>> From The Cube studios in Palo Alto, in Boston, bringing you data driven insights from the cube and ETR. This is breaking analysis with Dave Vellante. >> Last year, we noted in a breaking analysis that the cloud ecosystem is innovating beyond the idea or notion of multi-cloud. We've said for years that multi-cloud is really not a strategy but rather a symptom of multi-vendor. And we coined this term supercloud to describe an abstraction layer that lives above the hyperscale infrastructure that hides the underlying complexities, the APIs, and the primitives of each of the respective clouds. It interconnects whether it's On-Prem, AWS, Azure, Google, stretching out to the edge and creates a value layer on top of that. So our vision is that supercloud is more than running an individual service in cloud native mode within an individual individual cloud rather it's this new layer that builds on top of the hyperscalers. And does things irrespective of location adds value and we'll get into that in more detail. Now it turns out that we weren't the only ones thinking about this, not surprisingly, the majority of the technology ecosystem has been working towards this vision in various forms, including some examples that actually don't try to hide the underlying primitives. And we'll talk about that, but give a consistent experience across the DevSecOps tool chain. Hello, and welcome to this week's Wikibon, Cube insights powered by ETR. In this breaking analysis, we're going to share some recent examples and direct quotes about supercloud from the many Cube guests that we've had on over the last several weeks and months. And we've been trying to test this concept of supercloud. Is it technically feasible? Is it business rational? Is there business case for it? And we'll also share some recent ETR data to put this into context with some of the players that we think are going after this opportunity and where they are in their supercloud build out. And as you can see I'm not in the studio, everybody's got COVID so the studios shut down temporarily but breaking analysis continues. So here we go. Now, first thing is we uncovered an article from earlier this year by Lori MacVittie, is entitled, Supercloud: The 22 Answer to Multi-Cloud Challenges. What a great title. Of course we love it. Now, what really interested us here is not just the title, but the notion that it really doesn't matter what it's called, who cares? Supercloud, distributed cloud, someone even called it Metacloud recently, and we'll get into that. But Lori is a technologist. She's a developer by background. She works at F-Five and she's partial to the supercloud definition that was put forth by Cornell. You can see it here. That's a cloud architecture that enables application migration as a service across different availability zones or cloud providers, et cetera. And that the supercloud provides interfaces to allocate, migrate and terminate resources... And can span all major public cloud providers as well as private clouds. Now, of course, we would take that as well to the edge. So sure. That sounds about right and provides further confirmation that something new is really happening out there. And that was our initial premise when we put this fourth last year. Now we want to dig deeper and hear from the many Cube guests that we've interviewed recently probing about this topic. We're going to start with Chuck Whitten. He's Dell's new Co-COO and most likely part of the Dell succession plan, many years down the road hopefully. He coined the phrase multi-cloud by default versus multi-cloud by design. And he provides a really good business perspective. He's not a deep technologist. We're going to hear from Chuck a couple of times today including one where John Furrier asks him about leveraging hyperscale CapEx. That's an important concept that's fundamental to supercloud. Now, Ashesh Badani heads products at Red Hat and he talks about what he calls Metacloud. Again, it doesn't matter to us what you call it but it's the ecosystem gathering and innovating and we're going to get his perspective. Now we have a couple of clips from Danny Allan. He is the CTO of Veeam. He's a deep technologist and super into the weeds, which we love. And he talks about how Veeam abstracts the cloud layer. Again, a concept that's fundamental to supercloud and he describes what a supercloud is to him. And we also bring with Danny the edge discussion to the conversation. Now the bottom line from Danny is we want to know is supercloud technically feasible? And is it a thing? And then we have Jeff Clarke. Jeff Clark is the Co-COO and Vice Chairman of Dell super experienced individual. He lays out his vision of supercloud and what John Furrier calls a business operating system. You're going to hear from John a couple times. And he, Jeff Clark has a dropped the mic moment, where he says, if we can do this X, we'll describe what X is, it's game over. Okay. So of course we wanted to then go to HPE, one of Dell's biggest competitors and Patrick Osborne is the vice president of the storage business unit at Hewlett Packet Enterprise. And so given Jeff Clarke's game over strategy, we want to understand how HPE sees supercloud. And the bottom line, according to Patrick Osborne is that it's real. So you'll hear from him. And now Raghu Raghuram is the CEO of VMware. He threw a curve ball at this supercloud concept. And he flat out says, no, we don't want to hide the underlying primitives. We want to give developers access to those. We want to create a consistent developer experience in that DevsSecOps tool chain and Kubernetes runtime environments, and connect all the elements in the application development stack. So that's a really interesting perspective that Raghu brings. And then we end on Itzik Reich. Itzik is a technologist and a technical team leader who's worked as a go between customers and product developers for a number of years. And we asked Itzik, is supercloud technically feasible and will it be a reality? So let's hear from these experts and you can decide for yourselves how real supercloud is today and where it is, run the sizzle >> Operative phrase is multi-cloud by default that's kind of the buzz from your keynote. What do you mean by that? >> Well, look, customers have woken up with multiple clouds, multiple public clouds, On-Premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was, it can be and it should be. It is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they want to maintain an On-Premise cloud, On-Premise clouds are not going away, I mentioned edge clouds, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud by default we mean that's the state of the world today. Our goal is to bring multi-cloud by design as you heard. >> Really great question, actually, since you and I talked, Dave, I've been spending some time noodling just over that. And you're right. There's probably some terminology, something that will get developed either by us or in collaboration with the industry. Where we sort of almost have the next almost like a Metacloud that we're working our way towards. >> So we manage both the snapshots and we convert it into the Veeam portable data format. And here's where the supercloud comes into play. Because if I can convert it into the Veeam portable data format, I can move that OS anywhere. I can move it from physical to virtual, to cloud, to another cloud, back to virtual, I can put it back on physical if I want to. It actually abstracts the cloud layer. There are things that we do when we go between cloud some use BIOS, some use UEFI, but we have the data in backup format, not snapshot format, that's theirs, but we have it in backup format that we can move around and abstract workloads across all of the infrastructure. >> And your catalog is control in control of that. Is that right? Am I thinking about that the right way? >> Yeah it is, 100%. And you know what's interesting about our catalog, Dave, the catalog is inside the backup. Yes. So here's, what's interesting about the edge, two things, on the edge you don't want to have any state, if you can help it. And so containers help with that You can have stateless environments, some persistent data storage But we not not only provide the portability in operating systems, we also do this for containers. And that's true. If you go to the cloud and you're using say EKS with relational database services RDS for the persistent data later, we can pick that up and move it to GKE or move it to OpenShift On-Premises. And so that's why I call this the supercloud, we have all of this data. Actually, I think you termed the term supercloud. >> Yeah. But thank you for... I mean, I'm looking for a confirmation from a technologist that it's technically feasible. >> It is technically feasible and you can do it today. >> You said also technology and business models are tied together and enabler. If you believe that then you have to believe that it's a business operating system that they want. They want to leverage whatever they can. And at the end of the day, they have to differentiate what they do. >> Well, that's exactly right. If I take that in what Dave was saying and I summarize it the following way, if we can take these cloud assets and capabilities, combine them in an orchestrated way to deliver a distributed platform, game over. >> We have a number of platforms that are providing whether it's compute or networking or storage, running those workloads that they plum up into the cloud they have an operational experience in the cloud and they now they have data services that are running in the cloud for us in GreenLake. So it's a reality, we have a number of platforms that support that. We're going to have a a set of big announcements coming up at HPE Discover. So we led with Electra and we have a block service. We have VM backup as a service and DR on top of that. So that's something that we're providing today. GreenLake has over, I think it's actually over 60 services right now that we're providing in the GreenLake platform itself. Everything from security, single sign on, customer IDs, everything. So it's real. We have the proofpoint for it. >> Yeah. So I want to clarify something that you said because this tends to be very commonly confused by customers. I use the word abstraction. And usually when people think of abstraction, they think it hides capabilities of the cloud providers. That's not what we are trying to do. In fact, that's the last thing we are trying to do. What we are trying to do is to provide a consistent developer experience regardless of where you want to build your application. So that you can use the cloud provider services if that's what you want to use. But the DevSecOp tool chain, the runtime environment which turns out to be Kubernetes and how you control the Kubernetes environment, how do you manage and secure and connect all of these things. Those are the places where we are adding the value. And so really the VMware value proposition is you can build on the cloud of your choice but providing these consistent elements, number one, you can make better use of us, your scarce developer or operator resources and expertise. And number two, you can move faster. And number three, you can just spend less as a result of this. So that's really what we are trying to do. We are not... So I just wanted to clarify the word abstraction. In terms of where are we? We are still, I would say, in the early stages. So if you look at what customers are trying to do, they're trying to build these greenfield applications. And there is an entire ecosystem emerging around Kubernetes. There is still, Kubernetes is not a developer platform. The developer experience on top of Kubernetes is highly inconsistent. And so those are some of the areas where we are introducing new innovations with our Tanzu Application Platform. And then if you take enterprise applications, what does it take to have enterprise applications running all the time be entirely secure, et cetera. >> Well, look, the multi-cloud by default today are isolated clouds. They don't work together. Your data is siloed. It's locked up and it is expensive to move and make sense of it. So I think the word you and I were batting around before, this is an interconnected tissue. That's what the world needs. They need the clouds to work together as a single platform. That's the problem that we're trying to solve. And you saw it in some of our announcements here that we're starting to make steps on that journey to make multi-cloud work together much simpler. >> It's interesting, you mentioned the hyperscalers and all that CapEx investments. Why wouldn't you want to take advantage of a cloud and build on the CapEx and then ultimately have the solutions machine learning as one area. You see some specialization with the clouds. But you start to see the rise of superclouds, Dave calls them, and that's where you can innovate on a cloud then go to the multiple clouds. Snowflakes is one, we see a lot of examples of supercloud... >> Project Alpine was another one. I mean, it's early, but it's its clearly where you're going. The technology is just starting to come around. I mean it's real. >> Yeah. I mean, why wouldn't you want to take advantage of all of the cloud innovation out there? >> Is that something that's, that supercloud idea is a reality from a technologist perspective. >> I think it is. So for example Katie Gordon, which I believe you've interviewed earlier this week, was demonstrating the Kubernetes data mobility aspect which is another project. That's exactly part of the it's rationale, the rationale of customers being able to move some of their Kubernetes workloads to the cloud and back and between different clouds. Why are we doing? Because customers wants to have the ability to move between different cloud providers, using a common API that will be able to orchestrate all of those things with a self-service that may be offered via the APEX console itself. So it's all around enabling developers and meeting them where they are today and also meeting them into tomorrow's world where they actually may have changed their mind to do those things. So yes we are walking on all of those different aspects. >> Okay. Let's take a quick look at some of the ETR data. This is an X-Y graph. You've seen it a number of times on breaking analysis, it plots the net score or spending momentum on the Y-axis and overlap or pervasiveness in the ETR dataset on the X-axis, used to be called market share. I think that term was off putting to some people, but anyway it's an indicator of presence in the dataset. Now that red dotted line that's rarefied air where anything above that line is considered highly elevated. Now you can see we've plotted Azure and AWS in the upper right. GCP is in there and Kubernetes. We've done that as reference points. They're not necessarily building supercloud platforms. We'll see if they ever want to do so. And Kubernetes of course not a company, but we put 'em in there for context. And we've cherry picked a few players that we believe are building out or are important for supercloud build out. Let's start with Snowflake. We've talked a lot about this company. You can see they're highly elevated on the vertical axis. We see the data cloud as a supercloud in the making. You've got pure storage in there. They made the public, the early part of its supercloud journey at Accelerate 2019 when it unveiled a hybrid block storage service inside of AWS, it connects its On-Prem to AWS and creates that singular experience for pure customers. We see Hashi, HashiCorp as an enabling infrastructure, as code. So they're enabling infrastructure as code across different clouds and different locations. You see Nutanix. They're embarking on their multi-cloud strategy but it's doing so in a way that we think is supercloud, like now. Now Veeam, we were just at VeeamON. And this company has tied Dell for the number one revenue player in data protection. That's according to IDC. And we don't think it won't be long before it holds that position alone at the top as it's growing faster than in Dell in the space. We'll see, Dell is kind of waking up a little bit and putting more resource on that. But Veeam, they're a pure play vendor in data protection. And you heard their CTO, Danny Allan's view on Supercloud, they're doing it today. And we heard extensive comments as well from Dell that's clearly where they're headed, project Alpine was an early example from Dell technologies world of Supercloud in our view. And HPE with GreenLake. Finally beginning to talk about that cross cloud experience. I think it in initially HPE has been more focused on the private cloud, we'll continue to probe. We'll be at HPE discover later on the spring, actually end of June. And we'll continue to probe to see what HPE is doing specifically with GreenLake. Now, finally, Cisco, we put them on the chart. We don't have direct quotes from recent shows and events but this data really shows you the size of Cisco's footprint within the ETR data set that's on the X-axis. Now the cut of this ETR data includes all sectors across the ETR taxonomy which is not something that we commonly show but you can see the magnitude of Cisco's presence. It's impressive. Now, they had better, Cisco that is, had better be building out a supercloud in our view or they're going to be left behind. And I'm quite certain that they're actually going to do so. So we have a lot of evidence that we're putting forth here and seeing in the marketplace what we said last year, the ecosystem is take taking shape, supercloud is forming and becoming a thing. And really in our view, is the future of cloud. But there are always risks to these predictive scenarios and we want to acknowledge those. So first, look, we could end up with a bunch of bespoke superclouds. Now one supercloud is better than three separate cloud native services that do fundamentally the same thing from the same vendor. One for AWS, one for GCP and one for Azure. So maybe that's not all that bad. But to point number two, we hope there evolves a set of open standards for self-service infrastructure, federated governance, and data sharing that will evolve as a horizontal layer versus a set of proprietary vendor specific tools. Now, maybe a company like Veeam will provide that as a data management layer or some of Veeam's competitors or maybe it'll emerge again as open source. As well, and this next point, we see the potential for edge disruptions, changing the economics of the data center. Edge in fact could evolve on its own, independent of the cloud. In fact, David Floria sees the edge somewhat differently from Danny Allan. Floria says he sees a requirement for distributed stateful environments that are ephemeral where recovery is built in. And I said, David, stateful? Ephemeral? Stateful ephemeral? Isn't that an oxymoron? And he responded that, look, if it's not ephemeral the costs are going to be prohibitive. He said the biggest mistake the companies could make is thinking that the edge is simply an extension of their current cloud strategies. We're seeing that a lot. Dell largely talks about the edge as retail. Now, and Telco is a little bit different, but back to Floria's comments, he feels companies have to completely reimagine an integrated file and recovery system which is much more data efficient. And he believes that the technology will evolve with massive volumes and eventually seep into enterprise cloud and distributed data centers with better economics. In other words, as David Michelle recently wrote, we're about 15 years into the most recent cloud cycle and history shows that every 15 years or so, something new comes along that is a blind spot and highly disruptive to existing leaders. So number four here is really important. Remember, in 2007 before AWS introduced the modern cloud, IBM outpost, sorry, IBM outspent Amazon and Google and RND and CapEx and was really comparable to Microsoft. But instead of inventing cloud, IBM spent hundreds of billions of dollars on stock buybacks and dividends. And so our view is that innovation rewards leaders. And while it's not without risks, it's what powers the technology industry it always has and likely always will. So we'll be watching that very closely, how companies choose to spend their free cash flow. Okay. That's it for now. Thanks for watching this episode of The Cube Insights, powered by ETR. Thanks to Stephanie Chan who does some of the background research? Alex Morrison is on production and is going to compile all this stuff. Thank you, Alex. We're all remote this week. Kristen Nicole and Cheryl Knight do Cube distribution and social distribution and get the word out, so thank you. Robert Hof is our editor in chief. Don't forget the checkout etr.ai for all the survey action. Remember I publish each week on wikibon.com and siliconangle.com and you can check out all the breaking analysis podcasts. All you can do is search breaking analysis podcast so you can pop in the headphones and listen while you're on a walk. You can email me at david.vellante@siliconangle.com. If you want to get in touch or DM me at DVellante, you can always hit me up into a comment on our LinkedIn posts. This is Dave Vellante. Thank you for watching this episode of break analysis, stay safe, be well and we'll see you next time. (upbeat music)

>> Hi I'm Peter Burris and welcome to another Wikibon Action Item. (electronic music) One of the most pressing strategic issues that businesses face is how to get more value out of their data, In our opinion that's the essence of a digital business transformation, is the using of data as an asset to improve your operations and take better advantage of market opportunities. The problem of data though, it's shareable, it's copyable, it's reusable. It's easy to create derivative value out of it. One of the biggest misnomers in the digital business world is the notion that data is the new fuel or the new oil. It's not, You can only use oil once. You can apply it to a purpose and not multiple purposes. Data you can apply to a lot of purposes, which is why you are able to get such interesting and increasing returns to that asset if you use it appropriately. Now, this becomes especially important for technology companies that are attempting to provide digital business technologies or services or other capabilities to their customers. In the consumer world, it started to reach a head. Questions about Facebook's reuse of a person's data through an ad based business model is now starting to lead people to question the degree to which the information asymmetry about what I'm giving and how they're using it is really worth the value that I get out of Facebook, is something that consumers and certainly governments are starting to talk about. it's also one of the bases for GDPR, which is going to start enforcing significant fines in the next month or so. In the B2B world that question is going to become especially acute. Why? Because as we try to add intelligence to the services and the products that we are utilizing within digital business, some of that requires a degree of, or some sort of relationship where some amount of data is passed to improve the models and machine learning and AI that are associated with that intelligence. Now, some companies have come out and said flat out they're not going to reuse a customer's data. IBM being a good example of that. When Ginni Rometty at IBM Think said, we're not going to reuse our customer's data. The question for the panel here is, is that going to be a part of a differentiating value proposition in the marketplace? Are we going to see circumstances in which companies keep products and services low by reusing a client's data and others sustaining their experience and sustaining a trust model say they won't. How is that going to play out in front of customers? So joining me today here in the studio, David Floyer. >> Hi there. >> And on the remote lines we have Neil Raden, Jim Kobielus, George Gilbert, and Ralph Finos. Hey, guys. >> All: Hey. >> All right so... Neil, let me start with you. You've been in the BI world as a user, as a consultant, for many, many number of years. Help us understand the relationship between data, assets, ownership, and strategy. >> Oh, God. Well, I don't know that I've been in the BI world. Anyway, as a consultant when we would do a project for a company, there were very clear lines of what belong to us and what belong to the client. They were paying us generously. They would allow us to come in to their company and do things that they needed and in return we treated them with respect. We wouldn't take their data. We wouldn't take their data models that we built, for example, and sell them to another company. That's just, as far as I'm concerned, that's just theft. So if I'm housing another company's data because I'm a cloud provider or some sort of application provider and I say well, you know, I can use this data too. To me the analogy is, I'm a warehousing company and independently I go into the warehouse and I say, you know, these guys aren't moving their inventory fast enough, I think I'll sell some of it. It just isn't right. >> I think it's a great point. Jim Kobielus. As we think about the role that data, machine learning play, training models, delivering new classes of services, we don't have a clean answer right now. So what's your thought on how this is likely to play out? >> I agree totally with Neil, first of all. If it's somebody else's data, you don't own it, therefore you can't sell and you can't monetize it, clearly. But where you have derivative assets, like machine learning models that are derivative from data, it's the same phenomena, it's the same issue at a higher level. You can build and train, or should, your machine learning models only from data that you have legal access to. You own or you have license and so forth. So as you're building these derivative assets, first and foremost, make sure as you're populating your data lake, to build and to do the training, that you have clear ownership over the data. So with GDPR and so forth, we have to be doubly triply vigilant to make sure that we're not using data that we don't have authorized ownership or access to. That is critically important. And so, I get kind of queasy when I hear some people say we use blockchain to make... the sharing of training data more distributed and federated or whatever. It's like wait a second. That doesn't solve the issues of ownership. That makes it even more problematic. If you get this massive blockchain of data coming from hither and yon, who owns what? How do you know? Do you dare build any models whatsoever from any of that data? That's a huge gray area that nobody's really addressed yet. >> Yeah well, it might mean that the blockchain has been poorly designed. I think that we talked in one of the previous Action Items about the role that blockchain design's going to play. But moving aside from the blockchain, so it seems as though we generally agree that data is owned by somebody typically and that the ownership of it, as Neil said, means that you can't intercept it at some point in time just because it is easily copied and then generate rents on it yourself. David Floyer, what does that mean from a ongoing systems design and development standpoint? How are we going to assure, as Jim said, not only that we know what data is ours but make sure that we have the right protection strategies, in a sense, in place to make sure that the data as it moves, we have some influence and control over it. >> Well, my starting point is that AI and AI infused products are fueled by data. You need that data, and Jim and Neil have already talked about that. In my opinion, the most effective way of improving a company's products, whatever the products are, from manufacturing, agriculture, financial services, is to use AI infused capabilities. That is likely to give you the best return on your money and businesses need to focus on their own products. That's the first place you are trying to protect from anybody coming in. Businesses own that data. They own the data about your products, in use by your customers, use that data to improve your products with AI infused function and use it before your competition eats your lunch. >> But let's build on that. So we're not saying that, for example, if you're a storage system supplier, since that's a relatively easy one. You've got very, very fast SSDs. Very, very fast NVMe over Fabric. Great technology. You can collect data about how that system is working but that doesn't give you rights to then also collect data about how the customer's using the system. >> There is a line which you need to make sure that you are covering. For example, Call Home on a product, any product, whose data is that? You need to make sure that you can use that data. You have some sort of agreement with the customer and that's a win-win because you're using that data to improve the product, prove things about it. But that's very, very clear that you should have a contractual relationship, as Jim and Neil were pointing out. You need the right to use that data. It can't come beyond the hand. But you must get it because if you don't get it, you won't be able to improve your products. >> Now, we're talking here about technology products which have often very concrete and obvious ownership and people who are specifically responsible for administering them. But when we start getting into the IoT domain or in other places where the device is infused with intelligence and it might be collecting data that's not directly associated with its purpose, just by virtue of the nature of sensors that are out there and the whole concept of digital twin introduces some tension in all this. George Gilbert. Take us through what's been happening with the overall suppliers of technology that are related to digital twin building, designing, etc. How are they securing or making promises committing to their customers that they will not cross this data boundary as they improve the quality of their twins? >> Well, as you quoted Ginni Rometty starting out, she's saying IBM, unlike its competitors, will not take advantage and leverage and monetize your data. But it's a little more subtle than that and digital twins are just sort of another manifestation of industry-specific sort of solution development that we've done for decades. The differences, as Jim and David have pointed out, that with machine learning, it's not so much code that's at the heart of these digital twins, it's the machine learning models and the data is what informs those models. Now... So you don't want all your secret sauce to go from Mercedes Benz to BMW but at the same time the economics of industry solutions means that you do want some of the repeatability that we've always gotten from industry solutions. You might have parts that are just company specific. And so in IBM's case, if you really parse what they're saying, they take what they learn in terms of the models from the data when they're working with BMW, and some of that is going to go into the industry specific models that they're going to use when they're working with Mercedes-Benz. If you really, really sort of peel the onion back and ask them, it's not the models, it's not the features of the models, but it's the coefficients that weight the features or variables in the models that they will keep segregated by customer. So in other words, you get some of the benefits, the economic benefits of reuse across customers with similar expertise but you don't actually get all of the secret sauce. >> Now, Ralph Finos-- >> And I agree with George here. I think that's an interesting topic. That's one of the important points. It's not kosher to monetize data that you don't own but conceivably if you can abstract from that data at some higher level, like George's describing, in terms of weights and coefficients and so forth, in a neural network that's derivative from the model. At some point in the abstraction, you should be able to monetize. I mean, it's like a paraphrase of some copyrighted material. A paraphrase, I'm not a lawyer, but you can, you can sell a paraphrase because it's your own original work that's based obviously on your reading of Moby Dick or whatever it is you're paraphrasing. >> Yeah, I think-- >> Jim I-- >> Peter: Go ahead, Neil. >> I agree with that but there's a line. There was a guy who worked at Capital One, this was about ten years ago, and he was their chief statistician or whatever. This was before we had words like machine learning and data science, it was called statistics and predictive analytics. He left the company and formed his own company and rewrote and recoded all of the algorithms he had for about 20 different predictive models. Formed a company and then licensed that stuff to Sybase and Teradata and whatnot. Now, the question I have is, did that cross the line or didn't it? These were algorithms actually developed inside Capital One. Did he have the right to use those, even if he wrote new computer code to make them run in databases? So it's more than just data, I think. It's a, well, it's a marketplace and I think that if you own something someone should not be able to take it and make money on it. But that doesn't mean you can't make an agreement with them to do that, and I think we're going to see a lot of that. IMSN gets data on prescription drugs and IRI and Nielsen gets scanner data and they pay for it and then they add value to it and they resell it. So I think that's really the issue is the use has to be understood by all the parties and the compensation has to be appropriate to the use. >> All right, so Ralph Finos. As a guy who looks at market models and handles a lot of the fundamentals for how we do our forecasting, look at this from the standpoint of how people are going to make money because clearly what we're talking about sounds like is the idea that any derivative use is embedded in algorithms. Seeing how those contracts get set up and I got a comment on that in a second, but the promise, a number of years ago, is that people are going to start selling data willy-nilly as a basis for their economic, a way of capturing value out of their economic activities or their business activities, hasn't matured yet generally. Do we see like this brand new data economy, where everybody's selling data to each other, being the way that this all plays out? >> Yeah, I'm having a hard time imagining this as a marketplace. I think we pointed at the manufacturing industries, technology industries, where some of this makes some sense. But I think from a practitioner perspective, you're looking for variables that are meaningful that are in a form you can actually use to make prediction. That you understand what the the history and the validity of that of that data is. And in a lot of cases there's a lot of garbage out there that you can't use. And the notion of paying for something that ultimately you look at and say, oh crap, it's not, this isn't really helping me, is going to be... maybe not an insurmountable barrier but it's going to create some obstacles in the market for adoption of this kind of thought process. We have to think about the utility of the data that feeds your models. >> Yeah, I think there's going to be a lot, like there's going to be a lot of legal questions raised and I recommend that people go look at a recent SiliconANGLE article written by Mike Wheatley and edited by our Editor In Chief Robert Hof about Microsoft letting technology partners own right to joint innovations. This is a quite a difference. This is quite a change for Microsoft who used to send you, if you sent an email with an idea to them, you'd often get an email back saying oh, just to let you know any correspondence we have here is the property of Microsoft. So there clearly is tension in the model about how we're going to utilize data and enable derivative use and how we're going to share, how we're going to appropriate value and share in the returns of that. I think this is going to be an absolutely central feature of business models, certainly in the digital business world for quite some time. The last thing I'll note and then I'll get to the Action Items, the last thing I'll mention here is that one of the biggest challenges in whenever we start talking about how we set up businesses and institutionalize the work that's done, is to look at the nature of the assets and the scope of the assets and in circumstances where the asset is used by two parties and it's generating a high degree of value, as measured by the transactions against those assets, there's always going to be a tendency for one party to try to take ownership of it. One party that's able to generate greater returns than the other, almost always makes move to try to take more control out of that asset and that's the basis of governance. And so everybody talks about data governance as though it's like something that you worry about with your backup and restore. Well, that's important but this notion of data governance increasingly is going to become a feature of strategy and boardroom conversations about what it really means to create data assets, sustain those data assets, get value out of them, and how we determine whether or not the right balance is being struck between the value that we're getting out of our data and third parties are getting out of our data, including customers. So with that, let's do a quick Action Item. David Floyer, I'm looking at you. Why don't we start here. David Floyer, Action Item. >> So my Action Item is for businesses, you should focus. Focus on data about your products in use by your customers, to improve, help improve the quality of your products and fuse AI into those products as one of the most efficient ways of adding value to it. And do that before your competition has a chance to come in and get data that will stop you from doing that. >> George Gilbert, Action Item. >> I guess mine would be that... in most cases you you want to embrace some amount of reuse because of the economics involved from your joint development with a solution provider. But if others are going to get some benefit from sort of reusing some of the intellectual property that informs models that you build, make sure you negotiate with your vendor that any upgrades to those models, whether they're digital twins or in other forms, that there's a canonical version that can come back and be an upgraded path for you as well. >> Jim Kobielus, Action Item. >> My Action Item is for businesses to regard your data as a product that you monetize yourself. Or if you are unable to monetize it yourself, if there is a partner, like a supplier or a customer who can monetize that data, then negotiate the terms of that monetization in your your relationship and be vigilant on that so you get a piece of that stream. Even if the bulk of the work is done by your partner. >> Neil Raden, Action Item. >> It's all based on transparency. Your data is your data. No one else can take it without your consent. That doesn't mean that you can't get involved in relationships where there's an agreement to do that. But the problem is most agreements, especially when you look at a business consumer, are so onerous that nobody reads them and nobody understands them. So the person providing the data has to have an unequivocal right to sell it to you and the person buying it has to really understand what the limits are that they can do with it. >> Ralph Finos, Action Item. You're muted Ralph. But it was brilliant, whatever it was. >> Well it was and I really can't say much more than that. (Peter laughs) But I think from a practitioner perspective and I understand that from a manufacturing perspective how the value could be there. But as a practitioner if you're fishing for data out there that someone has that might look like something you can use, chances are it's not. And you need to be real careful about spending money to get data that you're not really clear is going to help you. >> Great. All right, thanks very much team. So here's our Action Item conclusion for today. The whole concept of digital business is predicated in the idea of using data assets in a differential way to better serve your markets and improve your operations. It's your data. Increasingly, that is going to be the base for differentiation. And any weak undertaking to allow that data to get out has the potential that someone else can, through their data science and their capabilities, re-engineer much of what you regard as your differentiation. We've had conversations with leading data scientists who say that if someone were to sell customer data into a open marketplace, that it would take about four days for a great data scientist to re-engineer almost everything about your customer base. So as a consequence, we have to tread lightly here as we think about what it means to release data into the wild. Ultimately, the challenge there for any business will be: how do I establish the appropriate governance and protections, not just looking at the technology but rather looking at the overall notion of the data assets. If you don't understand how to monetize your data and nonetheless enter into a partnership with somebody else, by definition that partner is going to generate greater value out of your data than you are. There's significant information asymmetries here. So it's something that, every company must undertake an understanding of how to generate value out of their data. We don't think that there's going to be a general-purpose marketplace for sharing data in a lot of ways. This is going to be a heavily contracted arrangement but it doesn't mean that we should not take great steps or important steps right now to start doing a better job of instrumenting our products and services so that we can start collecting data about our products and services because the path forward is going to demonstrate that we're going to be able to improve, dramatically improve the quality of the goods and services we sell by reducing the assets specificities for our customers by making them more intelligent and more programmable. Finally, is this going to be a feature of a differentiated business relationship through trust? We're open to that. Personally, I'll speak for myself, I think it will. I think that there is going to be an important element, ultimately, of being able to demonstrate to a customer base, to a marketplace, that you take privacy, data ownership, and intellectual property control of data assets seriously and that you are very, very specific, very transparent, in how you're going to use those in derivative business transactions. All right. So once again, David Floyer, thank you very much here in the studio. On the phone: Neil Raden, Ralph Finos, Jim Kobielus, and George Gilbert. This has been another Wikibon Action Item. (electronic music)