(bright upbeat music) >> Hi well, this is Dave Vellante. We're digging deeper into the world of database. You know, there are a lot of ways to skin a cat and different vendors take different approaches and we're reaching out to the technologists to get their perspective on the major trends that they're seeing in the market, 'cause we want to understand the different ways in which you can solve problems. So look, if you have thoughts and the technical chops on this topic, I'd love to interview you. Just ping me at at DVellante, on Twitter, a lot of ways to get ahold of me. Anyway, we recently spoke with Andrew Mendelsohn, who is Oracle's EVP and he's responsible for database server technologies. And we talked a lot about Oracle's ADW, Autonomous Data Warehouse. And we looked at the cloud database strategy that Oracle is taking and the company's plans and how they're different maybe from other solutions in the marketplace, but I wanted to dig deeper. And so today we have two members of Mendelsohn's team on The Cube, and we're going to probe a little bit. George Lumpkin, is the Vice President of Autonomous Data Warehouse. And Neil Mendelson is the VP of Modern Data Warehouse, that business for Oracle. They're both 20-year veterans of Oracle. When I reached out to Steve Savannah, who's a colleague of mine for many years, he's always telling me how great Oracle is relative to the competition. So I said, okay, come on The Cube and talk about this, give me your best people. And he said, whatever these two don't know about cloud data warehouse, it isn't worth knowing anyway. So with that said gentlemen, welcome to The Cube. Thanks so much for coming on. >> Thank you. >> Hey, glad to be here. >> So George, let's start with you. And maybe we could recap for some of the viewers who might not be familiar with the interview that I did with Andy. In your words, what exactly is an Autonomous Data Warehouse? Is this cloud native? Is it an Oracle buzzword? What is it? >> Well, I mean, Autonomous Data Warehouse is Oracle's cloud data warehouse. It's a service that built to allow business users to get more value from their data. That's what the cloud data warehouse market is. Autonomous Data Warehouse is absolutely cloud native. This is a huge misconception that people might have when they first sort of hear about something, this service because they think this is a Oracle database, right? Oracle makes databases. This is the same old database I knew from 10 years ago. And that's absolutely not true. We built a cloud native service or data warehousing built it with cloud features. You know, if your understanding of the cloud data warehouse market is based upon how you thought things look 10 years ago, like Snowflake wouldn't have even existed, right? You can't base your understanding of Oracle based upon that. We have a modern service that's highly elastic, provides cloud capabilities like online patching and it's fully autonomous. It's really built the business users so they don't need to worry about administering their database. >> So I want to come back and actually ask you some questions about that, but let me follow up and talk about some of the evolution of the ADW. And where did you start? I think it was 2018, maybe where you came from, where you are today, maybe you can take us through the technological progression and maybe the path you took to get here. >> And so 2018, was when we released the service and made generally available, but of course, you know we started much earlier than that. And this was started within my product management team, and other organization. So we really sat down with a blank sheet of paper and we said, what should the data warehouse in the cloud look like? You know, let's put aside everything that Oracle does for its on-prem customers and think about how the cloud should be different. And the first thing that we said was, well, you know, if Oracle writes the database software, and Oracle builds its own hardware, and Oracle has created its own cloud, why do we need customers to manage a database? And that's where the idea of autonomous database came from. That Oracle is managing the entire ecosystem. And therefore we built a database that we believe it's far and away the simplest to use simplest data warehouse in the market. And that's been our focus since we started with 2018. And that continues to be our focus, looking at more ways that we can make an Autonomous Data Warehouse as simpler and easier for business users to get more value out of their data. >> Awesome, one more question. And actually Neil, you might want to chime in on this as well. So just from a technical perspective, you know forget the marketing claims and all the BS. How do you compare ADW to the so-called born in the cloud data warehouses? You mentioned Snowflake, you know Redshift, is Redshift born in the cloud. Well, it was par XL but Amazon's done some good work around Redshift. I think big query is maybe probably a better example 'cause it was, you know, like Snowflake started in the cloud but how do you compare ADW to some of these other so-called born in the cloud data warehouses? >> I think part of this, you mentioned Redshift wasn't important in the cloud. It was, you know, a code base taken from a prior company that was on-premise company. So they adapted it to the cloud, right? And you know, we have done, as George said, much of the same, which is, you know, our starting point was not you know another company's code base, but our starting point was our own code base. But as George said, it's less about the starting point and it's more about where you envision the end point, right? Which is that, you know, whatever your starting point is, I think we have a fundamental different view of the endpoint. Amazon talks about how they're literally built for you know, a cloud built for developers, right? You know, builders, right? And you know Oracle wasn't first in the infrastructure business, we entered through applications business. And all of a sudden, you know we began taking on 100s of 1000s and 100s of even more customers that were SAS customers. Underneath was the database and all the infrastructure. One of the things that we took away from that was that we couldn't possibly hire enough people DBA, to manage all the infrastructure below our applications customers. So one of the things that influenced this is that, you know customers expect SAS applications to just take care of themselves, right? So we had to essentially modify the infrastructure to allow it to do so as well, right? And we're bringing that capability to those people who, you know, may or may not have an application, but their interest is, you know more of this self-service agility type of aspect. >> So it seems to me and Georgia was sort of alluding to this before. I mean, when you mentioned Snowflake a couple of times, and then Neil, something you just said, I'm going to pick up on is you've been around for a long time. And you know, when I talked to the Snowflake people, they know Oracle, a lot of them came from Oracle. They understand I think how you can't just build Oracle overnight and build in the capabilities that Oracle has and the recovery. And you talk to customers and you know you are the gold standard of, you know especially mission critical databases, so I get that. But now you just sort of hit on it, is it takes a lot of people and skill to run the database. So that's the problem that you're saying you were attacking, is that, am I getting that right? >> Right, right, so the people that you talked about who originally built Snowflake came from Oracle, but they came from Oracle more than a decade ago. So their context is over a decade old, right? In the meantime, we've been busy, you know building a economies and many other capabilities, right? Their view of Oracle is that view that was back more than 10 years ago, right? They're still adding capability. So a really good example of this illustration is Oracle as you said, it's the most capable system that's out there and has been for many years. We've been focusing on how do we simplify that and how do we use machine learning embedded within the system itself? Because core to the concept of autonomous is that inside, is this machine learning system that's continually improving, right? That's the whole notion. Where in Snowflakes case, they're still adding functionality. Last year, they added masking which you know functionality they didn't have, but when they added the capability, they added it without, you know, the ability for a business user to actually take advantage of it. There's no capability for a business user to actually find the information that needs to be masked. And then after the information is found, you require a technical person to actually implement the mask. In Oracle's case, we've had masking and those capabilities for a long time, our focus was to be able to provide a simple tool that a business user can use that doesn't need technical or security experience. Find the data that needs to be masked PII data, and then hit a button and have it masked for you. So, you know, they're still, you know, without this notion of a strategy to move toward the system to heal itself and to manage itself, they're just going to continue. As they continue to add more capability, they will in turn add more complexity. What we're trying to do is take complexity out while others are adding it in, its an ironic twist. >> It is an ironic twist. It is interesting to look at it. And I don't want to make this about Snowflake. But I mean, Hey, I like what they're doing. I like them. I know the management, they're growing like crazy and you know and the customers tell me, hey, this is really simple. And it's simple by design. I mean, to your point over time it's going to get, you know, more and more complex. I was talking to Andy, I think it was Andy. He was saying, you know, they've got the different sizes you've got to shape some, you know, they call it t-shirt sizes. And I was like, okay, I got a small, I got a medium and a large, maybe that's okay. But you guys would say, we give more granular you know, a scaling, I guess is the point there, right? I mean George, I don't know if you can comment on that. It just a different strategy. You've got a company that was founded well, I guess, 2015 versus one that was founded in 1977. So you would think the latter has, you know way more function than the former, but George, anything you'd add to this conversation? >> Yeah, I mean, I'm always amazed that there are these database systems that are perceived as cloud native and they do things like sell you database sizes by t-shirt sizes, as you described. I mean, if you look at Snowflake, it's small, medium, large extra large too extra large, but they're all factors of two. You're getting a size of your database of two, four, eight, six, 32, et cetera. Or if you look at AWS Redshift, you're buying your database by the nodes. You say, how many nodes do you want? And in both those cases, this is a cloud native. This is saying we have some hardware underneath our database and we need you, Mr. Customer, to tell us how many servers you want. That's not the way the clouds should work, right? And I think this is one of the things that we did with Autonomous Data Warehouse. We said, no, that's not how the rules should work. We still run our database on hardware, we still have nodes and servers. We should tell the customer, how many CPU's you would like for your data warehouse? You want 16? Sounds good. You want 18? Yeah, we can give you 18. We're not, you know, we're not selling these to you in bundles of eight or bundles of six or powers of two. We'll sell you what you need. That's what cloud elasticity should be. Not this idea that oh, we are a database that should be managed by IT. IT already knows about servers and nodes. Therefore it's okay if we tell people your cloud data warehouse runs on nodes. Within Oracle as Neil said, we wouldn't. The data warehouse should be used by the people who want to actually analyze their data, should be used by the business users. >> Well, and so the other piece of cloud native that has become popular, is this idea of separating compute from storage and being able to scale those two independent of each other which is pretty important, right? Because you don't want to have to pay for a chunk of compute if you don't need the storage and vice versa. Maybe you could talk about that, how you solve that problem, to the extent that you solve that problem. >> Absolutely, we do separate compute print storage with Autonomous Data Warehouse. When you come in and you say, I need 10 CPU's for my data warehouse and I need two terabytes of storage. Those are two dependent decisions that you make. So they're not tied together in any way. And, you are exactly right, Dave, this is how things should work in the cloud. You should pay for what you need, pay for what you use, not be constrained by having big sets of storage you have to use for a given amount CPU or vice versa. >> Okay, go ahead Neil, please. >> Oh, just to add on to that, you know, the other aspect that comes into play is that, you know, so your starting point is X, whatever that happens to be. Over time that changes. And we all know that workloads vary right throughout the day throughout the month, throughout the year by various events that occur maybe the close of the year, close of business at the end of the quarter, it maybe you know, holiday season for retailers and so forth. So, you know, it's not only the starting point, but how do you actually manage the growth, right? scaling up and scaling down, right? In our case, we tried, as George said, we abstracted that completely for the customer basically said check a box, which has auto scale. So, if the system is required more resources, will apply more resources. And we do so instantaneously without any downtime whatsoever, right? Because you know, again, you know, people think in terms of these systems have now become business critical. So if the business critical, you can't just shut down to expand. Imagine during the holiday season is your business is ramping up. And then all of a sudden you have to scale, right? And your system either shuts down, reboots itself, right? Or it slows down to the point that it's a crawl and all your customers get frustrated. We don't do that. You click a button, auto scale and we take care of it for you smoothing out those lumps, right? Without any technical assistance. And again, if you look at Redshift, you look at all these various systems, they require technical assistance to be able to figure out not only your initial data, but how you scale out over time. >> Interesting, okay. So all is said, you know, a lot of companies are using Azure, AWS Google for infrastructure, why would these customers not just use their database? Why would they switch to Oracle or ADW? >> Well, I think Neil will probably add something. I want to start by saying a huge number of our existing Autonomous Data Warehouse customers today are customers of AWS and Azure. They are pulling data from AWS and Azure and bringing it into an Oracle Autonomous Data Warehouse. And we built feature Joe, I focused on product managers. We feel featured for that. And so it's perfectly viable and it it's almost commonplace, that the very largest enterprises to be doing that. But then coming to the question of why would they want to do it? I don't know, Neil, you want to take that? >> Yeah, yeah, so one of the things that we've really see emerge here is you know, a data warehouse doesn't generate the transactions on itself, right? So the data has to come from somewhere, right? And you ask yourself, well, where does the data come from? Well, in a lot of cases, that data is coming from applications and increasingly SAS applications that the company has deployed. And those are, you know, HR applications, you know, CRM applications, you know ERP applications and many vertical applications. In Oracle's case, what we've done is we say, okay, well, we have the application, this transactional thing, we have the infrastructure from the economist data warehouse, why don't we just make it really, really easy? And if you're an Oracle applications customer, that's already running on the Oracle cloud, we will essentially provide you the ability to create a data warehouse from that information, right? With a clicker, with largely either with a product and service or quick start kit. You don't start from scratch, you start from where you are. And there are many cases that where you are has data, very much as George mentioned before telcos, banks, insurance companies, governments, all of the data that they want to analyze, a lot of that data guess where it's coming from, it's coming from Oracle applications. So it makes sense to be able to have both the data that's generated and the data that's being analyzed close to the same place. Because at the end of the day, the payoff pitch for any form of analysis is not coming up with an insight, oh, I realized X, Y, Z, but it's rather putting the insight directly into production. And that's where, when you have this stuff spread all over God's greener trying to go from insight into action can take months, if not years. The reason that a lot of customers are now turning to us is that they need to be much more agile and they need to be able to turn that insight into action immediately without it being a science project. >> Okay, thank you for that. So let's tick them off. Like what are the top things that customers can get from Oracle Autonomous Data Warehouse, that they couldn't get from say a Snowflake or Redshift or Big query or SQL server or something yet. I appreciate you guys' willingness to talk about the competition. Let's tick them off. What are the most important things that we should know about that they can't get elsewhere? >> So first, I mean, we already talked about a couple of what we think are really the major themes of Autonomous Data Warehouse. The services is autonomous. You don't need to worry about managing it, anyone can manage the data warehouse. The service is elastic. You can buy and pay for what you use. You know, those are just what we think of as being the general characteristics of Autonomous Data Warehouse. But you know, when you come to your question of, hey, what do we give that other vendors don't provide? And I think the one angle that Autonomous Data Warehouse does a really good job is and Neil was just discussing this, it focuses on the business problems, right? We have years and years of experience with not just database security, but data security, right? You know, every cloud vendor can say, oh we encrypt all your data, we have these compliance certifications, all of these things. And what they're saying is, we are securing your database, we are securing your database infrastructure. At Oracle of course has to do those as well. But where we go further, is we say, hey, no, no, no, no, no, we know what business users want. They want to secure their data. What kind of data am I storing? Do I have PII data? Could you detect whether there's PII data and tell me about it in case some user loaded something that I wasn't aware of? What kind of privileges did I give my users? Can you make sure that those privileges are right? And can you tell me if users were given privileges that they're not using maybe I need to take them away. These are the problems that Oracle's tackled in security over the last 20 years. It's really more about the business problem. Yeah, some other, oh, go ahead. >> Oh, I'm sorry, I got so many questions for you guys. We'll get back to that 'cause it sounds like there's a long list. (laughs) >> We have nowhere to go.(laughs) I want to pick up with George on something you said about elasticity. Is it true pay by the drink? Do you have a consumption pricing? I mean, can I dial it up and dial it down whenever I want? How does that work? >> Yes, I mean not to be too many technical details, but you say, I want 14 CPU's that's what your database runs at. You can change that default number anytime you want online, right? You can say, okay, I'm coming up on my quarter end, I'm going to raise my database 20 CPU. We just do it on the ply. We just adjust the size--- >> What about the other way? What about coming down? Can I go down to one? >> You go down, you can go down to one--- >> And you're not going to charge me for 14 if I go down to one? >> No, if you set it down to one, you get charged for one, right? >> Okay, that's good, that's good. >> In the background, you know we are also allowing levels of auto scaling. You say, if you say hey, I want to charged for 14 and Oracle, can you take care of all those scaling for me? So if a bunch of people jump on at 5:00 PM, to run some queries, 'cause the executive said, hey, I need a report by tomorrow morning. We'll take care of that for you. We'll let you go beyond 14 and only charge you for exactly what you use for those extra CPU's beyond 14. >> Okay, thank you. Go ahead, Neil. >> And maybe, if we add, you know, Andy talked about this when he was on that show with you last week, right? And you know, he talked about this concept of a converged database, but let me talk about it in the way that we see it from a business point of view, right? You know, business users are looking to, you know ask a variety of questions, right? And those questions need to be able to relate to both you know, the customer themselves, the relationship that the customer might have with others. You know, today we talk about like the social network and who are influencers within that, and then where they actually conduct business. Which is really, you know, in every case, it's on some form of increasingly on a mobile device. So in that case, you want to be able to ask questions, which is not only, you know, who should I focus on, but who are the key influencers within this community, right? That could influence others? And does that happen in a particular place in time? Meaning, you know, let's say pre COVID, it might happen at a coffee shop or somewhere else. We can answer all of those questions and more inside of the autonomous system without having to replicate the data out to one system that does graph and another system that does spatial, a third system that does this. It's like a business user. It's like, wait a minute, come on, you're trying to tell me that I need a separate system and replicate the data just be able to understand location? The answer in many cases is yes, you have to have separate, which a business person says, well, that's absurd. Can't I just do this all in one system? You can with Oracle. >> So look, I'm not trying to be the snarky journalist or analyst here but I want to keep pushing on this issue. So here we are, it's 2021. It's April. We're like a third of the way through the year. And so far, nobody has come out and said, okay, we're going to deliver Autonomous Data Warehouse just like Oracle. So I asked myself, well, why is Oracle doing this? You guys answered, you know, to reduce the labor cost. But I asked myself, is this how they're solving the problem of keeping relevant a database that spans five decades? And you guys said, no, no, this is cloud native born in the cloud, you know started essentially with a new mindset. But is this a trend that others are going to follow? You know, and if so, why haven't we seen it this idea of a self-driving databases? Why is it right now unique to Oracle? What's really going on here? >> So I think there's a really interesting thing that's happening, it's not visible outside of Oracle. It's very visible for those of us who work inside of the development organization. You know, if you look at Oracle, I can tell you bad. I mean, I think it's safe to presume Oracle has the largest database development organization on the planet, right? I mean, it was kind of the largest database or large most used database for the past two decades. And what's happened is we pivoted to building a cloud platform. We're not just building a database, we're taking all of these resources that we have with all these expertise of building database software. We were saying, we now have to build the platform to run and manage the database software in the cloud, right? And it's a little bit like, you know I think to make people relate to it a little better, there was a really good quote from Elon Musk couple of years ago, talking about Tesla. Like everyone looks at the car, right? Tesla, the car is really great. The hard part of this, is building the factory, and that's analogy holds for Oracle. What we're building is the cloud battery. And what we have transitioned is our database development organization is now building as robust a cloud as possible. So that you know, when we increase the number of databases by 10 X, we don't add 10 X, more cloud ops people to manage it. We are ramping up developer building features to automate the management of our cloud infrastructure. And with that automation, we get better ability, less errors, more security. We give benefits to our cloud data warehouse customers with it. And I think this something really important to realize, right? We build database software. We build, you know, an engineered system built for databases called exit data, and we build a cloud platform. And these are really equal tiers in what we are building and developing today in 2021 from Oracle database development organization. >> Well, you mentioned exit data, I want to shift gears here a little bit and talk about we're seeing this hybrid cloud on-premises clouds, they're finally gaining some traction. I got to give props Oracle's cloud of customers really the early to that game. I think it was the first in my view anyway, true same same vision, took you guys a little while to get there but it was the right vision. And the thing I always say about Oracle people don't understand is Oracle invest in R and D, your chairman is also the CTO. You guys are serious about technical investment so you know, that's where innovation comes from. But, and we heard during your recent earnings call, we heard some positive comments on this. So what's your take on delivering autonomous data warehouse on-prem and how do you compare with say Snowflake and AWS in that area? Snowflake, Frank Slootman, I've had him on record saying we're not going to do that halfway house. Forget it, we are always going to be in the cloud. We're never going to do an on-prem installation. AWS, we'll see to date. Yeah, I don't think you can get a Redshift for instance in outposts, but maybe that'll come. But, how do you see that emerging? What's your difference there? Maybe Neil, you could talk about that. >> Yeah, so, you know, I think, you know, customers had a lot of regulated industries, right? Still have concerns about the public cloud. And I think that when you hear statements like, you know, we're never going to do, you know, on-prem. Well, economist cloud at customer, it's not a classic on-prem solution. What it is, it's a piece of our cloud delivered in your data center. It's still the cloud software. Oracle manages it, Oracle, you know, the system itself manages itself and we take care of that responsibility so you don't have to. The differences is that we can make that available in a public cloud as well as in a private cloud, right? And there are so many use cases, you know, that you can imagine from a regulatory point of view, or just from a comfort point of view, where customers are choosing, they want the ability to decide for themselves where to place this stuff as compared to only having one option, right? And you know, you look at a lot of what's happening in the emerging world where, you know, there are a lot of places in the world that may not have, you know, really really high-speed internet connections to make, you know a public cloud feasible. Well, in that case, whether you're talking about, you know an oil rig or you're talking about something else, right? We can put that capability where it needs to be close to the operation that you're talking about, irrespective of the deployment option. >> Well, let me just follow up on that because I think it's interesting that, you know Frank Slootman said that to me, I oftentimes around AWS I say, never say never 'cause they'll surprise you, right? And I've learned that with Andy Jassy, but one of the things that seems difficult for on-prem, would be to separate that compute from storage because you have to actually physically move in resources. I think about Vertica Xeon mode. It's not quite the same, same. So, I mean, in that regard, maybe you're not the same same. And maybe that dogma makes sense for some companies. For Oracle, obviously you've got a huge on-prem state, thoughts on that. >> So, you know, clearly, you know, so typically what we'll do is that we'll provide additional hardware beyond what the customer might expect and that allows them to use the capabilities of expansion, right? We also have the ability to allow the customer to expand from their cloud of customer into the public cloud as well, of which we have a lot of those situations. So we can provide a level of elasticity, even on-premises by over provisioning the systems, well not charging the customer until they use only based on what they consume, right? Combined together with the ability for us to augment their usage in the public cloud as well, right? Where others, again are constraint, right? Because they only have a single option. >> Right, well, you've got the capital resources to do that as well which is not to be overlooked. Okay, I mean, I've blown our time here but you guys are so awesome. (laughs) I appreciate the candor. So last question and George, if you want to throw in a couple of those other tick boxes, you know the differentiators, please feel free, but for both of you, if you can leave customers with the one key point or the top key points on how Oracle Autonomous Data Warehouse can really help them improve their business in the near term, what would they be? Maybe George, you could start and then Neil you bring us home. >> Yeah, I mean, I think that, as I said before, our starting point with Autonomous Data Warehouse, is how can we build a better customer experience in the cloud? And I think, and this continues throughout 2021, and I think that the big theme here is the business users should be able to get value directly from their data warehouses. We talked a few times about how a line of business user should be able to manage their own data, should be able to load their own data warehouse, should be able to start to work with their own data, should be able to run machine learning, model of build machine learning, models against that data and all of that built in, and delivered in Autonomous Data Warehouse. And we think that this is, you know we see our customer organizations large and small, the light bulbs starting to go on how easy the services to use to and how completed it is for helping business users get value from their data. And just adding onto what George said, you know, the development organization has done a tremendous job of really simplifying this cooperation. What we also tried to do that on the business side. You know, when a customer has an on-prem situation, they're looking at moving to the cloud, whether lift and shift or modernized, they're looking at costs, they're looking at risk and they're looking at time. So one of the things we look at is how do we mitigate that? How do we mitigate the cost, the risk and the time? Well, this week, I think we announced our new cloud lift program and the cloud lift program is what Oracle will provide to its cloud engineering resources around the world is that we will do, we will take the cost, the risk and the time out of the equation and Oracle will work directly with the customer or the customer's partner of choice, maybe an Accenture or Deloitte, and we will move them, right? You know, at little or no cost, most cases there's no cost whatsoever, right? We mitigate the risk because we're taking the risk on. And we've built a lot of automated tools to make that go very quickly, right? And securely, and then finally, we do it in a very very short amount of time as compared to what you would need to do with, you know 'cause there is no Redshift on-premises. There is no Snowflake on-premises. You have to convert from what you already have to that, right? And, but the company beyond the technological barriers that George talked about were also trying to smooth the operation so that a business itself can make a decision that not only did they not need the technical people to operate it, they won't need an entire consulting contract with millions of dollars in order to actually do the movement to the cloud. >> Well, guys, I really appreciate you coming on the program and again, your candor to speak openly about you know, your approach, the competitors. And so it's great having you, really really thank you for, for your time. >> Appreciate it. >> And thank you for watching everybody. Look, if you guys want to come back, go toe to toe with these guys, say the word you're always welcome to come on The Cube. One thing for sure, Oracle are serious, when it comes to database. Thank you for watching. This is Dave Vellante. We'll see you next time. (bright music)

(downtempo beats) >> Hi, I'm Peter Burris. Welcome to The Cube. We're having a conversation with Oracle about how to create business value out of data. This is the second segment that we're going to be looking at the first segment focused on, "what is data value "and how does a business "think about generating value with data?" This section is going to focus more on and what path do you follow? What journey do you take, to actually achieve the outcomes that you want using data to generate overall better business working with customers, with operations, whatever else it might be. Now to have that conversation, we've got Neil Mendelson with us today. >> Hey Peter. >> Neil is the vice president of big data advance analytics in Oracle. Welcome to The Cube. >> Thank you, good to be here. >> So Neil, in the first section, in the first segment that we talked about, the idea of "what is data value?" How do we think about data capital, how we think about how business uses data capital to generate business. No we're going to get practical and talk about the journey once the business is thinking about using data differently to differentiate itself, it then has to undertake a journey to do something. So, what's the first step that a business has to think about as it starts to conceive of the role that data's going to play in their business differently? >> Well I think, you know, you correctly tagged it as being a journey and starting with the business. I think part of where sometimes this goes awry is when we start with the technology first, right? It really begins with the business, right? So we're starting really with business analysts, people within the line of business, and what we're looking for is things that we can actually measure, right? Things that we can measure and quantify that drive a real value to the business. >> But those things are specific outcomes and have a consequence to the business right? >> They are specific, right? So it's not like, "Oh, we're interested in improving our overall business." That's not specific enough, right? You can't give a data scientist the charter to go build an algorithm for improving the overall business, right? It's got to be much more specific. So, let's say we're going to pick something like churn, right? And even down to churn to a particular segment, right? So you want to specific measurable outcome and then you want to be able to understand which executive in the business actually owns that outcome. Because if you can't find the executive that owns the outcome, then it may never really matter, right? >> Now that's all the business analysts job, is try to make sure that the question's being framed properly and that the right people are participating in the process of answering the question. Do I got that right? >> Correct, and that the outcome that you hope to achieve is material enough to make a difference in the business, and the key executive that's responsible for that cares and knows about the endeavor that you're embarking upon. >> So a material outcome that's not so abstract, like the business, but also not so pedantic as, change the air filter on time. That is then, has clear measures associated with it where you can test whether or not you have achieved the outcome, and an executive identified that ultimately has responsibility for improving those metrics in the business. >> Exactly. >> Okay, what's the next step? >> So the next step on the journey is to look at how you can pull together the data necessary to begin to answer that question. So, that brings in the data engineer, we used to call them "data wranglers," and you're beginning to look at, "what kind of data, right, can I obtain "from inside of the business or outside "that is material to answering that question?" Now sometimes what happens is that you end up finding out that we're not capturing that key information. And you've got to go back to the business analyst again and say, "Hey, we could begin a process "to begin capturing that information." But you know, is there something else you know, what's priority number two? What's the next thing on the list, in an agile-type method, that we could go to, let's see if that data is readily available, because one of the things you want to do, obviously, is create as much success early in the process as possible so things that will elongate this whole process, right, like, now I have to invent a whole way to collect data in order to actually examine it, maybe we ought to move on to the next material measurable outcome to the business and then go examine that. >> So we're really tryin' to develop habits here and habits don't form if the process of getting even started is just too difficult and there is no success. So, identify the outcome but then the data engineers response look for, "what's the data "and can we economically gather it and acquire it?" >> Right, and not just economically gather it, but can we legally, alright, gather it. Because, just because we have it doesn't necessarily mean the intended use that we look to put for it is one that either would pass regulatory control or policy of the company. So, that's important as well, you don't want to get too far down the line only to find out that what you're pursuing is something that your company is not comfortable yet doing. Even if there's an adjacent company that's doing exactly the same thing. >> Right, so we've got the outcome, we've got the measures, we got the executive support, we've also got the data, we've determined we can economically and legally, and ethically acquire it, what's next? >> So, next we're going to, the business analyst is going to collaborate together with both the data wrangler, we got the data, and now the data scientist or the mathematician, alright, gets involved. And what you're beginning to do is to begin to look at the data that's been derived and for the business analyst, looking at it is more of a visual metaphor, and for the data scientist, looking at it is more from a quantitative point of view. And you want to spend enough time to understand that you're now looking at the data and some of your original assumptions about the data and about your business are actually holding true, because it's possible at that point that you find out that your original assumption that you're working toward, toward changing this outcome, needs to actually shift a little bit because what you thought was happening is actually different, right? We're working with a Japanese financial services company and they thought that a lot of their business was essentially coming from younger people that are comfortable using computers and it turned out that there was a much older demographic that was actually using their systems than they thought. So, sometimes you have to rejigger, and you have to be open to being agile not to be so fixed on that particular outcome. You know, the data itself and being able to initially examine the data might shift you a little bit left or a little bit right. You got to be open to that. >> So this process has allowed us to, started putting in place in the habits to be empirical, iterative, optimistic, around data. We've actually now got the data scientists has started building out the data models, we've even started the process of training those models getting them up to creating some value, and improving and refining them over time. But where the industry sometimes falls down, is now you get a bunch of technology people involved who say, "Oh, I want to do this without anybody else knowing about it, "I'm going to download a bunch of open-source software "I'll go secure some stuff over here, some capacity, "maybe in the cloud or "maybe I'll just borrow some cycles somewhere." And we end up in this 12, 15, this long process of trying to implement a technology. Let's now talk about how we take the habits that are being formed, the outcomes we want to achieve, this working group that's actually making progress, and then turn that into a practical solution in the business. >> So just as you said, what we're starting with is trying to become, specific in terms of our outcome, is to be able to make sure that it's measurable and to be agile in our process. Where time, right, is an important factor. Costs is an important factor, time is a factor, and so for is risk, right? And when it comes to building the technology platform necessary to enable all this, time, cost, and risk are still factors, right? So starting off with trying to build everything yourself, from a technological point of view, doesn't make a lot of sense anymore, right? The value that you're going to get from the business is not by assembling computers into racks, right? People've done that stuff for you, right? It's not about taking, you know, any kind of software and integrating it together to the extent that you can get higher level components and begin working with those, that will give you the ability to turn that data into actual monetary value faster, right? So don't take the time necessarily, all the extra time necessary to assemble the stuff, see if you can already get it in a prepackaged form. >> So timed value becomes a primary criteria overall, 'cause in many respects, and certainly to what our research has shown, is that costs go up as you take longer, and risk goes up, at least in these complex kinds of initiatives, as you take longer because more people get involved and there's all kinds of crazy things happen, so the ability to stay agile and make things happen in a valuable way is crucial. Another thing we've seen Neil, I want to ask you about this, is we've talked to a number of CIOs who were making the observation that while there's a lot things, a lot of ways that they could procure stuff, that their shop itself has to go through some transformation and they are looking at how they can buy options on some of these changes right now and deliver value while setting themselves up for the future. What's the right way of thinking about that process? >> So it's easy for us sitting here in Silicon Valley to immediately jump to the conclusion that everybody just ought to move to the public cloud, right? And we're very much a huge proponent of that ourselves, right? In fact, we've transformed our business to essentially you know, to heavily weight entirely toward the cloud, right? And you know, there are real benefits in obviously doing that, right? When you're getting infrastructure in the cloud it's immediately available to you, you know, you don't have to pay for it all up front you can scale it over time, it has all those obvious benefits, right? But there are times when, either because of a governmental regulation, or because of a policy, your company policy, or because of just latency issues, it's not really possible to go to the public cloud. In which case you need to do that work behind your firewall. >> You need to bring the cloud to the data. >> Exactly, right? And as you said, even when that option is available, and in fact Oracle does have that option now available to customers, with this notion of cloud and customer, where we're literally taking a piece of the Oracle cloud and putting it behind your firewall. But, for some companies, that in and of itself, may be a leap too far. So, you know, being able to consolidate systems together, being able to move a more simplistic option, that gives you still that open ability to move to the cloud either on premises or in the public cloud over time is important to people. So, what we find is that, companies are looking for different paths, right? They may be looking to go directly to the public cloud, if they're comfortable to doing so, and if they're the kind of use case that they're working on is capable of doing that. Or, they may need to stay behind their firewall and entertain the notion of cloud a customer, or depending upon where they are in terms of their organizational readiness, they also may find that they'd rather move toward an engineered system or and appliance model which gives them the ability to move to the cloud when they're ready but doesn't force that seat-change on an organization that may not yet be ready for it. >> Right, so we're looking at a couple of different options predicated on the characteristics of the problem that we're trying to solve, the maturity of the shop that's trying to solve it, or the combination of the shop and the business, and then obviously, where we want to put our time and energy? Do we want to put it into the infrastructure? Or do we want to put it in solving the problem? And increasingly, people want to solve the problem. >> Well, in the end, that's what we're expected to do as a business. And that's the, some of the key differences, there's shifts that's happening in the IT or technology segment. Today, we have to be focused from a technologists point of view, and understand how we can help the business solve the problem. And technology is a means to that end, not a thing unto itself. >> So Neil, as you think form your perspective, in big data, in analytics, as you think about what the world's going to look like differently in three years, what is the one or two things you would focus your attention on if you were a CIO, and about to undertake this journey of finding new and better ways of turning data into value within the business? >> Well I think we mentioned a few, right? One, we want to make sure that we're driving it from a business perspective. We want to make sure that we have tangible outcomes that we've identified. We want to make sure that the data is more readily available for those use cases we want to pursue. And we want to make sure that the infrastructure that we put into play is appropriate, not only from a regulatory and policy point of view, but is good fit for where the organization happens to be at that time. >> And doesn't cut us off from future options. >> Exactly, it's important not to be able to invest in something that will become a dead end. And we're really working hard to ensure that at whatever place the customers are at in this journey that we can on-board them, right, in a place that they're comfortable with, but still allow them to move through the different stages as they see fit. >> Right, so, overall we've talked about the value of data, we've talked about some of the practical things that an IT shop with the business can do to achieve value in data, it doesn't diminish the role that the cause going to play, it positions it in the context of the nature of the problem, the nature of the shop. You know, this has been a great discussion. >> Neil: I've enjoyed it, thank you. >> So, once again, Peter Burris from The Cube, talking about the journey to creating value, business value, out of data with the appropriate combination of agile data methods, and an infrastructure approach that allows businesses to stay focus in the problem and not the infrastructure. Once again, thank you for joining us from The Cube, and we hope to see you again soon.

(dynamic music) >> Hi, I'm Peter Burris, welcome to The Cube. We're having a conversation with Oracle about how to create business value out of data. This is the second segment that we're going to be looking at, the first segment focused on what is data value and how does a business think about generating value with data. This section is going to focus more on what path do you follow, what journey do you take to actually achieve the outcomes, that you want, using data to generate overall better business working with customers, with operators, whatever else it might be. Now, to have that conversation, we've got Neil Mendelson with us today >> Thank you. >> Neil is the Vice President of Big Data and Advanced Analytics at Oracle, welcome to The Cube. >> Thank you, good to be here. >> So Neil, in the first segment that we talked about the idea of what is data value, how do we think about data capital, how we think about how business uses data capital to generate business. Now we're going to get practical and talk about the journey. Once the business is thinking about using data differently, to differentiate itself it then has to undertake a journey to do something. So, what's the first step that a business has to think about as it starts to conceive of the role that data's going to play in their business differently. >> Well, I think you correctly tagged it as being a journey and starting with the business. I think part of where sometimes this goes awry is when we start with it technology first, right? It really begins with the business, right? So, we're starting really with business analysts, people within the line of business. Now, what we're looking for is things that we can actually measure, right, things that we can measure and quantify that drive a real value to the business. >> But, those things are specific outcomes that have a consequence to the business, right? >> They are specific, right? So, it's not like, "Oh, we're interested in "improving our overall business." That's not specific enough, right? You can't give a data scientist the charter to go build an algorithm for improving the overall business, right? It's got to be much more specific. So, let's say we're going to pick something like churn, right, even down to churn to a particular segment, right? So, you want a specific measurable outcome and then you want to be able to understand which executive in the business actually owns that outcome. Because if you can't find the executive that owns the outcome then it may never really matter, right? >> Now that's all the business analyst's job is to try to make sure that the question is being framed properly and that the right people are participating in the process of answering that question. Have I got that right? >> Correct and that the outcome that you hope to achieve is material enough to make a difference in the business and that th key executive that's responsible for that cares and knows about the endeavor that you're embarking upon. >> So, a material outcome that's not so abstract like the business, but also not so pedantic as change the air filter on time that is then, has clear measures associated with it where you can test whether or not you have achieved the outcome and an executive identified that ultimately has responsibility for improving those measure in the business. >> Exactly. >> Okay. What's the next step? >> So, the next step on the journey is to look at how you can pull together the data necessary to begin to answer that question. So, that brings in the data engineer, we used to call them data wranglers. And, you're beginning to look at what kind of data, right, can I obtain from the inside of the business or outside that is material to answering that question. Now, sometimes what happens is that you end up finding out that we're not capturing that key information. And, you've got to go back to the business analyst again and say, "Hey, we can begin a process to being "capturing that information, but is there something else? "What's priority number two? "What's the next thing on the list "in an agile type method that we could go to? "Let's see if that data is readily available." Because, one of the things that you want to do, obviously, is create as much success early in the process as possible. So, things that will elongate this whole process, right? Like, now I have to invent a whole way to collect data in order to actually examine it. Maybe we ought to move on to the next material measurable outcome to the business and then go examine that. >> So, we're really trying to develop habits here. And, habits don't form if the process of getting even started is just too difficult and there is no success. So, identify the outcome, but then the data engineer is responsible for what's the data and can we economically gather it and acquire it. >> Right, and not just economically gather it. But, can be legally gather it because just because we have it doesn't necessarily mean that the intended use that we looked to put forward is one either would pass regulatory control or policy of the company. So, that's important as well. You don't want to get too far down the line only to find out that what you're pursuing is something that your company is not comfortable yet doing. Even if there's an adjacent company that's doing exactly the same thing. >> Right, so we've got the outcome, we've got the measures, we've got the executive support. We've also got the data and we've determined that we can economically and legally and ethically acquire it. What's next? >> So, next the business analyst is going to collaborate together with both the data wrangler, we've got the data, and now the data scientist or the mathematician, right, gets involved. And, what you're beginning to do is to begin to look at the data that's been derived and for the business analysts looking at it it's more of a visual metaphor and for the data scientist looking at it is more from a quantitative point of view. And, you want to spend enough time to understand that you're now looking at the data and some of your original assumptions about the data and about your business are actually holding true. Because, it's possible at that point that you find out that your original assumption that you're working toward, toward changing this outcome needs to actually shift a little bit because what you thought was happening was actually different, right? We were working with a Japanese financial services company and they thought that a lot of their business was essentially coming from younger people that are comfortable using computers. And, it turned out that there was a much older demographic that was actually using their systems than they thought. So, sometimes you have to rejigger and you have to be open to being agile, not to be so fixed on that particular outcome. The data itself and being able to initially examine the data might shift you a little bit left or a little bit right. You got to be open to that. >> So, this process has allowed us to, started to putting in place some of the habits to be empirical, iterative, opportunistic around data. We've actually, now got the data scientists have started building out the data models. We've even started the process of training those models, get them up to creating some value and improving and refining them over time. But, where the industry sometimes falls down is now you get a bunch of technology people involved who say, "Oh, I want to do this without anybody else "knowing about it. "I'm going to download a bunch of open source software. "I'll go secure some stuff over here, "some capacity, maybe in the cloud or maybe "I'll just borrow some cycles somewhere." And, we end up in this 12, 15 this long process of trying to implement the technology. Let's now talk about how we take the habits that are being formed, the outcomes we want to achieve, this working group that's actually making progress and then turn that into a practical solution in the business. >> So, just as you said what we're starting with is trying to become specific in terms of our outcomes, to be able to make sure that it's measurable and to be agile in our process where time, right, is an important factor, cost is an important factor, time is factor and so for is risk, right? And, when it comes to building that technology platform necessary to enable all this time, cost, and risk are still factors, right? So, starting off with trying to build everything yourself from a technological point of view doesn't make a lot of sense, anymore, right? The value that you're going to get from the business is not by assembling computers into racks, right? People have done this stuff for you, right? It's not about taking any kind of software and integrating it together to the extent that you can get higher level components and begin working with those that will give you the ability to turn that data into actual monetary value faster, right? So, don't take the time, necessarily, all the extra time necessary to assemble the stuff. See if you can already get it in a prepackaged form. >> So, time to value becomes a primary criteria overall. 'Cause in many respects, certainly what our research has shown, is that costs go up as you take longer and risk goes up, at least in these complex kinds of initiatives as you take longer, because more people get involved and there's all kinds of crazy things happening. So, the ability to stay agile and make things happen in a valuable away is crucial. Another thing we've seen here, I want to ask you about this, is we've talked to a number of CIOs who were making the observation that while there's a lot of things, a lot of ways they could procure stuff that their shop, itself, has to go through some transformation. And, they are looking at how they can buy options on some of these changes right now and deliver value while setting themselves up for the future. What's the right way of thinking about that process? >> So, it's easy for us sitting here in Silicon Valley to immediately jump to the conclusion that everybody just ought to move to the public cloud, right? And, we're very much a huge proponent of that ourselves, right? In fact, we've transformed our business to essentially heavily weight entirely toward the cloud, right? And, there are real benefits in obviously doing that, right? When you're getting infrastructure in the cloud it's immediately available to you. You don't have to pay for it all up front. You can scale it over time. It has all those obvious benefits, right? But, there are times when either because of a governmental regulation or because of a policy, your company policy or because of just latency issues, it's not really possible to go to the public cloud. In which case you need to do that work behind your firewall. >> You need to bring the cloud to the data. >> Exactly right? And, as you said, even when that option is available and in fact Oracle does have that option now available to customers with this notion of cloud a customer where we're literally taking a piece of the Oracle cloud and putting it behind your firewall. But, for some companies, that in and of itself may be a leap too far. So, being able to consolidate systems together being able to move to a more simplistic option that gives you still that open ability to move to the cloud either on premises or in the public cloud over time is important to people. So, what we find is companies are looking for different paths, right? They may be looking to go directly to the public cloud if they're comfortable doing so and if the kind of use case that they're working on is capable of doing that. Or, they may need to stay behind their firewall and entertain the notion of cloud a customer. Or, depending upon where they are in terms of their organizational readiness they also may find that they'd rather move toward an engineered system or an appliance model which gives them the ability to move to the cloud when they're ready but doesn't force that sea change on an organization that may not yet be ready for it. >> Right, so we're looking at a couple of different options predicated on the characteristics of the problem that we're trying to solve, the maturity of the shop that's trying to solve it or the combination of the shop and the business, and then obviously, where we want to put our time and energy. Do we want to put it into the infrastructure or do we want to put it into solving the problem? And increasingly, people want to solve the problem. >> Well, in the end that's what we're expected to do as a business. And, that's some of the key differences or shifts that's happening in IT or technology segment. Today, we have to be focused, from a technologists point of view and understand how we can help the business solve the problem and technology is a means to that end not a thing unto itself. >> So Neil, as you think from your perspective in big data and analytics, as you think about what the world's going to look like differently in three years what is the one or two things that you would focus your attention on if you were a CIO and about to undertake this journey of finding new and better ways of turning data into value within the business? >> I think we mentioned a few, right? One, we want to make sure that we're driving it from a business perspective. We want to make sure that we have tangible outcomes that we've identified. We want to make sure that the data is more readily available for those use cases that we want to pursue. And, we want to make sure that the infrastructure that put into play is appropriate not only from a regulatory and policy point of view but is a good fit for where the organization happens to be at that time. >> And, doesn't cut us off from options. >> Exactly, it's important not to be able to invest in something that will become a dead end, right? We're really working hard to ensure that at whatever place the customers are at in this journey, right, that we can on board them, right, in a place that they're comfortable with but still allow them to move through the different stages as they see fit. >> Right, so over all we've talked about the value of data. We've talked about some of the practical things that an IT shop with the business can do to achieve valued data. It doesn't diminish the role that the cloud is going to play. It positions it in the context of the nature of the problem, the nature of the shop. Neil, this has been a great discussion. >> I've enjoyed it, thank you. >> So, once again Peter Burris from The Cube talking about the journey to creating value, business value out of data with the appropriate combination of agile data methods and an infrastructure approach that allows business to stay focused on the problem and not the infrastructure. Once again, thank you for joining us from The Cube and we hope to see you again soon. (dynamic music)

>> Announcer: theCUBE presents "On the Ground." (light techno music) >> Hello there and welcome to SiliconANGLE's theCUBE, On the Ground, here at Oracle's Headquarters. I'm John Furrier, the host of theCUBE, and I'm here with Neil Mendelson, the Vice President of Product Management for the Big Data Team at Oracle. Welcome to On the Ground, thanks for having us here, at Headquarters. >> Good to be here. >> So big data, obviously a big focus of Oracle OpenWorld, is right around the corner but in general, big data breadth of products from Oracle, has been around for awhile. What's your take on this? Because Oracle is doing very well with this new Cloud storing. My interview with Mark Hurd, 100% of the code has been cloudified. Big data now is a big part of the Cloud dynamic. What are some of the things that you're seeing out in the marketplace around big data, and where does Oracle fit? >> Well, you know, when this whole big data thing started years ago, I mean Hadoop just hit its 10th anniversary, right? Everybody was talking about throwing everything out that they had and there was no reason for SQL anymore and you're just going to throw a bunch of stuff together yourself and put it together and off you go, right? And now I think people have realized that to get the real value out of these new technologies, it's not a question of just the new technologies alone, but how do you integrate those with your existing estates. >> So Oracle obviously is a big database business, you know, I mean Tom Curry, with "Hey the database, take your swim lane", but what's interesting is with Hadoop and some of these other ecosystems, what customers are looking for is to not just use Oracle database but to use whatever they might see as a feature of some use case. >> Neil: Absolutely. >> Hadoop for batch. So you guys have been connecting these systems, so could you just quickly explain for a minute how you guys look at this choice factor from a customer standpoint because there's a role for Hadoop, but Hadoop isn't going to take over the whole world as we see in the ecosystem. What's your role, vis-a-vis the database choice? >> Yeah, so we very much believe when Oracle started, it was all about Database, and it was all about SQL. And we believe now that the new normal is really one that includes both Hadoop, NoSQL, and Relational, right? SQL is of course still a factor, but so are the ability to interface, in via rest interfaces and scripting languages. So for us, it's really a big tent, and we've been taking what we had done previously in Database and really extending that to Data Management over Hadoop and NoSQL. >> We had a great chat at Oracle OpenWorld last year, and you talked about your history at Oracle before you did you run with start-ups. You've seen this movie go on early days with data warehousing, so I got to ask you, big data's not new to Oracle, obviously the database business has been thriving and changing with the Cloud around the corner and certainly here on the doorstep but could you explain Oracle's Database, I mean, big data product offerings? >> Sure. >> What was the first product? Take us through the lineage of where it is, because you guys have products. >> We do. >> And a slew of stuff is coming, I can imagine, I'm sure you can't share much about that but talk about the lineage right now. >> Okay, so we started about three years ago on the Hadoop side by making an appliance made for Hadoop and then in the future, which followed on with Spark. And that appliance has been doing well on the marketplace for a number of years and we've obviously continued to enhance that. We then took what we perfected on premises and we moved that up to the Cloud, so we have a big data cloud service for customers that offer them high-performance access to Hadoop and Spark and without necessarily the need to actually manage security and all the things with it. At OpenWorld, we'll be making a series of announcements, we'll be creating yet another big data Cloud service. This one will be fully managed, fully elastic for customers who only want to take advantage of a Hadoop or Sparks service, as an example, and don't want to deal with the ability to specifically tweak the environment, right? We also announced a little while ago, our family of Cloud Machines, right? So you'll see, a, the first Cloud Machine is one that provides Oracle IaaS and PaaS services and then we'll add to the family. >> John: That's shipping already, though. >> That's shipping already, right? And then we'll add to the family, an Exadata Cloud Machine and a big data Cloud Machine and the Cloud Machines are really kind of a cool concept. They're cool because for a lot of customers from a regulatory point of view or otherwise, they're just not ready for the public Cloud, but everybody wants to take advantage of what the Cloud provides. So how do you do that behind your firewall, right? How do you provide IT as a service? So what Oracle has done essentially, is to package up its Cloud services and able to deliver that to customers behind the firewall and they get the exact same technology that they have on the public Cloud, they build to one architecture and then deploy it wherever they choose. They get the advantages of the Cloud, it's a subscription service, right, but they can deal with but they can adhere to whatever data sovereignty or issues that they might have. >> So let's get to that regulatory dynamic in a second but I just want to back up, so Big Data Appliance, B-D-A you guys call it, Big Data Appliance, that's been out. Big data service... >> Neil: Cloud service started about a year ago. >> Done a year, that's out there. Those laces that connect Appliance that's on-pem with the Cloud. >> Neil: Right. >> And then now you have the cloud machine series of enhancements coming in Oracle Openworld. >> Right, as well as a fully elastic, fully managed cloud service that will add to the mix as well >> Okay, so let's get down, so that's going to bring us fully cloud-enabled. >> Yep. >> Cloud on-premise, >> Both. >> All that kind of dynamic flexibility and an option for cloud configurations and depressuring. Okay, back to the regulatory thing. So what's the big deal about that, because you mentioned that most companies we talk to love the cloud, they love the economics, but there's a lot of fund and fear internally amongst their own team about getting sued, losing data, you know, certain industries that they might have to play, is that a fact and can you explain that for someone and what's important about that. >> Yeah I mean, for some customers it's a real concern, right, and the world is dynamically shifting, I mean, look at what happened a few months ago with you know the Brexit, right, I mean all of a sudden it was OK to have, you know, the data as long as it was in the EU, well the EU is now shifting, so where does the data go, right? So from a regulatory point of view we haven't fully settled in terms of where customer data can be held, exactly how its treated, and you know those things are evolving. So for a number of companies, they want the advantages of the cloud but they don't necessarily want it on the public cloud and that's why we're offering these new cloud machines because they can essentially have their cake and eat it too. >> So interesting, the dynamic then is is that this whole regulatory thing is a moving train. >> Right. >> Relative to the whole global landscape. >> Right. >> Who knows what's going to happen with China and other things, right? >> Right and I think that's what's really terrific is that our history is, of course, were a company that's been around for a while so we started on premises and we moved up to the cloud and our customers are ones that are going to have, kind of, this hybrid kind of a system, right. Other companies started much later and their cloud only and you know while that's great for companies that want the public cloud. What do you do if you're in a regulatory environment that isn't ready to boot public cloud? Now you have to have two architectures, one for on-premises and one for cloud and then how do you deal with a moving landscape where a year from now things that are on premises can move to the cloud and other things that are in the cloud may have to move to back on premises, right? How do you deal with that dynamic going forward and not get stuck. >> So, is it fair to say that Oracle is a big data player in the cloud and on-premise? >> Absolutely, and not just for data management. I think that you know while we started at that core, that's our heritage, we've so much built out our portfolio, we have big data products in the data integration space, in the machine learning space, we have big data products that connect up with our IoT strategy, with data visualization, we've really blossomed as the marketplace has matured bringing additional technology for customers to utilize. >> Okay, so let's get down to the reality and get into the weeds with customer deployments. How do you guys compare vis-a-vis the competition now you got the on-prem with the BDA, Big Data Appliance with the cloud service, cloud machines to create some provisioning, flexibility on whether architectures the customers may choose. >> Yeah. For whatever reason that they would have. >> Okay. How does that compare to the competition? >> On the on-premises side, if we start there, there was a recent Forrester Wave that looked at various Hadoop appliances and we took the number one category or the number one position across all the three categories that they looked at, they looked at the strategy, they looked at the market presence and they looked at the capability of what we offered and we ended up number one in that space. On the cloud side, of course, we're maturing in terms of that offering as well but you know we're really the only company out there that can offer the same architecture both on cloud and on-premises, where you don't necessarily have to go all in on one or the other, and for many companies that's exactly what they're, you know, what they need right. They can't necessarily go all in one way or the other. >> So I got to ask you kind of a, put your Oracle historian tech historian hat on as well as your Oracle executive hat on and talk about some of the technologies that have come and gone over the years and how does that relate to some of the things that are hyped up now? I mean certainly Hadoop, what's supposed to be this new industry, it's going to disrupt the database and Oracle's going to be put out of business and this is how people are going to store stuff, MapReduce. Now people are saying, why even have Hadoop in the cloud when you got object store. So, things come and go, I'm not saying Hadoop is going to come and go but it's good for batch but so, what's your comments on it can you point to industry technology, say okay, that's going to be a feature of something else, that's a real deal? What are some of the things that you look at that you can say... >> So you know we're seeing exactly as you described, a few years ago you go to a conference and it was all about MapReduce. Right now, a seminar in MapReduce, nobody goes, right. Everybody's going to Spark, right, and there's already things that potentially will replace Spark, things like Flink, and we're going to see that continual change and a lot of what we focused on is to be able to provide some level of abstraction between the customers architecture and these moving technology. So, I'll give an example. Our data integration technology, historically that was, you know, you're able to visually describe a set of transformations and then we generated code in SQL or PL/SQL. Now we generate code, not only in SQL and PL/SQL but we generate that same code in Spark. If tomorrow Spark gets replaced with Flink or something else, we simply replace the code generator underneath and all of what the customers built gets preserved and moved into the future. I think a lot of people are now becoming concerned that as they take advantage of open source really really at the very low levels they have the potential to essentially get stuck in a technology which has essentially become obsoleted, right? >> Yeah. >> As any new technology evolves we move from people who just code, right, with all the lower level stuff up to a set of tools and you know we talk to companies now that have huge amounts of now legacy MapReduce code, right, you think only a few years ago... >> It's kinds like cobalt. >> Neil: Yeah. (John laughing) >> Neil: So... >> I's going to be around but not really pervasive. >> Right. So how can you take advantage of these technologies, without necessarily having to get stuck to any one of them. >> So, I'm going to ask you the philosophical question, so Oracle database business has been the star over the years since the founding but even now it seems to me that the role of the database becomes even more important as you connect subsystems, call it, Hadoop, Spark, whatever technology's going to evolve as a feature of an integrated system, if you will, software-based and or engineered system coming together. So that seems to be obvious that you can connect in an open way and give customers choice but that's kind of different from the old Oracle. I have a database everything runs on Oracle, Oracle on Oracle's grade, certainly it runs well but what's the philosophy internally obviously the database team's sitting there it must be like, wow big data is an opportunity for Oracle. >> That's right. Or do they go, no the database business is different. How do you guys talk about that internally and then how do customers take away from that dynamic between the database crown jewel and the opening it up and being more big data driven? >> I think it's ironic because, externally, when you talk to people, they just assume that we're going to be like "Oh my god this is a threat" and we're going to just double down on what we're doing on the database side and we're just going to hunker down and I don't know try to hide, right? But that's exactly the opposite of what we're really been doing internally. We really have embraced these technologies of Hadoop and Spark and NoSQL, and we're essentially seeing data management evolve, that is the new normal. So rather than looking at, not only what we might have said, we did say when we introduced Oracle in the data warehousing market back in '95, We said "Put all your data in the Oracle database." We're not saying that anymore because there are reasons to put data in Hadoop, there are reasons to put data in graph databases, in NoSQL databases, we need to be able to provide those choice while still integrating that data management platform as one integrated entity. >> Would you say then it was fair to say that, from a customer standpoint, by having that open approach gives more faster access to different data types in real time? >> Absolutely. >> John: Then isn't that the core value proposition of big data. >> Yeah, again when the Hadoop new craze first started it was all about unload and put everything in this one store and for a lot of companies today, they still are faced with the this conundrum which says, in order to analyze data, I have to put it all in one place. So that means that you have to move your operational data into one place, you have to move your data warehousing stuff into one place, but then at the same time you mentioned real time. How do you get into the business of moving data from Place A to Place B on a constant basis while still being able to offer real-time access and real-time analytics? The answer is you can't. >> And the value of the data, the data capital, as we've been talking about, McGee bond is an IoT piece of data from a turbine could have really big relevance to the system of record in another database and that has to be exposed and integrated quickly to surface some insight about the quality of that... >> It's the thing that gives you context, right. Today what's going on is that we are getting all access to all these rich data sources and rich data types that we didn't have before, whether that's text information or information coming off sensors and alike, and the relevance of that information is, when we combined it together with the corporate information, the stuff that we have in our existing systems to really reap the true benefit. How do you know, when you get a log file the log file doesn't have anything about the customer in it, the log file just has a, a number associating itself to a customer. You have to tie that together with the customer profile which data which might not exist in Hadoop, maybe it's in a NoSQL store. >> And certainly the Open Source is booming with Oracle. You guys are actively involved in all the different open source ecosystems. >> Sure, we drive a number of open source projects whether it's MySQL or Java or, the list goes on and on. Many people don't think of, you know, they're not even aware that Oracle's behind my MySQL. As an example, right, I mean, I remember talking to my son recently he says, "Do you know anything about MySQL" and I'm like well a little bit. And then as we're talking and were looking through his code, finally I say, "You know this is Ooracle product," He's like no it's not. You know cause... >> It's too cool to be Oracle. >> That's right. That's not a bad thing, right. >> Yeah. I mean the reality of it is, is that you know we've invested a whole lot of time and energy in these technologies and we're really looking to commercialize them to mainstream them, to make them less scary for more people to be able to get value from. Well your son's example's a great illustration of the new Oracle that's out there now this whole new philosophy. Final, give you the last word real quick, for folks watching, what's one thing you'd want to share with them that they may or may not know about Oracle and it's big data strategy? >> Give us a look. Right, I mean I think that when you think of big data and you think of these new technologies, you may not think of Oracle, right. You may think of the new companies that you're more familiar with in the light. The reality of is, is that Oracle has an extraordinarily rich portfolio of technology and services on the cloud as well as like cloud machines. So give us a look, I think you'll be surprised at how open we are, how much of the open source technology we've embedded in our products and how fast were essentially evolving into, what is the new normal. >> Neil thanks so much for spending the with me here On the Ground. I'm john Furrier, you're watching exclusive "On the Ground" coverage here at Oracle Headquarters. Thanks for watching. >> Neil: Thank you.

>> Announcer: TheCube presents, On the Ground. (upbeat music) Hi everyone, welcome to this special On the Ground, Cube coverage here at Oracle headquarters. I'm John Furrier, the host of theCube, here with Chris Linskey, who's the Vice President, Product Management for Oracle Big Data. Welcome to On the Ground, good to see you. >> Thanks John, nice to meet you. >> So let's talk about big data, and the concepts going on now for analytics. What is going on in your mind around big data, and some of the ideas that with customers are kicking around, because the number one thing we hear is, I got to store the data. Solved, check, database, system of record. But now other databases are popping up. Different types of databases, you got graph databases, you've got unstructured databases. Do I run Oracle for all those? When do I use Oracle? When do I don't use Oracle? So the first question is, what are some of the obstacles that are facing the companies? Is it integration? Is it the choice? What's going on? >> There's a lot. There's a lot of interest in the market around big data. But in terms of companies that are actually using that in kind of a productized fashion to build competitive insight, are less than you would think, because of some of these obstacles. So, we look at it in a few different ways, and we try to tackle the obstacles at Oracle in each of these categories. One of the first big questions to solve, is what you raised. How do I manage the data? I've got a lot of gravity in my data warehouse and in my databases, but now I've got all this new content coming in. It might be social media. It might be log data. Things that you're not sure of the value, so it may not make sense to store in that enterprise data warehouse. That's really where customers are looking at alternate technologies like big data, like Hadoop, to give you both that cost savings, but also to give you specialized access, whether you're doing, like you said, spacial queries, or graph queries. Oracle can give you the right engine for the right job, but what's also important in that data management layer, is doing it in a way that breeds simplicity of ownership. If the cost of ownership is too expensive, no one's going to do that. So we also have an initiative called Big Data SQL, that let's you use that common Oracle database as your front-end, but then queried back to Hadoop, queried back to a spatial or graph engine. You can leave that data there, where it makes the most sense. >> I mean SQL on Hadoop for instance has proven that SQL is the language of most people querying. So, that's out there, so that's done. But it doesn't mean run relational databases all the time, but that's what people are interfacing into other databases. >> Chris: Yeah. >> Is that a pretext to what's really happening? Is that, interfacing to other data sets is really the more important than actually having whole new systems. Because that seems to be ... >> It's a bit of both. The way I look at it is, some companies look at Hadoop as just another data source. I've got some log data, some social data, let me put it in a place that's cost-effective to store. And there using your database as a front-end makes sense. Other customers look at Hadoop and big data more as a data platform, where they want to use that cluster, that compute environment, to do more than just query things and build a chart. And that's where you see some new technologies coming out. In Oracle we call it our data factory. That's around, how can I use all of that compute power to actually do data integration? Right, how can I keep up with that one hour of ETL window I'm given a night to deal with all these new sources? So we see people adopting Hadoop for ... >> That's a tough window, one hour is a tough window. If you're Wall Street, backing up. >> Yeah, some of it's tough. >> Talk about Data Lab. What is this concept that you have been kicking around called Data Lab? >> Exactly. >> What does that mean? >> So I think that's the third pillar. We talked about data management, giving you the right engine. We talked about data factory, giving you that integration capability. But, why go through all that effort, if not to start driving innovation? And that's what we think about as the Data Lab. It's a place where you can experiment with advanced analytics. It's a place where you can experiment with data mashup, and new data combinations. And you do it in a cost-effective way, and a way that breeds this notion of agility. You mentioned the word, system of record before. That's a very great description for the warehouse. You're not going to change your revenue definition, or your customer dimension, in the warehouse. That's what everyone uses. But Hadoop, people look at as a system of innovation. It sits alongside the warehouse. You can put a lot of that same data in there. Often you'll put data that never made it into the warehouse. So you get that big data variety, and then you can use that to come up with new ideas. So that's really the essence of the lab, is bringing in more data sets. Trying more combinations of data. And then also seeing if you can move beyond just descriptive and diagnostic analytics, into predictive. >> So let me just get this right. Factory is all the ingestion, Data Lab is like your, I'd say sandbox, my word. So system of record is the most important data. That's a customer name, a key variable for that, that's in the company's business model. So that's where all the hardcore data is. Social media data might be, hey, I'm geo, piece of geo data, and it's at retail store, says I'm going to buy something, or has local presence. Has my name, which is in the system of record. So, that data is in a different database. Has to go over there and get to the system of record. That's hard. That's actually a hard problem. >> Chris: It is. >> But that's a realistic thing that people want to take is this gestural data pieces, small data, that means something to the system of record, or some engagement data, cross-connected to system of record. Do you guys solve that problem? This is what people want to look for, right? >> We do. What's interesting is, that's an age old problem. We had that with data warehousing. We have it even more now with all the big data sources. And, I think the opportunity here is to decide who should solve that problem. Is it a scarce ETL developer that you have in IT. They have limited cycles. >> That's true. >> Do I have a data scientist? People actually use data scientists to do this sort of data integration work. It's hard to come up with a new predictive model if the data sets don't match up. And, its unfortunate, because that's the PhD guy. And, that's menial labor to a large degree. >> Hard to find PhD's, too. >> It is. I like to call them unicorns. You hear about them, you never really see them. And you definitely don't want the scientist doing that menial labor. The joke we say is that the data scientist has been turned into a data janitor, because of all these tasks that get put on their shoulders. So, we think at Oracle that's an opportunity. With this combination of data management, data factory, and Data Lab on top, you can actually push that work out to your business analyst teams. They can collaborate with IT. They can collaborate with your data scientist if you have them, but the spirit of the Lab is not ... >> So making the analysts and the business folks, make them like data scientists. >> Chris: Exactly. >> As functional as data scientists, without having them being ... >> One of the phrases in the industry is citizen data scientist, and I manage a product called Oracle Big Data Discovery, and that is really our goal. Can we build these very intuitive UI's, that make these analysts produce more output like a data scientist would. >> So what's the architecture to make that happen? Because I think that's right on the money. I think that's a great solution. I think and the example I used is just a small piece of data, but that's a database problem. So by abstracting out to another level with software, you can let people wire their own solutions together. I get that. How do you guys do that from an architecture standpoint? What do you say to customers? How do I do this? What's the playbook? >> It's a good question, because at its core, there's no reason to go about solving this problem, unless it works at the big data scale, right? If you can't analyze petabytes, terabytes of content, you would use a regular BI solution. There's no reason to move over to big data. So, a key aspect of the architecture is scale. But also if you're going to support these analysts, they're not happy if they click on the screen and then they wait five minutes for something to come back. So, interactivity performance is critical too for this user base. Because of that, in products like BDD, and really across a lot of our different initiatives, Apache Spark has become a key piece of our architecture. And that's something you might not expect from Oracle, that we're moving into open source, adopting a lot of those technologies, but we really do see the value of Spark. >> So I asked Neil Mendelson just today the question, where he sees the market going. So I want to ask you a little bit different question, but same question on a different task. What's the next big thing? Because we are on the front end of this really pioneering analytics mindset. >> Chris: Yep. >> Horizontally scalable data sets. Software value propositions, applied to data as currency, if you will. Soon data will be on the balance sheets. Some say, certainly the analysts at Wikibon are saying that, some day it should be an asset class. >> Chris: Data capital is a phrase we use. >> Data capital, love that. And so that is a trend, that could be right around the corner. But that's where it's going. What's the next big thing to get us there? >> I think the first hurdle was just making sense of big data. It took organizations a couple years just to get their head around that, and to build that architecture, so it will scale and people will adopt the system. I think the opportunity now is, at least as we see it in our analytic portfolio is, you've got these users on the system. You've got these Hadoop clusters in place. What can you do with that power? And, we think the big opportunity, especially as we create these data scientists, these citizen data scientists, is machine learning. How can we embed, especially the Spark machine learning libraries, into our products more natively? Such that, you don't have to have the PhD at the outset. You can use that compute power, and you can use the Spark open source libraries, to help bootstrap that process. >> So do you guys solve what I call the data swamp problem? Because, let me explain in more color. Most people are dumping everything in what they call a data lake. And, just store all the data, we'll get to it later. Some of it, mostly it's Hadoop, it's a bunch of batch data. Because they don't know what to do with it yet. So it just sits there. And it gets dirty, and it turns into a swamp. That's what the joke is, data swamp. Ironically we're looking at the lake here at the Oracle headquarters. >> Chris: Pristine, pristine. >> Pristine, the water's flying up through thing, it's beautiful. This is a big problem, because data that's idle, that's not being used in this case, not being intelligently acted upon, can turn into a swamp, is only valuable when needed. Meaning, if something's happening in real time, you go to the data lake, and pull out a piece of data, to your earlier reference, and make it in real time, it's important. So you never know the potential energy of that data, and the value. It could be perfectly useless one minute, extremely valuable the next. Is your value proposition with the big data appliance of analyst tools to connect to those lakes and bring them back? Is that the whole, you guys save the data lake >> There's two pieces >> problem? >> There's two pieces. One is giving you the infrastructure, and for that we have our big data cloud service, our big data appliance. Because, lots of people think big data is just commodity hardware. As you move into analytics and do more in memory, you're going to want that extra capacity. So that's one piece, making sure you've got the horsepower. But then, you need those tools on top. And that's where our big data discovery product focuses. And to your point, what we've done is actually integrate the things that those analysts need when they're in that discovery moment. First thing they need, like you said, I never knew I needed this data set before. It just came up to me. So we give you almost a shopping experience for data. You can go in, type in keywords. I want to look for social media log data. And we actually search into Hadoop, and index all that content. So, it's just like you were on our website. >> So you're kind of keeping the lake moving and clean, because you're indexing it, so you can service data at any given time. >> That's the first piece. The second piece though is again, in your discovery process, you have to recognize this is the first time people will be working with this data. And that's where a lot of these data scientists shine, because they know all the techniques as to how do I interrogate it? What's important? What's not? And that's what we build into our product now. So the analyst can just look at a very visual screen, and it helps them figure out where to focus. Is it worth me spending time? >> It's like almost this bot craze that's going on. You guys are abstracting away the scientist's knowledge into software, and providing almost an interface. >> That's the hope. If you can get a data scientist, trust me, keep them. They're very valuable. >> Catch that unicorn. >> Yes. >> No, it's true though. There's not enough PhD's, or data scientists out there. Soon, there's new curriculum out there, but still. The idea is to scale up, and make the normal person, the citizen be the data scientist. >> And also, it's funny, if you look at the advanced analytic tools, and the data science tools out there, they're very dated. A lot of them were built 15, 20 years ago with that data miner statistician. There's now this new breed of data scientists that they want more compelling interfaces. They expect more. >> Chris, final question. Top three conversations that you have with customers, where they're most challenged. If you had to look at the patterns, applying all the big data techniques in your brain to the three top problems that customers are trying to solve that you guys help. >> Excellent. So the first one I would say by far, and I wish it wasn't the case, but it's, help me justify building out my big data cluster. That's the first one. Lots of companies want to do more with big data, but they're struggling ... >> Just their ROI, or cost, or both. >> The ROI, the cost, really, why should I make that investment? How do I justify it? And I really do think that cloud is going to change that picture dramatically. When I can shift to looking at the CapEx versus OpEx ... >> So you're saying the cloud lowers the bar, in terms of getting value generated, or is it ... >> It does two things. It lowers the financial entry point, and how much you have to justify up front. And it lowers the IT skillset to manage those clusters in the data center. So, two very big problems. >> Great, that's awesome. Second one? >> No that I've solved that. Second one is, okay, well what do I do next? How do I find things? Where should I be looking? And that is where this Data Lab concept is meant to come into play. Some customers will have a perfect use case in mind. That's how they justified the project. They can go and execute that. But a lot of them, again it's this notion of a data lake. I need to pursue a range of experiments. Where do I start? And tools like big data discovery help a lot there. >> So Data Lab is just play with the data, and get a feel for it. >> Yep. And do it in a way that breeds that experimentation. Not just to visualize the data, but change it. Reshape it. Build new models, build new classifications. The last thing I'd say is okay, did I get my ROI, do I have a cluster? Yes. Did I figure out something that looks interesting? Yes. Now I have an idea. What do I do next? It's how do I connect my insights from big data back to the tools that we use every day. >> So this is where the value of the data capital thing you're talking about. The Lab is essentially formulating the key connections for data pipes to connect in. >> Yep. >> Is that kind of the best way to think about it? >> Roughly, yes. Yeah you come up with new ideas, new data products ... >> So you've operationalized it by the third step. >> Yes. And then, how do you do that? In some cases it's, oh, I just push the data, I move the data over to my data warehouse. Which may make sense. But Oracle also has, I think I mentioned it before, Big Data SQL as a product. Which will let you keep that data in Hadoop, keep everything else in your data warehouse, and productization is that easy. So you don't have to worry about moving data. It helps a lot. >> Well that highlights one of the things we always hear all the time, which is skills. >> Chris: Yep. >> And people know SQL. >> Chris: They do. Everyone does. >> Everyone does. Chris, thanks so much for spending the time here On the Ground. Really appreciate chatting with you. This is theCube. Exclusive coverage on the ground at Oracle headquarters. I'm John Furrier, thanks for watching.