live from the Computer History Museum in Mountain View California it's the cube covering Devon that create 2018 brought to you by Cisco hey welcome back everyone live here at dev net create Cisco's event here at this Museum in Mountain View California art a Silicon Valley I'm here with Laura Cooney we here for two days wall-to-wall coverage breaking down Cisco's move into the DevOps Wow developer world separate from the dev net community which is the Cisco Developer Program and we've been breaking down Lauren great to have you this past two days so we talked to a lot of the Cisco folks a lot of the practitioners let's analyze it let's discuss kind of what's going on first of all Cisco creates a new group almost a year ago next month called dev net create to get out of the Cisco bubble and go out into the cloud world and see if they can't connect the cloud ecosystem cloud native kubernetes all the micro services goodness is going on the application side on infrastructures code and bring that with the Cisco network engineering community who are plumbers network plumbers their network engineers they deal with provisioning gear routes well I think it's interesting because you have this CCIE number that has been decreasing over the past couple of years and that's that's not because the network is less important it's actually because new skill sets are emerging and folks need to take on these new skills to learn and to really flourish in their careers so I think what definite is doing is just tremendous in terms of enabling developers to move up stack to look at things like kubernetes to look at things like you know cloud native to look at new applications you can build new things that you can extend to API integration into you know new types of applications you know we had folks here that we're learning to code in Python for the first time and I think that's awesome I think that's great and the timing is perfect I mean I got to give credit to Susie we and her team at Cisco they have they doing they're doing all the right things I think the way they're handling this is they're not overly aggressive they're not arrogant they're humble they're learning they're listening and they're doing all the right things are bringing a lot to the table from the Cisco table to this community and they've got you know this is very cool but the timing is critical if they tried to do this four years ago how hard would have it been you know you've been there okay I mean they pull this off four years ago I think there was the the goal was always there four years ago but I think the timing was you know you you have to kind of put the the mission in order and get things up and running first you can't just you don't launch a community you build one and I think we you know Cisco really needed to build that core community first and that was that was super credit but even four years ago let's just go back and rewind the clock we was cloud then so it was still the purest DevOps culture it was certainly hard-charging was definitely flying but still even like a lot of the on-premise enterprise folks we're like still kind of poopoo in the cloud you even saw it four years ago Oracle just made their move a couple years ago to the cloud and they're still trying to catch up so you know these legacy vendors and Cisco is one they've pivoted nicely Cisco into this because now the timings there as kubernetes there's enough code to get glued in plugged in with the stack so I think timing has also been a tailwind timing was critical I mean back then we were talking about software-defined networking and you know new services that you could deliver to the cloud and new ways and then DevOps came in is like really the glory child right saying like this DevOps was gonna solve world hunger and and what she came what it came down to basically is you know it is a critical part but there are certain piece parts that needed to come together especially in the open source world to make these things happen I mean to me if I had to like point out I'm just riffing here but you know to me the seminal moment for a cloud and you know agile was happening that's a key driver but it was the fact that was horizontally scalable tech unstructured data the roles of databases software that was becoming this new lightweight glue layer control planes or moving up and down the stack so there wasn't one thing combination of these awesome things were happening that made people go whoa holy-holy we could do more if we think about scale differently skill differently and really how do you bring this you know and this is where you get to edge computing it's how do you actually bring these to the masses how do you go where the people are how do you store data where people are how do you extend security in new ways I mean that's that's gonna be super critical I think the other thing that's also pretty evident is that when you start having new entrants into a market start eating some of your breakfasts then they start eating some of your lunch then you go wait a minute if I don't do something my dinner is gonna be eaten I mean you starting to see people see their business at risk yeah this is a huge thing that that lights up to see XO the CEO the CEO o CDO CIO now it's like okay we got to make a move definitely I think that's that's the way that it has to be and in terms of Cisco I want to get your thoughts because I've always been talking about this and I'm a big Cisco fan I know a lot of people who work there been a big admirer of the company from day one and what they did in the internet generation they did bought a lot of cubbies which create a little bit of a mash mash but that's nothing I issue they really ran the networks what a great culture however we're now seeing applications driving a lot of value and the network needs to be programmable and the challenge that cisco has always been how do we will if the stack as a company and all the little scuttle butts and conversations and parties have been to hallway conversations francisco executives employees is that's been the internal debate how does cisco should cisco move up the stack and if so how so it's been kind of this internal thing good timing now to start moving up the stack because the automations here I think it was great timing four years ago to move up the stack to be honest I think that there were efforts then I know that I was engaged in some to do that rather quickly you know those turned into things that you know went one way or the other I think that there are the right people in the right places at Cisco now to make that actually happen I think you know we're a little early on that I think Suzy Zephyr is just tremendous in terms of driving the users up stack to have them learn these new skills and as they learn these new skills they're learning it on Cisco and that's gonna be really critical that's gonna have the pull power yeah I think this is got a chance a real great chance to and it's not a far reaching of a accomplishment either for them to do this is they can now actually build a developer program now because before they didn't have enough software but what Suzy's doing if I'm Chuck Roberts CEO of Cisco I'm doubling down and what's going on with definite definite create and I can take that def net component and almost kind of expand it out because Cisco has a developer option you look at what they're doing on the collaborative software side the stuff with video they have a total core confidence in video I mean they were early on so many things but now with I got WebEx they're still and so for video conferencing but still beyond that IOT is a video application well huge opportunity in these these communities that pop up and right now you don't have a product if you don't have a supporting community and so salutely be doubling down on this they need to double down on that they probably need to invest more in it than they are now I see it as absolutely critical as they move forward because you know Cisco wants to be one or two in the market for all their products all their solutions to have that they need to have the supporting community dude yeah we did two days here and you know and in terms of events it's not the big glam event it's really a early stage the only the second event within the it hasn't even been 12 months since the first dev net create what I'm impressed by what I love about the cube is we when you get at these early moments when you see it magic happening you get into the communities and you realize wow this is a team that could pull it off and I think Cisco's a company at Cisco live in Barcelona you know it really became apparent to me that Cisco's really pulling in the right direction on a couple things I feel that the big company thing that gotta kind of clean that up a bit just make it more nimble but they got their eye on the prize on video they could really crush the IOT opportunity and the leverage of the network is a huge asset and if they could make that programmable with an open source community behind it man this could be a whole nother Cisco almost bring back that look at the glory days I fully agree Lauren what are you up to these days I mean you got a new gig I do care about your new company and what you're working on you guys write in code you do Advisory do consulting actually stuff I mean you know I like my hands and lots of things so I think it's important to say that you know I've taken my experience at IBM and Microsoft and juniper and Cisco driving new innovation to market faster and new revenue channels and I've taken that and I've started a consulting firm called spark labs and what we do is we use new models like Minimum Viable Product and business model canvas to actually drive you know whether it's product whether it's service whether it's these you know new channels whether it's partner or whether you're just trying to kind of pull together your team in a new way we actually take this and and help you do it in a faster way and you know we've got the models we've got the background and you know we're working with companies that are big and small what kind of engagement you working on what kind of problems you saw so you know we have a larger company that we're working with and one of the things that they ran into is they had just changed around kind of their leadership and we've gone in and worked with their leadership team to kind of establish what this new team needs to look like what are they going to deliver on what are the metrics what are the you know kind of success things that that people are really trying to achieve and how do we empower this new team that has this new leader and you know how do we make sure that everyone's aligned I think that's part of it and the line that's critical alignment is you know you don't if you don't it's it's great to have an amazing vision but if you don't have the execution you're just not going to get there yeah Andy Jesse one of my favorite execs that I've interviewed he's pragmatic he's strong went to Harvard Law hold it against him but great super great guy but he's got a great philosophy I think I come from the Amazon culture is you argue all day long but once a decisions made you align behind it yeah so bring some constructive discourse to the table yep but once it's done they don't tolerate any you know yeah a dysfunctional aggressed passive-aggressive behavior okay say and if say your piece fuck a lot that's exactly it I mean we pull people together for a day or a day and a half and actually run them through the business model canvas which will align with like what their goals are what their mission is how their how their you know being seen in the market and lots of other things but the real goal there is to pull the team together on on you know what exactly those things are and the value that their organization has because if you can't deliver on that message you can't deliver on much more so you do need that alignment and teams are so all over the place often when you're running fast you kind of forget and so sometimes they need to be reminded what's your take on dev net create this year what's your thoughts I think it's great I mean I love the fact that you know there's folks from so many different backgrounds and so many different you know kind of technical areas here I love you know muraki's giving away 1.2 million dollars of equipment and software licenses I think that's phenomenal I'm impressed by a Cisco folks here not too overly overboard and and give them too many compliments because you know they'll get cocky no but still serious dis Cisco people that are here are kicking ass they're doing a great job they're got the microphones on they're doing the demos they're doing a lot they are jazz and they're they're not mailing it in either doing a great job and I think that's that's authentic genuine I think that's going to be a great you know seed in the in the community to grow that up again still they got a lot of work to do but I don't think it's too far of a bridge for Network guys to be cloud guys and to kind of find some middle ground so I think it's the timings perfect I think I'm super impressed with the team and I think this is a great path a Cisco to double down on and and really invest more in because it's definitely got legs and a big fan of the camp thing too we talked about the camp create where they had competitive teams hacking and spending two days on so you know love it love the culture but again early let's see where they go with it I mean if they can get the network ops go on there's DevOps for networks concepts yeah and bring it up and make it programmable couldn't ask for a better time with kubernetes all the coolness going on that microservices good time definitely a great time well great to host with you and we're here live at dev net create wrapping up two days of wall-to-wall coverage of the cube dev net create again this is the cloud ecosystem for cisco separate from the cool or dev net which is the Cisco developer program for all of Cisco a great opportunity for them of course the cubes here covering it we're gonna wrap this up and thanks for watching cube coverage here in the Computer History Museum in Mountain View California thanks for watching

>> Announcer: From the Silicon Valley Media Office in Boston, Massachusetts, it's The Cube! Now, here's your host, Dave Vellante. >> Hi, everybody, welcome to a special Silicon Angle, The Cube on the ground. We're going to be talking about data capital with Paul Sonderegger, who is a big data strategist at Oracle, and he leads Oracle's data capital initiative. Paul, thanks for coming in, welcome to The Cube. >> Thank you, Dave, it's good to be here. >> So data capital, it's a topic that's gaining a lot of momentum, people talking about data value, they've talked about that for years, but what is data capital? >> Well, what we're saying with data capital, is that data fulfills the literal economic textbook definition of capital. Capital is a produced good, as opposed to a natural resource that you have to invest to create it, and it is then an necessary input into some other good or service. So when we define data capital, we say that data capital is the recorded information necessary to produce a good or service. Which is really boring, so let me give you an example. So imagine, picture a retailer. A retailer wants to go into a new market. To do that, the retailer has to expand its inventory, it has to extend its supply chain, it has to buy property, all of these kinds of investments. If it lacks the financial capital to make all of those investments, it can't go, cannot go into that new region. By the same token, if this retailer wants to create a new dynamic pricing algorithm, or a new recommendation engine, but lacks the data to feed those algorithms, it cannot create that ability. It cannot provide that service. Data is now a kind of capital. >> And for years, data was viewed by a lot of organizations, particularly general counsel, as a liability, and then the big data meme sort of took off and all of a sudden, data becomes an asset. Are organizations viewing data as an asset? >> A lot of organizations are starting to view data as an asset, even though they can't account for it that way. So by current accounting standards, companies are not allowed to treat the money that they spend on developing information, on capturing data, as an asset. However, what you see with these online consumer services, the ones that we know, Uber, Airbnb, Netflix, Linkedin, these companies absolutely treat data as an asset. They treat it, not just as a record of what happened, but as a raw material for creating new digital products and services. >> You too, you tweeted out an article recently on Uber, and Uber lost about, what is it? 1.2 billion- >> At least. >> Over six months, at least. >> At least. >> And then the article calculated how much it was actually paid, I mean basically, the conclusion was it paid 1.2 billion for data. >> Yeah. >> It was about $1.20 per data for ride record, which actually is not a bad deal, when you think about it that way. >> Well, that's the thing, it's not a bad deal when you consider that the big picture they have in view is the global market for personal transportation, which The Economist estimates is about 10 trillion dollars annually. Well, to go after a 10 trillion dollar market, if you can build up a unique stock of data capital, of a billion records at about a billion dollars per record, that's probably a pretty good deal, yeah. >> So, money obviously is fungible, it's currency. Data is not a currency, but digital data is fungible, right, I mean, you can use data in a lot of different ways, can't you? >> No, no, it's, and this actually is a really important point, it's a really important point. Data is actually not fungible. This is part of data's curious economic identity. So data, contrary to popular wisdom, data is not abundant. Data consists of countless unique observations, and one of the issues here is that, two pieces of data are usually not fungible. You can't replace one with the other because they carry different information. They carry different semantics. So just to make it very, very concrete, one of the things that we see now, a huge use of data capital is in fraud detection. And one of our customers handles the fraud detection for person-to-person mobile payments. So say you go away for a weekend with a friend, you come back, you want to split the tab, and you just want to make a payment directly to the other person. You do this through your phone. Those transactions, that account to account transfer, gets checked for possible fraudulent activity in the moment, as it happens, and there is a scoring algorithm that sniffs those transactions and gives it a score to indicate whether or not it may be fraudulent or if it's legitimate. Well, this company, they use the information they capture about whether their algorithm captured, caught, all of the fraudulent transactions or missed some, and whether that algorithm mistakenly flagged legitimate transactions as fraudulent. They capture all of those false positives and false negatives, feed it back into the system, and improve the performance of the algorithm for the next go around. Here's why this matters: the data created by that algorithm about its own performance, is a proprietary asset. It is unique. And no other data with substitute for it. And in that way, it becomes the basis for a sustainable competitive advantage. >> It's a great example. So the algorithm maybe is free, you can grab an algorithm, it's how you apply it that is proprietary, and now, okay, so we've established that the data is not fungible. But digital data doesn't necessarily have high asset specificity. Do you agree with that? In other words, I can use data in different ways, if it's digital. Yeah, absolutely, as a matter of fact, this is one of the other characteristics of data. It is non-rivalrous, is what economists would call it. And this means that two parties can use the same piece of data at the same time. Which is not the case with, say, a tractor. One guy on a tractor means that none of the other people can ride that tractor. Data's not like that. So data can be put to multiple uses simultaneously. And what becomes very interesting is that different uses of data can command different prices. There's actually a project going on right now where Harvard Law School is scanning and digitizing the entire collection of US case law. Now this is The Law, the law that we all as Americans are bound to. Yet, it is locked up in a way, in just, in all of these 43,000 books. Well, Harvard and a startup called Ravel Law, they are working on scanning and digitizing this data, which can then be searched, for free, all of these, you can search this entire body of case law, for free, so you can go in and search "privacy," for example, and see all of the judgements that mention privacy over the entire history of US case law. But, if you want, for example, to analyze how different judges, current sitting judges, rule on cases related to privacy, well, that's a service that you would pay for from Ravel. The exact same data, their algorithms are working on the same body of data. You can search it for free, but the analysis that you might want on that same data, you can only get for a fee. So different uses of data can command different prices. >> So, some excellent examples there. What are the implications of all of this for competitive strategies, what should companies, how should they apply this for competitive strategies? >> Well, when we think about competitive strategy with data capital, we think in terms of three principles of data capital, is what we call them. The first one is that data comes from activity. The second one is, data tends to make more data, and the third is that platforms tend to win. So these three principles, even if we just run through them in their turn, the first one, data comes from activity, this means that, in order to capture data, your company has to be part of the activity that produces it at the time that activity happens. And the competitive strategy implication here is that, if your company is not part of that activity when it happens, your chance to capture its data is lost, forever. And so this means that interactions with customers are critical targets to digitize and datify before the competition gets in there and shuts you out. The second principle, data tends to make more data, this is what we were talking about with algorithms. Analytics are great, they're very important, analytics provide information to people so that they can make better choices, but the real action is in algorithms. And here is where you're feeding your unique stock of data capital to algorithms, that not only act on that data, but create data about their own performance, that then improve their future performance, and that data capital flywheel becomes a competitive advantage that's very hard to catch. The third principle is that platforms tend to win. So platforms are common in information-intensive industries, we see them with a credit card, for example, we see them in financial services. A credit card is a payment platform between consumers on the one side, merchants on the other. A video game console is a platform between developers on the one side and gamers on the other. The thing about platform competition is that it tends to lead toward a winner-take-all outcome. Not always, but that's how it tends to go. And with the digitization and datification of more activities, platform competition is coming for industries that have never seen it before. >> So platform beats product, but it's winner-take-all, or number two maybe breaks even, right? >> That tends to be the way it goes. >> And number three loses money, okay. The first point you were making about, you've got to be there when the transaction occurs, you've got to show up. The second one's interesting, data tends to make more data. So, and you talked about algorithms and improving and fine-tuning in that feedback loop. I would imagine customers are challenged in terms of investments, do they spend money on acquiring more data, or do they spend money on improving their algorithms, and then the answer is got to do both, but budgets are limited. How are customers dealing with that challenge? >> Well, prioritization becomes really critical here. So not all data is created equal, but it's very difficult to know which data will be more valuable in the future. However, there are ways to improve your guess. And one of the best ways is to, go after data that your competition could get as well. So this is data that comes from activities with customers. Data from activities with suppliers, with partners. Those are all places where the competition could also try to digitize and datify those activities. So companies should really look outside their own four walls. But the next part, you know, figuring out, what do you do with it? This is where companies really need to take a page out of actual science as they approach data science, and science is all about argument. It's all about experimentation, testing, and keeping the hypotheses that are proven and discarding the ones that are disproven. What this means is that companies need a data lab environment, where they can cut the time, the cost, the effort, of forming and testing new hypotheses, getting new answers to new questions from their data. >> Okay, so, data has value, you've got to prioritize. How do you actually value the data so that I can prioritize and figure out what I should be focusing on in the lab and in production? >> Yeah, well, the basic answer is to go where the money is. So there are a couple things you can do with data. One is that you can improve your operational effectiveness, and so here, you should go look at your big cost areas, and focus your limited data science and managerial resources on trying to figure out, hey, can we become more efficient in whatever your big cost driver is? If it's shipping and logistics, if it's inventory management, if it's customer acquisition, if it's marketing and advertising, so that's one way to go. The next big thing that you can do with data is try to create a new product or service, a new ... create new value in a way that generates revenue. Here, there is a little caveat, which is that, companies may also want to consider creating new capabilities, maybe enriching the customer experience, making connections across multiple channels, that they can't actually charge for, not today. But, what they get, is data that no one else has. What they get from, let's say, making an investment into, bring together the in-store shopping experience with the, with the targeted emails, with, with communication through social feeds and through Twitter. Let's say that they invest in trying to tie that data together, to get a richer picture of their consumers' behavior. They might not be able to charge for that today. But, they may get insight into the way that shopping experience works that no one else can see, which then leads to a value-added service tomorrow. And I know it all sounds very speculative, but this is basically the nature of prototyping, of new product creation. >> Well, Uber's overused as an example, but this is a good application of Uber because they, essentially they pay for driver acquisition, which doesn't scale well. >> Yeah. >> But they get data. >> That's right. >> Because they're there at the point of the transaction and the activity and they've got data that nobody else has. >> Yeah, yeah, that's exactly right, and, you know, one of the ways to think about that is that, you're like a blackjack player, counting cards, and every time you play a hand as a company, you get data, information that may help you improve your future bets. This is why Vegas kicks out card counters, because it's an advantage for the future. But what we're talking about here, in digitizing activity with customers, every time you capture data about your interaction with those customers, you gain something simply for having carried out that activity. >> And so, thinking about, back to value for a minute, I mean I can envision some kind of value flow methodology where you assess the data intensity of the activity, and then assign some kind of, I don't know, score or a value to that activity, and then you can then look at that in relation to other activities. Is that a viable approach? >> It absolutely is. What companies need here is a new way to measure how much data they've got, how much they use, and then ascribe ... value created, you know, by that data. So the, how much they've got, you know, we can think about this, we always talk in terms of gigabytes and petabytes. But really we need some finer measurements. Data is an observation about something in the real world. And so, companies should start to think about measuring their data in terms of observations, in terms of attribute-value pairs. So even thinking about the record captured per activity, that's not enough. Companies should start thinking in terms of, how many columns are in that record? How many attributes are captured in these observations we make from that activity? The next issue, you know, how much do they use? Well, now, companies need to look at, how many of these observations are being touched, are being tapped by queries? Whether they're automatically generated, whether they are generated ad hoc by some data scientist, rooting around for some new understanding. So there's a set of questions there about, what percentage of these observations we possess are we actually using in queries of some kind? And then the third piece, how much value do we create from it? This is where ... This is a tough one, and it's really an estimation. It's, most likely what we need here is a new method for attributing the, profitabilty of a particular business unit to its use of that data. And I realize this is an estimation, but this is, there's a precedent for this in brand valuation, this is the coin of the realm when you're talking about putting a value to intangible assets. >> Well, as long as you're consistently applying that methodology across your portfolio, then, then at least you've got a relative measure and you can get back to prioritization, which is a key factor here. Is there an underlying technical architecture that has to be in place to take advantage of all this data capital momentum? >> There is, there is, companies are moving toward a hybrid cloud, big data architecture. >> What does that mean? >> It means that almost all the buzzwords are used, and we're going to need new ones. No, what it means is that, companies are going to find themselves in a situation where some of their computing activities, storage, processing, application execution, analytics, some of those activities will take place in a public cloud environment, some of it will take place within their own data centers, reconfigured to act as private clouds. And there are lots of potential reasons for this. There could be, companies have to deal with, not only existing regulations, which sometimes will prevent them from putting data up into a cloud, but they are also going to have to deal with regulatory arbitrage, maybe the regulations will change, or maybe they've got agreements with partners that are embodied in service level agreements that again require them to keep the data under their own observation. Even in that case, even in that case, the business still wants to consume all of those computing resources inside the data center as if they were services. The business doesn't care where they come from. And so this is one of the things that Oracle is providing, is an architecture for Oracle public cloud, and private cloud in the data center. It is the same on both sides of the wire. And in fact, can even be purchased in the same way so that even these, this Oracle cloud at customer, these machines, they are purchased on a subscription basis, just as public cloud capabilities are. And the reason this is good is because it allows IT leaders to provide to the business, computing capabilities, storage capabilities, you know, as needed, that can be consumed as services, regardless of where they come from. >> Yeah, so you've got the data locality issue, which is speed of light problems, you don't want to move data, then you've got compliance and governance, and you're saying, that hybrid approach allows you to have the cake and eat it, too. >> Yeah. >> Essentially. Are there other sort of benefits to taking this approach? >> Well, one of the, you know, the, one of the other pieces that we should talk about here is the big data aspect, and really what that means is, that, relational, Hadoop, NoSQL, graph database, repositories, they're all going to, they're all peers. They're all peers now, and, you know, this is Oracle's perspective, and as I'm sure you know, Oracle makes a relational database, it's very popular. Yeah, we've been doing it for a while, we're pretty good at it. Oracle's perspective on the future of data management is that Hadoop, NoSQL, graph, relational, all of these methods of data management will be peers and act together in a single high-performance enterprise system. And here's why. The reason is that, as our customers digitize and datify more of their activities, more of the world, they're creating data that's born in shapes and formats that don't necessarily lend themselves to a relational representation. It's more convenient to hold them in a Hadoop file system, and it's more convenient to hold them in just a great big key value store like NoSQL. And yet, they would like to use these data sources as if they were in the same system and not really have to worry about where they are. And we see this with, we see this with telecom providers who want to combine call data records with customer, warehouse, you know, customer data in the data warehouse. We see it with financial services companies who want to do a similar thing of combining research with portfolio investments records of what their high net worth customers have invested, with transaction data from the equities markets. So we see this polyglot future, the future of all of these different data management technologies, and their applications in the analytics built on top, working together, and existing in this hybrid cloud environment. >> So that's different than the historical Oracle, at least perceived messaging, where a lot of people believe that Oracle sees its Oracle database as a hammer, and every opportunity is a nail. You're telling a completely different story now. >> Well, it turns out there are many nails. So, you know, the hammer's still a good thing, but it turns out that, you know, there are also brads and tacks and Philips and flathead screwdrivers too. And this is just one of the consequences of our customers creating more kinds of data. Images, audio, JSON, XML, you know, spectrographic images from drones that are analyzing how much green is in a photograph because that indicates the chlorophyll content. We know, we know that our customers' ability to compete is based on how they create value from data capital. And so Oracle is in the business of making the things that make data more valuable, and we want to reinvent enterprise computing as a set of services that are easier to buy and use. >> And SQL is the lowest common denominator there, because of the skill sets that are available, is that right or? >> Well, it's funny, it's not necessarily a lowest common denominator, it turns out it's just incredibly useful. (laughs) Sequel is not just a technology standard, it's actually, in a manner of speaking, it's sort of a thinking standard. SQL is based on literally hundreds of years of hard thinking about how to think straight. You can trace SQL back to predicate logic, which was one of the critical ideas in the renaissance of mathematics and logic in the 1800s. So SQL embodies this way to think about, to think logically, to think about the attributes of things and their values and to reason about them in an automated fashion. And that is not going away, that in fact is going to become more powerful, more useful. >> Business processes are wired to that way of thinking, is what you're saying. >> That's exactly right. If you want to improve your operational effectiveness as a company, you're going to have to standardize some of your procedures and automate them, and that means you're going to standardize the information component of those activities. You can automate them better. And you're going to want to ask questions about, how's it going? And SQL is incredibly useful for doing that. >> So we went way over our time, this is very interesting discussion, but I have to ask you, what is it you do at Oracle? Do you work with customers to help them understand data strategies and catalyze new thinking? What's your day-to-day like? >> Yeah, I do a lot of this, a lot of telling the story, because we're in a huge time of change. Every 20 years or so, the IT industry goes through an architectural shift, and that changes, not just the technologies used to create value from data, but it changes the very value created from data itself. It changes what you can do with information. So, I spend a lot of time explaining these ideas of data capital, and sitting down with executives at our customers, helping them understand how to look out at the world and see the data that is not there yet, and what that means for the way that they compete, and then we talk through the competitive strategies that follow from that, and the technical architecture required to execute those strategies. >> Excellent. Well, Paul, thanks very much for sharing your knowledge with our Cube audience and coming into the Silicon Angle Media Studios here at Marlborough. >> Well, it's my pleasure. Thanks for having me. >> All right, you're welcome. Okay, thanks for watching, everybody. This is The Cube, Silicon Angle Media's special on the ground production. We'll see you next time. (peppy synth music)