(upbeat music) >> Welcome to theCUBE's coverage of HPE Discover 2021. I'm Lisa Martin. Robert Christiansen joins me, one of our alumni the VP of Strategy in the Office of the CTO at HPE. Robert, it's great to see you, welcome back to the program. >> It's nice being here, Lisa. Thank you so much for having me. >> So here we are still in this virtual world. Things are opening up a little bit, which is nice but one of the things I'm excited to talk to you about today is Edge to Cloud from the customer's perspective. Obviously, that's why HPE does what it does for its customers. So let's talk about some of the things that you see from your perspective, with respect to data. We can't have a Cube conversation without talking about data, there's more and more of it, value but getting access to it quickly, getting access to it in real-time and often cases to make data-driven decisions is a challenging thing to do. Talk to me about what you see from the customer's lens. >> Well, the customer at a very highest level from the board level on down they're saying, "Hey, what is our data strategy? How are we going to put the value of data in place? Are we going to have it manifest its value in an internal fashion where it makes us run better as an organization? Can we get cost improvements? Can we move quicker with that? And then can we monetize that data if it's like very specific to an industry like healthcare or pharma or something like that? Can we expose that data to the rest of the world and give them access into what we call like data sets?" And there's a lot of that going on right now too. So we're seeing these two different angles about how they're going to manage and control that data. And you were talking about, and you mentioned it, you know the Edge related focus around that. You know, the Edges where business is done is where people actually do the transaction whether it's in a healthcare like in a hospital or a manufacturing facility et cetera. And then, but that data that they're using at that location is really important to make a decision at that location. They can't send it back to a Cloud. They can't send it back to someplace, wait for a decision to happen and then shoot it back again and say, "Hey, stop the production line because we found a defect." You need to act at that moment which the clients are saying, "Hey, can you improve my reliability? Can you give me better SLS? Can you improve the quality of my products? Can you improve healthcare in a hospital by immediate decisions?" And that is a data problem. And that requires the movement of compute and networking and storage and fundamentally the core piece of HPE's world. But in addition to that, the software necessary to take the action on that data when they detect that there's some action that needs to be taken. >> And I mentioned a minute ago, you know real-time and we've learned in the last 15 months plus. One of the things we learned is for a lot of cases, access to real-time data is no longer a nice to have. It's really going to be something, an element that separates those that succeed versus those that aren't as competitive. But I want to talk about data from a consumption perspective consumers, producers, obviously, meeting to ensure that the data consumers have what they need, what is it? What is your thought when you talk with customers, the consumers versus the producers? >> Yeah, that's a great question, Lisa. One of the key fundamental areas that HPE and the Office of the CTO has really been focused on over the last six months is something that we call data spaces and that is putting in place a platform, a set of services that connect data consumers with data producers. And when you think about that, that really isn't nothing new. I mean, you could go all the way back, if you've been around for a while remember the company called TRW and they used to have credit reporting, and they used to sell that stuff. And then it moved into Experian and those things. But you've got Bloomberg and next LexisNexis and all these companies that sell data. And they've been doing it, but it's very siloed. And so the explosion of data, the valuableness the value of the data for the consumers of it has put the producers in a position where they can't readily be discovered. And whether it be a private source of data like an IoT device and an industrial control, or a set of data that might say, "Hey, here's credit card for our data on a certain geography." Those sets need to be discovered, curated, and be made available to those who would want that. You know, for example, the folks that want to know how IoT device is working inside an industrial control or a company who's trying to lower their fraud rates on credit card transactions, like in stadiums or something like that. And so this discoverability in this space, or what you just talked about is such a core piece of what we're working on right now. And we haven't, our strategy is not only to just work on what HPE has to bring that and manifest that to the marketplace. But more importantly, how are we working with our partners to really bridge that gap and bring that next generation of services to those clients that can make those connections. >> So connecting and facilitating collaboration, absolutely key, as well as that seamless flow of data sharing without constraints. How are customers working with HPE and some of your partners to be able to create a data strategy, launch it, and start gleaning value from data faster than they can before? (Robert chuckles) >> This is the big question because it's a maturity curve. Organizations are in various states of what we call data maturity or data management maturity. They can be in very early stages. You know what we consider, you know, they just more worried about just maintaining the lights on DR strategies and make sure that data doesn't go away versus all the way through a whole cycle where they're actually governing it and putting it into what I call those discoverable buckets that are made available. And there's a whole life cycle about that. And so we see a big opportunity here for our A&PS and other professional services organizations to help people get up that maturity curve. But they also have to have the foundational tools necessary to make that happen. This is really where the Ezmeral product line or software applications really shines being able to give that undercarriage that's necessary to help that data maturity and the growth of that client to meet those data needs. And we see the data fabric being a key element to that, for that distributed model, allowing people to get access and availability to have a highly redundant, highly durable data fabric and then to build applications specifically as data-intensive applications on top of that with the Ezmeral platform all the way into our GreenLake solutions. So it's quite a journey here, Lisa. I want to just, point to the fact that HPE has done a really, really good job of positioning itself for the explosion of all of these data-intensive AI/ML workloads that are making their way into every single conversation every single enterprise to this day that wants to take advantage of the value of the data they have and to augment that data through other sources. >> One, when you think about data-intensive applications the first one that pops into my mind is Uber. And it's one of those applications that we just expect. We kind of think of as a taxi service when really it's logistics and transportation, but all of the data on the backend that it is organizing to find the ride for me at my location to take me where I'm going. The explosion of data-intensive applications is great but there's also so much more demand from consumers whether we're in business or we're consuming in our personal lives. >> It's so true and that's a very popular example. And you know, you think about the real-time necessity of what's the traffic patterns at the time I order my thing. Is it going to route me the right way? That's a very real consumer facing one, but if we click into our clients and where HPE very much is like the backbone of the global economy. We provide probably one third of the compute for the global economy and it's a staggering stat if you really think about it. Our clients, I was just talking with a client here earlier, very, very large financial services company. And they have 1200 data sets that have been selling to their clients globally. And a lot of these clients want to augment that data with their existing real-time data to come up with a solution. And so they merge it and they can determine some value through a model, an AI model. And so we're working hand-in-hand with them right now to give them that backbone so that they can deliver data sets into these other systems and then make sure they get controlled and secured. So that the company we're working with, our client has a deep sense of security that that data set is not going to find itself out into the wild somewhere. And uncontrolled for a number of reasons, from security and governance mind. But the number of use cases, Lisa are as infinite as the number of opportunities for people see value in business today. >> When you're talking about 1200 data sets that a company is selling, and of course there are many, many data sets that many types of companies consume. How do you work with them to ensure that they don't just proliferate silos, but that they get more of a unified data repository that they can act on? >> Yeah, that's a great question. A key tenant of the strategy at HPE is Open-source. So we believe in a hybrid, multi-Cloud environment meaning that as long as we all agree that we are going to standardize on Open-source technologies and APIs, we will be able to write and build applications that can natively run on any abstract platform. So for example, it's very important that we containerize, for example, and we use storage and data tools that adhere to Open standards. So if you think about that, if you write a Spark application you want that Spark application potentially to run on any of the hyperscalers, the Amazon's or the Microsoft to GCPS, or you want it to run on-premises and specifically like on HPE equipment. But the idea here is I consider one of our clients right now. I mean, think about that. One of our clients specifically ask that question that you just said. They said, "Hey, we are building out this platform, this next generation platform. And we don't want the lock-in. We want to be, we want to create that environment where that data and the data framework." So they use very specific Open -source data frameworks and they open, they use very specific application frameworks the software from the Open-source community. We were able to meet that through the Ezmeral platform. Give them a very high availability, five nines high availability, redundant, redundant geographically to geographic data centers to give them that security that they're looking for. And because of that, it's opened so many other doors for us to walk in with a Cloud strategy that is an alternative, not just the one bet to public Cloud but you haven't other opportunity to bring a Cloud strategy on-premises that is compatible with Cloud-native activities that are going on in the public Cloud. And this is at the heart of HPE strategy. I think it's just, it's been paying off. It continues to pay off. We just keep investing and keep moving down that path. I think we're going to be doing really well. >> It sounds to me that the strategy that HP is developing is highly collaborative and synergistic with your customers. Talk to me a little bit about that, especially in the last year, as we've seen a massive acceleration in digital transformation about the rapid pivot to work from home, the necessity to collaborate electronically. Talk to me a little bit about that yin and yang with HPE and its customers in terms of your strategy. >> Yeah, well, I think when COVID hit one of the very first things that just took off with VDI. Rohit Dixon and I were talking on a podcast we had earlier around the work from home strategy that was implemented almost immediately. Well, we had it already in the can, we already were doing it for many clients already but it went from like a three priority to a 12, 10 being the max. Super, super charged up on how do we get work from home secured, work from home applications and stuff in the hands of people doing, you know, when data sensitivity is super important, VDI kicks in that's on that side. But then if you start looking at the digital transformation that has to happen in the supply chain that's going on right now. The opening up of our economies it's been various starts and stops if you look around the globe. The supply chains have absolutely gone under a huge amount of pressure, because, unlike in the United States, everybody just wants everything now because things are starting to open up. I was talking to a meat packing company and a restaurant business a little while ago. And they said, "Everybody wants to order the barbecue. Now we can't get the meat for the barbecues 'cause everybody's going to the barbecues." And so the supply, this is a multi-billion dollar industry supplying meat to all of the rest of the countries and stuff like that. And so they don't have optics into that supply chain today. So they're immediately having to go through a digitization process, the transformation in something as what you would call as low tech as delivering meat. So no industry is immune, none anywhere in this whole process. And it will continue to evolve as we exit and change how we live our life going into these next couple of years. I think it's going to be phenomenal just to watch. >> Yeah, it's one of the things I call a COVID catalyst some of the silver linings that have come out of this 'cause I wouldn't have thought of the meatpacking industry as a technology field as well, but now thanks to you, I will. Last question for you. When customers in this dynamic world in which we're still living talk about Edge to Cloud are they working with you to develop a Cloud initiatives, Cloud mandates, Cloud everywhere? And if so, how do you help them start? >> Yeah, that's a great question. So again, it's like back into the data model, everybody has a different degree or a starting point that they will engage us with a strategy but specifically with what you're talking about. Almost everybody already has a Cloud strategy. So they may be at different maturity levels with that Cloud strategy. And there's almost always a Cloud group. Now, historically HPE has not had much of a foot in the Cloud group because they never really historically looked at us says that HPE is a Cloud company. But what's happened over the last couple of years with the acceleration of the acceptance of Cloud on-premises and GreenLake, specifically, and the introduction of Ezmeral and the Cloud-native infrastructure services and past layer stuff that's coming up through the Ezmeral product into our clients. It's immediately opened the door for conversations around Cloud that is available for what is staying on-premises which is in excess of 70% of the applications today. Now, if you were to take that now and extend that into the Edge conversation, what if you were able to take a smaller form factor of a GreenLake Cloud and push it more closer to an Edge location while still giving the similar capabilities, Cloud-native functions that you had before? When we're provocative with clients in that sense they suddenly open up and see the art of the possible. And so this is where we are really, really breaking down a set of paradigms of what's possible by introducing, you know, not just from the Silicon all the way up but the set of services all the way to the top of stack to the actual application that they're going to be running. And we say, "Hey, we can offer it to you in as a pay as you go model, we can get you the consumption models that are necessary, that lets you buy at the same way as the Cloud offers it. But more importantly, we'll be able to run it for you and provide you an abstraction out of that model. So you don't have to send your people out into the field to do these things. We have the software, the tools, and the systems necessary to manage it for you." But the last part is I want to be really really focused on when clients are writing that application for the Edge that matters. They are putting it into new Cloud-native architectures containers, microservices, they're using solid pipelines development pipelines, they've implemented what they call their DevOps or their DataOps practices in field, in country, if you would say. That's where we shine. And so we had a really, really good conversation start there. And so how we start that is we arrive with a set of blueprints to help them establish what that roadmap looks like. And then our professional services staff, or A&PS groups around the globe are really really set up well to help them take that trip. >> Wow, that's outstanding, Robert. We could have a whole conversation on HPE's transformation. Internet itself that was my first job in tech was at Hewlett Packard back in the day. But this has been really interesting, really getting it your vision of the customer's experience and the customer's perspective from the Office of the CTO. Great to talk to you, Robert. Thank you for sharing all that you did. This could have been a Part 2 conversation. >> Well, I'm hopeful then that we'll do Part 3 and 4 here as the months go by. So I look forward to seeing you again, Lisa. >> Deal, that's a deal. All right. >> All right. >> For Robert Christiansen, I'm Lisa Martin. You're watching theCUBE's coverage of HPE Discover 2021. (upbeat music)

(upbeat music) >> Welcome to theCUBE's coverage of HPE Discover 2021. I'm Lisa Martin. Robert Christiansen joins me, one of our alumni the VP of Strategy in the Office of the CTO at HPE. Robert, it's great to see you, welcome back to the program. >> It's nice being here, Lisa. Thank you so much for having me. >> So here we are still in this virtual world. Things are opening up a little bit, which is nice but one of the things I'm excited to talk to you about today is Edge to Cloud from the customer's perspective. Obviously, that's why HPE does what it does for its customers. So let's talk about some of the things that you see from your perspective, with respect to data. We can't have a Cube conversation without talking about data, there's more and more of it, value but getting access to it quickly, getting access to it in real-time and often cases to make data-driven decisions is a challenging thing to do. Talk to me about what you see from the customer's lens. >> Well, the customer at a very highest level from the board level on down they're saying, "Hey, what is our data strategy? How are we going to put the value of data in place? Are we going to have it manifest its value in an internal fashion where it makes us run better as an organization? Can we get cost improvements? Can we move quicker with that? And then can we monetize that data if it's like very specific to an industry like healthcare or pharma or something like that? Can we expose that data to the rest of the world and give them access into what we call like data sets?" And there's a lot of that going on right now too. So we're seeing these two different angles about how they're going to manage and control that data. And you were talking about, and you mentioned it, you know the Edge related focus around that. You know, the Edges where business is done is where people actually do the transaction whether it's in a healthcare like in a hospital or a manufacturing facility et cetera. And then, but that data that they're using at that location is really important to make a decision at that location. They can't send it back to a Cloud. They can't send it back to someplace, wait for a decision to happen and then shoot it back again and say, "Hey, stop the production line because we found a defect." You need to act at that moment which the clients are saying, "Hey, can you improve my reliability? Can you give me better SLS? Can you improve the quality of my products? Can you improve healthcare in a hospital by immediate decisions?" And that is a data problem. And that requires the movement of compute and networking and storage and fundamentally the core piece of HPE's world. But in addition to that, the software necessary to take the action on that data when they detect that there's some action that needs to be taken. >> And I mentioned a minute ago, you know real-time and we've learned in the last 15 months plus. One of the things we learned is for a lot of cases, access to real-time data is no longer a nice to have. It's really going to be something, an element that separates those that succeed versus those that aren't as competitive. But I want to talk about data from a consumption perspective consumers, producers, obviously, meeting to ensure that the data consumers have what they need, what is it? What is your thought when you talk with customers, the consumers versus the producers? >> Yeah, that's a great question, Lisa. One of the key fundamental areas that HPE and the Office of the CTO has really been focused on over the last six months is something that we call data spaces and that is putting in place a platform, a set of services that connect data consumers with data producers. And when you think about that, that really isn't nothing new. I mean, you could go all the way back, if you've been around for a while remember the company called TRW and they used to have credit reporting, and they used to sell that stuff. And then it moved into Experian and those things. But you've got Bloomberg and next LexisNexis and all these companies that sell data. And they've been doing it, but it's very siloed. And so the explosion of data, the valuableness the value of the data for the consumers of it has put the producers in a position where they can't readily be discovered. And whether it be a private source of data like an IoT device and an industrial control, or a set of data that might say, "Hey, here's credit card for our data on a certain geography." Those sets need to be discovered, curated, and be made available to those who would want that. You know, for example, the folks that want to know how IoT device is working inside an industrial control or a company who's trying to lower their fraud rates on credit card transactions, like in stadiums or something like that. And so this discoverability in this space, or what you just talked about is such a core piece of what we're working on right now. And we haven't, our strategy is not only to just work on what HPE has to bring that and manifest that to the marketplace. But more importantly, how are we working with our partners to really bridge that gap and bring that next generation of services to those clients that can make those connections. >> So connecting and facilitating collaboration, absolutely key, as well as that seamless flow of data sharing without constraints. How are customers working with HPE and some of your partners to be able to create a data strategy, launch it, and start gleaning value from data faster than they can before? (Robert chuckles) >> This is the big question because it's a maturity curve. Organizations are in various states of what we call data maturity or data management maturity. They can be in very early stages. You know what we consider, you know, they just more worried about just maintaining the lights on DR strategies and make sure that data doesn't go away versus all the way through a whole cycle where they're actually governing it and putting it into what I call those discoverable buckets that are made available. And there's a whole life cycle about that. And so we see a big opportunity here for our A&PS and other professional services organizations to help people get up that maturity curve. But they also have to have the foundational tools necessary to make that happen. This is really where the Ezmeral product line or software applications really shines being able to give that undercarriage that's necessary to help that data maturity and the growth of that client to meet those data needs. And we see the data fabric being a key element to that, for that distributed model, allowing people to get access and availability to have a highly redundant, highly durable data fabric and then to build applications specifically as data-intensive applications on top of that with the Ezmeral platform all the way into our GreenLake solutions. So it's quite a journey here, Lisa. I want to just, point to the fact that HPE has done a really, really good job of positioning itself for the explosion of all of these data-intensive AI/ML workloads that are making their way into every single conversation every single enterprise to this day that wants to take advantage of the value of the data they have and to augment that data through other sources. >> One, when you think about data-intensive applications the first one that pops into my mind is Uber. And it's one of those applications that we just expect. We kind of think of as a taxi service when really it's logistics and transportation, but all of the data on the backend that it is organizing to find the ride for me at my location to take me where I'm going. The explosion of data-intensive applications is great but there's also so much more demand from consumers whether we're in business or we're consuming in our personal lives. >> It's so true and that's a very popular example. And you know, you think about the real-time necessity of what's the traffic patterns at the time I order my thing. Is it going to route me the right way? That's a very real consumer facing one, but if we click into our clients and where HPE very much is like the backbone of the global economy. We provide probably one third of the compute for the global economy and it's a staggering stat if you really think about it. Our clients, I was just talking with a client here earlier, very, very large financial services company. And they have 1200 data sets that have been selling to their clients globally. And a lot of these clients want to augment that data with their existing real-time data to come up with a solution. And so they merge it and they can determine some value through a model, an AI model. And so we're working hand-in-hand with them right now to give them that backbone so that they can deliver data sets into these other systems and then make sure they get controlled and secured. So that the company we're working with, our client has a deep sense of security that that data set is not going to find itself out into the wild somewhere. And uncontrolled for a number of reasons, from security and governance mind. But the number of use cases, Lisa are as infinite as the number of opportunities for people see value in business today. >> When you're talking about 1200 data sets that a company is selling, and of course there are many, many data sets that many types of companies consume. How do you work with them to ensure that they don't just proliferate silos, but that they get more of a unified data repository that they can act on? >> Yeah, that's a great question. A key tenant of the strategy at HPE is Open-source. So we believe in a hybrid, multi-Cloud environment meaning that as long as we all agree that we are going to standardize on Open-source technologies and APIs, we will be able to write and build applications that can natively run on any abstract platform. So for example, it's very important that we containerize, for example, and we use storage and data tools that adhere to Open standards. So if you think about that, if you write a Spark application you want that Spark application potentially to run on any of the hyperscalers, the Amazon's or the Microsoft to GCPS, or you want it to run on-premises and specifically like on HPE equipment. But the idea here is I consider one of our clients right now. I mean, think about that. One of our clients specifically ask that question that you just said. They said, "Hey, we are building out this platform, this next generation platform. And we don't want the lock-in. We want to be, we want to create that environment where that data and the data framework." So they use very specific Open -source data frameworks and they open, they use very specific application frameworks the software from the Open-source community. We were able to meet that through the Ezmeral platform. Give them a very high availability, five nines high availability, redundant, redundant geographically to geographic data centers to give them that security that they're looking for. And because of that, it's opened so many other doors for us to walk in with a Cloud strategy that is an alternative, not just the one bet to public Cloud but you haven't other opportunity to bring a Cloud strategy on-premises that is compatible with Cloud-native activities that are going on in the public Cloud. And this is at the heart of HPE strategy. I think it's just, it's been paying off. It continues to pay off. We just keep investing and keep moving down that path. I think we're going to be doing really well. >> It sounds to me that the strategy that HP is developing is highly collaborative and synergistic with your customers. Talk to me a little bit about that, especially in the last year, as we've seen a massive acceleration in digital transformation about the rapid pivot to work from home, the necessity to collaborate electronically. Talk to me a little bit about that yin and yang with HPE and its customers in terms of your strategy. >> Yeah, well, I think when COVID hit one of the very first things that just took off with VDI. Rohit Dixon and I were talking on a podcast we had earlier around the work from home strategy that was implemented almost immediately. Well, we had it already in the can, we already were doing it for many clients already but it went from like a three priority to a 12, 10 being the max. Super, super charged up on how do we get work from home secured, work from home applications and stuff in the hands of people doing, you know, when data sensitivity is super important, VDI kicks in that's on that side. But then if you start looking at the digital transformation that has to happen in the supply chain that's going on right now. The opening up of our economies it's been various starts and stops if you look around the globe. The supply chains have absolutely gone under a huge amount of pressure, because, unlike in the United States, everybody just wants everything now because things are starting to open up. I was talking to a meat packing company and a restaurant business a little while ago. And they said, "Everybody wants to order the barbecue. Now we can't get the meat for the barbecues 'cause everybody's going to the barbecues." And so the supply, this is a multi-billion dollar industry supplying meat to all of the rest of the countries and stuff like that. And so they don't have optics into that supply chain today. So they're immediately having to go through a digitization process, the transformation in something as what you would call as low tech as delivering meat. So no industry is immune, none anywhere in this whole process. And it will continue to evolve as we exit and change how we live our life going into these next couple of years. I think it's going to be phenomenal just to watch. >> Yeah, it's one of the things I call a COVID catalyst some of the silver linings that have come out of this 'cause I wouldn't have thought of the meatpacking industry as a technology field as well, but now thanks to you, I will. Last question for you. When customers in this dynamic world in which we're still living talk about Edge to Cloud are they working with you to develop a Cloud initiatives, Cloud mandates, Cloud everywhere? And if so, how do you help them start? >> Yeah, that's a great question. So again, it's like back into the data model, everybody has a different degree or a starting point that they will engage us with a strategy but specifically with what you're talking about. Almost everybody already has a Cloud strategy. So they may be at different maturity levels with that Cloud strategy. And there's almost always a Cloud group. Now, historically HPE has not had much of a foot in the Cloud group because they never really historically looked at us says that HPE is a Cloud company. But what's happened over the last couple of years with the acceleration of the acceptance of Cloud on-premises and GreenLake, specifically, and the introduction of Ezmeral and the Cloud-native infrastructure services and past layer stuff that's coming up through the Ezmeral product into our clients. It's immediately opened the door for conversations around Cloud that is available for what is staying on-premises which is in excess of 70% of the applications today. Now, if you were to take that now and extend that into the Edge conversation, what if you were able to take a smaller form factor of a GreenLake Cloud and push it more closer to an Edge location while still giving the similar capabilities, Cloud-native functions that you had before? When we're provocative with clients in that sense they suddenly open up and see the art of the possible. And so this is where we are really, really breaking down a set of paradigms of what's possible by introducing, you know, not just from the Silicon all the way up but the set of services all the way to the top of stack to the actual application that they're going to be running. And we say, "Hey, we can offer it to you in as a pay as you go model, we can get you the consumption models that are necessary, that lets you buy at the same way as the Cloud offers it. But more importantly, we'll be able to run it for you and provide you an abstraction out of that model. So you don't have to send your people out into the field to do these things. We have the software, the tools, and the systems necessary to manage it for you." But the last part is I want to be really really focused on when clients are writing that application for the Edge that matters. They are putting it into new Cloud-native architectures containers, microservices, they're using solid pipelines development pipelines, they've implemented what they call their DevOps or their DataOps practices in field, in country, if you would say. That's where we shine. And so we had a really, really good conversation start there. And so how we start that is we arrive with a set of blueprints to help them establish what that roadmap looks like. And then our professional services staff, or A&PS groups around the globe are really really set up well to help them take that trip. >> Wow, that's outstanding, Robert. We could have a whole conversation on HPE's transformation. Internet itself that was my first job in tech was at Hewlett Packard back in the day. But this has been really interesting, really getting it your vision of the customer's experience and the customer's perspective from the Office of the CTO. Great to talk to you, Robert. Thank you for sharing all that you did. This could have been a Part 2 conversation. >> Well, I'm hopeful then that we'll do Part 3 and 4 here as the months go by. So I look forward to seeing you again, Lisa. >> Deal, that's a deal. All right. >> All right. >> For Robert Christiansen, I'm Lisa Martin. You're watching theCUBE's coverage of HPE Discover 2021. (upbeat music)

>> Narrator: From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now here is your host, Dave Vellante. >> Hi, everybody. This is Dave Vellante. Welcome to a special presentation, Conquering Big Data. This is part one: Data as Capital, and this is sponsored by Oracle. With me is Paul Sonderegger, a big data strategist from Oracle. Paul, it's good to see you in theCUBE again. >> It's good to be here. >> Okay, so we were talking earlier. This whole thing for us at SiliconANGLE Media started around 2010 when we started to pay attention to the dupe trend, and data is the new source of competitive advantage, data is the new oil, and in six or seven short years, we've come quite a long way. Everybody says that they want to be data-driven. Where are we today from your perspective? >> I think the cover article of the Economist just a couple of weeks ago captured it pretty well where it said the data is the world's most valuable resource, and part of the evidence for that is that the top five most valuable listed firms or publicly listed firms worldwide are all data-heavy technology companies, so we're at the point now where the effect of accumulating data, stocks of data capital is obvious and using it is obvious but nonetheless, we are still at the beginning of the changes that the rise of data capital are going to bring. >> As I said, most executives would say they want their companies to be data-driven. Many actually say, "Oh yes, our company is data-driven," but when you start to peel the onion, do you agree that most companies aren't really as data-centric as they may claim to be? >> A lot of companies, they just struggle with the philosophy of what data is and what effect it has on the way they compete. Don't get me wrong. All executives understand that more data helps you make better decisions. That's evergreen. That's a good idea. But a lot of companies fail to appreciate that data. Contrary to popular wisdom, is not abundant. There's a lot of it but it consists of countless unique observations, and so really, the way that executives need to think about data is that it is scarce. Data really consists of observations of things that are going on in the world, and if you are not there when those activities happen, when these events take place, your opportunity to capture those observations is lost. It doesn't come back. >> Okay, so let's get into this. You've written about and talked about the three principles of data capital, so let's start there and go through them. Principle one is data comes from activity. Okay. I guess that sounds obvious but what does it mean? >> This is the issue that we were just talking about. This is the first principle of data capital, that data comes from activity and a lot of executives will say, "Yes, obviously. "We put in this big ERP application back in the '90s, "and it captured all of this data about our own processes, "so then we reported on it "so we can see what's going on." All of that is true but what a lot of executives miss is that they're in competition for data. So the data that ERP apps and CRM apps and all of these enterprise applications produce, those are all data from the company's own activities but what's happening now is the digitization and datafication of activities outside the company, activities that customers carry on. It could be in everyday consumer life, it could be in B2B environments as well, it could be the movement of trucks, the movement of inventory done through supply chains run by partners. Executives have to get the habit of looking out at the world and seeing the data that is not there yet, information coming from these activities that is lost. It's either captured on paper or it's not captured at all, and putting sensors and mobile apps into those activities before their rivals do because when an activity happens, if you are not part of it, your opportunity to capture its data is lost. It doesn't come back. >> So data, raw data is abundant but the data that is actually valuable to organizations you're saying is scarce and takes a lot of refinement to use the oil analogy. >> Think about it this way. Remember Sir Edmund Halley, the guy who predicted the comet? >> Dave: Right. >> Sir Edmund Halley predicted when you will die. This is actually one of his signal achievements a lot of people have forgotten about. Halley was the first one to work out mortality tables, what is expected, what is life expectancy. The reason that that could be valuable is that he showed that life insurance policies that the British government was offering were mispriced depending on how old you were and how much longer you expected to live. The data that he used to make those calculations was not his. It came from Breslau. It came from another city, and it came from a particular church, which had kept really rigorous records during that time. Before the priests of Breslau said, "Hey, you could use this data," Halley had no ability to make this prediction. He had no ability to identify the mispricing of life insurance policies. That data, those observations was a scarce resource concentrated in another city that he needed in order to figure all this out. We have exactly the same situation now. Exactly the same situation now where companies taking observations of activities that they conduct with their partners, activities that they conduct with their customers build up into these concentrations of observations that are unique, they're proprietary, and they are the necessary fuel for creating new digital products and services. >> And many of those observations come from data outside of the organization. Okay, let's look at the second principle. Data makes more data. What are you talking about here? Are you talking about metadata? Can you explain? >> Sure. Providing data to people so they can make better decisions is always a good thing. It has been a good thing for a long time. It will continue to be a good thing. But the real money is in algorithms. The real money is in using these stocks of data capital to feed algorithms for two reasons. One is that algorithms can take decisions beyond human scale either in a more situations per unit time or simply faster than human beings can. The second reason it's important is because algorithms produce data about their own performance, which can be fed back into the model to improve their future performance. This is true of dynamic pricing algorithms, which capture data about what change did this price switch have on conversion rates, for example. It applies in fraud detection. We have customers who are banks who look at how many legitimate transactions did our current fraud detection algorithm wrongly flagged because they get complaints about it, how many fraudulent transactions did our current algorithm actually missed because investigations get kicked off through other processes. Those observations about the performance of the algorithm go back into the model improving its future performance. This applies to algorithms for inventory detection and fleet movement. So the second principle is the data tends to make more data, and this virtuous cycle with algorithms creates a competitive advantage that is very, very hard to catch. >> And I'm hearing you have to act on that data and continue to iterate. It's not obviously a one-shot static deal. We kind of all know that but it's this constant improvement that's going to give you that competitive edge. >> That's really the key, and this is at the very heart of machine learning, so all the talk about AI and all the talk about machine learning, one of the tactics of machine learning algorithms is that they learn from their own behaviors and improve their behaviors over time, so really, this particular kind of competitive advantage is baked in to the practice of machine learning and AI. >> Okay, great. Now your third principle is that platforms tend to win. You've written that this is where the real money is, so what do you mean by platforms? Are you talking about platforms versus products? What do you mean? >> Here, we're talking about platforms not as technologists often think about it where there is a foundational technology and then you build on top. We're talking about platforms as economists see them, so through the eyes of an economist, a platform is an intermediary that serves a two-sided market, and usually it makes it easier, cheaper, faster for the two sides to do business with each other. So just to use a very familiar example, credit cards are a payment platform, and they serve a two-sided market. On one side, you have merchants. On the other side, you have consumers. And of course, we as consumers, we want to carry the card more merchants will take. Merchants want to take the card more consumers have in their pocket. And so growth on one side of the market tends to encourage growth on the other side of the market. They kind of ladder up like that, and that means that platform competition tends toward a winner-take-all outcome, and so we have seen this in, say, the competition for the desktop operating system. That was a platform competition. We see it in the competition for the mobile operating system but it's also something that you see in gaming platforms, for example. More game developers want to develop for the platforms where there are more gamers. Gamers want to have the platform where there are more games. The reason that this matters now is because the digitization and datafication of more daily activities brings platform competition to industries that have never see it before. So just to use a simple example, look at farming. You can now have a drone. It will go out and take pictures of a field, and the drone will do spectrographic analysis of the images, and it's looking for green, which is a proxy for the degree of chlorophyll in the plants. It uses that information to inform the fertilizer spreader about how to tailor the fertilizer to the plants, not to the field but to the individual plants. The tractor in the middle is in competition to be the platform for digital agricultural services, and that is not how makers of large agricultural equipment typically think about competition. >> Okay, so let's move on. If data is so important, it's the new source of competitive advantage, we're talking today about data as capital, but the accounting field doesn't look at data as the same way in which they do a financial asset. You don't see companies recognizing the value of data on their balance sheets yet at the same time, you said the top five firms worldwide in terms of market value are data-oriented. So I'm sure that's much greater than the capital assets that they have on their books. So what's going on there? Should the accounting world be coming into the 21st century? Should companies wait until they do? What are your thoughts on that? >> I won't presume to give the accounting industry any advice on what they ought to but I will say that regardless of how the accounting standards look at data. The most successful data-driven companies, they already recognize that data is a true asset despite the fact that they cannot put it on the balance sheet as an asset with a certain dollar value. These firms, they already recognize that data is not just a record of what happened, it is a raw material for creating new digital products and services. In that way, it is capital like capital equipment, like financial capital, like if you do not have this input, you cannot create the service that you have in mind. And so that's why these data-heavy companies are not satisfied with the stocks of data capital they've got. These platform businesses are constantly on the lookout for new activities they can go digitize and datafy, adjacent activities that are next to the ones that they have already captured in order to further build out this stock of data capital, in order to create more raw material for new products and services. I will presume to give corporations in general advice, and the advice is that you've got to get this idea that data is not just a record of what happened, it is a raw material for new digital products and services. Digital products and services are the competitive field for providing value to your customers. >> So don't wait for the accounting industry to catch up is really your advice there. >> Not at all. >> So you said digitize, datafy, and that's leads us what you've talked in the past about data trade, the monetization question, so let's talk about monetization. How should organizations think about monetizing data? Should they be selling data? Should they be thinking about it differently? Why should they be monetizing data? >> The first thing to remember is that data trade is a decades-old practice. Credit bureaus were one of the first kinds of companies to build an entire business on the trade of data, and so they're accumulating information about consumers and then providing them to banks so the banks can more easily, quickly, effectively make lending decisions, and that increases access to credit, which is a good thing overall. It's a very, very useful thing. But what's happening now is that the data trade is massively expanding, buying and selling of data about different kinds of aspects of consumer buying and shopping behavior, for example but we're also starting to see the buying and selling of data in the world of the Internet of Things. As you may know, Oracle has a very large data marketplace, the largest online marketplace, a data marketplace of consumer shopping and browsing behavior, so we have five billion consumer profiles, 400 million business profiles, $3 trillion in transactions. One of the things to note about this whole business is that the data in our marketplace is created by a whole set of other firms. Just to give you one example, there's 15,000 websites which are the sources for online browsing behavior, those websites have no idea what value that data will provide to the companies who use it. They don't know. Instead, they are originating this data, and they are selling it on for these secondary purposes, and those secondary purposes really are discovered by the companies who buy the data and use it, and that data then goes into targeting marketing campaigns. It goes into refining product launch plans. It goes into redesigning social media publishing calendars and activities. The reason all this matters is because data consists of observations. The value from those observations only happens when it gets used. There is this curious issue. Just like Edmund Halley needed data from Breslau in order to figure out life expectancy and figure out the proper pricing of these insurance policies, we have the same issue today where data originates in one set of activities but the firms that create it may not create the greatest value from it, and so we need these data marketplaces in order to grow the overall value created from this digitization and datafication. >> Paul, are there pitfalls that people should, I'm sure there are many but maybe a couple you could point to that people need to think about when they enter this data monetization journey? >> Sure. One of the ones that comes out right away is personally identifiable information and invasions of privacy. So one of the ways to deal with that is to anonymize these records, strip out all the personally identifiable information, and then the next step that you can take is to aggregate them. So on that first piece about stripping out personally identifiable information, there are obvious pieces like name, first name, last name, and social security number, taxpayer ID number but new regulations in Europe, the General Data Protection Regulation, the GDPR has expanded the notion of personally identifiable information to any piece of data that could be uniquely tied back to a specific individual, so for example, something like an IMEI number, that unique code for your phone as it connects to the cellular network, in some cases perhaps even IP address. So this notion of personally identifiable information is expanding, so that's one thing for companies to be aware of. This notion of aggregation is an interesting one because even the GDPR says that if you aggregate a whole bunch of records together, and reidentification of those individual records is no longer possible, the GDPR doesn't even apply to those data products, so one of the things companies should be thinking about is can they create data products that provide observations about a part of the world that other firms are interested in and yet at a high enough, at a large enough level of aggregation that the issues are around personally identifiable information are all resolved. >> And this becomes really important. GDPR goes in effect next May, next May 18. >> Next May. >> So things to think about. All right. Last question before we summarize this. Metrics, even though the accounting industry isn't counting data as an asset, are there new metrics that organizations are using or should be using to quantify the value of their data? >> There are. McKinsey writes about this occasionally. They have taken just a really simple, back of the envelope calculation for looking at revenue per employee for companies in a given industry, and then calling out the radical differences in revenue per employee for firms known to be highly data-centric versus others who perhaps are older or have been in the business longer or who have greater traditional capital assets, so something even that simple can be a useful tool but I suspect that we're going to need a new family of metrics. There has been talk for a while about data productivity, about measuring that. It's often been difficult to do but we've entered into a new world now where observations about how data gets used within a company, looking at the queries going against the data management infrastructure is now not only possible but cost-effective. I suspect that we're actually going to see a new metric of data productivity that is related to traditional measures of labor productivity and capital productivity, which economists have known about for a long time, but I think we'll see a way of measuring the work done, the value-creating work done by a company's digital data infrastructure which can then be related to what's their return on invested capital as well as what is their labor productivity. I think we'll start to see a new set of metrics like that. >> And it maybe is implicit in even the McKinsey example of revenue per employee, something as simple as that. Maybe if you could isolate that and identify the input of labor and capital, maybe you can get to that. >> And then if you could isolate the input of work done by queries acting on data, then yeah, you ought to be able to establish that relationship. >> Okay, good. Let's summarize. Before I do, I just want to remind people to think about some questions. We're going to have a Q&A session right after this in the chat area right below. Okay, so we kind of introduced the notion of data capital and talked about why it's important. You mentioned the top five firms worldwide in terms of value are data-oriented companies, and then we talked about your three principles around data capital. Why don't you summarize the three for us? >> Sure. Data comes from activity, so digitize and datafy activities outside your firms before your rivals do. Data tends to make more data, so feed the data you've got into algorithms so that they can create data about their own performance creating a virtuous cycle. And then the third is platforms tend to win, and here, companies really need an active imagination to look at their industries and their business models and imagine them, either imagine their own business model reinvented as a platform, an intermediary between two side of the market where the digitization and datafication helps them create a new kind of value, or imagine another firm like that that comes to attack them. >> Okay, and then we talked about the accounting industry, how it has not begun to recognize data as value, put in a balance sheet, et cetera. You chose not to suggest that they should or should not. Rather, you chose to focus on the companies, the organizations that they should not wait for the accounting industry to catch up, that they should really dive in and begin thinking about how to digitize, you call it datafy, and that led to a conversation on monetization, and then you talked about data markets as a critical emerging, re-emerging entity and dynamic that's occurring there. Maybe some comments? >> Sure. For decades now, we've had businesses with traditional business models working as data sellers. Again, credit bureaus are a good example, market research firms are another good one, LexisNexis, Bloomberg but I think what we're going to see is a rise in data marketplaces where you've got a new kind of business model. It's an exchange. And you've got data originators providing data into the marketplace for sale, and you've got buyers on the other side, probably mostly companies but there could be nonprofits, there could be governments as well actually, and those, those are actually really exciting because exchanges like that, increases in data trade help to spread the wealth of data capital to more parties. It makes it possible for companies who need data but have not datafied the activities that they just discovered they care about go and source that data. It also helps firms who have managed to create these data capital assets but they're not sure what to do with them themselves make them available to places where they can create value. >> Excellent. Then you talked about ways to avoid some of the pitfalls, particularly those associated with personal information and the upcoming GDPR, and then we wrapped with a conversation around metrics, some simple metrics have been posed like revenue per employee, and you noted a McKinsey study that those data-oriented companies have a higher revenue per employee but then you suggested that we're going to start peeling back those metrics and looking at the contribution of labor plus capital in terms of what you call, a new metric called data productivity, so we're going to follow that and hopefully talk to you down the road and learn more about that. Paul, thanks so much for spending some time with us. I really appreciate it. >> Thank you. >> You're welcome. Okay, now as I say, think about your questions. Go down below. Paul and I will be here for a Q&A in the chat below. Thanks for watching, everybody. We'll see you next time. (light music)