>>Good day. And thanks for joining us as we continue our series here on the Coupa, the AWS startup showcase featuring today, big ID and what this is, will Murphy was the vice president of business development and alliances at big idea. Well, good day to you. How are you going today? Thanks John. I'm doing well. I'm glad to be here. That's great. And acute belong to, I might add, so it's nice to have you back. Um, let's first off, let's share the big ID story. Uh, you've been around for just a handful of years accolades coming from every which direction. So obviously, uh, what you're doing, you're doing very well, but for our viewers who might not be too familiar with big ID, just give us a 30,000 foot level of your core competence. Yeah, absolutely. So actually we just had our five-year anniversary for big ID, uh, which we're quite excited about. >>Um, and that five-year comes with some pretty big red marks. We've raised over $200 million for a unicorn now. Um, but where that comes to and how that came about was that, um, we're dealing with, um, longstanding problems with modern data landscape security governance, privacy initiatives, um, and starting in 2016 with the, uh, authorship of GDPR, the European privacy law organizations, how to treat data differently than they did before they couldn't afford to just sit on all this data that was collected for a couple of reasons, right? Uh, one of them being that it's expensive. So you're constantly storing data, whether that's on-prem or in the cloud is we're going to talk about there's expense that you have to pay to secure the data and keep it from being leaked. You have to pay for access control. It's paid for a lot of different things and you're not getting any value out of that. >>And then there's the idea of like the customer trust piece, which is like, if anything happens to that data, um, your reputational, uh, your reputation as a company and the trust you have between your customers and your organization is broken. So big ID. What we did is we decided that there was a foundation that needed to be built. The foundation was data discovery. If you even an organization knows where its data is, whose data it is, where it is, um, and what it is, and also who has access to it, they can start to make actionable decisions based on the data and based on this new data intelligence. So we're trying to help organizations keep up with modern data initiatives and we're empowering organizations to handle their data sensitive, personal regulated. And what's actually quite interesting is we allow organizations to define what's sensitive to them because like people, organizations are all different. >>And so what's sensitive to one organization might not be to another, it goes beyond the wall. And so we're giving organizations that new power and flexibility, and this is what I still find striking is that obviously with this exponential growth of data and machine learning, just bringing billions of inputs, it seems like right. All of a sudden you have this fast reservoir data, is that the companies in large part, um, don't know a lot about the data that they're harvest state and where it is. And so it's not actionable, it's kind of dark data, right. Just out there reciting. >>Um, and so as I understand it, this, this is your focus basically is tell people, Hey, here's your landscape. Uh, here's how you can better put it to action, why it's valuable and we're going to help them protect it. Um, and they're not aware of these things, which I still find a little striking in this day and age, >>And it goes even further. So, you know, when you start to, when you start to reveal the truth and what's going on with data, there's a couple things that some organizations do. Uh, and I think human instincts, some organizations want to bury their head in the sand. I'm like, everything's fine. Uh, which is, as we know, and we've seen the news frequently, not a sustainable approach. Uh, there's the, there's the, like, let's be a, we're overwhelmed. We don't, we don't even know. We don't even know where to start. Then there's the natural reaction, which is okay. We have to centralize and control everything which defeats the purpose of having, um, shared drives and collaboration and, um, geographically disparate workforces, which we've seen particularly over the last year, how important that resiliency within organizations is to be able to work in different areas. And so, um, it really restricts the value that, um, organizations can get from their data, which is important. And it's important in a ton of ways. Um, and for customers that have allowed their, their data to be, to be stored and harvested by these organizations, they're not getting value out of it either. It's just risk. And we've got to move data from the liability side of the balance sheet, um, to the assets out of the balance sheet. And that comes first and foremost with knowledge. >>So everybody's vote cloud, right? Everybody was on prem and also we build a bigger house and build a bigger house, better security, right in front of us, got it, got to grow. And that's where I assume AWS has come in with you. And, and this was a two year partnership that you've been engaged with in AWS. So maybe shine a little light on that, about the partnership that you've created with AWS, and then how you then in turn transition that, to leverage that for the betterment of your >>Customer base. Yeah. So AWS has been a great partner. Um, they are very forward-looking for an organization, as large as they are very forward looking that they can't do everything that their customers need. And it's better for the ecosystem as a whole to enable small companies like us. And we were very small when we started our relationship with them, uh, to, to join their partner organization. So we're an advanced partner. Now we're part of ISV accelerate. So it's a slightly more lead partner organization. Um, and we're there because our customers are there and AWS like us, but we both have a customer obsessed culture. Um, but organizations are embracing the cloud and there's fear of the cloud. There's there really shouldn't be in the, in the way that we thought of it, maybe five or 10 years ago. And that, um, companies like AWS are spending a lot more money on security than most organizations can. >>So like they have huge security teams, they're building massive infrastructure. And then on top of that, companies themselves can do, can use, uh, products like big ID and other products to make themselves more secure, um, from outside threats and from, from inside threats as well. So, um, we are trying to with them approach modern data challenge as well. So even within AWS, if you put all the information in, like, let's say S3 buckets, that doesn't really tell you anything. It's like, you know, I, I make this analogy. Sometimes I live in Manhattan. If I were to collect all the keys of everybody that lived in a 10 block radius around me and put it into a dumpster, uh, and keep doing that, I would theoretically know where all the keys were there in the dumpster. Now, if somebody asked me, I'd like my keys back, uh, I'd have a really hard time giving them that because I've got to sort through, you know, 10,000 people's keys. >>And I don't really know a lot about it, but those key sale a lot, you know, it says, are you in an old building, are you in a new building? You have a bike, do you have a car? Do you have a gym locker? There's all sorts of information. And I think this analogy holds up for data because of the way you store your data is important, but, um, you can gain a lot of theoretically innocuous, but valuable information from the data that's there while not compromising the sensitive data. And as an AWS has been a fabulous partner in this, they've helped us build a AWS security, have integration out of the box. Um, we now work with over 12 different AWS native, uh, applications from anything like S3 Redshift and Sienna, uh, Kinesis, as well as, um, apps built on AWS like snowflake and Databricks that we, that we connect to. >>And AWS, the technical team of department teams have been an enormous part of our success there. We're very proud of joining the marketplace to be where our customers want to buy enterprise software more and more. Um, and that's another area that we're collaborating, uh, in, in, in joint accounts now to bring more value in simplicity to our joint customers. What's your process in terms of your customer and, uh, evaluating their needs because you just talked about earlier, you had different approaches to security. Some people put their head in the sand, right? Some people admit that there's a problem. Some people fully engaged. So I assume there's also different levels of sophistication in terms of whatever you have in place and then what their needs are. So if you would shine a little light on that, you know, where they are in terms of their data landscape and AWS and its tools, but you just touched them on multiple tools you have in your service. >>Now, all that comes together to develop what would be, I guess, a unique program for a company's specific needs. It is. We started talking to the largest enterprise accounts when we were founded and we still have a real proclivity and expertise in that area. So the issues with the large enterprise accounts and the uniqueness there is scale. They have a tremendous amount of data, HR data, financial data, customer data, you name it, right? Like, we'll go. We can, we can go dry mouth talking about how many you're saying data. So many times with, with these large customers, um, freight Ws scale, wasn't an issue. They can store it, they can analyze it. They can do tons. It where we were helping is that we could make that safer. So if you want to perform data analytics, you want to ensure that sensitive data is not being, or that you want to make sure you're not violating local, not national or industry specific regulations. >>Financial services is a great example. There's dozens of regulations at the federal level in the United States and each state has their own regulations. This becomes increasingly complex. So AWS handles this by, by allowing an amazing amount of customization for their customers. They have data centers in the right places. They have experts on, on, uh, vertical, specific issues. Big ID handles this similarly in some ways, but we handle it through ostensive ability. So one of our big things is we have to be able to connect to every everywhere where our customers have data. So we want to build a foundation of like, let's say first let's understand the goals is the goal compliance with the law, which it should be for everybody that should just be like, we need to, we need to comply with the law. Like that's, that's easy. Yeah. Then as the next piece, like, are we dealing with something legacy? >>Was there a breach? Do we need to understand what happened? Are we trying to be forward-looking and understanding? We want to make sure we can lock down our most sensitive data, tier our storage tier, our security tier are our analytics efforts, which also is cost-effective. So you don't have to do, uh, everything everywhere, um, or is the goal a little bit like we needed to get a return on investment faster, and we can't do that without de-risking some of that. So we've taken those lessons from the enterprise where it's exceedingly difficult, uh, to work because of the strict requirements, because the customers expect more. And I think like AWS, we're bringing a down market. Uh, we have some, a new product coming out. Uh, it's exclusive for, uh, AWS now called small ID, which is a cloud native, a smaller version, lighter weight version of our product for customers in the more commercial space in the SMB space where they can start to build a foundation of understanding their data or, um, protection for security for, for, for privacy. >>And, and before I let you go here, what I'd like to hear about is practical application. You know, somebody that, that you've, you know, that you were able to help and assist you evaluated. Cause you've talked about the format here. You've talked about your process and talk about some future, I guess, challenges, opportunities, but, but just to give our viewers an idea of maybe the kind of success you've already had to, uh, give them a perspective on that, this share a couple stories. If you wouldn't mind with some work that you guys did and rolled up your sleeves and, and, uh, created that additional value >>For your customers. Yeah, absolutely. So I'll give a couple examples. I'm going to, I'm going to keep everyone anonymized, uh, as a privacy based company, in many ways, what we, we try to respect colors. Um, but let's talk about different types of sensitive data. So we have customers that, um, intellectual property is their biggest concern. So they, they do care about compliance. They want to comply with all local and national laws where they, where they, their company resides all their offices are, but they were very concerned about sensitive data sprawl around intellectual property. They have a lot of patents. They have a lot of sensitive data that way. So one of the things we did is we were able to provide custom tags and classifications for their sensitive data based on intellectual property. And they could see across their cloud environment, across their on-premise environment across shared drives, et cetera. >>We're sensitive data had sprawl where it had moved, who's having access to it. And they were able to start realigning their storage strategy and their content management strategy, data governance strategy, based on that, and start to, uh, move sensitive data back to certain locations, lock that down on a higher level could create more access control there, um, but also proliferate and, uh, share data that more teams needed access to. Um, and so that's an example of a use case that I don't think we imagined necessarily in 2016 when we were focused on privacy, but we've seen that the value can come from it. Um, so yeah, no, I mean, the other piece is, so we've worked with some of the largest AWS customers in the world. Their concern is how do we even start to scan the Tedder, terabytes and petabytes of data in any reasonable fashion? >>Uh, without it being out of date, if we create this data map, if we prayed this data inventory, uh, it's going to be out of date day one, as soon as we say, it's complete, we've already added more. That's where our scalability fit Sam. We were able to do a full scan of their entire AWS environment and, uh, months, and then keep up with the new data that was going into their AWS environment. This is a, this is huge. This was groundbreaking for them. So our hyper scan capability, uh, that we've wrote, brought out that we rolled out to AWS first, um, was a game changer for them to understand what data they had and where it is who's it is et cetera at a way that they never thought they could keep up with. You know, I I'm, I brought back to the beginning of code when the British government was keeping track of all the COVID cases on spreadsheets and spreadsheet broke. >>Um, it was also out of date, as soon as they entered something else. It was already out of date. They couldn't keep up with them. Like there's better ways to do that. Uh, luckily they think they've moved on from, from that, uh, manual system, but automation using the correct human inputs when necessary, then let, let machine learning, let, uh, big data take care of things that it can, uh, don't waste human hours that are precious and expensive unnecessarily and make better decisions based on that data. You know, you raised a great point too, which I hadn't thought of about the fact is you do your snapshot today and you start evaluating all their needs for today. And by the time you're going to get that done, their needs have now exponentially grown. It's like painting the golden gate bridge, right. You get that year and now you've got to pay it again. I said it got bigger, but anyway, they will. Thanks for the time. We certainly appreciate it. Thanks for joining us here on the sort of showcase and just remind me that if you ever asked for my keys, keep them out of that dumpster to be here.

(upbeat music) >> Well, good day and thank you for joining us as we continue our series here on theCUBE of the AWS Startup Showcase featuring today BigID. And with us is Will Murphy, who's the Vice President of the Business Development and Alliances at BigID. Will, good day to you, how are you doing today? >> Thanks John, I'm doing well. I'm glad to be here. >> Yeah, that's great. And theCUBE alum too, I might add so it's nice to have you back. Let's first off, let's share the BigID story. You've been around for just a handful of years. Accolades coming from every which direction so obviously what you're doing, you're doing very well. But for our viewers who might not be too familiar with BigID, just give us a 30,000 foot level of your core competence. >> Yeah absolutely. So actually we just had our five-year anniversary for BigID, which we're quite excited about. And that five year comes with some pretty big red marks. We've raised over $200 million for a unicorn now. But where that comes to and how that came about was that we're dealing with longstanding problems with modern data landscapes, security governance, privacy initiatives. And starting in 2016 with the authorship of GDPR, the European privacy law organizations had to treat data differently than they did before. They couldn't afford to just sit on all this data that was collected. For a couple reasons, right? One of them being that it's expensive. So you're constantly storing data whether that's on-prem or in the cloud as we're going to talk about. There's expense to that. You have to pay to secure the data and keep it from being leaked, You have to pay for access control, you have to pay for a lot of different things. And you're not getting any value out of that. And then there's the idea of the customer trust piece, which is like if anything happens to that data, your reputation as a company and the trust you have between your customers and your organization is broken. So BigID, what we did is we decided that there was a foundation that needed to be built. The foundation was data discovery. If an organization knows where its data is, whose data it is, where it is, and what it is and also who has access to it, they can start to make actionable decisions based on the data and based on this new data intelligence. So, we're trying to help organizations keep up with modern data initiatives. And we're empowering organizations to handle their data, sensitive, personal regulated. What's actually quite interesting is we allow organizations to define what's sensitive to them because like people, organizations are all different. And so what's sensitive to one organization might not be to another. It goes beyond the wall. And so we're giving organizations that new power and flexibility. >> And this is what I still find striking is that obviously with this exponential growth of data you got machine learning, just bringing billions of inputs. It seems like right now. Also you had this vast reservoir of data. Is that the companies in large part don't know a lot about the data that they're harvesting and where it is, and so it's not actionable. It's kind of dark data, right? Just out there residing. And so as I understand it, this is your focus basically is to tell people, hey here's your landscape, here's how you can better put it to action why it's valuable and we're going to help you protect it. And they're not aware of these things which I still find a little striking in this day and age >> And it goes even further. So you know, when you start to reveal the truth and what's going on with data, there's a couple things that some organizations do. And enter the human instincts. Some organizations want to bury their head in the sand like everything's fine. Which is as we know and we've seen the news frequently not a sustainable approach. There's the like let's be we're overwhelmed. Yeah. We don't even know where to start. Then there's the unnatural reaction, which is okay, we have to centralize and control everything. Which defeats the purpose of having shared drives and collaboration in geographically disparate workforces, which we've seen in particularly over the last year, how important that resiliency within organizations is to be able to work in different areas. And so it really restricts the value that organizations can get from their data, which is important. And it's important in a ton of ways. And for customers that have allowed their data to be stored and harvested by these organizations, like they're not getting value out of it neither. It's just risk. And we've got to move data from the liability side of the balance sheet to the assets side of the balance sheet. And that comes first and foremost with knowledge. >> So everybody's going cloud, right? Used to be, you know, everybody's on prem. And all of a sudden we build a bigger house. And so because you build a bigger house, you need better security, right? Your perimeter's got to grow. And that's where I assume AWS has come in with you. And this is a two year partnership that you've been engaged with in AWS. So maybe shine a little light on that. About the partnership that you've created with AWS and then how you then in turn transition that to leverage that for the benefit of your customer base. >> Yeah. So AWS has been a great partner. They are very forward-looking for an organization as large as they are. Very forward looking that they can't do everything that their customers need. And it's better for the ecosystem as a whole to enable small companies like us, and we were very small when we started our relationship with them, to join their partner organization. So we're an advanced partner now. We're part of ISV Accelerate. So it's a slightly more lead partner organization. And we're there because our customers are there. And AWS like us, we both have a customer obsessed culture. But organizations are embracing the cloud. And there's fear of the cloud, but there really shouldn't be in the way that we thought of it maybe five or 10 years ago. And that companies like AWS are spending a lot more money on security than most organizations can. So like they have huge security teams, they're building massive infrastructure. And then on top of that, companies themselves can can use products like big ID and other products to make themselves more secure from outside threats and from inside threats as well. So we are trying to with them approach modern data challenges well. So even within AWS, if you put all the information in like let's say S3 buckets, it doesn't really tell you anything. It's like, you know, I make this analogy sometimes. I live in Manhattan and if I were to collect all the keys of everybody that lived in a 10 block radius around me and put it into a dumpster and keep doing that, I would theoretically know where all the keys were. They're in the dumpster. Now, if somebody asked me, I'd like my keys back, I'd have a really hard time giving them that. Because I've got to sort through, you know, 10,000 people's keys. And I don't really know a lot about it. But those key say a lot, you know? It says like, are you in an old building? Are you in a new building? Do you have a bike? Do you have a car? Do you have a gym locker? There's all sorts of information. And I think that this analogy holds up for data but ifs of the way you store your data is important. But you can gain a lot of theoretically innocuous but valuable information from the data that's there, while not compromising the sensitive data. And as an AWS has been a fabulous partner in this. They've helped us build a AWS security, have integration out of the box. We now work with over 12 different AWS native applications from anything like S3, Redshift, Athena, Kinesis, as well as apps built on AWS, like Snowflake and Databricks that we connect to. And in AWS, the technical teams, department teams have been an enormous part of our success there. We're very proud to have joined the marketplace, to be where our customers want to buy enterprise software more and more. And that's another area that we're collaborating in joint accounts now to bring more value and simplicity to our joint customers. >> So what's your process in terms of your customer and evaluating their needs? 'Cause you just talked about it earlier that you had different approaches to security. Some people put their head in the sand, right? Some people admit that there's a problem. Some people fully are engaged. So I assume there's also a different level of sophistication in terms of what they already have in place and then what their needs are. So if you were to shine a little light on that, about assessing where they are in terms of their data landscape. And now AWS and its tools, which you just touched on. You know, the multiple tools you have in your service. Now, all that comes together to develop what would be I guess, a unique program for a company's specific needs. >> It is. We started talking to the largest enterprise accounts when we were founded and we still have a real proclivity and expertise in that area. So the issues with the large enterprise accounts and the uniqueness there is scale. They have a tremendous amount of data: HR data financial data, customer data, you name it. Right? Like, we could go dry mouth talking about how many insane data so many times with these large customers. For AWS, scale wasn't an issue. They can store it. They can analyze it. They can do tons with it. Where we were helping is that we could make that safer. So if you want to perform data analytics, you want to ensure that sensitive data is not being part of that. You want to make sure you're not violating local, national or industry specific regulations. Financial services is a great example. There's dozens of regulations at the federal level in United States. And each state has their own regulations. This becomes increasingly complex. So AWS handles this by allowing an amazing amount of customization for their customers. They have data centers in the right places. They have experts on vertical specific issues. BigID handles this similarly in some ways, but we handle it through extensibility. So one of our big things is we have to be able to connect to everywhere where our customers have data. So we want to build a foundation of like let's say first, let's understand the goals. Is the goal compliant with the law? Which it should be for everybody. That should just be like, we need to comply with the law. Like that's easy. Yeah. Then there's the next piece, like are we dealing with something legacy? Was there a breach? Do we need to understand what happened? Are we trying to be forward-looking and understanding? We want to make sure we can lock down our most sensitive data. Tier our storage, tier our security, tier are our analytics efforts which also is cost-effective. So you don't have to do everything everywhere. Or is the goal a little bit like we needed to get our return on investment faster. And we can't do that without de-risking some of that. So we've taken those lessons from the enterprise where it's exceedingly difficult to work because of the strict requirements because the customers expect more. And I think like AWS, we're bringing it down market. We have some new product coming out. It's exclusive for AWS now called SmallID, which is a cloud native. A smaller version, lighter weight version of our product for customers in the more commercial space. In the SMB space where they can start to build a foundation of understanding their data for protection and for security, for privacy. >> Will, and before I let you go here what I'd like to hear about is practical application. You know, somebody that you've, you know, that you were able to help and assist, you evaluated. 'Cause you've talked about the format here. You talked about your process and talked about some future, I guess, challenges, opportunities. But just to give our viewers an idea of maybe the kind of success you've already had. To give them a perspective on that. Just share a couple of stories, if you wouldn't mind. Whether there's some work that you guys did and rolled up your sleeves and created that additional value for your customers. >> Yeah, absolutely. So I'll give a couple examples. I'm going to keep everyone anonymized. As a privacy based company, in many ways, we try to respect-- >> Probably a good idea, right? (Will chuckles) >> But let's talk about different types of sensitive data. So we have customers that intellectual property is their biggest concern. So they do care about compliance. They want to comply with all the local and national laws where their company resides and all their offices are. But they were very concerned about sensitive data sprawl around intellectual property. They have a lot of patents. They have a lot of sensitive data that way. So one of the things we did is we were able to provide custom tags and classifications for their sensitive data based on intellectual property. And they could see across their cloud environment, across their on-premise environment, across shared drives et cetera, where sensitive data had sprawl. Where it had moved, who's having access to it. And they were able to start realigning their storage strategy and their content management strategy, data governance strategy, based on that. And start to move sensitive data back to certain locations, lock that down on a higher level. Could create more access control there, but also proliferate and share data that more teams needed access to. And so that's an example of a use case that I don't think we imagined necessarily in 2016 when we were focused on privacy but we've seen that the value can come from it. Yeah. >> So it's a good... Please, yeah, go ahead. >> No, I mean, the other (mumbles). So we've worked with some of the largest AWS customers in the world. Their concern is how do we even start to scan the Tedder terabytes and petabytes of data in any reasonable fashion without it being out of date. If we create this data map, if we create this data inventory, it's going to be out of date day one. As soon as we say, it's complete, we've already added more. >> John: Right. >> That's where our scalability fits in. We were able to do a full scan of their entire AWS environment in months. And then keep up with the new data that was going into their AWS environment. This is huge. This was groundbreaking for them. So our hyper scan capability that we brought out, that we rolled out to AWS first, was a game changer for them. To understand what data they had, where it is, who's it is et cetera, at a way that they never thought they could keep up with. You know, I brought back to the beginning of code when the British government was keeping track of all the COVID cases on spreadsheets and spreadsheets broke. It was also out of date. As soon as they entered something else it was already out of date. They couldn't keep up with it. Like there's better ways to do that. Luckily they think they've moved on from that manual system. But automation using the correct human inputs when necessary. Then let machine learning, let big data take care of things that it can. Don't waste human hours that are precious and expensive unnecessarily. And make better decisions based on that data. >> Yeah. You raised a great point too which I hadn't thought of about. The fact is, you do your snapshot today and you start evaluating all their needs for today. And by the time you're able to get that done their needs have now exponentially grown. It's like painting the golden gate bridge. Right? You get done and now you got to paint it again, except it got bigger. We added lanes, but anyway. Hey, Will. Thanks for the time. We certainly appreciate it. Thanks for joining us here on the startup showcase. And just remind me that if you ever asked for my keys keep them out of that dumpster. Okay? (Will chuckles) >> Thanks, John. Glad to be here. >> Pleasure. (soft music)

>> Narrator: From around the globe, it's the CUBE with coverage of SUSECON Digital, brought to you by SUSE. >> Hi, and welcome back. I'm Stu Miniman, and this is the CUBE's coverage of SUSECON Digital '20. Happy to welcome to the program Glenn Fitzgerald, he is the Chief Data Officer for Fujitsu Products in Europe, coming to me from across the pond. Ah, Glenn, great to see you, so thanks so much for joining us. >> Hi Stu, thanks, very glad to be here. >> All right, so, first of all, you know, Fujitsu Products Europe, Chief Data Officer, give us a little bit, your role and responsibility inside Fujitsu. >> Of course, the Fujitsu Products Europe is as the name suggests, that part of the Fujitsu Corporation that is dedicated to delivering our products out through the European geography. Fujitsu's product sets runs the full range of ITC components from... tablets to PCs to servers to big storage devices to networks, which is to integrated systems and the software stacks that sit on top of them. It's a wide profile, yeah. And my role has been to be the Chief Technology Officer for that organization for several years. Recently, we have as an organization adopted a new approach to take to the marketplace. And that has necessitated a slight change in my role to one that's more focused on enabling customers to get value out of their data and their data repositories and the correlation of that data to generate business value. A long description, Stu, but I think necessary. >> Yeah, no, super important, Glenn. One thing we've actually been saying for more than a year on the CUBE now, is when you have that discussion of digital transformation, one of the things that differentiates companies before they've gone digital and if they are truly to call themself, you know, have gone through this transformation, is they need to be data-driven, you know. Data needs to be how they're making their decisions. It was definitely a key theme that we heard from SUSE in the keynote. So maybe talk a little bit about how digital transformation and the partnership with SUSE fits into your world. >> Absolutely. So, in terms of the transformation of our business and the changes that we're trying to make to it, as a product organization, traditionally our relationships with our customers is kind of transactional. You know, we sell stuff and they buy stuff. And that relationship with customers is increasingly less viable. It's increasingly challenged. And I think it's challenged by the many things that have happened in the marketplace. It's a sign of a maturing industry. So, you have the Cloud and you have the ISVs who are providing compute power and storage capacity and network capability to our customers in a different way. They're delivering it on the click of a button on an internet browser. Now, that's suitable for some customers in some situations, it isn't suitable for others, but it's definitely here to stay and it's definitely going to change the way the marketplace works, and it has. So we've recognized inside our organization that we need to leverage some of the capabilities that exists inside the Fujitsu services organizations. Fujitsu is a large company. It also has very significant manage services capabilities, we deliver to huge customers all across Europe in terms of German government, British government, a lot of the big manufacturing industrials in Europe and a lot of the travel and insurance financial sectors. So leveraging some of that to take a more consultant-led approach to our marketplace, to our customers. So what we want to do with them is take them through the story of data transformation. And as you said, and I quite agree, the marketplace is becoming increasingly data-driven. You've only got to look at some of the well-known examples, and I'm not going to rehearse them again because everybody's heard them and knows who they are. But, every organization, however large or small, has to derive business advantage and discrimination from its data. Otherwise, they'll go the way of... I hate to say it, the High Street. You can see, in this recent pandemic, the COVID-19 stuff. I don't know what it's like in the US, but absolutely in the UK and in Europe, those retailers that have been able to provide a online presence have survived, and some of them have thrived. And those retailers that haven't been able to provide that presence aren't here anymore. And that's just, it's a current and rather violent example of this change of how to manage data and get the best value out of it. Now, in order to take that to our customers, the Fujitsu Product team needs to change some of its capabilities, it needs to introduce some of those consulting capabilities into its portfolio, which we do. It needs to work with some of our partners to deliver the capabilities either as an installation or a service and SUSE are one of our prime partners in that sense. Both in terms of delivering the computing platform standards, the SUSE Data Hub, I believe it's changed its name now. The SUSE Data Hub as I know it, is core to our offerings in this space. We have just launched in Germany, for example, a manufacturing optimization application which runs off the SUSE infrastructure and uses the SAP database and database management systems above that to deliver things like predictive maintenance and just-in-time parts delivery, and in-factory automated routine of little robots carrying the bits to the right place. And that's an example of something that was led by a consulting activity between Fujitsu and our customer, in this case, a large manufacturer. We recognized during that consultancy that some of the stuff we needed to do to deliver the solution, that would deliver the data-derived business benefit the customer needed, was not in our immediate scope. We got some of our larger partners, SUSE and SAP in this case, involved in it, and they outcome has been happy for everybody. There are some lessons in all that. The Fujitsu is still learning, if I'm frank, like how to price it. When you have consultant-led activities that are generating very great benefit for your client, it's not too great for the supplier to still be charging that just on consultant day-rate. That can lead you to not getting the value out of what you're providing to your client. So there's lessons there. There's lessons in how to interact between ourselves and some of our services partners and clients. And making sure that the optimum route to market is delivered. But that essentially, Stu, is the story. It's a change from a transactional approach to a consultant-led approach, and the generation of a large ecosystem of partners, like SUSE, like SAP, with the capabilities to build stuff with us and deliver business outcomes to clients, not a stack of tin. >> Excellent. So, Glenn, what about kind of emerging requirements, what you're hearing from your customers, you know, AI is an area that we heard quite a bit in the keynote from SUSE. Where do you see that fitting into the entire discussion? Obviously, the key, leverage of data, when you talk about AI. >> Absolutely, and to talk about that in two ways. The first way, the first issue with that is exactly the point you make, Stu, around data. So, AI, which is not artificial and not intelligent, it's just maths. It's statistical mathematics acting upon a large set of data. And if you have a large set of the right data, it can produce fantastic results for the client. But without that data, it is a relatively meaningless exercise. Once that data are assembled, we're beginning to see very significant results produced by the application of new networking, the machine learning. To technology-based, data-derived solutions for our clients, and there are many examples. I'll give you just one or two. We are working with a large financial institution in the city of London that wants to produce, basically, an artificial knowledge base that will perform the task of insurance underwriting. Don't ask me how that works, I'm not a financial guy. But apparently, insurance underwriting is a relatively mechanical task. You have a set of actuarial tables, you have a set of risks, you compare one with the other and produce a premium. We're working with them on that. There is a lot in the manufacturing space, and a surprising amount in healthcare. One of the most personally rewarding examples I've been involved with was the delivery of intelligent heart monitoring to clients with pacemakers. So, the pacemaker is made intelligent and it dumps to a Bluetooth-connected device in the patient's home, and that uploads to an AI-based knowledge system in the Cloud, and the Cloud says, "Sit down, you're going to have a heart attack." And the important element of that is that it says, "Sit down, you're going to have a heart attack" before you've had the heart attack, so you don't have one. A really fantastic example of human-centric interest. So, I think, as a separate subject, AI is largely of academic interest. But as a component of a data-driven solution for a customer, it's rapidly emerging as an important element in our armory, as indeed some other technologies. Like data annealing, and like data analytics, and to a slightly lesser extent at the moment, but I think it will come, blockchain. >> Excellent. So, Glenn, one of the things we always like talking about when we talk to a CDO is how are companies getting along with their data strategy? And I think back four or five years ago when we were first hearing about CDO as a role, it was, you know, the CDO, where do they fit compared to the CIO, what is the changing role of the CIO? So, like you were saying about some of these things, data often can be an afterthought or not necessarily connected, but just as we were saying, data needs to be a critical piece of how companies plan. You gave a great example of medical, obviously. You know, the data can really help transform lives in that environment. So, bring us inside what you're hearing from customers, how are they structuring, and are they really being, I guess, data-driven is one of the terms that I... >> That's a very good question. And the answer is yes to everything. So, one of the most difficult things to estimate, if you're going in to a customer with a client, especially if it's a client that you don't know very well, is exactly what their point of reference is going to be, what their comfort with some of these things is. As a result, we at Fujitsu invested a good deal of effort in going out to our client base and asking them the necessary questions to generate a thing we call the Data Maturity Model. Now the Data Maturity Model is not a new concept, it's a very solid and sound concept, it's been around for a long time. I think what we're trying to do is bring more rigor to that with a very large sample base of our customers. And the model is what you'd expect. There are five levels within it from at Level 1, what is data? To Level 5, where data is continually monitored, continually exploited, and continually developed as part of the business that the organization delivers. So there's a spectrum. In my experience, slightly controversially perhaps, the state of organizations on that Maturity Index varies with geography. And I think it's something to do with acceptance of risk, I think it's something to do with security concerns and liabilities. It's my observation that in the Anglophone world, in England and in the US certainly, there is a higher average awareness of the importance of data and the need to derive business benefit from it than there is, for example, in the Germanophone world, where there are more concerns around security and more regulations around security. They're quite constraining. And as a result, where organizations are a bit more traditional and a bit less aware of the value to be derived from the data. So, people, organizations hit everywhere on this scope, this plane of awareness of data and its potential. But it's definitely the case that the average is always going up. >> Yeah-. >> You only have to look at some of the public stocks, under the stuff in the public domain, to observe that that's happening all the time. >> Yeah, Glenn, I'm curious with the global pandemic happening, are you seeing any impact on that? I've heard some anecdotal data that you talk about some of the companies that are, you know, might not be interested in doing Cloud adoption because they're concerned about security, and all of a sudden realizing they need to take advantage of certain solutions. Or if you look at something like the tracking and tracing, obviously, people are rightfully concerned about personal information and having rights infringed upon. So, what will, in your opinion, are you seeing any early indications as to what this impact will be on how we think about data? >> I think there, again, there are two different dimensions. There's a Darwinian element in the attitudes towards commercial data. As we said right at the start of the conversation, in the current environment, you can see large retailers disappearing at a rate of knots because they haven't been data-aware and data-adopting. That lesson is not lost on other retailers. So, retailers are beginning to do things that in the past they wouldn't have done because of that sort of security concern, but also because of concerns about things like function and performance... and the sheer security that you have in owning your own stuff and therefore being certain of its ownership by you and your retention of the IPR involved. So there is definitely a slackening of that concern and a faster adoption of data exploitation technologies in the commercial sector. In the domestic sector, I think it's very mixed. And again, extremely geography different. In the UK, we have, if I could just talk about my own country for a second, we have this trial of a smartphone COVID-19 tracking app going on on the Isle of Wight. The British media is full to brimming with discussions of the implications of that upon individual liberty, of whether or not it's the nanny state gone mad, of whether or not we should all be not cooperating with it and catching the damn disease anyway because it's a step too far. In Germany, they just implemented it. And everybody went, "Right." (makes click sound) So there are all these different cultural adoptions of these things. But always and forever the trend is upwards. Similar debates around video surveillance technology. So you've got the pressure of security and protecting the public, against intrusion and violation of individual rights. And that debate has got to the stage now where there have been some pilots for threat detection based on video surveillance in the UK that have been stopped. Not so much in Germany. In the US, I don't know, but I guess, you're even more Libertarian than we Brits are, so it's probably more the other way. But with all of these discussions of differences, of culture and nation and area and geography, the trend is definitely upward. So, however the British people resolve that stress, you have to have a tracking app if you want to beat this disease. And that will happen in due course. >> Excellent. Well, Glenn, I'll give you the final takeaway, SUSECON '20, talk about the importance of the Fujitsu and SUSE partnership. >> I think it's a growing part of the base of an ecosystem that's required for all organizations like Fujitsu, like SUSE, that want to reach out and deliver solutions to our customers' business problems, which is after all, what we're here for and what we're all about. Because let's face it. In any sizable organization, the data landscape is unbelievably complicated. You have different formats of data, in RBDs, in unstructured file store, in whatever floats around employees' devices, on social media, for God's sake. Getting all of that out, understanding its relationship to infrastructure, understanding its relation through infrastructure, through application stacks, and service delivery, and then being able to transform that into new applications and new service paradigms that deliver the business benefits that our customers are looking for, is an incredibly complex act. And no one organization is going to be able to do it on its own. So I see the future as one of these growing ecosystems of people that work together some of the time, compete some of the time. Are in what we might call a frenemy relationship. Because we all have to work together to deliver what the customers need. Fujitsu is working with SUSE and our other partners at the forefront of that trying to build economic and commercial and technical partnerships. And I'm sure that will continue through SUSECON '20 and into the future. >> All right, well, Glenn Fitzgerald, thank you so much for joining us. Really appreciate the updates. >> I've enjoyed it. Thank you for having me. >> All right, much more coverage from SUSECON '20 Digital. I'm Stu Miniman and thank you for watching the CUBE. (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)