>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from the cube in ETR. This is Breaking Analysis with Dave Vellante. >> On Friday, December 10th, Oracle announced a strong earnings beat and raise, on the strength of its licensed business, and slightly better than expected cloud performance. The stock was up sharply on the day and closed up nearly 16% surpassing 280 billion in market value. Oracle's success is due largely to its execution, of a highly differentiated strategy, that has really evolved over the past decade or more, deeply integrating its hardware and software, heavily investing in next generation cloud, creating a homogeneous experience across its application portfolio, and becoming the number one platform. Number one for the world's most mission critical applications. Now, while investors piled into the stock, skeptics will point to the beat being weighed toward licensed revenue and likely keep one finger on the sell button until they're convinced Oracle's cloud momentum, is more consistent and predictable. Hello and welcome to this week's Wikibond CUBE insights powered by ETR. In this breaking analysis, we'll review Oracle's most recent quarter, and pull in some ETR survey data, to frame the company's cloud business, the momentum of fusion ERP, where the company is winning and some gaps and opportunities that we see. The numbers this quarter was strong, particularly top line growth. Here are a few highlights. Oracle's revenues that grew 6% year on year that's in constant currency, surpassed $10 billion for the quarter. Oracle's non-gap operating margins, were an impressive 47%. Safra Catz has always said cloud is more profitable business and it's really starting to show in the income statement. Operating cash and free cash flow were 10.3 billion and 7.1 billion respectively, for the past four quarters, and would have been higher, if not for charges largely related to litigation expenses tied to the hiring of Mark Hurd, which the company said would not repeat in the future quarters. And you can see in this chart how Oracle breaks down its business, which is kind of a mishmash of items they lump into so-called the cloud. The largest piece of the revenue pie is cloud services, and licensed support, which in reading 10Ks, you'll find statements like the following; licensed support revenues are our largest revenue stream and include product upgrades, and maintenance releases and patches, as well as technical support assistance and statements like the following; cloud and licensed revenue, include the sale of cloud services, cloud licenses and on-premises licenses, which typically represent perpetual software licenses purchased by customers, for use in both cloud, and on-premises, IT environments. And cloud license and on-prem license revenues primarily represent amounts earned from granting customers perpetual licenses to use our database middleware application in industry specific products, which our customers use for cloud-based, on-premise and other IT environments. So you tell me, "is that cloud? I don't know." In the early days of Oracle cloud, the company used to break out, IaaS, PaaS and SaaS revenue separately, but it changed its mind, which really makes it difficult to determine what's happening in true cloud. Look I have no problem including same same hardware software control plane, et cetera. The hybrid if it's on-prem in a true hybrid environment like exadata cloud@customer or AWS outposts. But you have to question what's really cloud in these numbers. And Larry in the earnings call mentioned that Salesforce licenses the Oracle database, to run its cloud and Oracle doesn't count that in its cloud number, rather it counts it in license revenue, but as you can see it varies that into a line item that starts with the word cloud. So I guess I would say that Oracle's reporting is maybe somewhat better than IBM's cloud reporting, which is the worst, but I can't really say what is and isn't cloud, in these numbers. Nonetheless, Oracle is getting it done for investors. Here's a chart comparing the five-year performance of Oracle to some of its legacy peers. We excluded Microsoft because it skews the numbers. Microsoft would really crush all these names including Oracle. But look at Oracle. It's wedged in between the performance of the NASDAQ and the S&P 500, it's up over 160% in that five-year timeframe, well ahead of SAP which is up 59% in that time, and way ahead of the dismal -22% performance of IBM. Well, it's a shame. The tech tide is rising, it's lifting all boats but, IBM has unfortunately not been able to capitalize. That's a story for another day. As a market watcher, you can't help but love Larry Ellison. I only met him once at an IDC conference in Paris where I got to interview Scott McNealy, CEO at the time. Ellison is great for analysts because, he's not afraid to talk about the competition. He'll brag, he'll insult, he'll explain, and he'll pitch his stories. Now on the earnings call last night, he went off. Educating the analyst community, on the upside in the fusion ERP business, making the case that because only a thousand of the 7,500 legacy on-prem ERP customers from Oracle, JD Edwards and PeopleSoft have moved Oracle's fusion cloud ERP, and he predicted that Oracle's cloud ERP business will surpass 20 billion in five years. In fact, he said it's going to bigger than that. He slammed the hybrid cloud washing. You can see one of the quotes here in this chart, that's going on when companies have customers running in the cloud and they claim whatever they have on premise hybrid, he called that ridiculous. I would agree. And then he took an opportunity to slam the hyperscale cloud vendors, citing a telco customer that said Oracle's cloud never goes down, and of course, he chose the same week, that AWS had a major outage. And so to these points, I would say that Oracle really was the first tech company, to announce a true hybrid cloud strategy, where you have an entirely identical experience on prem and in the cloud. This was announced with cloud@customer, two years, before AWS announced outposts. Now it probably took Oracle two years to get it working as advertised, but they were first. And to the second point, this is where Oracle differentiates itself. Oracle is number one for mission critical applications. No other vendor really can come close to Oracle in this regard. And I would say that Oracle is recent quarterly performance to a large extent, is due to this differentiated approach. Over the past 10 years, we've talked to hundreds literally. Hundreds and hundreds of Oracle customers. And while they may not always like the tactics and licensing policies of Oracle in their contracting, they will tell you, that business case for investing and staying with Oracle are very strong. And yes, a big part of that is lock-in but R&D investments innovation and a keen sense of market direction, are just as important to these customers. When you're chairman and founder is a technologist and also the CTO, and has the cash on hand to invest, the results are a highly competitive story. Now that's not to say Oracle is not without its challenges. That's not to say Oracle is without its challenges. Those who follow this program know that when it comes to ETR survey data, the story is not always pretty for Oracle. So let's take a look. This chart shows the breakdown of ETR is net score methodology, Net score measures spending momentum and works ETR. Each quarter asks customers, are you adding in the platform, That's the lime green. Increasing spend by 6% or more, that's the fourth green. Is you're spending E+ or minus 5%, that's the gray. You're spending climbing by 6%, that's the pinkish. Or are you leaving the platform, that's the bright red retiring. You subtract the reds from the greens, and that yields a net score, which an Oracle's overall case, is an uninspiring -4%. This is one of the anomalies in the ETR dataset. The net score doesn't track absolute actual levels, of spending the dollars. Remember, as the leader in mission critical workloads, Oracle commands a premium price. And so what happens here is the gray, is still spending a large amount of money, enough to offset the declines, and the greens are spending more than they would on other platforms because Oracle could command higher prices. And so that's how Oracle is able to grow its overall revenue by 6% for example, whereas the ETR methodology, doesn't capture that trend. So you have to dig into the data a bit deeper. We're not going to go too deep today, but let's take a look at how some of Oracle's businesses are performing relative to its competitors. This is a popular view that we like to share. It shows net score or spending momentum on the vertical axis, and market share. Market share is a measure of pervasiveness in the survey. Think of it as mentioned share. That's on the x-axis. And we've broken down and circled Oracle overall, Oracle on prem, which is declining on the vertical axis, Oracle fusion and NetSuite, which are much higher than Oracle overall. And in the case of fusion, much closer to that 40% magic red horizontal line, remember anything above that line, we consider to be elevated. Now we've added SAP overall which has, momentum comparable to fusion in the survey, using this methodology and IBM, which is in between fusion and Oracle, overall on the y-axis. Oracle as you can see on the horizontal axis, has a larger presence than any of these firms that are below the 40% line. Now, above that 40% line, you see companies with a smaller presence in the survey like Workday, salesforce.com, pretty big presence still, Google cloud also, and Snowflake. Smaller presence but much much higher net score than anybody else on this chart. And AWS and Microsoft overall with both a strong presence, and impressive momentum, especially for their respective sizes. Now that view that we just showed you excluded on purpose Oracle specific cloud offering. So let's now take a look at that relative to other cloud providers. This chart shows the same XY view, but it cuts the data by cloud only. And you can see Oracle while still well below the 40% line, has a net score of +15 compared to a -4 overall that we showed you earlier. So here we see two key points. One, despite the convoluted reporting that we talked about earlier, the ETR data supports that Oracle's cloud business has significantly more momentum than Oracle's overall average momentum. And two, while Oracle is smaller and doesn't have the growth of the hyperscale giants, it's cloud is performing noticeably better than IBM's within the ETR survey data. Now a key point Ellison emphasized on the earnings call, was the importance of ERP, and the work that Oracle has done in this space. It lives by this notion of a cloud first mentality. It builds stuff for the cloud and then, would bring it on-prem. And it's been attracting new customers according to the company. He said Oracle has 8,500 fusion ERP customers, and 28,000 NetSuite customers in the cloud. And unlike Microsoft, it hasn't migrated its on-prem install base, to the cloud yet. Meaning these are largely new customers. Now this chart isolates fusion and NetSuite, within a sector ETR calls GPP. The very giant, public and private companies. And this is a bellwether of spending in the ETR dataset. They've gone back and it correlates to performance. So think large public companies, the biggest ones, and also privates big privates like Mars or Cargo or Fidelity. The chart shows the net score breakdown over time for fusion and NetSuite going back to 2019. And you can see, a big uptick as shown in the blue line from the October, 2020 survey. So Oracle has done a good job building and now marketing its cloud ERP to these important customers. Now, the last thing we want to show you is Oracle's performance within industry sectors. On the earnings call, Oracle said that it had a very strong momentum for fusion in financial services and healthcare. And this chart shows the net score for fusion, across each industry sector that ETR tracks, for three survey points. October, 2020, that's the gray bars, July 21, that's the blue bars and October, 2021, the yellow bars. So look it confirms Oracles assertions across the board that they're seeing fusion perform very well including the two verticals that are called out healthcare and banking slash financial services. Now the big question is where does Oracle go from here? Oracle has had a history of looking like it's going to break out, only to hit some bumps in the road. And so investors are likely going to remain a bit cautious and take profits off the table along the way. But since the Barron's article came out, we reported on that earlier this year in February, declaring Oracle a cloud giant, the stock is up more than 50% of course. 16 of those points were from Friday's move upward, but still, Oracle's highly differentiated strategy of integrating hardware and software together, investing in a modern cloud platform and selectively offering services that cater to the hardcore mission critical buyer, these have served the company, its customers and investors as well. From a cloud standpoint, we'd like to see Oracle be more inclusive, and aggressively expand its marketplace and its ecosystem. This would provide both greater optionality for customers, and further establish Oracle as a major cloud player. Indeed, one of the hallmarks of both AWS and Azure is the momentum being created, by their respective ecosystems. As well, we'd like to see more clear confirmation that Oracle's performance is being driven by its investments in technology IE cloud, same same hybrid, and industry features these modern investments, versus a legacy licensed cycles. We are generally encouraged and are reminded, of years ago when Sam Palmisano, he was retiring and leaving as the CEO of IBM. At the time, HP under the direction ironically of Mark Hurd, was the now company, Palmisano was asked, "do you worry about HP?" And he said in fact, "I don't worry about HP. I worry about Oracle because Oracle invests in R&D." And that statement has proven present. What do you think? Has Oracle hit the next inflection point? Let me know. Don't forget these episodes they're all available as podcasts wherever you listen, all you do is search it. Breaking Analysis podcast, check out ETR website at etr.plus. We also publish a full report every week on wikibon.com and siliconANGLE.com. You can get in touch with me on email David.vellante@siliconangle.com, you can DM me @dvellante on Twitter or, comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights. Powered by ETR. Have a great week everybody. Stay safe, be well, and we'll see you next time. (upbeat music)

>> Hello everyone, and welcome back to theCUBEs coverage of the Snowflake Data Cloud Summit 2020. We're tracking the rise of the data cloud Sunny Bedi is here with me. He's the CIO and Chief Data Officer for Snowflake. Sunny, thanks for making the time today. Good to see you. >> Same here, Dave. Thanks for having me over. >> Yeah, so you're welcome. So before we get into it, I got to ask you, I mean, you recently left Nvidia to join Snowflake. I mean(chuckles) one of the few companies that are almost as hot as Snowflake, how come? >> Well, you know Dave I joined Nvidia 12 years ago. I was there for 12 years, when Nvidia was less than 2000 people company. And Nvidia have an unbelievable growth trajectory, and then from 2000 employees to 16,000, when I left in December of 2019. And Snowflake kind of provided the same opportunity to come in, and help scale the company. I thrive in an environment where I can be creative, I thrive in an environment where I can build things, I can scale things, I can grow things. And its been just a perfect opportunity to come and repeat that success over here. >> Awesome, Well we wish you the best. Talk about your role a little bit. I mean, it's like totally unique. I mean, especially in certain smaller organizations that have the same person, in the role of Chief Information Officer and Chief Data Officer, but, which are you? Are you more CIO, CDO, how do you balance that out? >> I would say that I'm both, to be an effective CIO, you need immersion with automation, you need immersion with data, you need immersion with security, and you also need immersion with compliance. So if all of these things are together, things are integrated. You have a cohesive way of handling all the pieces that come together. We believe if you keep them separated, you create silos and we definitely don't want silos. We want integration. We want seamless integration to drive and scale the company for future. >> I always felt-- >> So my time is balanced between both areas. >> I mean, I always felt like a lot of the CIOs I talked to, they'd love to get more involved in the data, but they're just too busy trying to keep the lights on. So, maybe what are your thoughts on the priorities of each hats CIO and CDO? >> Yeah, so look, I mean, I think because we're a full cloud company, we don't have anything on-prem. I don't have any workloads on-prem. we don't have a data center. I really don't have to worry about all the operational challenges that you have to deal with being an on-prem company. So the cycles that I can be involved from a transformation prospect, driving transformation for the company, both on the data side, as well as on the IT side. I have that cycles to invest that time and energy into both areas. Typically in a traditional company, which has not yet migrated towards the cloud, a major portion of their bandwidth gets wasted. CIOs bandwidth and IT professionals bandwidth gets wasted, in dealing with the operational challenges that you have in an on-prem environment. So having not to worry about that over here, it gives me all the cycles to be investing my time on both areas. >> Yeah, a lot of wasted IT labor over the decades. Let me ask you, how is running a data company? We were inside of a fast moving Silicon Valley Tech Company. What are the similarities and the differences from some of the customers? I mean, on the one hand, you're moving faster than your customers at least most of them, and you don't have the technical that you just described, CX on Nirvana. On the other hand, you're an example of what's possible. You can sort of set the best practice mark. How do you see that dynamic? >> So, in our firm world-class IT organization, it needs to be data-driven, it needs to be highly automated, it needs to enable world-class user experience, and then to secure and make the environment compliant resilient. The cloud platform that we have, inside Snowflake, allows us to achieve all of that. Now that is, an ideal situation to be in. But you don't have to deal with, all the on-prem type of workloads. So finding that balance is what we're going after. And, however this is a journey, right. For other companies who are not on the cloud, its a journey. They have to prioritize that. They have to start moving things to the cloud, and that's where we are different and similar, right. We're different that we don't have to worry about that. Everything is in the cloud for us. And then, that's kind of how we see it. >> So, you know, used to call it the dogfooding segment, but Oliver Bussmann was the CIO of SAP. So no, no, Dave, we call it drinking your own champagne. (laughs) which is how you guys are referring to it. But, sometimes still in such situations you're (laughs) inside the sausage factory, which is good in a way because you see it before it goes into production. But, so what's your journey with, with Snowflake been like? >> Yeah, so that's a really good question. That's a major portion of what I do at work. And, let's start with the first principles of we believe, that we want to measure everything in the company, that's important for companies performance. If we measure the right things, we believe we can drive the best outcomes. We are driven through those first principles and we leveraged our business applications, our data, our security, our automation, and our compliance to integrate with our product to power, all these use cases and workloads. In our own environment, we call that snow house. Which is nothing but a Snowflake instance. So, for all the new products that we are coming into market with, we work very closely with the engineering team, with the product management team, to make sure that we actually become customer zero, and try to use as much functionality of that, inside our own enterprise and give as much feedback to our engineering and our product management teams, so that they can make the customer one experience to be world-class. So that's kind of in a nutshell how we go to market with those products. >> So you're customer zero. So all the product guys that they suck up to you, or are they afraid of you? (laughs) >> Well, I think it's a very neutral, beneficial relationship. So, they know that my team's feedback, is important to how they are kind of shaping up the product, and it's just not necessarily IT, right. We have folks in finance, folks in sales, marketing, everybody is drinking the champagne, right. And IT and the data team actually enabled that deployment, but the use cases are pretty much in the entire enterprise of the company in every aspect of it. Well you know-- >> Including security. >> Well, that's what we say. We always talk about alignment, but it's like, it's almost alignment by design, as opposed to being this forced thing. I'm interested in this, sort of Snowflake on Snowflake concept that you guys talk about. What were your objectives going in and maybe thinking about the outcomes, what did you expect? Did you work backwards from that? What were you trying to achieve? >> Yeah, I mean, look again back to the first principles. We believe we want to measure everything that's important to our business. That will drive the right outcomes. We then layer the application layer. We then overlay the business process layer. We then overlay the compliance and security layer. And the end result really is operationalizing Snowflake internally to drive our business, making the right choices, right decisions for the company. So we have a ton of use cases that are just ideal, using Snowflake on Snowflake. You know I can give you some examples of that if you like, >> Yes. >> But, >> Go on please. >> Security being one of the biggest use cases. We use the entire monitoring and remediation work that goes in the security compliance world, all through Snowflake. And we are finding real time events through data sharing, with our key suppliers. And we're ensuring that we're protecting our environment as much as possible with that whole infrastructure. >> You talked about layering, governance, security, et cetera. Yeah (laughs) I'm imagining a coat of primer paint in a nice and smooth over, it's not a bolt-on. I want to press you on that, because it can't be an afterthought. And what you're describing is much more of a modern approach. And I want you to totally differentiate between the layers that you talked about and what you've surely seen in your experience over the years as a bolt-on, what's the difference? >> Well, I mean the security wall, there's a lot of data, and a lot of the data that is critical to your environment. You want to make sure it is fully complete, you're getting it in the right hands, in the right platform to understand that, and doing the correlation work that needs to happen real time. Our platform allows all that data to be ingested and real time and anything that is suspicious, that's being out there. We're finding that stuff in real time. The monitoring has to be real time. And if there is an event, somebody needs to take an action real time. So the platform allows it to integrate altogether. And basically, the suppliers that we're using are also doing data sharing with us on this platform. So it makes the whole security remediation to be really really fantastic experience. >> Well I think too, I'd to share often with my audience when I talk to practitioners that are using Snowflake they, surprising to me when I first heard this, they said, "Well we choose Snowflake as the security," and I went, what! But the simplicity and the workflow is simpler, and it just means less human labor involved in setting these things up. So I wonder if you could talk about the team that you put together, the culture that you're building, and what's the makeup look like? >> Sure, so are you specifically asking about the characteristics of how we're building up the culture? >> Yeah, absolutely. >> Okay, so I think we're looking for, obviously very much high energy folks, people who have, high accountability, they're data-driven. We want to measure everything that's important to us. We're looking for folks who have situational awareness, and then finally high sense of urgency. I think all of these elements, allows IT organization to be integrated with the business. In large traditional companies, IT organizations kind of disintegrate with the business. We want to integrate with the business, to drive the best outcomes that are needed for the company. >> I Want to ask you about some of your favorite use cases, but you mentioned measurement. How do you measure? What are you measuring? >> Sure, so I would say that, let's just take security. Cause we talked about security. Let's just use security as a use case. So in security, there are many different frameworks, as you may know, right. There is the NIST framework, there is the CIS framework, there is an ISO framework. We have adopted towards a CIS framework inside Snowflake. That framework has 20 controls and that 20 controls has another 20 sub-controls. So we're talking about 400 controls potentially. Not every control is applicable to us, but majority of them are. And so for every control, there is a source of data, that's being ingested in Snowflake. I'll give you an example of that is asset management. So, asset management for end points, asset management for our servers, or asset management for our network gear. All of that data gets ingested inside Snowflake. We measure that. We can tell you exactly how many end points I have. I can tell you exactly when an employee gets onboarded, what laptop we have given them, when the employee leaves the company, I'll be collecting that laptop back on time, I'll be revoking all that access. That's part of CIS Control 1 as an example, and we're measuring all of that. And I can tell you exactly at my real time inside Snowflake. How effective I am for that specific control. That's just an example of that Dave. Now imagine 400 of these items that make up the whole security CIS framework. You want to measure everything on that 400 controls or 400 sub-controls. And you want to make sure that if any of that control is not being managed properly, you're alerted about it and you're remediating it to prevent a security issue that may pop up. >> Awesome, visibility and automation component. Are you a CSO too Sunny? We don't really have that title. We don't really have a CSO title, but I do wear a security hat as well. It's actually a joint responsibility between... I manage the corporate security. The product security is inside the product team, but we use the same common framework. We use the same common telemetry. We use the same common methodology. Incident management response teams are very similar, and it's all powered through a Snowflake. >> Awesome. Sunny Bedi you're great guests, I would imagine the sales guys love dragging you on zooms these days to sales calls, just to (laughs) share best practice, but love to have you back and continue the conversation. Sunny Bedi, really appreciate your time. Thank you. >> Thank you Dave. Thank you very much. >> All right, keep it right there everybody. We'll be right back with our next guest, right after this short break.

>> Voiceover: Live from midtown Manhattan, it's theCUBE! Covering Big Data New York City 2017. Brought to you by, SiliconANGLE Media and as ecosystems sponsors. >> Okay, welcome back everyone, live in New York City this is theCUBE's coverage of, eighth year doing Hadoop World now, evolved into Strata Hadoop, now called Strata Data, it's had many incarnations but O'Reilly Media running their event in conjunction with Cloudera, mainly an O'Reilly media show. We do our own show called Big Data NYC here with our community with theCUBE bringing you the best interviews, the best people, entrepreneurs, thought leaders, experts, to get the data and try to project the future and help users find the value in data. My next guest is Rob Thomas, who is the General Manager of IBM Analytics, theCUBE Alumni, been on multiple times successfully executing in the San Francisco Bay area. Great to see you again. >> Yeah John, great to see you, thanks for having me. >> You know IBM is really been interesting through its own transformation and a lot of people will throw IBM in that category but you guys have been transforming okay and the scoreboard yet has to yet to show in my mind what's truly happening because if you still look at this industry, we're only eight years into what Hadoop evolved into now as a large data set but the analytics game just seems to be getting started with the cloud now coming over the top, you're starting to see a lot of cloud conversations in the air. Certainly there's a lot of AI washing, you know, AI this, but it's machine learning and deep learning at the heart of it as innovation but a lot more work on the analytics side is coming. You guys are at the center of that. What's the update? What's your view of this analytics market? >> Most enterprises struggle with complexity. That's the number one problem when it comes to analytics. It's not imagination, it's not willpower, in many cases, it's not even investment, it's just complexity. We are trying to make data really simple to use and the way I would describe it is we're moving from a world of products to platforms. Today, if you want to go solve a data governance problem you're typically integrating 10, 15 different products. And the burden then is on the client. So, we're trying to make analytics a platform game. And my view is an enterprise has to have three platforms if they're serious about analytics. They need a data manager platform for managing all types of data, public, private cloud. They need unified governance so governance of all types of data and they need a data science platform machine learning. If a client has those three platforms, they will be successful with data. And what I see now is really mixed. We've got 10 products that do that, five products that do this, but it has to be integrated in a platform. >> You as an IBM or the customer has these tools? >> Yeah, when I go see clients that's what I see is data... >> John: Disparate data log. >> Yeah, they have disparate tools and so we are unifying what we deliver from a product perspective to this platform concept. >> You guys announce an integrated analytic system, got to see my notes here, I want to get into that in a second but interesting you bring up the word platform because you know, platforms have always been kind of reserved for the big supplier but you're talking about customers having a platform, not a supplier delivering a platform per se 'cause this is where the integration thing becomes interesting. We were joking yesterday on theCUBE here, kind of just kind of ad hoc conceptually like the world has turned into a tool shed. I mean everyone has a tool shed or knows someone that has a tool shed where you have the tools in the back and they're rusty. And so, this brings up the tool conversation, there's too many tools out there that try to be platforms. >> Rob: Yes. >> And if you have too many tools, you're not really doing the platform game right. And complexity also turns into when you bought a hammer it turned into a lawn mower. Right so, a lot of these companies have been groping and trying to iterate what their tool was into something else it wasn't built for. So, as the industry evolves, that's natural Darwinism if you will, they will fall to the wayside. So talk about that dynamic because you still need tooling >> Rob: Yes. but tool will be a function of the work as Peter Burris would say, so talk about how does a customer really get that platform out there without sacrificing the tooling that they may have bought or want to get rid of. >> Well, so think about the, in enterprise today, what the data architecture looks like is, I've got this box that has this software on it, use your terms, has these types of tools on it, and it's isolated and if you want a different set of tooling, okay, move that data to this other box where we have the other tooling. So, it's very isolated in terms of how platforms have evolved or technology platforms today. When I talk about an integrated platform, we are big contributors to Kubernetes. We're making that foundational in terms of what we're doing on Private Cloud and Public Cloud is if you move to that model, suddenly what was a bunch of disparate tools are now microservices against a common architecture. And so it totally changes the nature of the data platform in an enterprise. It's a much more fluid data layer. The term I use sometimes is you have data as a service now, available to all your employees. That's totally different than I want to do this project, so step one, make room in the data center, step two, bring in a server. It's a much more flexible approach so that's what I mean when I say platform. >> So operationalizing it is a lot easier than just going down the linear path of provisioning. All right, so let's bring up the complexity issue because integrated and unified are two different concepts that kind of mean the same thing depending on how you look at it. When you look at the data integration problem, you've got all this complexity around governance, it's a lot of moving parts of data. How does a customer actually execute without compromising the integrity of their policies that they need to have in place? So in other words, what are the baby steps that someone can take, the customers take through with what you guys are dealing with them, how do they get into the game, how do they take steps towards the outcome? They might not have the big money to push it all at once, they might want to take a risk of risk management approach. >> I think there's a clear recipe for doing this right and we have experience of doing it well and doing it not so well, so over time we've gotten some, I'd say a pretty good perspective on that. My view is very simple, data governance has to start with a catalog. And the analogy I use is, you have to do for data what libraries do for books. And think about a library, the first thing you do with books, card catalog. You know where, you basically itemize everything, you know exactly where it sits. If you've got multiple copies of the same book, you can distinguish between which one is which. As books get older they go to archives, to microfilm or something like that. That's what you have to do with your data. >> On the front end. >> On the front end. And it starts with a catalog. And that reason I say that is, I see some organizations that start with, hey, let's go start ETL, I'll create a new warehouse, create a new Hadoop environment. That might be the right thing to do but without having a basis of what you have, which is the catalog, that's where I think clients need to start. >> Well, I would just add one more level of complexity just to kind of reinforce, first of all I agree with you but here's another example that would reinforce this step. Let's just say you write some machine learning and some algorithms and a new policy from the government comes down. Hey, you know, we're dealing with Bitcoin differently or whatever, some GPRS kind of thing happens where someone gets hacked and a new law comes out. How do you inject that policy? You got to rewrite the code, so I'm thinking that if you do this right, you don't have to do a lot of rewriting of applications to the library or the catalog will handle it. Is that right, am I getting that right? >> That's right 'cause then you have a baseline is what I would describe it as. It's codified in the form of a data model or in the form on ontology for how you're looking at unstructured data. You have a baseline so then as changes come, you can easily adjust to those changes. Where I see clients struggle is if you don't have that baseline then you're constantly trying to change things on the fly and that makes it really hard to get to this... >> Well, really hard, expensive, they have to rewrite apps. >> Exactly. >> Rewrite algorithms and machine learning things that were built probably by people that maybe left the company, who knows, right? So the consequences are pretty grave, I mean, pretty big. >> Yes. >> Okay, so let's back to something that you said yesterday. You were on theCUBE yesterday with Hortonworks CEO, Rob Bearden and you were commenting about AI or AI washing. You said quote, "You can't have AI without IA." A play on letters there, sequence of letters which was really an interesting comment, we kind of referenced it pretty much all day yesterday. Information architecture is the IA and AI is the artificial intelligence basically saying if you don't have some sort of architecture AI really can't work. Which really means models have to be understood, with the learning machine kind of approach. Expand more on that 'cause that was I think a fundamental thing that we're seeing at the show this week, this in New York is a model for the models. Who trains the machine learning? Machines got to learn somewhere too so there's learning for the learning machines. This is a real complex data problem and a half. If you don't set up the architecture it may not work, explain. >> So, there's two big problems enterprises have today. One is trying to operationalize data science and machine learning that scale, the other one is getting the cloud but let's focus on the first one for a minute. The reason clients struggle to operationalize this at scale is because they start a data science project and they build a model for one discreet data set. Problem is that only applies to that data set, it doesn't, you can't pick it up and move it somewhere else so this idea of data architecture just to kind of follow through, whether it's the catalog or how you're managing your data across multiple clouds becomes fundamental because ultimately you want to be able to provide machine learning across all your data because machine learning is about predictions and it's hard to do really good predictions on a subset. But that pre-req is the need for an information architecture that comprehends for the fact that you're going to build models and you want to train those models. As new data comes in, you want to keep the training process going. And that's the biggest challenge I see clients struggling with. So they'll have success with their first ML project but then the next one becomes progressively harder because now they're trying to use more data and they haven't prepared their architecture for that. >> Great point. Now, switching to data science. You spoke many times with us on theCUBE about data science, we know you're passionate about you guys doing a lot of work on that. We've observed and Jim Kobielus and I were talking yesterday, there's too much work still in the data science guys plate. There's still doing a lot of what I call, sys admin like work, not the right word, but like administrative building and wrangling. They're not doing enough data science and there's enough proof points now to show that data science actually impacts business in whether it's military having data intelligence to execute something, to selling something at the right time, or even for work or play or consume, or we use, all proof is out there. So why aren't we going faster, why aren't the data scientists more effective, what does it going to take for the data science to have a seamless environment that works for them? They're still doing a lot of wrangling and they're still getting down the weeds. Is that just the role they have or how does it get easier for them that's the big catch? >> That's not the role. So they're a victim of their architecture to some extent and that's why they end up spending 80% of their time on data prep, data cleansing, that type of thing. Look, I think we solved that. That's why when we introduced the integrated analytic system this week, that whole idea was get rid of all the data prep that you need because land the data in one place, machine learning and data science is built into that. So everything that the data scientist struggles with today goes away. We can federate to data on cloud, on any cloud, we can federate to data that's sitting inside Hortonworks so it looks like one system but machine learning is built into it from the start. So we've eliminated the need for all of that data movement, for all that data wrangling 'cause we organized the data, we built the catalog, and we've made it really simple. And so if you go back to the point I made, so one issue is clients can't apply machine learning at scale, the other one is they're struggling to get the cloud. I think we've nailed those problems 'cause now with a click of a button, you can scale this to part of the cloud. >> All right, so how does the customer get their hands on this? Sounds like it's a great tool, you're saying it's leading edge. We'll take a look at it, certainly I'll do a review on it with the team but how do I get it, how do I get a hold of this? What do I do, download it, you guys supply it to me, is it some open source, how do your customers and potential customers engage with this product? >> However they want to but I'll give you some examples. So, we have an analytic system built on Spark, you can bring the whole box into your data center and right away you're ready for data science. That's one way. Somebody like you, you're going to want to go get the containerized version, you go download it on the web and you'll be up and running instantly with a highly performing warehouse integrated with machine learning and data science built on Spark using Apache Jupyter. Any developer can go use that and get value out of it. You can also say I want to run it on my desktop. >> And that's free? >> Yes. >> Okay. >> There's a trial version out there. >> That's the open source, yeah, that's the free version. >> There's also a version on public cloud so if you don't want to download it, you want to run it outside your firewall, you can go run it on IBM cloud on the public cloud so... >> Just your cloud, Amazon? >> No, not today. >> John: Just IBM cloud, okay, I got it. >> So there's variety of ways that you can go use this and I think what you'll find... >> But you have a premium model that people can get started out so they'll download it to your data center, is that also free too? >> Yeah, absolutely. >> Okay, so all the base stuff is free. >> We also have a desktop version too so you can download... >> What URL can people look at this? >> Go to datascience.ibm.com, that's the best place to start a data science journey. >> Okay, multi-cloud, Common Cloud is what people are calling it, you guys have Common SQL engine. What is this product, how does it relate to the whole multi-cloud trend? Customers are looking for multiple clouds. >> Yeah, so Common SQL is the idea of integrating data wherever it is, whatever form it's in, ANSI SQL compliant so what you would expect for a SQL query and the type of response you get back, you get that back with Common SQL no matter where the data is. Now when you start thinking multi-cloud you introduce a whole other bunch of factors. Network, latency, all those types of things so what we talked about yesterday with the announcement of Hortonworks Dataplane which is kind of extending the YARN environment across multi-clouds, that's something we can plug in to. So, I think let's be honest, the multi-cloud world is still pretty early. >> John: Oh, really early. >> Our focus is delivery... >> I don't think it really exists actually. >> I think... >> It's multiple clouds but no one's actually moving workloads across all the clouds, I haven't found any. >> Yeah, I think it's hard for latency reasons today. We're trying to deliver an outstanding... >> But people are saying, I mean this is head room I got but people are saying, I'd love to have a preferred future of multi-cloud even though they're kind of getting their own shops in order, retrenching, and re-platforming it but that's not a bad ask. I mean, I'm a user, I want to move from if I don't like IBM's cloud or I got a better service, I can move around here. If Amazon is too expensive I want to move to IBM, you got product differentiation, I might want to to be in your cloud. So again, this is the customers mindset, right. If you have something really compelling on your cloud, do I have to go all in on IBM cloud to run my data? You shouldn't have to, right? >> I agree, yeah I don't think any enterprise will go all in on one cloud. I think it's delusional for people to think that so you're going to have this world. So the reason when we built IBM Cloud Private we did it on Kubernetes was we said, that can be a substrate if you will, that provides a level of standards across multiple cloud type environments. >> John: And it's got some traction too so it's a good bet there. >> Absolutely. >> Rob, final word, just talk about the personas who you now engage with from IBM's standpoint. I know you have a lot of great developers stuff going on, you've done some great work, you've got a free product out there but you still got to make money, you got to provide value to IBM, who are you selling to, what's the main thing, you've got multiple stakeholders, could you just clarify the stakeholders that you're serving in the marketplace? >> Yeah, I mean, the emerging stakeholder that we speak with more and more than we used to is chief marketing officers who have real budgets for data and data science and trying to change how they're performing their job. That's a major stakeholder, CTOs, CIOs, any C level, >> Chief data officer. >> Chief data officer. You know chief data officers, honestly, it's a mixed bag. Some organizations they're incredibly empowered and they're driving the strategy. Others, they're figure heads and so you got to know how the organizations do it. >> A puppet for the CFO or something. >> Yeah, exactly. >> Our ops. >> A puppet? (chuckles) So, you got to you know. >> Well, they're not really driving it, they're not changing it. It's not like we're mandated to go do something they're maybe governance police or something. >> Yeah, and in some cases that's true. In other cases, they drive the data architecture, the data strategy, and that's somebody that we can engage with right away and help them out so... >> Any events you got going up? Things happening in the marketplace that people might want to participate in? I know you guys do a lot of stuff out in the open, events they can connect with IBM, things going on? >> So we do, so we're doing a big event here in New York on November first and second where we're rolling out a lot of our new data products and cloud products so that's one coming up pretty soon. The biggest thing we've changed this year is there's such a craving for clients for education as we've started doing what we're calling Analytics University where we actually go to clients and we'll spend a day or two days, go really deep and open languages, open source. That's become kind of a new focus for us. >> A lot of re-skilling going on too with the transformation, right? >> Rob: Yes, absolutely. >> All right, Rob Thomas here, General Manager IBM Analytics inside theCUBE. CUBE alumni, breaking it down, giving his perspective. He's got two books out there, The Data Revolution was the first one. >> Big Data Revolution. >> Big Data Revolution and the new one is Every Company is a Tech Company. Love that title which is true, check it out on Amazon. Rob Thomas, Bid Data Revolution, first book and then second book is Every Company is a Tech Company. It's theCUBE live from New York. More coverage after the short break. (theCUBE jingle) (theCUBE jingle) (calm soothing music)

(busy music) >> Announcer: Live from Midtown Manhattan, it's the Cube, covering Big Data New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hello, and welcome to the special Cube presentation here in New York City for Big Data NYC, in conjunction with all the activity going on with Strata, Hadoop, Strata Data Conference right around the corner. This is the Cube's special annual event in New York City where we highlight all the trends, technology experts, thought leaders, entrepreneurs here inside the Cube. We have our three days of wall to wall coverage, evening event on Wednesday. I'm John Furrier, the co-host of the Cube, with Jim Kobielus, and Peter Burris will be here all week as well. Kicking off day one, Jim, the monster week of Big Data NYC, which now has turned into, essentially, the big data industry is a huge industry. But now, subsumed within a larger industry of AI, IoT, security. A lot of things have just sucked up the big data world that used to be the Hadoop world, and it just kept on disrupting, and creative disruption of the old guard data warehouse market, which now, looks pale in comparison to the disruption going on right now. >> The data warehouse market is very much vibrant and alive, as is the big data market continuing to innovate. But the innovations, John, have moved up the stack to artificial intelligence and deep learning, as you've indicated, driving more of the Edge applications in the new generation of mobile and smart appliances and things that are coming along like smart, self-driving vehicles and so forth. What we see is data professionals and developers are moving towards new frameworks, like TensorFlow and so forth, for development of the truly disruptive applications. But big data is the foundation. >> I mean, the developers are the key, obviously, open source is growing at an enormous rate. We just had the Linux Foundation, we now have the Open Source Summit, they have kind of rebranded that. They're going to see explosion from code from 64 million lines of code to billions of lines of code, exponential growth. But the bigger picture is that it's not just developers, it's the enterprises now who want hybrid cloud, they want cloud technology. I want to get your reaction to a couple of different threads. One is the notion of community based software, which is open source, extending into the enterprise. We're seeing things like blockchain is hot right now, security, two emerging areas that are overlapping in with big data. You obviously have classic data market, and then you've got AI. All these things kind of come in together, kind of just really putting at the center of all that, this core industry around community and software AI, particular. It's not just about machine learning anymore and data, it's a bigger picture. >> Yeah, in terms of a community, development with open source, much of what we see in the AI arena, for example, with the up and coming, they're all open source tools. There's TensorFlow, there's Cafe, there's Theano and so forth. What we're seeing is not just the frameworks for developing AI that are important, but the entire ecosystem of community based development of capabilities to automate the acquisition of training data, which is so critically important for tuning AI, for its designated purpose, be it doing predictions and abstractions. DevOps, what are coming into being are DevOps frameworks to span the entire life cycle of the creation and the training and deployment and iteration of AI. What we're going to see is, like at the last Spark Summit, there was a very interesting discussion from a Stanford researcher, new open source tools that they're developing out in, actually, in Berkeley, I understand, for, related to development of training data in a more automated fashion for these new challenges. The communities are evolving up the stack to address these requirements with fairly bleeding edge capabilities that will come in the next few years into the mainstream. >> I had a chat with a big time CTO last night, he worked at some of the big web scale company, I won't say the name, give it away. But basically, he asked me a question about IoT, how real is it, and obviously, it's hyped up big time, though. But the issue in all this new markets like IoT and AI is the role of security, because a lot of enterprises are looking at the IoT, certainly in the industrial side has the most relevant low hanging fruit, but at the end of the day, the data modeling, as you're pointing out, becomes a critical thing. Connecting IoT devices to, say, an IP network sounds trivial in concept, but at the end of the day, the surface area for security is oak expose, that's causing people to stop what they're doing, not deploying it as fast. You're seeing kind of like people retrenching and replatforming at the core data centers, and then leveraging a lot of cloud, which is why Azure is hot, Microsoft Ignite Event is pretty hot this week. Role of cloud, role of data in IoT. Is IoT kind of stalled in your mind? Or is it bloating? >> I wouldn't say it's stalled or that it's bloating, but IoT is definitely coming along as the new development focus. For the more disruptive applications that can derive more intelligence directly to the end points that can take varying degrees of automated action to achieve results, but also to very much drive decision support in real time to people on their mobiles or in whatever. What I'm getting at is that IoT is definitely a reality in the real world in terms of our lives. It's definitely a reality in terms of the index generation of data applications. But there's a lot of the back end in terms of readying algorithms and in training data for deployment of really high quality IoT applications, Edge applications, that hasn't come together yet in any coherent practice. >> It's emerging, it's emerging. >> It's emerging. >> It's a lot more work to do. OK, we're going to kick off day one, we've got some great guests, we see Rob Bearden in the house, Rob Thomas from IBM. >> Rob Bearden from Hortonworks. >> Rob Bearden from Hortonworks, and Rob Thomas from IBM. I want to bring up, Rob wrote a book just recently. He wrote Big Data Revolution, but he also wrote a new book called, Every Company is a Tech Company. But he mentions, he kind of teases out this concept of a renaissance, so I want to get your thoughts on this. If you look at Strata, Hadoop, Strata Data, the O'Reilly Conference, which has turned into like a marketing machine, right. A lot of hype there. But as the community model grows up, you're starting to see a renaissance of real creative developers, you're starting to see, not just open source, pure, full stack developers doing all the heavy lifting, but real creative competition, in a renaissance, that's really the key. You're seeing a lot more developer action, tons outside of the, what was classically called the data space. The role of data and how it relates to the developer phenomenon that's going on right now. >> Yeah, it's the maker culture. Rob, in fact, about a year or more ago, IBM, at one of their events, they held a very maker oriented event, I think they called it Datapalooza at one point. What it's looking at, what's going on is it's more than just classic software developers are coming to the fore. When you're looking at IoT or Edge applications, it's hardware developers, it's UX developers, it's developers and designers who are trying to change and drive data driven applications into changing the very fabric of how things are done in the real world. What Peter Burris, we had a wiki about him called Programming in the Real World. What that all involves is there's a new set of skill sets that are coming together to develop these applications. It's well beyond just simply software development, it's well beyond simply data scientists. Maker culture. >> Programming in the real world is a great concept, because you need real time, which comes back down to this. I'm looking for this week from the guests we talked to, what their view is of the data market right now. Because if you want to get real time, you've got to move from that batch world to the real time world. I'm not saying batch is over, you've still got to store data, and that's growing at an exponential rate as well. But real time data, how do you use data in real time, how do the modelings work, how do you scale that. How do you take a DevOps culture to the data world is what I'm looking for. What are you looking for this week? >> What I'm looking for this week, I'm looking for DevOps solutions or platforms or environments for teams of data scientists who are building and training and deploying and evaluating, iterating deep learning and machine learning and natural language processing applications in a continuous release pipeline, and productionizing them. At Wikibon, we are going deeper in that whole notion of DevOps for data science. I mean, IBM's called it inside ops, others call it data ops. What we're seeing across the board is that more and more of our customers are focusing on how do we bring it all together, so the maker culture. >> Operationalizing it. >> Operationalizing it, so that the maker cultures that they have inside their value chain can come together and there's a standard pattern workflow of putting this stuff out and productionizing it, AI productionized in the real world. >> Moving in from the proof of concept notion to actually just getting things done, putting it out in the network, and then bringing it to the masses with operational support. >> Right, like the good folks at IBM with Watson data platform, on some levels, is a DevOPs for data science platform, but it's a collaborative environment. That's what I'm looking to see, and there's a lot of other solution providers who are going down that road. >> I mean, to me, if people have the community traction, that is the new benchmark, in my opinion. You heard it here on the Cube. Community continues to scale, you can start seeing it moving out of open source, you're seeing things like blockchain, you're seeing a decentralized Internet now happening everywhere, not just distributed but decentralized. When you have decentralization, community and software really shine. It's the Cube here in New York City all week. Stay with us for wall to wall coverage through Thursday here in New York City for Big Data NYC, in conjunction with Strata Data, this is the Cube, we'll be back with more coverage after this short break. (busy music) (serious electronic music) (peaceful music) >> Hi, I'm John Furrier, the Co-founder of SiliconANGLE Media, and Co-host of the Cube. I've been in the tech business since I was 19, first programming on mini computers in a large enterprise, and then worked at IBM and Hewlett Packard, a total of nine years in the enterprise, various jobs from programming, training, consulting, and ultimately, as an executive sales person, and then started my first company in 1997, and moved to Silicon Valley in 1999. I've been here ever since. I've always loved technology, and I love covering emerging technology. I was trained as a software developer and love business. I love the impact of software and technology to business. To me, creating technology that starts a company and creates value and jobs is probably one of the most rewarding things I've ever been involved in. I bring that energy to the Cube, because the Cube is where all the ideas are, and where the experts are, where the people are. I think what's most exciting about the Cube is that we get to talk to people who are making things happen, entrepreneurs, CEO of companies, venture capitalists, people who are really, on a day in and day out basis, building great companies. In the technology business, there's just not a lot real time live TV coverage, and the Cube is a non-linear TV operation. We do everything that the TV guys on cable don't do. We do longer interviews, we ask tougher questions. We ask, sometimes, some light questions. We talk about the person and what they feel about. It's not prompted and scripted, it's a conversation, it's authentic. For shows that have the Cube coverage, it makes the show buzz, it creates excitement. More importantly, it creates great content, great digital assets that can be shared instantaneously to the world. Over 31 million people have viewed the Cube, and that is the result of great content, great conversations. I'm so proud to be part of the Cube with a great team. Hi, I'm John Furrier, thanks for watching the Cube. >> Announcer: Coming up on the Cube, Tekan Sundar, CTO of Wine Disco. Live Cube coverage from Big Data NYC 2017 continues in a moment. >> Announcer: Coming up on the Cube, Donna Prlich, Chief Product Officer at Pentaho. Live Cube coverage from Big Data New York City 2017 continues in a moment. >> Announcer: Coming up on the Cube, Amit Walia, Executive Vice President and Chief Product Officer at Informatica. Live Cube coverage from Big Data New York City continues in a moment. >> Announcer: Coming up on the Cube, Prakash Nodili, Co-founder and CEO of Pexif. Live Cube coverage from Big Data New York City continues in a moment. (serious electronic music)