tell you some of the experiences we had. And we weren't a digital native. But we were one of the first companies to put trading online, and to build APIs so our customers could self service, and they could do that all online. We did mobile trading. I remember we had to test our software on like twenty different phone sets. Today, it's actually, so much easier. >> It's only three. Or two. Or one. Depending on how you look at it. >> That's right. We couldn't even test on all the phone sets that were out then. But that was such a great experience, and I still, that Schwab network, is still people I'm in touch with today. And we all sort of sprinkled out to different places. I think, I dunno, there's just something special about that company in terms of what we learned, and what we were able to accomplish. >> You have a fantastic background. Again the waves of innovation you have lived through, been apart of, tackling hard problems, taking it head on. Great ethos, great management discipline. Now more than ever, it seems to be needed, because we're living in an age of massive change. Cause you have the databases are changing, the networks changing, the coding paradigms changing. Dev ops, you've got the role of data. Obviously, mobile clearly is proliferated. And now the business models are evolving. Now you got business model action, technical changes, cultural people changes. All of those theaters are exploding with opportunity, but also challenges. What's your take on that as you look at that world? >> You know, I'm a change junkie, I think. I love when things are changing, when organizations are changing, when companies are coming apart and coming together. So for me, I feel like, I've been again, so fortunate I'm in the perfect place. But, one of the things that I really prided myself on early in my career, is being what I call the bridge, or the, the translator between the different lines of business folks that I work with. Whether it was head of marketing, or somebody in a sales or customer relationship, or service organization, and the technology teams I built and led. And I think I've had a natural curiosity about what makes a business tick, and not so much over indexing on the technology itself. So technology is going to come and go, there's going to be different flavors. But actually, how to really take advantage of that technology, to better engage your customers, which as you said, their needs and their demands are changing, their expectations are so high. They really set the pace now. Who would have though that ten years ago we'd live in an environment where industries and businesses are changing because consumers have sort of set the bar on the way we all want to interact, engage, communicate, buy, pay. So there's this huge impact on organizations, and you know, I have a lot of empathy for large established enterprises that are challenged to make it through this transformation, this change, that somehow, they have to make. And I always try to pay attention on which companies have done it. And I call out Microsoft as an example. I can still remember several years ago, being at a conference. I think it was Jeffrey Moore who was speaking, and he had on one slide... Here's all the companies in technology that have had really large success. Leading up to the internet boom days, there would be a recipe for the four companies that would come together. I think it was Sun, Oracle, and Microsoft. And then he said, and now here's the companies of today. And most young people coming out of college, or getting computer science degrees won't use any of these old technology companies. But Microsoft proved us all wrong, but they did it, focused on people, culture, being willing to say where they screwed up, and where they're not going to focus anymore, and part ways with those parts of their business. And really focus on who are their customers, what are their customer needs. I think there's something to be learned from those changes they made. And I think back to the Tech Whisperer, there's no excuse for an executive today, not to at least understand the fundamentals of technology. So many decisions have to be made around investment, capital, hiring, investment in your people. That without that understanding, you're sort of operating blind. >> And this is the thing that I think I love, and was impressed by that Tech Whisperer article. You know, a play on the Horse Whisperer, the movie. You're kind of whispering in the ears of leaders who won't admit that they're scared. But they're all scared! They're all scared. And so they need to get, maybe it's cognitive dissonance around decision making, or they might not trust their lead. Or they don't know what they're talking about So this certainly is there, I would agree with that. But there's dynamics at play, and I want to get your thoughts on this. I think this plays into the Tech Whisperer. The trend we're seeing is the old days was the engineers are out coding away, hey they're out there coding away, look at them coding away. Now with Cloud they're in the front lines. They're getting closer to the customer, the apps are in charge. They're dictating to the infrastructure what can be done. With data almost every solution can be customized. There's no more general purpose. These are the things we talk about, but this changes the personnel equation. Now you got engineering and product people talking to sales and marketing people, business people. >> And customers. >> They tend not to, they traditionally weren't going well. Now they have to work well, engineers want to work with the customers. This is kind of a new business practice, and now I'm a scared executive. Beth, what do I do? What's your thoughts on that dynamic? >> You know, I'm not sure I would have had insight in that if I hadn't had the oppurtunity to work at this little start up, which we were a digital native. And it was the first time I worked in an environment where we did true extreme programming, pair programming, we had really strong product leads, and engineers. So we didn't have project managers, business analysts, a lot of things that I think enterprise I.T tends to have. Because the folks, historically, at an enterprise, the folks that are specifying the need, the business need, are folks in the lines of business. And they're not product managers, and even product managers, I say in banking for example, they aren't software product managers. And so that change, if you really do want to embrace these new methods and dev ops, and a lot of the automation that's available to engineering and software development organizations today, you really do have to make that change. Otherwise it's just going to be a clumsy version of what you use to do, with a new name on it. The other thing though that I would say, is I don't want to discount for large enterprises is partnerships with start up companies or other tech partners. You don't need to build everything. There's so much great technology out there. You brought up the Cloud. Look at how rich these Cloud stacks are getting. You know, it's not just now, can you provision me some compute, and some storage, and help me connect to the internet. There's some pretty sophisticated capabilities in there around A.I and machine learning, and data management, and analysis. So, I think overtime, we'll see richer and richer Cloud stacks, that enables you know, every company to benefit from the technology and innovation that's going on right now. >> Andy Jassy, the CEO of Amazon Web Search, has always said whenever I've interviewed him, he always talks publicly now about it is, two pizza teams, and automate the undifferentiated heavy lifting. In tech we all know what that is, the boring, mundane, patching, provisioning, ugh. And deploying more creative research. Okay so, I believe that. I'm a big believer of that philosophy. But it opens up the role, the question of the roles of the people. That lonely DBA, that you once were, I did some DBA work myself. System admins, storage administrator, these were roles, network administrator, the sacred God of the network, they ran everything. They're evolving to be much more coding oriented, software driven changes. >> It's a huge change. And you know, one thing that I think is sad, is I run into folks often that are, I'll just say, technology professionals, just say, you know, we're at large. Who are out of work. You know, who sort of hang their head, they're not valued, or maybe there's some ageism involved, or they get marked as, oh that's old school, they're not going to change. So, I really do believe we're at a point, where there's not enough resources out there. And so how we invest in talent that's available today, and help people through this change, not everybody is going to make it. It starts with you, knowing yourself, and how open-minded you are. Are you willing to learn, are you willing to put some effort forth, and sort of figuring out some of these new operating models. Because that's just essential if you want to be part of the future. And I'll tell you, it's hard, and it's exhausting. So I don't say this lightly, I just think. You know about my career, how many changes and twists and turns their have been. Sometimes you're just like, okay I'm ready, I'm ready to just go hiking. (Beth laughs) >> It can be, there's a lot of institutional baggage, associated with the role you had, I've heard that before. Old guard, old school, we don't do that, you're way too old for that, we need more women so lets get women in. So there's like a big dynamic around that. And I want to get your thoughts on it because you mentioned ageism, and also women in tech has also grown. There's a need for that. So there's more opportunities now than ever. I mean you go to the cyber security job boards, there are more jobs for cyber security experts than any. >> Oh, I'll tell you, yesterday, we held an event at our office, in partnership with some different start ups. Because that's one of the things you do when you're in a corporate venture group, and it was all on the future of authentication. So it was really targeted at an audience of information security professionals and chief information security officers. And it was twenty men and one woman. And I thought, wow, you know I'm use to that from having been a CIO that a lot of the infrastructure roles in particular, like as you were saying, the rack and stack, the storage management, the network folks, just tend to be more male dominant, than I think the product managers, designers, even software engineers to some extent. But here you know, how many times can you go online and see how many openings there are for that type of role. So I personally, am not pursuing that type of role, so I don't know what all the steps would need to be, to get educated, to get certified, but boy is there a need. And that needs not going to go away. As more, if everything is digitized and everything is online. Then security is going to be a constant concern and sort of dynamic space. >> Well, we interview a lot of women in tech, great to have you on, you're a great leader. We also interview a lot of people that are older. I totally believe that there's an ageism issue out there. I've seen it first hand, maybe because I'm over fifty. And also women in tech, there's more coming but not enough. The numbers speak for themselves. There's also an opportunity, if you look at the leveling up. I talked to a person who was a network engineer, kind of the same thing as him, hanging his head down. And I said, do you realize that networking paradigm is very similar to how cyber works. So a lot of the old is coming back. So if you look at what was in the computer science programs in the eighties. It was a systems thinking. The systems thinking is coming back. So I see that as a great opportunity. But also the aperture of the field of computer science is changing. So it's not, there are some areas that frankly, women are better than men at in my opinion. In my opinion, might get some crap for that. But the point, I do believe that. And there are different roles. So I think it's not just, there's so much more here. >> Oh, that's what I try to tell people. It's not just coding, right. There's so many different types of roles. And unfortunately I think we don't market ourselves well. So I encourage everyone out there that knows somebody. (Beth laughs) Who's looking-- >> If someone was provisioned Sun micro-systems, or mini computers, or workstations, probably has a systems background that could be a Cloud administrator or a Cloud architect. Same concepts. So I want to get your thoughts on women in tech since you're here. What's your thoughts on the industry, how's it going, things you advise, other folks, men and women, that they could do differently. Any good signs? What's your thoughts in general? >> Yeah so, first of all, I'm just a big advocate for women in general. Young girls, and, young women, just getting into the work force, and always have been. Have to say again, very fortunate early in my career working for companies like a phone company, and Schwab, we had so many amazing female leaders. And I don't even think we had a program, it was just sort of part of the DNA of the company. And it's really only in the last couple of years I really seen we have a big problem. Whether it's reading about some of the cultures of some of the big tech companies, or even spending more time in the valley. I think there's no one answer, it's multifaceted. It's education, it's families, it's you know, each one of us could make a difference in how we hire, sort of checking in what our unintended biases are, I know at Citi right now, there's a huge program around diversity and inclusion. Gender, and otherwise. And one of the ways I think it's going to be impactful. They've set targets that I know are controversial, but it holds people accountable, to make decisions and invest in developing people, and making sure there's a pipeline of talent that can step up into even bigger roles with a more diverse leadership team. It will take time though, it will take time. >> But mind shares are critical. >> It absolutely is. Self-awareness, community awareness, very much so. >> What can men do differently, it's always about women in tech, but what can we, what can men do? >> I think it's a great question. I would say, women can do this too. I hate when I see a group together, and it's all women working on the women issue. Shame on us, for not inviting men into the organization. And then I think it's similar to the Tech Whisperer. Don't be nervous, don't be worried, just step in. Because, you know, men are fathers, men are leaders, men are colleagues. They're brothers, they're uncles. We have to work on this together. >> I had a great guest, and friend, I was interviewing. And she was amazing, and she said, John, it's not diversity and inclusion, it's inclusion and diversity. It's I-N-D not D-I. First of all, I've never heard of it, what's D-N-I? My point exactly. Inclusion is not just the diversity piece, inclusion first is inclusive in general, diversity is different. So people tend to blend them. >> Yes they do. >> Or even forget the inclusion part. >> Final question, since you're a change junkie, which I love that phrase, I'm kind of one myself. Change junkies are always chasing that next wave, and you love waves. Pat Gelsinger at VMWare, wave junkie, always love talking with him. And he's a great wave spotter, he sees them early. There's a big set of waves coming in now, pretty clear. Cloud has done it's thing. It's only going to change and get bigger, hybrid, private, multi Cloud. Data, AI, twenty year cycle coming. What waves are you most excited about? What's out there? What waves are obvious, what waves aren't, that you see? >> Yeah, oh, that's a tough one. Cause we try to track what those waves are. I think one of the things that I'm seeing is that as we all get, and I don't just mean people, I mean things. Everything is connected, and everything has some kind of smarts, some kind of small CPU senser. There's no way that our existing, sort of network, infrastructure and the way we connect and talk can support all of that. So I think we're going to see some kind of discontinuous change, where new models are going to, are going to absolutely be required cause we'll sort of hit the limit of how much traffic can go over the internet, and how many devices can we manage. How much automation can the people and an enterprise sort of oversee and monitor, and secure and protect. That's the thing that I feel like it's a tsunami about to hit us. And it's going to be one of these perfect storms. And luckily, I think there is innovation going on around 5G and edge computing, and different ways to think about securing the enterprise. That will help. But it couldn't come soon enough. >> And model also meaning not just technical business. >> Absolutely. Machine the machine. Like who's identity is on there that's taken an action on your behalf, or the companies behalf. You know, we see that already with RPA, these software robots. Who's making sure that they're doing what they're suppose to do. And they're so easy to create, now you have thousands of them. In my mind, it's just more software to manage. >> And a great contrary to Carl Eschenbach, former VMware CEO now at Sequoia, he's on the board of UIPath, they're on the front page of Forbes today, talking about bots. >> Yes, yes, yes, I've heard them speak. >> This is an issue, like is there a verification. Is there a fake bots coming. If there's fake news, fake bots are probably going to come too. >> Absolutely they will. >> This is a reality. >> And we're putting them in the hands of non-engineers to build these bots. Which there's good and bad, right. >> Regulation and policy are two different things, and they could work together. This is going to be a seminal issue for our industry. Is understanding the societal impact, tech for good. Shaping the technologies. This is what a Tech Whisperer has to do. You have a tough job ahead of you. >> But I love it. >> Jeff: Beth thank you for coming on. >> Thank you for having me. >> I'm Jeff Furrier for the People First Network here at Sand Hill Road at Mayfield as part of theCUBE and SiliconANGLE's co-creation with Mayfield Fund, thans for watching.

live from Washington DC if the queue covering in forum DC 2018 brought to you by in for and welcome back here on the cube inform 2018 we're live in Washington DC continuing our day to coverage here on the cube along with de Ville on tape I'm John Wallace it's now a pleasure as well to welcome Eric Noren to the cube is the managing director of the CFO and enterprise value consulting at Accenture good morning Eric Harry a good morning to see you guys glad to have you with us we appreciate the time yeah let's talk about first the relationship assurance your and in for I know you've had you've been elsewhere right doing some other things with other folks and have kind of migrated back into the in four fold what led to that and what kind of successes are you having well so we're very excited about the partnership with with in for this is kind of like the really the second year for us right now as we go into the second year the first year was really driven from the partnership and the work that we do at Koch Industries and that that client experience kind of led us into a variety of different paths of partnership with with in for we've been doing work with with in for products for many years but we just our alliances just kind of blossomed in this past year into a variety of different areas focusing on the cloud suite financials focusing on GT Nexus in the supply chain space and now we're getting more and more excited about bursts and we're also getting very excited about the the whole the way the infor OS platform is just blossoming and and being tailored to a variety different industries and you've got you've got three offerings right if I remember right that you're taking out that you're taking to your client base as we speak once you give us a rundown of what you're up to well in our practice we have in our CFO and enterprise value practice we have an offering that's all around digital finance that's one of our biggest areas and that's really all just about the intersection of platform technology and how it enables the next generation of the finance function for the CFO so that we cloud that could also include things like you know automation and artificial intelligence applied to the finance function we see in our recent research here that CFO role as pivoting really not to be not really as focused on the books and records and being the controllers right but the CFOs role is now becoming more focused on being the digital steward the value architect of the enterprise and so the core of Finance is being digitized so that the transaction handling can be done more in an automated and efficient way and then freeing up the talent to focus on analytics and value-add and that really allows the CFO to focus more on driving insights into the business driving growth and what we call enterprise value so I totally agree the role of the CFO is transforming quite dramatically you know long gone in my view anyway are the days of CFO equals bean-counter this is a little there's a controller for that and no bean counter by the way is not a pejorative I run a business and I'm happy when people are counting those beans but it's not the CFO's role they're really transforming you see some Rockstar CFOs certainly in the tech industry like Scarpelli Tom sweet to just name a couple right reporting still matters compliance still matters but the CFO is taking a much more strategic role I'm really interested in this this this digitization of finance double-click on that yeah what does that specifically mean maybe you could give us some examples well I think that a couple things one is cloud right also I would say one thing is how transaction handling is moving from paper into all aspects of touchless transaction handling one is that harnessing the data to for transaction so it's touchless between vendors and customers and how that just flows through the system in a more digital way less paper more digital more touchless integration more automation right and then with that platform enabling things like artificial intelligence or machine learning being applied to these patterns of transaction handling so it can do the compliance checking in the reconciliation and so that the accountants right are enabling these algorithms to check things and don't have to do it themselves right but then there's also this whole context of of digital sort of process automation that that yields new ways of working you know new ways of looking at efficiency in terms of how and where the work is done right there was a view of like shared services and how we enable a digital operating model where there is there's work that can be done you know in with business unit intimacy and then there's work that can be done from other locations but then enabled by digital technology that's common and standardized right in a common platform that's also scalable and flexible and so putting all those things together is what we call digital finance I love this conversation and Accenture is like the best of the best you guys gets deep industry expertise and domain expertise I'm interested in Eric and in what the organizational structure looks like because when we talk about digital you're talking about data yeah and when you talking about data you're talking about monetization in some way shape or form not people I think got confused in the early days of big data so we can sell our data and more importantly as how data contributes to the monetization of the company sure and and how you can harness that and invest in that and that's really where the CFO comes in but he or she is not an expert at at digital not an X not a chief data officer or chief digital officer but they are an enabler they got to understand the strategy they got to pay for the strategy and maybe help course-correct it so what are you seeing is the right organizational regime to take advantage of digital well I think it first off it's integrated and it's and it's and it's focused on integration and collaboration for sure I think that there's a role where finance has the the business acumen and the insights to find out where the the story of enterprise value where it is now where it could be relative to the drivers of the business and but what's going on in the industry or the adjacent industries they can take advantage of so it's really all about you know a partnership between you know let's say finance right and let's say bringing in new talent and skills like data scientists and all those kind of you know digital skills and integrating it into finance so that it could be more accessible and then and then translate it into opportunities for for the business units so so a couple examples could be just one just getting a when we say monetization I think there's two things one is cost reduction where could you just use data to just understand the business in all aspects of where costs and how they're behaving and just being farm Warp know precise about where there are opportunities to reduce costs increase your bottom line right and that that in of those is value then there's the other side on you know revenue up left where there could be optimization of pricing optimization of your discounting strategy all those things that get into maintaining and improving your revenue without any additional cost of goods sold correct cost of sales right exactly that's a great example rights right your your operating structures it stays the same they're getting more leverage out of that that's writing and then there's other things where there's adjacent opportunities in to just gain market share right just to say well where there's opportunities with and really what we want to say is that by applying all this intelligence it's focused on really the theme is focused on customer experiences like what are the customer experiences that could be enabled with digital digital technologies in a seamless touchless way that are just differentiating the company you know in the market customers are and I think the world is changing its disrupting so the ways in which customers are interacting with businesses are expecting these kind of digital experiences very much inspired by a lot of the digital native companies they're out there in the market so the traditional companies that don't have those experience need to catch up and invest in these kind of customer experiences give me an example I mean how about expectations and and so let's say for example if you're a telco alright and you've got experiences that are about paying your bill or experiences have to do with services that you need by going to a call center all right now maybe you can have you know the traditional route of talking to someone or maybe there's a way you can go between the information and the channels that you have between your telephone your the mobile app between the website being able to talk to someone and having chat bots and the mix and how you coordinate all those different experiences so that that the customer can come in and get their questions answered in a very efficient way in some cases the the chat BOTS and the kind of sophistication that they can have to to to address the customers question right on the spot in a very timely way helps them just say I got my question solved and I'm happy with that experience right same thing with having information about I'm getting a you know service supply to my home how do I know that I'm having that same certainty of the service supply to the home much like the certainty that consumers are experiencing kind of like when they get an uber and they're like hey I know that the car is only five minutes away and it's coming and I have that certainty of an experience now that's being applied to other kind of customer experience it's a lot of situation I'm there at three things so first was saved money you know example RP a jerk something to help you drop money to the bottom line just cutting out mundane tasks yeah the top the top line operating leverage and that's around analytics may be optimizing pricing was the example you gave now the third I'll call Tam expansion which is which is really gaining share you leveraging your digital strategy to maybe try to be an incumbent disruptor just disrupt before you get disrupted now that last one has more risk associated with it because there are there are additional cost you've got other cost of goods sold you go to market cost but the reward could be you know huge these are the conversations is a great great proxy for the conversations that are going on with your clients yeah absolutely and I think that look you know there's the the market is going through changes constant disruption is coming in different forms whether it be through technology or other kind of industry integrations and you know they're different in the different we I specialize and more the communications me in technology industry alright and so those those are where I spend most of my time and and what's going on in communications right now and what's going on with communications and media is a quite interesting time on how content and distribution of content is changing and the way that the next generation of consumers are going to think about you know consuming media and how advertising is distributed we're going through a tremendous transformation in that space and all the companies are kind of racing to to be have that advantage of how they connect with the consumers at scale in a seamless connected way so that they have that that that ability to continue to serve them in new and innovative ways so let's talk about them so you said comms and media are we talking telecoms yeah okay and then tech industry is in IT technical yeah I mean tech suppliers tech suppliers yes girls just go and and companies like novo those kind of companies that are in that those guys are pretty forward-thinking in terms of technology adoption oh absolutely okay the telco business is really interesting right now though absolutely hardened infrastructures they get over the top suppliers coming in the cost per per bit is going down but they can't charge more you know this you know very well yeah they're going through some really radical transformation at the same time they have a huge opportunity with content yes you see and people make some moves yes absolutely about what's going on in that business a little bit more well you know there was the recent you know Comcast just an acquisition of sky is quite Norway we got 18 t going through the Time Warner thing and then you have so that's a Content play that I think is just frees up some opportunities for for companies like Comcast and AT&T you know to start really servicing their customers and a new profound way you know to be able to say it could be you know content that is suited to different demographics and to get those consumers at scale not only to keep them you know comfortable with the and and very delighted if you will with the kind of wireless service and flexibility they have with that but then to be able to see all the range of content that it could be consuming all of which is coming back to those companies as data as the consumers are watching all this content and having better control visibility of all the different patterns that they're seeing in the use of this content so they can then in turn shape different kinds of programming and shape different kinds of advertising programs that are tailored to those demographics and there's an it there's an underlying infrastructure transformation that's going on so it's something as basic as you know things like network function virtualization not to get too geeky out here but I'm trying to to make their their infrastructure more agile so they can compete with the OTT suppliers and they're trying to vertically integrate as content yes Rogers absolutely in this whole next wave of 5g is is a huge thing that's gonna come to us and that's that's a big disruption that's just starting and will happen in the next three to five years that will level be coming due so everybody's trying to get digital right yeah yeah yo that you talk to but do you do you when you go beneath that to the organization it's harder to get people you know to actually move do you get do you see a sense of complacency of people saying well you know not we're doing pretty well in our industry or I'll be retired before this all happens I mean how do you compel well I think that I mean that does exist in certain industries and certain types of companies you know I think that's the whole point about talent right and I think when we come back and look at talent is really when we think about change not only is the technology changing but the the talent that's available not only in the finance function but in all parts of an enterprise the the the the next generation of folks that are going into the workforce are just coming from a different place in terms of how they use technology in their lifestyle but how they want to apply to their as a customer but then how they want to do it as an employee and so for when we have that conversation about well what is the future going to look like a lot of it will come down to well what does digital mean as an experience for your consumer and your customer but also what does it mean for the talent and and we believe that look talent is a critical asset in every and every company it's the biggest asset that we have in a center right so how do we inspire and have folks have been enabled to use digital technologies to have that entrepreneurial you know sort of platform to use these digitally native tools that's really the key and I think that any kind of you know CFO that's like thinking about betting on the future that talent is very much a part of that stories it's definitely about technology is very important it's an enabler it's a platform however it's the talent that will be using the platform to take those info sites and drive growth in the wild card is data all right that's the new oh yeah absolutely I mean when a variable in the equation yeah this data putting data at the sort of score of your organization and having the talent that knows how they'll exploit your day that's right and I think it's like when I think about talent there's I mean there's different specializations right but I think the talent is really about the collaboration you see people who are able to work with other different cross functions and say well how do we how do we build and find this together how do we discover where the opportunity the insight is together right and you know there's you know there's differences between you know stuff which I said like the you know things that are known and we just optimized what we have and then there's going into the new areas right that I haven't been discovered yet and I think that the thing about the the the talent that's curious you know we like the way to think about like okay curious about what could be or what's out there and using data not as a as a hurdle but harnessing the power of data to go into these areas and start exploring and using all those different tools to explore where could we go and one of the things it's doing is it's not about you know we talked about analytics and some of the tools that are out there it's not about necessarily precision in this moment it's about direction of where you can go and exploring and continuing to find the facts that support investment it's your point I mean the the tools and the tech aren't the hard part it's the it's the unknown it's the people right you know the processes around that right getting everybody on the same page to collaborate it's like old dogs new tricks I mean I mean so yeah never simplifying but you you are trying to bring new tricks yeah to folks and there's a generational awareness that you're the difference between the people they have coming up and where they said that's right and we think that look you know by bringing the fresh new talent in to the organization that and of itself has has the team operating and working differently because not only they have new tools but there's new a new way of talent being integrated you know new talent and experienced talent you know seeing how these things come together to wither with a mandate again on superior business outcomes like let's go after these prizes it's worth it to get this right to make these investments because if we get it right there's an opportunity to grow revenue to grow to grow profitably to gain market share right so there's a there's a it's hard okay there's culture change and change this is normal okay digital transformation is not an easy thing to do all companies go through you know different things but it's worth it in the end yeah and it enforced talked a lot at this show about new new ways to work what I call new ways to and I think there's some substance there yeah absolutely Eric thank you and for the record we are always open to new tricks we do like new tricks okay good it'd be good to have you with us okay my pleasure guys Norman ceinture back with more on the key we're live here in Washington DC [Music]

(soft music) (soft music) >> Narrator: Live from Las Vegas. It's the Cube. Covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. >> Okay welcome back everyone. We're live in Las Vegas. This is the Cube. SiliconAngles flagship program. We got out to the events and extract the (mumbles). Been here two full days of wall to wall coverage. I'm John Furrier. My cohost Peter Burris. Peter really good to see Oracle really move from modern marketing experience, the old show name, to a cleaner broader canvas called Modern CX. Which is modern customer experience. And you startin to see the new management which took the baton from the old management. Kevin Akeroyd. Andrea Ward who did a lot of work. I mean they basically did a ton of acquisitions. We talked last year if you remember. Look they have a data opportunity and we spelled it right out there and said if they can leverage that data horizontally and then offer that vertical specialism with differentiation, they could have their cake and eat it too. Meaning the pillars of solutions in a digital fabric with data. That's what they did. They essentially did it. >> Yeah they did. And it's been, it was a. We came here hoping that that's what we would see and that's what we saw John. Oracle not only has access to a lot of data but a lot of that first person data that really differentiates the business. Information about your finances. Information about your customers. Information about orders. That's really, really crucial data. And it's not easy to get. And if you could build a a strategy for your customers that says let's find ways of bringing in new sources of data. Leveraging that data so that we can actually help you solve and serve your customers better. You got a powerful story. That's a great starting point. >> And one of the things that I would observe here is that this event, the top story was that Mark Hurd came down and talked to the customers in the keynote. And also made a cameo visit to the CMO, some which they had separately. But really kind of basically putting it transparently out there. Look we got all this technology. Why are we spending all of this technology and effort to get a one percent conversion rate on something that happens over here. Let's look at it differently. And I think the big story here is that Oracle puts the arc to the future. Which I think is a very relevant trajectory. Certainly directionally correct using data and then figuring out your process and implementing it. But really looking at it from a people perspective and saying if you can use the data, focus your energies on that data to get new things going. And not rely on the old so much. Make it better but bring in the new. >> I think that's the one thing that we need to see more from Oracle in all honesty. At shows, this show, and shows like this. Is that and we asked the question to a couple quests. What exactly is modern marketing? Technology can allow a company to do the wrong things faster and cheaper. And in some cases that's bad. In marketing that's awful. Because more of the wrong thing amplifies the problem. That's how you take down a brand. You can really annoy the hell out of your customers pretty quickly. >> Well I think you made that point interesting I thought. On that just to reiterate that, validate that, and amplify. Is that if you focus more on serving the business as a marketer versus now it's about the customer. Okay which is why I like the CX and I know you do too. You can create enterprise value through that new way. Versus hey look what team. I'm helping you out with some leads and whatever. Support, content. Marketing now owns the customer relationship. >> Well marketers talk about a persona all the time John. They say what's the persona? It's a stylized type of customer, and now with data we can make it increasingly specific. Which is very, very powerful. I think Oracle needs to do the same thing with the marketing function. What is that marketing function persona that Oracle is, it's self driving to. Driving it's customers to. And trying to lead the industry into. So I would personally like to see a little bit more about what will be the role of marketing in the future. What exactly is the modern. What exactly is modern marketing? What is the road map that Oracle has, not just for delivering the technology, but for that customer transformation that they talk about so much. It's clear that they have an idea. I'd like to see a little bit more public. Cause I think a lot of marketers need to know where they're going to end up. >> I was a bit skeptical coming in here today. I was a little nervous and skeptical. I like the team though, the people here. But I wasn't sure they were going to be able to pull this off as well as they did. I'd give them a solid letter grade of an A on this event. Not an A plus because I think there's some critical analysis that's worth addressing in my opinion. In my opinion Oracle's missing some things. It's not their fault. They're only going as fast as they can. Not to get into your perspective too, but here's my take. They don't know how to deal with video. That came up as technical issue. But Jay -- >> But nobody really does. >> But nobody really does. And that's just again because we're in the video business it jumped out at me. But Jay Baer was on. Who's hosted the CMO Summit. And he's out there too like us. Content is a big thing. And I haven't heard a lot about the content equation in the marketing mix. So if you look at the modern marketing mix, content is data. And content is instrumental as a payload for email marketing. And we're in the content business so we know a lot about the engagement side of it. So I just don't see a lot of the engagement conversations that are happening around content. Don't see that dots connecting. >> And I think you're right. I think you're right John. And part of the reason is, and again I think Oracle needs to do a better job at articulating what this means. From our perspective, it's my perspective but you agree with me. I'll put words in your mouth. Is that marketing has to be a source of value to customers. Well what do customers find valuable? They find information in easily digestible, consumable chunks as they go on their journey. What are those chunks? Those chunks, in fact, are content. So to tie this back and show how crucial this is. At the end of the day, consumers, businesses need to learn about your brand. Need to learn about next best action. All that other stuff. In consumable interesting, valuable chunks. And it ultimately ends up looking like content. So your absolutely right to talk about how this all comes together and show how, that content is the mechanism by which a lot of this value's actually going to be delivered. Is really crucial. >> And now to give the praise sandwich, as we say in positive coaching alliance, two positives and then the critical analysis in the middle. That's the praise sandwich. So to give them some praise around the criticism. I will say that Oracle validates for me, and this is why I think they got a good strategy. That there's no silver bullet in marketing. Okay there's no silver bullet. This product will get you more engagement. This will do that. They do show that data is going to be an instruble part of creating a series of collections of silver bullets. Of bullets if you will. To create that value. And I think that's the key. And then the second praise is, this is kind of nuance in their analysis. But the third party data support, is a big deal in my mind. I want to expand more on that. I want to learn more about it. Because when you have the first party data, which is very valuable, and access to more data sources. That becomes increasingly interesting. So the extensibility for getting content data or other data can come in through third party. I think that opens the door for Oracle to innovate on the area we gave the criticism on. So I think that's a positive trend. I think that's a good outlook on having the ability to get that third party data. >> Yeah but it's also going to be one of the places where Oracle is going to have to compete very, very aggressively with some other leaders who are a little bit more oriented towards content. At least some of their marketing clients are a little bit more content oriented. I'm comfortable Oracle will get there because let's face it. At the end of the day, marketing's always done a pretty good job of created, creative, using data to figure out what creative to use or create is nice. Very important. But what we're really talking about is customer experience. Will the customer get something out of every interaction? And while content's crucial to that the end result is ultimately, is the customer successful? And Oracle is showing a better play for that. So I'll give you, I like the way you did it on the grading. I'll give them a B plus. But I'm not disagreeing with you. I think we saw A talent here. We saw an A minus story. And they're a year in. So there's still some work that needs to be done, but it's clearly -- >> Why you weighted as a B plus >> I give them an A on vector. And where they're going. >> I would agree with that. >> And the feedback that we've gotten from the customers walking the show floor. There's a lot of excitement. A lot of positive energy. The other thing that I would say -- >> Oh the band. I'd give the band, the band was a B minus. (Peter laughs) Yeah that takes it. That's going to kill the curve. >> What was the band last night? >> I don't even remember. We missed the good one, I know that. We had dinner so we came late. It was a good band. It wasn't like, it wasn't like Maroon 5 or One Republic. Or Imagine Dragons or U2. >> Or one of the good ones. Sting. C minus. But the other thing that I think is really important is at least it pertains to modern customer experience. Is that they are, they are absolutely committed to the role the data's going to play. And we talked about that right at the front. But they are demonstrating a deep knowledge of how data and data integration and data flows are really going to impact the way their customers businesses operate. And I think that there were a couple of, I'll give a really high point and one that I want to hear more about in terms of the interviews we had. Great high point was one, we talked a lot about data science and how data science technologies are being productized. And that we heard, for example, that Oracle's commitment to it's marketplace is that they are going to insure that their customers can serve their customer's customers with any request within 130 milliseconds anywhere in the world. That's a very, very powerful statement that you can only really make if you're talking about having an end to end role over, or influence -- >> Like we commented, that's a good point. Like we commented that this end to end architecture is going to be fundamental. If you read the tea leaves and look at other things happening, like at Mobile World Congress. Intel I think is a bellwether on this with 5G. Cause they have to essentially create this overlay for connectivity as well as network transformation to do autonomous vehicles. To do smart cities. To smart homes. All these new technologies. It's an end to end IPR (mumbles). It's connected devices. So they're super smart to have this connected data theme which I think's relevant. But the other one, Ron Corbusier's talked about this evolution. And I find some of these, and I want to get your reaction to this statement. So Ron was kind of like, "oh it's an evolution. "We've seen this movie before." Okay great. But when you talk to Marta Feturichie, who was a customer from Royal Phillips. >> Peter: Great interview. >> She's head of CRM. Now she's doing some other stuff. So okay. What does CRM mean? So if you think evolution. What the customers are doing. Time Warner and Royal. It's interesting. Certain things are becoming critical infrastructure and other things are becoming more dynamic and fluid. So if you believe in evolution, these are layers of innovation. So stuff can be hardened as critical infrastructure, say like email marketing. So I think that what's happening here is you start to see some hardening of some critical infrastructure, aka marketing technology. MarTech (mumbles). Maybe some consolidation. AdTech kind of comes together. Certain things are going to be hardened and platformized. >> Let's take the word hardened and change it cause I know what you mean. Let's say it's codified. Now why is that, why is that little distinction a little bit interesting is because the more codified it gets, the more you can put software on it. The more you can put software on it the more you can automate it. And now we're introducing this whole notion of the adaptive intelligence. Where as we start to see marketing practices and processes become increasingly codified. What works, what doesn't work? What should we do more of? What should we do less of? Where should we be spending out time and innovating? Versus where should we just be doing it because it's a road activity at this point in time. That's where introducing this adaptive intelligence technology becomes really interesting. Because we can have the adaptive technology elements handle that deeply codified stuff where there really is not a lot of room for invention. And give the more interesting ongoing, customer engagement, customer experience -- >> Right on. And I think we should challenge Oracle post event and keep an eye on them on this adaptive intelligence app concept. Because that is something that they should ride to the sunset cause that is just a beautiful positioning. And if they can deliver the goods on that, they say they have it. We'll expand on that. That's going to give them the ability to churn out a ton of apps and leverage the data. But to the codified point you're making, here's my take. One of the things that I hear from customers in marketing all the time is a lot of stuff if oh yeah mobile first all that stuff. But still stuff's web presence based. So you got all these coded URL's. You got campaigns running ten ways from Sunday. DNS is not built to be adaptive and flexible. So it's okay to codify some of those systems. And say, "look we just don't tinker with these anymore." They're locked and loaded. You build on top of it. Codify it. And make that data the enabling technology from that. >> Peter: Without it become new inflexible (mumbles). >> Yeah I can't say, "Hey let's just tweak the hardened infrastructure "to run an AB test on a campaign." Or do something. No, no. You set this codified systems. You harden them. You put software on top of them. And you make it a subsystem that's hardened. And that's kind of what I mean. That's where the market will go because let's face it. The systems aren't that intelligent to handle a lot of marketing. >> Peter: They're still computers. >> They're still computers. People are running around just trying to fix some of this spaghetti code in marketing. And as the marketing department gets more IT power. Hey you own it. They're owning now. Be afraid what you wish for you might get it. So now they own the problem. So I think Oracle on the surfaces side has a huge opportunity to do what they did with Time Warner. Come into the market and saying, "Hey we got that for you." And that's what Hurd's kind of subtle message was on his keynote. Hey we're IT pros, but by the way you don't need to be in the IT business to do this. We fix your problems and roll out this -- >> We're going to talk to you in your language. And your language is modern customer experience. Which is one of the reasons why they've got to be more aggressive. And stating what they mean by that. >> And we have all the data in our data cloud. And all the first party data in our Oracle database. >> Right, right exactly right. >> That system of record becomes the crown jewel. Oracle has a lock spec on the table. You think it's a lock spec? >> Uh no. And that's exactly why I think they need to articulate where this is all going a little bit. They have to be a leader in defining what the future of marketing looks like so they can make it easier for people to move forward. >> Alright putting you on the spot. What do you think a modern marketing looks like? And organization. >> We talked about this and the answer that I gave, and I'll evolve it slightly, cause we had another great guest and I thought about it a little bit more is. A brand continuously and always delivers customer value. Always. And one of the -- >> Kind of cliche-ish. >> Kind of cliche-ish. >> Dig into it. >> But modern marketing is focused on delivering customer value. >> How? >> If they're deliver - well for example when the customer has a moment in a journey of uncertainty. Your brand is first is first to the table with that content that gets them excited. Gets them comfortable. >> Lot of progression. >> Makes them feel ready to move forward. That your, and well I'll make another point in a second. And I would even say that we might even think about a new definition of funnel. At the risk of bringing up that old artifact. Historical funnel went to the sale. Now we can actually start thinking about what's that funnel look like to customer success. >> Well there's two funnel dynamics that are changing. This is important, I think. This is going to be one of those moments where wow the Cube actually unpacked a major trend and I believe it to be true. The vertical funnel has collapsed. And now the success funnel is not >> Peter: It's not baked. >> Not big. It's decimated from this perspective of if the sale is the end game of the funnel, pop out that's over. Your point is kind of like venture funding for starter. That's when the start line begins. So here it's, okay we got a sale. But now we have instrumentation to take it all the way through the life cycle. >> And you know John. That's a great way of thinking about it. That many respects when you, when you introduce a customer to a new solution that has complex business implications that you are jointly together making an investment in something. And you both have to see it through. >> I mean sales guys put investment proposal on the -- >> That's exactly right. And so I think increasingly. So I would say modern marketing, modern marketing comes down to customer success. A prediction I'll make for next year is that this session is called, you know we'll call it the modern marketing modern customer experience show. But the theme is going to be customer success. >> Heres what I'm going to do. Here's what we're going to do this year Peter. We're going to, we will, based upon this conversation which we're riffing in real time as we analyze and summarize the event. We, I will make it my mission. And you're going to work with me on this as a directive. We're going to interview people, we're going to pick people that are truly modern marketing executives. >> Peter: That's great. >> We're going to define a simple algorithm that says this is what we think a modern marketing executive looks like. And we're going to interview them. We're going to do a story on them. And we're going to start to unpack because I think next year. We should be coming here saying, "we actually did our work on this." We figured out that a modern marketing organization and an executive behave and look this way. >> Right I think it's a great idea. So I'll give you one more thought. Cause I know you'll like this one too. Doug Kennedy. The partner. The conversation that we had. >> Very good. >> Talking about clearly a grade A executive. Seven weeks into the job. But that is going to be, you know for this whole thing to succeed he's got a lot of work in front of him. It's going to be very interesting to see how over the course of time this show and other Oracle shows evolve. >> I have a lot of partner experience. You do too. He's got a zillion years under his belt. He's a pro. He did not have any deer in the headlights look for seven weeks on the job. He's been there. He's done that. He knows the industry. He's seen the cycles of change. He's ridden waves of innovation up and down. And I think Oracle has a huge opportunity with his new program. And that is Oracle knows how to make money. Okay Oracle knows how to price things. They know how to execute on the sales side and go to market. And partners relationships are grounded in trust. And profitability. I would say profitability first and trust second. And it's kind of a virtuous circle. >> But John they've got to start getting grown in customer experience right? >> John: Yeah, yep. >> And that's not, it's doable but it's going to be a challenge. >> Well we talk about swim lanes with his interview, and I thought that was interesting. If you look at a center for instance, Deloy, PWC and all the different players. They're picking their swim lanes where their core competency is. And that's what he was basically saying. They're going to look for core competency. Now I think they're not there yet. The major SI's and potential partners. So he's going to have to put the spec out and put the bar there and say this is what we got to do. But you got to make the channel serve the customer. It has to be profitable. And it has to be relevant. And the only dangerous strategy I would say is the co-selling thing is always dicey. >> Especially if one has customer experience as a primary. >> It requires equilibrium in the ecosystem. >> You got it, you got it. >> It isn't there. >> And also it's a multi-partner go to market. It's not just one or two now. >> So he's going to have to really spread the love at the same time have hardened rules. Stick to his knitting on that one. Okay Peter final word. What do you, bottom line the show. Encapsulate the show into a bumper sticker. >> Well we heard Amazon released today. Google released today. Beat their numbers. Two companies that are trying to build an ecosystem from their core of the cloud. And the question is. Is Oracle who has customers with applications and with that first person data. Are they going to be able to cloudify, sorry for using that word, but are they going to be able to gain that trust that this new operating model they're really committed to for the future. Before Amazon and Google can create applications to their platform. Because Oracle has the end to end advantage right now. And in the world where digital's important. Speed's important. The fidelity of the data's important. The customer experience is important. That end to end has a window of opportunity. >> And I would also add two other companies reported, Microsoft and Intel and missed. So you have Amazon and Google. New guard, newer guard. Old guard Intel, Microsoft. Oracle is considered old guard even though they have some modernization going on from CX and the cloud. But Oracle is cloud a hundred percent in the cloud. Their SAP, for instance, is going multi-class. So the wild card in all this is, if the multi-cloud game evolves. >> Think end to end. End to end. Because that has advantages. When you're talking data, one of the things that Jack Brookwood said. He said, "you know why we can hit that 150 millisecond target?" >> Cause you don't have to move the data around. >> Cause sometimes we don't have to move the data around. >> This can be very interesting. And this going to be fun to watch and participate in. Of course the Cube will covering Oracle, well we'll be there again this year. We don't have the exacts specifics on that, but certainly if your interested in checking us out. Were siliconangle.com. Peter's research is at wikibon.com as well as SiliconANGLE on the front page. SiliconAngle.tv has all the videos. And well will be documenting and following the modern marketing experience with people and companies. And documenting that on the Cube and SiliconANGLE. So that's a wrap from day two at Oracle Modern CX. Thanks for watching. (electronic music)

(upbeat music) >> Narrator: Live, from Las Vegas, it's the CUBE. Covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. >> Welcome back everyone, we are here live in Las Vegas for the CUBE's special coverage of Oracle's ModernCX, Modern Customer Experience, this is the Cube, I'm John Furrier, my cohost Peter Burris. Our next guest is Tony Nadalin. Tony Nadalin is the global vice president of the Global Consulting at Oracle for the marketing cloud. Welcome to the CUBE. >> Well, thank you. Thank you for having me. >> So you've got to implement this stuff, and we've heard a lot of AI magic and there's a lot of meat on the bone there. People are talking about there's a lot of real things happening. Certainly, Oracle's acquired some great technologies over the years, integrated it all together. The proof is in the pudding. When you roll it out, the results have to speak for themselves. >> Tony: Yes, absolutely. >> So share with us some of those activities. What's the score board look like? What's the results? >> I think what's really important, and Lewis spoke about this yesterday, it's people and product. The customers are buying visions. They're looking at creating and changing the customer experience. They're not just buying a piece of technology. They're buying a transformation. I think what's really important and what we do a lot in services, in all services, not just Oracle Marketing Cloud Services, but just healthy services, is when customers are implementing, they're not just implementing technology, they're not just plumbing the pipes. They are putting in changes. They're looking at the people, the process, the technology. We have a really good relationship with our customers and our partners and we're constantly looking at the complete set of services, the complete suite. From what I call transformational services, where we come in and try to understand what are you trying to change? How are you trying to change your customer experience? As a marketer, owning not only what you do, and how all the different channels are working together across all the different products that they are. They purchase Eloqua, Responsys, BlueKai, Maxymiser, et cetera. >> So you're laying it all out, it's like you're sitting in a room, now I'm oversimplifying it, but it's not just rolling out stuff. You've got planning. >> Tony: You've got to plan it. >> Put the pieces together. >> You do, and it's a readiness. It's a readiness of the organization, you think about it, you've got within a marketing organization, you've got many teams coming together that have to be united around the brand, the consistency, how they're engaging with customers. But also, not only across like an acquisition team, or loyalty or an upsell and cross sell team, how does that, as we were looking at the products key notes, how does that then extend into the services engagement? How does it extend into the sales engagement? How are we making sure that everyone is using the same messaging, the same branding, leveraging each other? It's a real transformation at a people, process and technology level. So that when you're then implementing, you're implementing changes. And so we've got some great services and great partners that make sure that when the customers are going through that transformation, they're sort of going it fully readied. And our role, from a services perspective, is to ensure then, sort of define the transformation, define the strategy, like plan the plan, and then go execute the plan. And then putting in the plumbing, getting everyone readied. The analogy I used, I'm sure you've got kids, right? When we have toddlers, and you build the kid's first bikes. Your goal is to build that bike, put the training wheels on the bike, and ultimately sort of stand behind your child to a point that when you let them go, they're not going to graze their knees. Then from an ongoing basis, continue to stand behind them, then get ready to take the training wheels off. Then training wheels come off. Maybe at one point they may become BMX champions, right? But you're sort of behind them through the whole-- >> John: There's progression. >> Progression, exactly. >> With my kids, it's simply man to man, then zone defense. (laughter) >> But it's progression, right? A lot of customers, we have not only the onboarding and implementation services, but these ongoing services that are so key. Because obviously it's important to ensure that your customers are realizing. When I think of our services and the journey, there's the discovery, the transformation, and the strategy. That's like the discovery. But you've then got the realization. And then the optimization and the realization to me is that you're realizing that initial step. You're realizing the technology and you're realizing people and process. You're getting people stood up. Skills, people, organizations, technology, data. You're realizing it all so they can then take the next step. >> Alright, so what's the playbook? A lot of times, in my mind's eye, I can envision in a white board room, board room, laying it all out, putting the puzzle pieces together, and then rolling out implementation plan. But the world is going agile, not waterfall anymore, so it's a combination of battle mode, but also architectural thinking. So not just fashion, real architectural, foundational. >> Peter: Design thinking. >> Tony: Exactly, architectural. >> John: Design thinking. What's the playbook? What's the current state of the art in the current-- >> Well we have obviously product consultants, architects, solution consultants, content creators. It's the whole spectrum of where the customer needs to focus on. And I think-- >> John: So you assemble them based upon the engagement. >> Based upon the engagement and understanding, like what are the customer's strengths? Where are they now? Where are they trying to get to? There's some customers, you know, we have a whole range of services, and we have a whole range of customers. So there are some customers who are like, "We have our own teams today, "we want to augment our teams with your teams, "we want to have hybrid models." Or, "We have our own teams today, but not only have you got great people, but you've got great processes." So like, look at Maxymiser as an example. A lot of our Maxymiser customers, not only use our platform, but they use our people. They're not just buying our people, they're buying a sort of agile, Kanban, JavaScript development practices that are a different level of software development. It's not just the people that can code, it's the development practices. So it's that whole operational services where we bring to the table just a different degree of operational excellence. But we're also to go in to our customers that have their own teams and provide them also consulting perspective around how they can also sharpen their edge. If they want to sort of keep, you know. So whole spectrum of services. >> So let me see if I can throw something out there, in kind of like the center, the central thesis of what you do and how it's changed from what we used to do. Especially a company like Oracle, which has been a technology company at the vanguard of a lot of things. It used to be that customers had an idea of what they wanted to implement. They wanted to implement an accounting system. The processes are relatively known. What was unknown was the technology. How do, what do I buy? How do I configure? How do I set it up? How do I train? How do I make the software run? How do I fix? So it was known process, unknown technology. As a consequence, technology companies could largely say, yeah, that value is intrinsic to the product. So you buy the product, you've got it now. But as we move more towards a service world, as we move more toward engaging the customer world where the process is unknown, and the technology, like the cloud, becomes increasingly known. Now we're focused on more of an unknown process, known technology, and the value is in, does the customer actually use it. >> I think the value is actually in does the customer get value. I think there's a, I've managed customer success organizations and customer service organizations, and the one thing I see in SAS, is usage doesn't always equate to value. So I think as a services organization, it's important to understand the roadmap to value. Because a lot of times, I would say in commodity software, sort of the use of it by default in itself was enough. That you were moving to a software platform. I think SAS customers, especially marketers, are looking for transformation. They're looking for a transformation and a change in value. A change in value in the conversation they're having with the customer. A change in acquisition, loyalty, retention, a change in being relevant. As Joseph was saying this morning, being relevant with the customer, and that value is more than just implementing some technology. >> So it's focusing on ensuring that the customer is getting value utility out of whatever they purchase. >> Tony: Correct. >> Not just that they got what they purchased. So as we move into a world where we're embedding technology more and more complex, it's two things happen. One is, you have to become more familiar of the actual utilization. And what does it mean, and I think marketing cog helps that. What is marketing, how does it work? And second one, the historical norm has been, yeah, we're going to spend months and years building something, deploying something, but now we're trying to do it faster, and we can. So how is your organization starting to evolve its metrics? Is it focused on speed? Is it focused on, obviously value delivered, utilization. What are some of the things that you are guiding your people to focus on? >> Well I think, I very much take a outside-in view. So to me, if I look at why a customer is buying, and what do they want. Obviously most customers want fast time to value, as reduced effort, obviously, and little surprises. I think having a plan and being able to execute your plan. And this whole, as we were talking like one-to-many versus one-to-one. >> And timing too, no surprises and they want to execute. >> And time to value, right? And speed. And I think as we were talking, similar to as a marketer is trying to engage any customer and sort of going from that one-to-many to that one-to-you, what's important now for any organization, a services organization, any company, is to understand what does your business look like? Because why you bought from Oracle, whether you be in a certain vertical or a certain space, or a certain maturity as a customer, it's important that we have the play books, and we do, that say that if you're a customer of this size, of these products in this vertical, then we have the blueprints for success. They may not be absolutely perfect, but they're directional, that we can sort of put you on the fast path. That we've seen the potholes before, we've seen the bumps, we understand the nuances of your data, your systems, your people, your regulations. So that we can actually, we have a plan. And it's a plan that's relevant to you. It's not a generic plan. And I think that's the biggest thing where good companies show up then deliver solutions that they're not learning 100%. There's always going to be nuances and areas of gray that you work through, where the customer's just as much as vendors as they transform. We're not just swapping like for like, but when you transform, there's changes that occur on the customer side. There's new awarenesses of I didn't realize we did that. I didn't realize I want to change doing that. And I've actually changed maybe my whole thought. >> What's the change coming from this event? If you look at the show here, ModernCX, some really good directional positioning. The trajectory of where this is going, I believe is on a great path. Certainly directionally relevant, 100%. Some stuff will maybe shift in the marketplace. But for the most part, I'm really happy to see Oracle go down this road. But there's an impact factor to the customers, and the communities, and that's going to come to you, right? So what are you taking away from the show that's important for customers to understand as Oracle brings in adaptive intelligence? As more tightly coupled, highly cohesive elements come together? >> I think to me, it's transformation. Customers really do understand what are they trying to achieve as they transform? Not just by a piece of technology, but come into it understanding, okay, what are we trying to transform? And have we got like all change management? All transformational management? Have I got the right buy-in across the organization? As a marketer, if I'm trying to transform the organization, have I got the right stakeholders in the room with me? Am I trying to influence the right conversations? You look at the conversation yesterday with Netflix. The discussion, or Time-Warner, sorry. Around their transformation around data. That wasn't a single entity determining that. That was a company driven strategy. A company driven transformation. And I think to really change the customer experience, and control the brand of that across all touchpoints of the company, it requires transformation and it requires being realistic around also how long that journey takes. Depending on the complexity and size of the company. It requires investment of people, of energy, or resources and really understanding where is your customer today? Where is your competition? And to Mark's point, it's like the market is being won here, you're having to compete against your competition, you're having to be better than them, you're having to understand your competition just as much as you understand yourself, so you're leapfrogging. Because just as much as you're going after your competitors customers, your customers are coming up for your customers, right, your competitors are coming up for your customers. I think transformation and understanding how to engage the right services leaders, be it Oracle or any of our partners, to really transform your business is to me the biggest take away. The technology then, be it Chatbox or AI, I mean they augment, they help, they're going to be channels, but I think transformation is key. >> It's really not the technology, it's really what you're doing it with, at the end of the day. Tony, thanks for coming on the CUBE. We really appreciate it, and again, when the rubber hits the road, as Peter was saying earlier, it's going to be what happens with the product technologies for the outcomes. >> Tony: Absolutely. >> Thanks for sharing your insights here on the CUBE. Sharing the data, bringing it to you. I'm John Furrier with the CUBE with Peter Burris, more live coverage for the Mandalay Bay in Las Vegas from Oracle's ModernCX after this short break. (upbeat music) >> Narrator: Robert Herjavec >> Interviewer: People obviously know you from Shark Tank. But the Herjavec Group has been really laser focused on cyber security.

(energetic upbeat music) >> Host: Live from Las Vegas, it's The CUBE. Covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. >> Welcome back, everyone. We are here live in Las Vegas at the Mandalay Bay for Oracle's Modern CX show, Modern Customer Experience. The Modern Marketing Experience converted into the Modern CX Show. I'm John Furrier with The Cube. My co-host Peter Burris. Day two of coverage. Our next guest is Catherine Blackmore, Global Vice President, Customer Success, Global Customer Success at Oracle Marketing Cloud. Catherine, welcome back to The CUBE. Great to see you. >> Thank you so much for having me here. It's been an incredible week, just amazing. >> Last year we had a great conversation. Remember we had. >> Yes. >> It was one of those customer focused conversations. Because at the end of the day, the customers are the ones putting the products to use, solving their problems. You were on stage at the keynote. The theme here is journeys, and the heroes involved. What was the summary of the keynote? >> Sure. As you say, this theme has really been around heroic marketing moments. And in a way, I wanted to take our marketers and the audience to an experience and a time where I think a lot of folks can either remember or certainly relate where, what was the beginning of really one experience, which was Superman. If you think about heroism and a superhero, well, Superman will come to mind. But I think what was interesting about that is that it was created at a time where most folks were not doing well. It was actually during the Great Depression. And most folks wouldn't realize that Superman almost never came to be. It was an image, an icon, that was created by two teenage boys, Jerry Shuster and Joe Siegal. And what they did is they got audience. They understood, just as two teenage boys, my parents, my family, my community is just not doing well. And we see that folks are trying to escape reality. So we're going to come up with this hero of the people. And in doing so, what's interesting is, they really were bold, they were brave. They presented a new way to escape. And as a result, DC Comics took it up. And they launched, and they sold out every single copy. And I think it's just a really strong message about being able to think about creativity and being bold. Jerry and Joe were really the heroes of that story, which was around. My challenge to the audience is, who's your Superman? What is your creative idea that you need to get out there? Because in many ways, we need to keep moving forward. At the same time, though, balance running a business. >> It's interesting, you did mention Superman and they got passed over. And we do a lot of events in the industry, a lot of them are big data events. And it's one little insight could actually change a business, and most times, some people get passed over because they're not the decision maker or they may be lower in the organization or they may just be, not be knowing what to do. So the question on the Superman theme, I have to ask you, kind of put you on the spot here is, what is the kryptonite for the marketer, okay, because >> (laughing) Yes. >> there's a lot of obstacles in the way. >> Catherine: It is. >> And so people sometimes want to be Superman, but the kryptonite paralyzes them. >> Catherine: Yeah. >> Where's the paralysis? >> It's funny that you say that. I think I actually challenge folks to avoid the kryptonite. There was three things that we really talked about. Number one is, Modern Marketing Experience, it's just an incredible opportunity for folks to think ahead, dream big, be on the bleeding edge. But guess what, we're all going to go on flights, we're going to head home, and Monday morning's going to roll around and we're going to be stuck and running the business. And my inspiration and, really, challenge to the audience and to all of our marketers is how do we live Modern Marketing Experience everyday? How do we keep looking ahead and balance the business? And, really, those heroic marketers are able to do both. But it doesn't stop there. We talked a lot about this week, about talent. Do we have the right team? Kryptonite is not having the right people for today and tomorrow, and then in addition to that, you can't just have a team, you can't just have a vision, but what's your plan? Where actually having the right stakeholders engaged, the right sponsorship, that's certainly probably the ultimate kryptonite if you don't. >> The sponsorships are interesting because the people who actually will empower or have empathy for the users and empower their people and the team have to look for the yes's, not the no's. Right. And that's the theme that we see in the Cloud success stories is, they're looking for the yes. They're trying to get that yes. But they're challenging, but they're not saying no. That's going to shut it down. We've seen that in IT. IT's been a no-no, I was going to say no ops but in this digital transformation with the emphasis on speed, they have to get to the yes. So the question is, in your customer interactions, what are some of those use cases where getting to that yes, we could do this, What are some of the things, is it data availability? >> Catherine: Absolutely. >> Share some color on that. >> I think, So I actually had a wonderful time connecting with Marta Federici, she met with you earlier. And I love her story, because she really talks about the culture and placing the customer at the center of everything they're doing, to the extent that they're telling these stories about why are we doing this? We're trying to save lives, especially in healthcare. And just to have stories and images. And I know some companies do an amazing job of putting the customer up on the wall. When we talk to our customers about how do we actually advance a digital transformation plan? How do we actually align everyone towards this concept of a connected customer experience? It starts with thinking about everyone who touches the customer every day and inspiring them around how they can be part of being a customer centric organization. And that's really, that's really important. That's the formula, and that's what we see. Companies, that they can break through and have that customer conversation, it tends to align folks. >> Interesting. We were talking earlier, Mark Hurd's comment to both the CMO Summit that was happening in a separate part of the hotel here in the convention center, as well as his keynote. He was saying, look, we have all this technology. Why are we doing this one percent improvement? And he was basically saying, we have to get to a model where there's no data department anymore. There never was. >> That's right. >> And there shouldn't be. There shouldn't be, that department takes care of the data. That's kind of the old way of data warehousing. Everyone's a data department, and to your point, that's a liberating, and also enables opportunities. >> It does. We talked a lot. Actually, the CMO Summit that we had as well this week, a lot of our CMOs were talking about the democratization of data. And Elissa from Tableau, I think you also talked to. We talked about, how do you do that? And why, what are those use cases, where, Kristen O'Hara from Time Warner talked about it as well. And I think, that's where we have to go. And I think there's a lot of great examples on stage that I would like to think our marketers, and quite frankly, >> Which one's your favorite, favorite story? >> My favorite story. >> John: Your favorite story. >> Wow, that's really putting me on the spot. >> It's like picking your favorite child. I have four. I always say "well, they're good at this sport, or this kid's good in school." Is there? >> I guess one. >> John: Or ones that you want to highlight. >> Well one that I, because we talked about it today. And it was really a combination of team and plan. Just really highlighting on what Marta's driving. If you think about the challenges of a multinational >> Peter: Again, this is at Philips. >> John: Marta, yeah. >> Catherine: This is Philips, Royal Philips. So Marta, what she's really, her team has been trying to accomplish, both B to C and B to B, and it speaks to data, and it talks about obviously having CRM be kind of that central nervous system so that you can actually align your departments. But then, being able to think about team. They've done a lot of work, really making certain they have the team for today and the future. They're also leveraging partners, which is also key to success. And then, having a plan. We spent time with Royal Philips actually at headquarters a number of weeks ago and they are doing this transformation, this disruptive tour with all of their top folks across, around the world that running their different departments, to really have them up and them think differently which is aligning them around that culture of looking out to the future. >> Peter: Let's talk a bit about thinking differently. And I want to use you as an example. >> Catherine: Sure. >> So your title is Customer Success. Global Vice President, Global Customer Success. What does that mean? >> Sure. I know a lot of folks, I'd like to think that, that's just a household name right now in terms of Customer Success. But I realize it's still a little new and nascent. >> We've seen it elsewhere but it's still not crystal clear what it means. >> Sure, sure. So when I think of Customer Success, the shorter answer is, we help our customers be successful. But that, what does it really mean? And when I think about the evolution of what Customer Success, the department, the profession, the role, has really come to be, it's serving a very important piece of this Cloud story. Go back a decade when we were just getting started actually operationalizing SaaS and thinking about how to actually grow our businesses, we found that there just needed to be a different way of managing our customers and keeping customers, quite frankly. Cause as easy as it is to perhaps land a SaaS customer, and a Cloud customer, because it's easier to stand them up and it's easier for them to purchase, but then they can easily leave you too. And so what we found is, the sales organization, while, obviously understands the customer, they need to go after new customers. They need to grow share. And then in addition to that, in some organizations, there still are services to obviously help our customers be successful. And that's really important, but that is statement-of-work-based. There's a start and a stop and an end to that work. And then obviously there's support that is part of a services experience, but they tend to be queue-based, ticket-based, break-fix. And what we found in all of this is, who ultimately is going be the advocate of the customer? Who's going to help the customer achieve ROI business value and help them ensure that they are managing what they've purchased and getting value, but also looking out towards the future and helping them see what's around the corner. >> Catherine I want to ask the question. One of the themes in your keynote was live in the moment every day as a modern marketing executive, build your team for today and tomorrow, and plan for the future. You mentioned Marta, who was on yesterday, as well as Kristen O'Hara from Time Warner. But she made an interesting comment, because I was trying to dig into her a little bit, because Time Warner, everyone knows Time Warner. So, I was kind of curious. At the same time, it was a success story where there was no old way. It was only a new way, and she had a pilot. And she had enough rope to kind of get started, and do some pilots. So I was really curious in the journey that she had. And one thing she said was, it was a multi-year journey. >> Catherine: Yes. >> And some people just want it tomorrow. They want to go too fast. Talk through your experience with your customer success and this transformation for setting up the team, going on the transformational journey. Is there a clock? Is there a kind of order of magnitude time frame that you've seen, that works for most companies? >> Sure. And actually I want to bring in one more experience that I know folks had here at Modern Marketing, which was, also, Joseph Gordon-Levitt, he actually talked about this very thing. I think a lot of folks related to that because what he's been doing in terms of building out this community and creating crowd-sourced, or I should say, I think he would want to say community-sourced content and creativity. It was about, you can't really think about going big. Like I'm not thinking about feature film. I'm thinking about short video clips, and then you build. And I think everyone, the audience, like okay I get that. And Kristen's saying, it took many little moments to get to the big moment. I think folks want to do it all, right at the very beginning. >> John: The Big Bang Theory, just add, >> Absolutely. >> Just add water, and instant Modern Marketing. >> It is, it is. >> John: And it's hard. >> And what we have found, and this is why the planning part is so important, because what you have to do, and it might not be the marketer. The marketer, that VP of Marketing, even that CMO may know, it's going to be a three year journey. But sometimes it's that CEO, Board of Director alignment that's really required to mark, this is the journey. This is what year one's going to look like. This is what we're going to accomplish year two. There may be some ups and downs through this, because we need to transform sales, we need to transform back in operations in terms of how we're going to retire old processes and do new. And in doing so, we're going to get to this end state. But you need all of your stakeholders to be engaged, otherwise you do get that pressure to go big because, you know what Mark was saying, I've got 18 months, we need to be able to show improvement right away. >> We were talking about CIOs on another show that I was doing with Peter. And I think Peter made the comment that the CIO's job sometimes doesn't last three years. So these transformations can't be three years. They got to get things going quicker, more parallel. So it sounds like you guys are sharing data here at the event among peers >> Catherine: Yes. >> around these expectations. Is there anything in terms of the playbook? >> Catherine: Yes. >> Is it parallel, a lot of AGILE going on? How do you get those little wins for that big moment? >> So I think this is where the, what I would call, the League of Justice. You got to call in that League of Justice. For all you Superman out there. Because in many ways you're really challenged with running the business, and I think that's the pressure all of us are under. But when you think about speeding up that journey, it really is engaging partners, engaging, Oracle Marketing Cloud, our success and services team. I know you're going to be talking to Tony a little bit about some of the things we're building but that's where we can really come in and help accelerate and really demonstrate business value along the way. >> Well one more question I had for you. On the show floor, I noticed, was a lot of great traffic. Did you guys do anything different this year compared to last year when we talked to make this show a little bit more fluid? Because it seems to me the hallway conversation has been all about the adaptive intelligence and data is in every conversation that we have right now. What have you guys done differently? Did it magically just come to you, (Catherine laughing) Say, we're going to have to tighten it up this year? What was the aha moment between last year and this year? It's like night and day. >> I would like to think that we are our first and best customer, because as we ourselves are delivering technology, we ourselves also have to live what we tell our customers to do every day. Look at the data, look at the feedback. Understand what customers are telling you. How can you help customers achieve value? And we think of this as an important moment for our partners and our companies, that are here spending money and spending time to be here, achieve value. What we've done is really create an experience where it's so much easier to have those conversations. Really understanding the flow of traffic, and how we can actually ensure people are able to experience our partners, get to know them, get to know other customers. A lot of folks, too, have been saying, love keynote, love these different breakout sessions, but I want to connect with other folks going through that same thing that I am, so I can get some gems, get some ideas that I can pick up. >> And peer review is key in that. They talk to each other. >> Exactly. That's right, that's right. And so we've really enabled that, the way that we've laid out the experience this year. And I know it's even going to be better next year. Cause I know we're going to collect a lot more data. >> Well last year we talked a lot about data being horizontally scalable. That's all people are talking about now, is making that data free. The question for you is, in the customer success journeys you've been involved, what's the progress bar of the customer in terms of, because we live in Silicon Valley. So oh yeah, data driven marketer! Everyone's that. Well, not really. People are now putting the training wheels on to get there. Where are we on the progress bar for that data driven marketer, where there's really, the empathy for the users is there. There's no on that doubts that. But there's the empowerment piece in the organization. Talk about that piece. Where are we in that truly data driven marketer? >> Oh, we're still early days. It was obvious in talking to our various CMO's. We were talking about talent and the change, and what the team and the landscape needs to look like to respond to certainly what we've experienced in technology over the last number of years and then even what was introduced today. That level of, I need to have more folks that really understand data on my team but I'll tell you, I think the thing that's really interesting though about what we've been driving around technology and specifically AI. I love what Steve said, by the way, which is if a company is presenting AI as magic, well the trick's on you. Because truly, it's not that easy. So I think the thing that we need to think about and we will work with our customers on is that there's certainly a need and you have to be data driven but at the same time, we want to be innovation ready and looking and helping our customers see the future to the extent that how we think about what we're introducing is very practical. There's ways that we can help our customers achieve success in understanding their audience in a way that is, I wouldn't say, it's just practical. We can help them with use cases, and the way the technology is helping them do that, I think we're going to see a lot of great results this year. >> AI is great, I love to promote AI hype because it just makes software more cooler and mainstream, but I always get asked the question, how do you evaluate whether something is BS in AI or real? And I go, well first of all, what is AI? It's a whole 'nother story. It is augmented intelligence, that's my definition of it. But I always say, "It's great sizzle. Look for the steak." So if someone says AI, you got to look on the grill, and see what's on there, because if they have substance, it's okay to put a little sizzle on it. So to me, I'm cool with that. Some people just say, oh we have an AI magical algorithm. Uh, it's just predictive analytics. >> Catherine: Yes. >> So that's not really AI. I mean, you could say you're using data. So how do you talk to customers when they say, "Hey, AI magic or real? How do I grok that?" How do I figure it out? >> I think it's an important advancement, but we can't be distracted by words we place on things that have probably been around for a little while. It's an important way to think about the technology, and I think even Steve mentioned it on stage. But I think we're helping customers be smarter and empowering them to be able to leverage data in an easier way, and that's what we have to do. Help them, and I know this is talked a lot, not take the human and the people factor out because that's still required, but we're going to help them be able to concentrate on what they do best, whether it's, I don't want to have to diminish my creative team by hiring a bunch of data scientists. We don't want that. We want to be able to help brands and companies still focus on really understanding customers. >> You know, AI may be almost as old as Superman. >> Catherine: (laughing) I think you're right. >> Yeah, because it all comes back to Turing's test of whether or not you can tell the difference between a machine and a human being, and that was the 1930s. >> Well, neural networks is a computer science. It's a great concept, but with compute and with data these things really become interesting now. >> Peter: It becomes possible. >> Yeah, and it's super fun. But it promotes nuanced things like machine learning and Internet Of Things. These are geeky under-the-hood stuff that most marketers are like, uh what? Yeah, a human wearing a gadget is an Internet of Things device. That's important data. So then if you look at it that way, AI can be just a way to kind of mentally think about it. >> That's right, that's right. >> I think that's cool for me, I can deal with that. Okay, final question, Catherine, for you. >> Catherine: Yes. >> What's the most important thing that you think folks should walk away from Modern CX this year? What would you share from this show, given that, on the keynote, CMO Summit, hallways, exhibits, breakouts, if there's a theme or a catalyst or one? >> Peter: What should they put in the trip report? >> It's all about the people. I think that, if I were to distill it down, you think about that word bubble chart, that's people. I think that's the biggest word that came out of this. As much as technology is important, it's going to enable us, it's going to enable our people, and it's going to put a lot of attention on our talent and our folks that are going to be able to take our customers to the next level. >> And then people are the ones that are generating the data too, that want experiences, to them. >> Catherine: That's right. >> It's a people centric culture. >> Catherine: It is. >> Catherine Blackmore here on site, The CUBE, at Modern CX's The CUBE, with more live coverage here from the Mandalay Bay in Las Vegas, live after this short break. (electronic music)