(calm electronic music) >> Okay, welcome back everyone to theCUBE's coverage here live in New York City for AWS Summit 2022. I'm John Furrier, host of theCUBE, but a great conversation here as the day winds down. First of all, 10,000 plus people, this is a big event, just New York City. So sign of the times that some headwinds are happening? I don't think so, not in the cloud enterprise innovation game. Lot going on, this innovation conversation we're going to have now is about the confluence of cloud scale integration data and the future of how FinTech and other markets are going to change with technology. We got Chris Thomas, the CTO of Slalom, and Rob Krugman, chief digital officer at Broadridge. Gentlemen, thanks for coming on theCUBE. >> Thanks for having us. >> So we had a talk before we came on camera about your firm, what you guys do, take a quick minute to just give the scope and size of your firm and what you guys work on. >> Yeah, so Broadridge is a global financial FinTech company. We work on, part of our business is capital markets and wealth, and that's about a third of our business, about $7 trillion a day clearing through our platforms. And then the other side of our business is communications where we help all different types of organizations communicate with their shareholders, communicate with their customers across a variety of different digital channels and capabilities. >> Yeah, and Slalom, give a quick one minute on Slalom. I know you guys, but for the folks that don't know you. >> Yeah, no problem. So Slalom is a modern consulting firm focused on strategy, technology, and business transformation. And me personally, I'm part of the element lab, which is focused on forward thinking technology and disruptive technology in the next five to 10 years. >> Awesome, and that's the scope of this conversation. The next five to 10 years, you guys are working on a project together, you're kind of customer partners. You're building something. What are you guys working on? I can't wait to jump into it, explain. >> Sure, so similar to Chris, at Broadridge, we've created innovation capability, innovation incubation capability, and one of the first areas we're experimenting in is digital assets. So what we're looking to do is we're looking at a variety of different areas where we think consolidation network effects that we could bring can add a significant amount of value. And so the area we're working on is this concept of a wallet of wallets. How do we actually consolidate assets that are held across a variety of different wallets, maybe traditional locations- >> Digital wallets. >> Digital wallets, but maybe even traditional accounts, bring that together and then give control back to the consumer of who they want to share that information with, how they want their transactions to be able to control. So the idea of, people talk about Web 3 being the internet of value. I often think about it as the internet of control. How do you return control back to the individual so that they can make decisions about how and who has access to their information and assets? >> It's interesting, I totally like the value angle, but your point is what's the chicken and the egg here, the cart before the horse, you can look at it both ways and say, okay, control is going to drive the value. This is an interesting nuance, right? >> Yes, absolutely. >> So in this architectural world, they thought about the data plane and the control plane. Everyone's trying to go old school, middleware thinking. Let's own the data plane, we'll win everything. Not going to happen if it goes decentralized, right, Chris? >> Yeah, yeah. I mean, we're building a decentralized application, but it really is built on top of AWS. We have a serverless architecture that scales as our business scales built on top of things like S3, Lambda, DynamoDB, and of course using those security principles like Cognito and AWS Gateway, API Gateway. So we're really building an architecture of Web 3 on top of the Web 2 basics in the cloud. >> I mean, all evolutions are abstractions on top of each other, IG, DNS, Key, it goes the whole nine yards. In digital, at least, that's the way. Question about serverless real quick. I saw that Redshift just launched general availability of serverless in Redshift? >> Yes. >> You're starting to see the serverless now part of almost all the services in AWS. Is that enabling that abstraction, because most people don't see it that way. They go, oh, well, Amazon's not Web 3. They got databases, you could use that stuff. So how do you connect the dots and cross the bridge to the future with the idea that I might not think Web 2 or cloud is Web 3? >> I'll jump in quick. I mean, I think it's the decentralize. If you think about decentralization. serverless and decentralization, you could argue are the same way of, they're saying the same thing in different ways. One is thinking about it from a technology perspective. One is thinking about it from an ecosystem perspective and how things come together. You need serverless components that can talk to each other and communicate with each other to actually really reach the promise of what Web 3 is supposed to be. >> So digital bits or digital assets, I call it digital bits, 'cause I think zero ones. If you digitize everything and everything has value or now control drives the value. I could be a soccer team. I have apparel, I have value in my logos, I have photos, I have CUBE videos. I mean some say that this should be an NFT. Yeah, right, maybe, but digital assets have to be protected, but owned. So ownership drives it too, right? >> Absolutely. >> So how does that fit in, how do you explain that? 'Cause I'm trying to tie the dots here, connect the dots and tie it together. What do I get if I go down this road that you guys are building? >> So I think one of the challenges of digital assets right now is that it's a closed community. And I think the people that play in it, they're really into it. And so you look at things like NFTs and you look at some of the other activities that are happening and there are certain naysayers that look at it and say, this stuff is not based upon value. It's a bunch of artwork, it can't be worth this. Well, how about we do a time out there and we actually look at the underlying technology that's supporting this, the blockchain, and the potential ramifications of that across the entire financial ecosystem, and frankly, all different types of ecosystems of having this immutable record, where information gets stored and gets sent and the ability to go back to it at all times, that's where the real power is. So I think we're starting to see. We've hit a bit of a hiccup, if you will, in the cryptocurrencies. They're going to continue to be there. They won't all be there. A lot of them will probably disappear, but they'll be a finite number. >> What percentage of stuff do you think is vapor BS? If you had to pick an order of magnitude number. >> (laughs) I would say at least 75% of it. (John laughs) >> I mean, there's quite a few projects that are failing right now, but it's interesting in that in the crypto markets, they're failing gracefully. Because it's on the blockchain and it's all very transparent. Things are checked, you know immediately which companies are insolvent and which opportunities are still working. So it's very, very interesting in my opinion. >> Well, and I think the ones that don't have valid premises are the ones that are failing. Like Terra and some of these other ones, if you actually really looked at it, the entire industry knew these things were no good. But then you look at stable coins. And you look at what's going on with CBDCs. These are backed by real underlying assets that people can be comfortable with. And there's not a question of, is this going to happen? The question is, how quickly is it going to happen and how quickly are we going to be using digital currencies? >> It's interesting, we always talk about software, software as money now, money is software and gold and oil's moving over to that crypto. How do you guys see software? 'Cause we were just arguing in the queue, Dave Vellante and I, before you guys came on that the software industry pretty much does not exist anymore, it's open source. So everything's open source as an industry, but the value is integration, innovation. So it's not just software, it's the free. So you got to, it's integration. So how do you guys see this software driving crypto? Because it is software defined money at the end of the day. It's a token. >> No, I think that's absolutely one of the strengths of the crypto markets and the Web 3 market is it's governed by software. And because of that, you can build a trust framework. Everybody knows it's on the public blockchain. Everybody's aware of the software that's driving the rules and the rules of engagement in this blockchain. And it creates that trust network that says, hey, I can transact with you even though I don't know anything about you and I don't need a middleman to tell me I can trust you. Because this software drives that trust framework. >> Lot of disruption, lot of companies go out of business as a middleman in these markets. >> Listen, the intermediaries either have to disrupt themselves or they will be disrupted. I think that's what we're going to learn here. And it's going to start in financial services, but it's going to go to a lot of different places. I think the interesting thing that's happening now is for the first time, you're starting to see the regulators start to get involved. Which is actually a really good thing for the market. Because to Chris's point, transparency is here, how do you actually present that transparency and that trust back to consumers so they feel comfortable once that problem is solved. And I think everyone in the industry welcomes it. All of a sudden you have this ecosystem that people can play in, they can build and they can start to actually create real value. >> Every structural change that I've been involved in my 30 plus year career has been around inflection points. There was always some sort of underbelly. So I'm not going to judge crypto. It's been in the market for a while, but it's a good sign there's innovation happening. So as now, clarity comes into what's real. I think you guys are talking a conversation I think is refreshing because you're saying, okay, cloud is real, Lambda, serverless, all these tools. So Web 3 is certainly real because it's a future architecture, but it's attracting the young, it's a cultural shift. And it's also cooler than boring Web 2 and cloud. So I think the cultural shift, the fact that it's got data involved, there's some disruption around middleman and intermediaries, makes it very attractive to tech geeks. You look at, I read a stat, I heard a stat from a friend in the Bay Area that 30% of Cal computer science students are dropping out and jumping into crypto. So it's attracting the technical nerds, alpha geeks. It's a cultural revolution and there's some cool stuff going on from a business model standpoint. >> There's one thing missing. The thing that's missing, it's what we're trying to work on, I think is experience. I think if you're being honest about the entire marketplace, what you would agree is that this stuff is not easy to use today, and that's got to be satisfied. You need to do something that if it's the 85 year old grandma that wants to actually participate in these markets that not only can they feel comfortable, but they actually know how to do it. You can't use these crazy tools where you use these terms. And I think the industry, as it grows up, will satisfy a lot of those issues. >> And I think this is why I want to tie back and get your reaction to this. I think that's why you guys talking about building on top of AWS is refreshing, 'cause it's not dogmatic. Well, we can't use Amazon, it's not really Web 3. Well, a database could be used when you need it. You don't need to write everything through the blockchain. Databases are a very valuable capability, you get serverless. So all these things now can work together. So what do you guys see for companies that want to be Web 3 for all the good reasons and how do they leverage cloud specifically to get there? What are some things that you guys have learned that you can point to and share, you want to start? >> Well, I think not everything has to be open and public to everybody. You're going to want to have some things that are secret. You're going to want to encrypt some things. You're going to want to put some things within your own walls. And that's where AWS really excels. I think you can have the best of both worlds. So that's my perspective on it. >> The only thing I would add to it, so my view is it's 2022. I actually was joking earlier. I think I was at the first re:Invent. And I remember walking in and this was a new industry. >> It was tiny. >> This is foundational. Like cloud is not a, I don't view like, we shouldn't be having that conversation anymore. Of course you should build this stuff on top of the cloud. Of course you should build it on top of AWS. It just makes sense. And we should, instead of worrying about those challenges, what we should be worrying about are how do we make these applications easier to use? How do we actually- >> Energy efficient. >> How do we enable the promise of what these things are going to bring, and actually make it real, because if it happens, think about traditional assets. There's projects going on globally that are looking at how do you take equity securities and actually move them to the blockchain. When that stuff happens, boom. >> And I like what you guys are doing, I saw the news out through this crypto winter, some major wallet exchanges that have been advertising are hurting. Take me through what you guys are thinking, what the vision is around the wallet of wallets. Is it to provide an experience for the user or the market industry itself? What's the target, is it both? Share the design goals for the wallet of wallets. >> My favorite thing about innovation and innovation labs is that we can experiment. So I'll go in saying we don't know what the final answer is going to be, but this is the premise that we have. In this disparate decentralized ecosystem, you need some mechanism to be able to control what's actually happening at the consumer level. So I think the key target is how do you create an experience where the consumer feels like they're in control of that value? How do they actually control the underlying assets? And then how does it actually get delivered to them? Is it something that comes from their bank, from their broker? Is it coming from an independent organization? How do they manage all of that information? And I think the last part of it are the assets. It's easy to think about cryptos and NFTs, but thinking about traditional assets, thinking about identity information and healthcare records, all of that stuff is going to become part of this ecosystem. And imagine being able to go someplace and saying, oh, you need my information. Well, I'm going to give it to you off my phone and I'm going to give it to you for the next 24 hours so you can use it, but after that you have no access to it. Or you're my financial advisor, here's a view of what I actually have, my underlying assets. What do you recommend I do? So I think we're going to see an evolution in the market. >> Like a data clean room. >> Yeah, but that you control. >> Yes! (laughs) >> Yes! >> I think about it very similarly as well. As my journey into the crypto market has gone through different pathways, different avenues. And I've come to a place where I'm really managing eight different wallets and it's difficult to figure exactly where all my assets are and having a tool like this will allow me to visualize and aggregate those assets and maybe even recombine them in unique ways, I think is hugely valuable. >> My biggest fear is losing my key. >> Well, and that's an experience problem that has to be solved, but let me give you, my favorite use case in this space is, 'cause NFTs, right? People are like, what does NFTs really mean? Title insurance, right? Anyone buy a house or refinance your mortgage? You go through this crazy process that costs seven or eight thousand dollars every single time you close on something to get title insurance so they could validate it. What if that title was actually sitting on the chain, you got an NFT that you put in your wallet and when it goes time to sell your house or to refinance, everything's there. Okay, I'm the owner of the house. I don't know, JP Morgan Chase has the actual mortgage. There's another lien, there's some taxes. >> It's like a link tree in the wallet. (laughs) >> Yeah, think about it, you got a smart contract. Boom, closing happens immediately. >> I think that's one of the most important things. I think people look at NFTs and they think, oh, this is art. And that's sort of how it started in the art and collectable space, but it's actually quickly moving towards utilities and tokenization and passes. And that's where I think the value is. >> And ownership and the token. >> Identity and ownership, especially. >> And the digital rights ownership and the economics behind it really have a lot of scale 'cause I appreciate the FinTech angle you are coming from because I can now see what's going on here with you. It's like, okay, we got to start somewhere. Let's start with the experience. The wallet's a tough nut to crack, 'cause that requires defacto participation in the industry as a defacto standard. So how are you guys doing there? Can you give an update and then how can people get, what's the project called and how do people get involved? >> Yeah, so we're still in the innovation, incubation stages. So we're not launching it yet. But what I will tell you is what a lot of our focus is, how do we make these transactional things that you do? How do we make it easy to pull all your assets together? How do we make it easy to move things from one location to the other location in ways that you're not using a weird cryptographic numeric value for your wallet, but you actually can use real nomenclature that you can renumber and it's easy to understand. Our expectation is that sometime in the fall, we'll actually be in a position to launch this. What we're going to do over the summer is we're going to start allowing people to play with it, get their feedback, and we're going to iterate. >> So sandbox in when, November? >> I think launch in the fall, sometime in the fall. >> Oh, this fall. >> But over the summer, what we're expecting is some type of friends and family type release where we can start to realize what people are doing and then fix the challenges, see if we're on the right track and make the appropriate corrections. >> So right now you guys are just together on this? >> Yep. >> The opening up friends and family or community is going to be controlled. >> It is, yeah. >> Yeah, as a group, I think one thing that's really important to highlight is that we're an innovation lab. We're working with Broadridge's innovation lab, that partnership across innovation labs has allowed us to move very, very quickly to build this. Actually, if you think about it, we were talking about this not too long ago and we're almost close to having an internal launch. So I think it's very rapid development. We follow a lot of the- >> There's buy-in across the board. >> Exactly, exactly, and we saw lot of very- >> So who's going to run this? A Dow, or your companies, is it going to be a separate company? >> So to be honest, we're not entirely sure yet. It's a new product that we're going to be creating. What we actually do with it. Our thought is within an innovation environment, there's three things you could do with something. You can make it a product within the existing infrastructure, you can create a new business unit or you can spin it off as something new. I do think this becomes a product within the organization based upon it's so aligned to what we do today, but we'll see. >> But you guys are financing it? >> Yes. >> As collective companies? >> Yeah, right. >> Got it, okay, cool. Well, let us know how we can help. If you guys want to do a remote in to theCUBE. I would love the mission you guys are on. I think this is the kind of work that every company should be doing in the new R and D. You got to jump in the deep end and swim as fast as possible. But I think you can do it. I think that is refreshing and that's smart. >> And you have to do it quick because this market, I think the one thing we would probably agree on is that it's moving faster than we could, every week there's something else that happens. >> Okay, so now you guys were at Consensus down in Austin when the winter hit and you've been in the business for a long time, you got to know the industries. You see where it's going. What was the big thing you guys learned, any scar tissue from the early data coming in from the collaboration? Was there some aha moments, was there some oh shoot moments? Oh, wow, I didn't think that was going to happen. Share some anecdotal stories from the experience. Good, bad, and if you want to be bold say ugly, too. >> Well, I think the first thing I want to say about the timing, it is the crypto winter, but I actually think now's a really great time to build something because everybody's continuing to build. Folks are focused on the future and that's what we are as well. In terms of some of the challenges, well, the Web 3 space is so new. And there's not a way to just go online and copy somebody else's work and rinse and repeat. We had to figure a lot of things on our own. We had to try different technologies, see which worked better and make sure that it was functioning the way we wanted it to function. Really, so it was not easy. >> They oversold that product out, that's good, like this team. >> But think about it, so the joke is that when winter is when real work happens. If you look at the companies that have not been affected by this it's the infrastructure companies and what it reminds me of, it's a little bit different, but 2001, we had the dot com bust. The entire industry blew up, but what came out of that? >> Everything that exists. >> Amazon, lots of companies grew up out of that environment. >> Everything that was promoted actually happened. >> Yes, but you know what didn't happen- >> Food delivery. >> But you know what's interesting that didn't happen- >> (laughs) Pet food, the soccer never happened. >> The whole Super Bowl, yes. (John laughs) In financial services we built on top of legacy. I think what Web 3 is doing, it's getting rid of that legacy infrastructure. And the banks are going to be involved. There's going to be new players and stuff. But what I'm seeing now is a doubling down of the infrastructure investment of saying okay, how do we actually make this stuff real so we can actually show the promise? >> One of the things I just shared, Rob, you'd appreciate this, is that the digital advertising market's changing because now banner ads and the old techniques are based on Web 2 infrastructure, basically DNS as we know it. And token problems are everywhere. Sites and silos are built because LinkedIn doesn't share information. And the sites want first party data. It's a hoarding exercise, so those practices are going to get decimated. So in comes token economics, that's going to get decimated. So you're already seeing the decline of media. And advertising, cookies are going away. >> I think it's going to change, it's going to be a flip, because I think right now you're not in control. Other people are in control. And I think with tokenomics and some of the other things that are going to happen, it gives back control to the individual. Think about it, right now you get advertising. Now you didn't say I wanted this advertising. Imagine the value of advertising when you say, you know what, I am interested in getting information about this particular type of product. The lead generation, the value of that advertising is significantly higher. >> Organic notifications. >> Yeah. >> Well, gentlemen, I'd love to follow up with you. I'm definitely going to ping in. Now I'm going to put CUBE coin back on the table. For our audience CUBE coin's coming. Really appreciate it, thanks for sharing your insights. Great conversation. >> Excellent, thank you for having us. >> Excellent, thank you so much. >> theCUBE's coverage here from New York City. I'm John Furrier, we'll be back with more live coverage to close out the day. Stay with us, we'll be right back. >> Excellent. (calm electronic music)

>> Voice Over: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Okay. Welcome back everyone. To theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We've got a great segment here on the power of hybrid cloud and AI. And I'm excited to have Rob Thomas, Senior Vice President of IBM's cloud and Data platform, CUBE alumni. Been on going back years and years talking about data. Rob, great to see you, a leader at IBM. Thanks for joining. >> John. Great to see you hope everybody is safe and well and great to be with you again. >> Yeah, love the progress, love the Hybrid Cloud distributed computing, meets operating systems, meets modern applications at the center of it is the new cloud equation. And of course data continues to be the value proposition as the platform. And as you quoted many times and I love your favorite quote. There's no AI without IA. So you got to have the architecture. So that still rings true today and it's just so evergreen and so relevant and cooler than ever with machine learning and AI operations. So let's just jump in. IBM's announced, host a new products and updates at Think. Tell us what you're most excited about and what should people pay attention to. >> Maybe I'll connect two thoughts here. There is no AI without IA, still true today. Meaning, customers that want to do AI need an information architecture. There was an IDC report just last year that said, "Despite all the progress on data, still 90% of data in organizations is either unused or underutilized." So what's amazing is after all the time we've been talking John, we're still really just getting started. Then that kind of connects to another thought, which is I still believe that AI is not going to replace managers, but managers that use AI will replace the managers that do not. And I'd say that's the backdrop for all the announcements that we're doing this week. It's things like auto SQL. How do you actually automate the creation of SQL queries in a large distributed data warehouse? It's never been done before, now we're doing it. It's things like Watson Orchestrate which is super powers in the hands of any business user, just to ask for something to get done. Just ask for a task to get completed. Watson Orchestrator will do that for you. It's maximo mobile. So anybody working in the field now has access to an AI system on their device for how they're managing their assets. So this is all about empowering people and users that use these products are going to have an advantage over the users that are not, that's what I'm really excited about. >> So one of the things that's coming out as Cloud Pak for Data, AI powered automation these are kind of two that you kind of touched upon the SQL thing their. Cloud Pak is there, you got it for Data and this automation trend. What is that about? Why is it important? Can you share with us the relevance of those two things? >> Let's talk broadly about automation. There's two huge markets here. There's the market for RPA business process, $30 billion market. There's the market for AIOps, which is growing 22%, that's on its way to $40 billion. These are enormous markets. Probably the biggest bet IBM has made in the last year is in automation. Explicitly in Watson AIOps. Last June in Think we announced Watson AIOps, then we did the acquisition of Instana, then we announced our intent to acquire Turbonomic. At this point, we're the only company that has all the pieces for automating how you run your IT systems. That's what I mean when I say AIOps. So really pleased with the progress that we've made there. But again, we're just getting started. >> Yeah. Congratulations on the Turbonomic. I was just commenting on that when that announced. IBM buying into the Cloud and the Hybrid cloud is interesting because the shift has happened. It's Public Cloud, it's on premises as Edge. Those two things as a system, it's more important ever than the modernization of the apps that you guys are talking about and having the under the cover capabilities. So as Cloud and Data merge, this kind of control plane concept, this architecture, as you'd said IA. You can't have AI without IA. What is that architecture look like? Can you break down the elements of what's involved? I know there's predictive analytics, there's automation and security. What are the pillars of this architecture? What are the four concepts? If you can explain that. >> Yeah, let's start with the basics. So Hybrid Cloud is about you build your software runs once and you run it anywhere you want, any public cloud,any private cloud. That assumes containers are important to the future of software. We are a hundred percent convinced that is true. OpenShift is the platform that we build on and that many software companies in the world are now building on because it gives you portability for your applications. So then you start to think about if you have that common fabric for Hybrid Cloud, how do you deliver value to customers in addition to the platform? To me, that's four big things. It's automation, we talked about that. It's security, it's predictions. How do you actually make predictions on your data? And then it's modernization. Meaning, how do you actually help customers modernize their applications and get to the Cloud? So those are the things we always talk about, automate, secure, modernize, predict. I think those are the four most important things for every company that's thinking about Cloud and AI. >> Yeah, it's interesting. I love the security side is one of the big conversations in AIOps and day two operations or whatever it's called is shifting left, getting security into the Cloud native kind of development pipeline. But speaking of secure, you have a customer that was talking about this Dow Chemical. About IB empowering Dow zero trust architecture. Could you explain that deal and how that's working? Because that's again, huge enterprise customer, very big scale at scale, zero trust is big, part of it. What is this? >> Let's start with the basics. So what is zero trust mean? It means to have a secure business, you have to start with the assumption that nothing can be trusted. That means you have to think about all aspects of your security practice. How do you align on a security strategy? How do you protect your data assets? How do you manage security threats? So we always talk about a line, protect, manage back to modernize, which is how do you bring all your systems forward to do this? That's exactly what we're doing with the Dow as you heard in that session, which is they've kind of done that whole journey from how they built a security strategy that was designed with zero trust in mind, they're protecting data assets, they're managing cyber threats in real time with a relatively low number of false positives which are the issue that most companies have. They're a tremendous example of a company that jumped on this and has had a really big impact. And they've done it without interfering with their business operations, meaning anybody can lock everything down but then you can't really run your business if you're doing that. They've done it, I think in a really intelligent way. >> That's awesome. We always talk about the big waves. You always give great color commentary on the trends. Right now though, the tsunami seems to be a confluence of many things coming together. What are some of the big trends in waves you're seeing now specifically on the tech side, on the technology side, as well as the business side right now? 'Cause coming out of post COVID, it's pretty clear cloud-native is powering a new growth strategy for customers. Dow was one of them, you just commented on it but there's a bigger wave happening here, both on the tech theater and in the business theater. Can you share your views on and your opinions and envision on these trends? >> I think there's three profound trends that are actually pretty simple to understand. One is, technology is going to decentralize again. We've always gone from centralized architectures to decentralized. Mainframe was centralized, internet mobile decentralized. The first version of public cloud was centralized, meaning bringing everything to one place. Technology is decentralized and again, with Hybrid Cloud, with Edge, pretty straight forward I think that's a trend that we can ride and lead for the next decade. Next is around automation that we talked about. There was a McKinsey report that said, "120 billion hours a year are going to be automated with things like Watson Orchestrator, Watson AIOps." What we're doing around Cloud Pak for automation, we think that time is now. We think you can start to automate in your business today and you may have seen the--example where we're doing customer care and they're now automating 70% of their inbound customer inquiries. It's really amazing. And then the third is around data. The classical problem, I mentioned 90% is still unused or underutilized. This trend on data is not about to slow down because the data being collected is still multiplying 10 X every year and companies have to find a way to organize that data as they collected. So that's going to be a trend that continues. >> You know, I just kind of pinched myself sometimes and hearing you talk with some of our earlier conversations in theCUBE, people who have been on this data mindset have really been successful because it's evolving and growing and it's changing and it's adding more input into the system and the technology is getting better. There's more cloud scales. You mentioned automation and scale are huge. And I think this really kind of wakes everyone up. And certainly the pandemic has woken everyone up to the fact that this is driving new experiences for users and businesses, right? So this is, and then those experiences become expectations. This is the classic UX paradigm that grows from new things. So I got to ask you, with the pandemic what is the been the most compelling ways you seen people operate, create new expectations? Because new things are coming, new big things, and new incremental things are happening. So evolution and revolutionary capabilities. Can you share some examples and your thoughts? >> We've collected a decent bit of data on this. And what's interesting is how much AI has accelerated since the pandemic started. And it's really in five areas, it's customer care that we talked about, virtual agents, customer service, how you do that. It's employee experience. So somewhere to customer care but how do you take care of your employees using AI? Third is around AIOps, we talked about that. Fourth is around regulatory compliance and fifth is around financial planning and budgeting. These are the five major use cases of AI that are getting into production in companies over the last year that's going to continue to accelerate. So I think it's actually fairly clarifying now that we really understand these are the five big things. I encourage anybody watching, pick one of these, get started, then pick the second, then pick the third. If you are not doing all five of these, 12, 18, 24 months from now, you are going to be behind. >> So give us an example of some things that have surprised you in the pandemic and things that blew you away. Like, wow, I didn't see that coming. Can you share on things that you've seen evolve? Cause you're a year ahead of the business units of Cloud and Data, big part of IBM and you see customer examples. Just quickly share some notable use cases or just anecdotal examples of just things that jumped out at you that said, "Wow, that's going to be a double-down moment or that's not going to be anymore." Exposes, the pandemic exposes the good, bad and the ugly. I mean, people got caught off guard, some got a tailwind, some had a headwind, some are retooling. What's your thoughts on what you can you share any examples? >> Like everybody, many things have surprised me in the last year. I am encouraged at how fast many companies were able to adjust and adapt for this world. So that's a credit to all the resiliency that they built into their processes, their systems and their people over time. Related to that, the thing that really sticks out to me again, is this idea of using AI to serve your customers and to serve your employees. We had a hundred customers that went live with one of those two use cases in the first 35 days of the pandemic. Just think about that acceleration. I think without the pandemic, for those hundred it might've taken three years and it happened in 35 days. It's proof that the technology today is so powerful. Sometimes it just takes the initiative to get started and to do something. And all those companies have really benefited from this. So it's great to see. >> Great. Rob, great to have you on. Great to have your commentary on theCUBE. Could you just quickly share in 30 seconds, what is the most important thing people should pay attention to and Think this year from your perspective? What's the big aha moment that you think they could walk away with? >> We have intentionally made this a very technology centric event. Just go look at the demos, play with the technology. I think you will be impressed and start to see, let's say a bit of a new IBM in terms of how we're making technology accessible and easy for anybody to use. >> All right. Rob Thomas, Senior Vice President of IBM cloud and Data platform. Great to have you on and looking forward to seeing more of you this year and hopefully in person. Thanks for coming on theCUBE virtual. >> Thanks, John. >> Okay. I'm John Furrier with theCUBE. Keep coverage of IBM Think 2021. Thank you for watching. (soft music)

(soft music) >> Voice Over: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Okay. Welcome back everyone. To theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We've got a great segment here on the power of hybrid cloud and AI. And I'm excited to have Rob Thomas, Senior Vice President of IBM's cloud and Data platform, CUBE alumni. Been on going back years and years talking about data. Rob, great to see you, a leader at IBM. Thanks for joining. >> John. Great to see you hope everybody is safe and well and great to be with you again. >> Yeah, love the progress, love the Hybrid Cloud distributed computing, meets operating systems, meets modern applications at the center of it is the new cloud equation. And of course data continues to be the value proposition as the platform. And as you quoted many times and I love your favorite quote. There's no AI without IA. So you got to have the architecture. So that still rings true today and it's just so evergreen and so relevant and cooler than ever with machine learning and AI operations. So let's just jump in. IBM's announced, host a new products and updates at Think. Tell us what you're most excited about and what should people pay attention to. >> Maybe I'll connect two thoughts here. There is no AI without IA, still true today. Meaning, customers that want to do AI need an information architecture. There was an IDC report just last year that said, "Despite all the progress on data, still 90% of data in organizations is either unused or underutilized." So what's amazing is after all the time we've been talking John, we're still really just getting started. Then that kind of connects to another thought, which is I still believe that AI is not going to replace managers, but managers that use AI will replace the managers that do not. And I'd say that's the backdrop for all the announcements that we're doing this week. It's things like auto SQL. How do you actually automate the creation of SQL queries in a large distributed data warehouse? It's never been done before, now we're doing it. It's things like Watson Orchestrate which is super powers in the hands of any business user, just to ask for something to get done. Just ask for a task to get completed. Watson Orchestrator will do that for you. It's Maximo Mbo. So anybody working in the field now has access to an AI system on their device for how they're managing their assets. So this is all about empowering people and users that use these products are going to have an advantage over the users that are not, that's what I'm really excited about. >> So one of the things that's coming out as Cloud Pak for Data, AI powered automation these are kind of two that you kind of touched upon the SQL thing their. Cloud Pak is there, you got it for Data and this automation trend. What is that about? Why is it important? Can you share with us the relevance of those two things? >> Let's talk broadly about automation. There's two huge markets here. There's the market for RPA business process, $30 billion market. There's the market for AIOps, which is growing 22%, that's on its way to $40 billion. These are enormous markets. Probably the biggest bet IBM has made in the last year is in automation. Explicitly in Watson AIOps. Last June in Think we announced Watson AIOps, then we did the acquisition of Instana, then we announced our intent to acquire Turbonomic. At this point, we're the only company that has all the pieces for automating how you run your IT systems. That's what I mean when I say AIOps. So really pleased with the progress that we've made there. But again, we're just getting started. >> Yeah. Congratulations on the Turbonomic. I was just commenting on that when that announced. IBM buying into the Cloud and the Hybrid cloud is interesting because the shift has happened. It's Public Cloud, it's on premises as Edge. Those two things as a system, it's more important ever than the modernization of the apps that you guys are talking about and having the under the cover capabilities. So as Cloud and Data merge, this kind of control plane concept, this architecture, as you'd said IA. You can't have AI without IA. What is that architecture look like? Can you break down the elements of what's involved? I know there's predictive analytics, there's automation and security. What are the pillars of this architecture? What are the four concepts? If you can explain that. >> Yeah, let's start with the basics. So Hybrid Cloud is about you build your software runs once and you run it anywhere you want, any public cloud,any private cloud. That assumes containers are important to the future of software. We are a hundred percent convinced that is true. OpenShift is the platform that we build on and that many software companies in the world are now building on because it gives you portability for your applications. So then you start to think about if you have that common fabric for Hybrid Cloud, how do you deliver value to customers in addition to the platform? To me, that's four big things. It's automation, we talked about that. It's security, it's predictions. How do you actually make predictions on your data? And then it's modernization. Meaning, how do you actually help customers modernize their applications and get to the Cloud? So those are the things we always talk about, automate, secure, modernize, predict. I think those are the four most important things for every company that's thinking about Cloud and AI. >> Yeah, it's interesting. I love the security side is one of the big conversations in AIOps and day two operations or whatever it's called is shifting left, getting security into the Cloud native kind of development pipeline. But speaking of secure, you have a customer that was talking about this Dow Chemical. About IB empowering Dow zero trust architecture. Could you explain that deal and how that's working? Because that's again, huge enterprise customer, very big scale at scale, zero trust is big, part of it. What is this? >> Let's start with the basics. So what is zero trust mean? It means to have a secure business, you have to start with the assumption that nothing can be trusted. That means you have to think about all aspects of your security practice. How do you align on a security strategy? How do you protect your data assets? How do you manage security threats? So we always talk about a line, protect, manage back to modernize, which is how do you bring all your systems forward to do this? That's exactly what we're doing with the Dow as you heard in that session, which is they've kind of done that whole journey from how they built a security strategy that was designed with zero trust in mind, they're protecting data assets, they're managing cyber threats in real time with a relatively low number of false positives which are the issue that most companies have. They're a tremendous example of a company that jumped on this and has had a really big impact. And they've done it without interfering with their business operations, meaning anybody can lock everything down but then you can't really run your business if you're doing that. They've done it, I think in a really intelligent way. >> That's awesome. We always talk about the big waves. You always give great color commentary on the trends. Right now though, the tsunami seems to be a confluence of many things coming together. What are some of the big trends in waves you're seeing now specifically on the tech side, on the technology side, as well as the business side right now? 'Cause coming out of post COVID, it's pretty clear cloud-native is powering a new growth strategy for customers. Dow was one of them, you just commented on it but there's a bigger wave happening here, both on the tech theater and in the business theater. Can you share your views on and your opinions and envision on these trends? >> I think there's three profound trends that are actually pretty simple to understand. One is, technology is going to decentralize again. We've always gone from centralized architectures to decentralized. Mainframe was centralized, internet mobile decentralized. The first version of public cloud was centralized, meaning bringing everything to one place. Technology is decentralized and again, with Hybrid Cloud, with Edge, pretty straight forward I think that's a trend that we can ride and lead for the next decade. Next is around automation that we talked about. There was a McKinsey report that said, "120 billion hours a year are going to be automated with things like Watson Orchestrator, Watson AIOps." What we're doing around Cloud Pak for automation, we think that time is now. We think you can start to automate in your business today and you may have seen the C QVS example where we're doing customer care and they're now automating 70% of their inbound customer inquiries. It's really amazing. And then the third is around data. The classical problem, I mentioned 90% is still unused or underutilized. This trend on data is not about the slow down because the data being collected is still multiplying 10 X every year and companies have to find a way to organize that data as they collected. So that's going to be a trend that continues. >> You know, I just kind of pinched myself sometimes and hearing you talk with some of our earlier conversations in theCUBE, people who have been on this data mindset have really been successful because it's evolving and growing and it's changing and it's adding more input into the system and the technology is getting better. There's more cloud scales. You mentioned automation and scale are huge. And I think this really kind of wakes everyone up. And certainly the pandemic has woken everyone up to the fact that this is driving new experiences for users and businesses, right? So this is, and then those experiences become expectations. This is the classic UX paradigm that grows from new things. So I got to ask you, with the pandemic what is the been the most compelling ways you seen people operate, create new expectations? Because new things are coming, new big things, and new incremental things are happening. So evolution and revolutionary capabilities. Can you share some examples and your thoughts? >> We've collected a decent bit of data on this. And what's interesting is how much AI has accelerated since the pandemic started. And it's really in five areas, it's customer care that we talked about, virtual agents, customer service, how you do that. It's employee experience. So somewhere to customer care but how do you take care of your employees using AI? Third is around AIOps, we talked about that. Fourth is around regulatory compliance and fifth is around financial planning and budgeting. These are the five major use cases of AI that are getting into production in companies over the last year that's going to continue to accelerate. So I think it's actually fairly clarifying now that we really understand these are the five big things. I encourage anybody watching, pick one of these, get started, then pick the second, then pick the third. If you are not doing all five of these, 12, 18, 24 months from now, you are going to be behind. >> So give us an example of some things that have surprised you in the pandemic and things that blew you away. Like, wow, I didn't see that coming. Can you share on things that you've seen evolve? Cause you're a year ahead of the business units of Cloud and Data, big part of IBM and you see customer examples. Just quickly share some notable use cases or just anecdotal examples of just things that jumped out at you that said, "Wow, that's going to be a double-down moment or that's not going to be anymore." Exposes, the pandemic exposes the good, bad and the ugly. I mean, people got caught off guard, some got a tailwind, some had a headwind, some are retooling. What's your thoughts on what you can you share any examples? >> Like everybody, many things have surprised me in the last year. I am encouraged at how fast many companies were able to adjust and adapt for this world. So that's a credit to all the resiliency that they built into their processes, their systems and their people over time. Related to that, the thing that really sticks out to me again, is this idea of using AI to serve your customers and to serve your employees. We had a hundred customers that went live with one of those two use cases in the first 35 days of the pandemic. Just think about that acceleration. I think without the pandemic, for those hundred it might've taken three years and it happened in 35 days. It's proof that the technology today is so powerful. Sometimes it just takes the initiative to get started and to do something. And all those companies have really benefited from this. So it's great to see. >> Great. Rob, great to have you on. Great to have your commentary on theCUBE. Could you just quickly share in 30 seconds, what is the most important thing people should pay attention to and Think this year from your perspective? What's the big aha moment that you think they could walk away with? >> We have intentionally made this a very technology centric event. Just go look at the demos, play with the technology. I think you will be impressed and start to see, let's say a bit of a new IBM in terms of how we're making technology accessible and easy for anybody to use. >> All right. Rob Thomas, Senior Vice President of IBM cloud and Data platform. Great to have you on and looking forward to seeing more of you this year and hopefully in person. Thanks for coming on theCUBE virtual. >> Thanks, John. >> Okay. I'm John Furrier with theCUBE. Keep coverage of IBM Think 2021. Thank you for watching. (soft music)

>> (vocalizing) >> Narrator: From theCube studios in Palo Alto and Boston, it's theCube. Covering IBM Think, brought to you by IBM. >> Hi everybody, this is Dave Vallante and this is our continuing coverage of Think 2020, the digital event experience. This is the post-thing, the sort of halo effect, the afterthoughts, and joining me is Rob Thomas, he's back. The Senior Vice president of Cloud and Data Platform. Rob, thanks for taking some time to debrief on Think. >> Absolutely Dave, great to be here, good to see you again. >> Yeah, so you have a great event, you guys put it together in record time. I want to talk about sort of your innovation agenda. I mean, you are at the heart of innovation. You're talking cloud, data, AI, really the pillars of innovation, I could probably add in edge to extend the cloud. But I wonder if you could talk about your vision for the innovation agenda and how you're bringing that to customers. I mean, we heard from PayPal, you talked about Royal Bank of Scotland, Credit Mutual, a number of customer examples. How are you bringing innovation forward with the customer? >> I wouldn't describe innovation, maybe I'd give it two different categories. One is, I think the classic term would be consumerization, and you're innovating by making interiorized technology really easy to use. That's why we built out a huge design capability, it's why we've been able to get products like Watson Assistant to get companies live in 24 hours. That's the consumerization aspect, just making enterprise products really easy to use. The second aspect is even harder, which is, how do you tap into an institution like IBM Research, where we're doing fundamental invention. So, one of our now strengths in the last couple of months was around taking technology out of IBM Debater, project Debater, the AI system that could debate humans and then putting that into enterprised products. And, you saw companies like PayPal that are using Watson Assistant and now they have access to that kind of language capability. There's only two aspects here, there's the consumerization and then there's about fundamental technology that really changes how businesses can operate. >> I mean, the point you made about speed and implementation in your key note was critical, I mean really, within 24 hours, very important during this pandemic. Talk about automation, you know, you would think by now right, everything's automation. But, now you're seeing a real boom in automation and it really is driven by AI, all this data, so there's seems to be a next wave, almost a renaissance, if you will, in automation. >> There is and I think automation, when people hear first of the term, it's sometimes a scary term. Because people are like hey, is this going to take my job? Gain a lot of momentum for automation is a difficult, repetitive tasks that nobody really wanted to do in the first place. Whether it's things like data matching, containerizing an application. All these are really hard things and the output's great, but nobody really wants to do that work, they just want the outcome. And, as we've started to demonstrate different use cases for automation that are in that realm, a lot of momentum has taken off, that we're seeing. >> I want to come back to this idea of consumerization and simplification. I mean, when you think about what's been happening over the last several years. And, you and I have talked about this a lot, AI for consumer versus AI for business and enterprise. And really, one of the challenges for the encumbrance, if you will, is to really become data driven, put data at the core and apply machine intelligence to that, just to that data. Now the good news is, they don't have to invent all this stuff, because guys like you are doing that and talk about how you're making that simple. I mean, cloud packs is an example of that, simplification, but talk about how customers are going to be able to tap into AI without having to be AI inventors. >> Well, the classic AI problem actually is a data problem, and the classic data problem is data slide over, which is a company has got a lot of data but it's spread across a hundred or a thousand or tens of thousands different repositories or locations. Our strategy when we say a hybrid cloud is about how do we unify those data storage. So, it's called PaaS, on red hat open shift. We do a lot of things like data virtualization, really high performance. So, we take what is thousands of different data sources and we have that packed like a single fluid item. So then, when you're training models, you can train your models in one place and connect to all your data. That is the big change that's happening and that's how you take something like hybrid cloud, and it actually starts to impact your data architecture. And once you're doing that, then AI becomes a lot easier, because the biggest AI challenge that I described is, where's the data? Is the data in a usable form? >> A lot of times in this industry, you know, we go whale hunting, there are a lot of big companies out there, a lot of times they take priority. You know, at the same time though, a lot of the innovations are coming from companies, you know, we've never even heard of that could be multi-billion dollar companies by the end of the decade. So, how can, you know, small companies and mid-sized companies tap into this trend? Is it just for the big whales or could the small guys participate? >> The thing that's pretty amazing about modern cloud and data technology, I'll call it, is it's accessible to companies of any size. When we talked about, you know, the hundred or so clients that have adopted Watson Assistant since COVID-19 started, many of those are very small institutions with no IT staff or very limited IT staff. Though, we're making this technology very accessible. when you look at something like data, now a small company may not have a hundred different repositories, which is fine, but what they do have is they do want to make better predictions, they do want to automate, they do want to optimize the business processes that they're running in their business. And, the way that we've transformed our model consumption base starting small, it's really making technology available to, you know, from anywhere from the local deli to the Fortune 50 Company. >> So, last question is, What are your big takeaways from Think? I would ask that question normally when we're in a live event. It's a little different with the digital event, but there are still takeaways. What was your reaction and what do to leave people with? >> Even as we get back to doing physical events, which I'm positive will happen at some point. What we learned is there is something great about an immersive digital experience. So, I think the future of events is probably higher than this. Meaning, a big digital experience, to complement the physical experience. That's one big takeaway because the reaction was so positive to the content and how people could access it. Second one is the, all the labs that we did. So, for developers, builders, those were at capacity, meaning we didn't even take any more. So, there's definitively a thirst in the market for developing new applications, developing new data products, developing new security products. That's clear just by the attendance that we saw, that's exciting. Now, I'd say third, that is that AI is now moving into the mainstream, that was clear from the customer examples, whether it was with Tansa or UPS or PayPal that I mentioned before, that was talking with me. AI is becoming accessible to every company, that's pretty exciting. >> Well, the world is hybrid, oh you know the lab, the point you're making about labs is really important. I've talked to a number of individuals saying, "Hey I'm using this time to update my skills. I'm working longer hours, maybe different times of the day, but I'm going to skill up." And you know, the point about AI, 37 years ago, when I started in this business AI was all the buzz and it didn't happen. It's real this time and I'm really excited Rob, that you're at the heart of all this innovation, so really, I appreciate you taking the time. And, best of luck, stay safe, and hopefully we'll see you face to face. >> Offscreen Man: Sure. >> Thanks Dave, same to you and the whole team at theCube, take care. >> Thank you Rob, and thank you for watching everybody, this is Dave Vellante for theCube and our coverage of IBM Think 2020, the digital event experience and the post-event. We'll see you next time. (music)

>>From the cube studios in Palo Alto in Boston. It's the cube covering the IBM thing brought to you by IBM. We're back and this is Dave Vellante and you're watching the cube and we're covering wall-to-wall the IBM 2020 I think digital experience. Rob Thomas is here. He's the senior vice president of clouds and data. Right. Warm rub. Always a pleasure to see you. I wish you were face to face, but Hey, we're doing the best we can. As you say, doing the best we can. Great to see you Dave. Hope family safe, healthy, happy as best you can be. Yeah. Ditto. You back out your Robin. Congratulations on on the new role, you and the cube. We've been riding this data wave for quite some time now. It's really been incredible. It really is. And last year I talked to you about how clients, we're slowly making progress on data strategy, starting to experiment with AI. >>We've gotten to the point now where I'd say it's game on for AI, which is exciting to see and that's a lot of what the theme of this year's think is about. Yeah, and I definitely want to dig into that, but I want to start by asking you sort of moves that you saw you're in there seeing your clients make with regard to the cobot night covert 19 crisis. Maybe how you guys are helping them in very interested in what you see as sort of longterm and even, you know, quasi permanent as a result of this. I would first say it this way. I don't, I'm not sure the crisis is going to change businesses as much as it's going to be accelerating. What would have happened anyway, regardless of the industry that you're in. We see clients aggressively looking at how do we get the digital faster? >>How do we automate more than we ever have before? There's the obvious things like business resiliency and business continuity, managing the distributed workforce. So to me, what we've seen is really about, and acceleration, not necessarily in a different direction, but an acceleration on. The thing is that that we're already kind of in the back of their minds or in the back of their plans now that as we'll come to the forefront and I'm encouraged because we see clients moving at a rate and pace that we'd never seen before that's ultimately going to be great for them, great for their businesses. And so I'm really happy to see that you guys have used Watson to really try to get, you know, some good high fidelity answers to the citizens. I wonder if you could explain that initiative. Well, we've had this application called Watson assistant for the last few years and we've been supporting banks, airlines, retailers, companies across all industries and helping them better interact with our customers and in some cases, employees. >>We took that same technology and as we saw the whole covert 19 situation coming, we said, Hey, we can evolve Watson assistant to serve citizens. And so it started by, we started training the models, which are intent based models in Watson assistant on all the publicly available data from the CDC as an example. And we've been able to build a really powerful virtual agent to serve really any citizen that has questions about and what they should be doing. And the response has been amazing. I mean, in the last two weeks we've gone live with 20 organizations, many of which are state and local governments. Okay. Also businesses, the city of Austin children's healthcare of Atlanta. Mmm. They local governments in Spain and Greece all over the world. And in some instances these clients have gotten live in less than 24 hours. Meaning they have a virtual agent that can answer any question. >>They can do that in less than 24 hours. It's actually been amazing to see. So proud of the team that built this over time. And it was kind of proof of the power of technology when we're dealing with any type of a challenge. You know, I had a conversation earlier with Jamie Thomas about quantum and was asking her sort of how your clients are using it. The examples that came up were financial institutions, pharmaceutical know battery manufacturers, um, airlines. And so it strikes me when you think about uh, machine intelligence and AI, the type of AI that you're yeah, at IBM is not consumer oriented AI. It's really designed for businesses. And I wonder if you could sort of add some color to that. Yeah, let's distinguish the difference there. Cause I think you've said it well consumer AI is smart speakers things in our home, you know, music recommendations, photo analysis and that's great and it enriches all of our personal lives. >>AI for business is very different. This is about how do you make better predictions, how do you optimize business processes, how do you automate things that maybe your employees don't want to do in the first time? Our focus in IBM as part of, we've been doing with Watson is really anchoring on three aspects of AI language. So understanding language because the whole business world is about communication of language, trust meaning trusted AI. You understand the models, you understand the data. And then third automation and the whole focus of what we're doing here in the virtual think experience. It's focused on AI for automation. Whether that's automating business processes or the new announcement this week, which is around automating AI opera it operations for a CIO. You, you've talked the years about this notion of an AI ladder. You actually, I actually wrote a book on it and uh, but, but it's been hard for customers to operationalize AI. >>Mmm. We talked about this last year. Thanks. What kind of progress, uh, have we made in the last 12 months? There's been a real recognition of this notion that your AI is only as good as your data. And we use the phrase, there's no AI without IAA, meaning information architecture, it's all the same concept, which is that your data, it has to be ready for AI if you want to too get successful outcomes with AI and the steps of those ladders around how you collect data, how you organize data, how you analyze data, how you infuse that into your business processes. seeing major leaps forward in the last nine months where organizations are understanding that connection and then they're using that to really drive initiatives around AI. So let's talk about that a little bit more. This notion of AI ops, I mean it's essentially the take the concept of dev ops and apply it to the data pipeline if you will. >>Everybody, you know, complains, you know, data scientists complained that all, they spent all their time wrangling data, improving data quality, they don't have line of sight across their organization with regard to other data specialists, whether it's data engineers or even developers. Maybe you could talk a little bit more about that announcement and sort of what you're doing in that area. Sure. So right. Let me put a number on it because the numbers are amazing. Every year organizations lose 2016 point $5 billion of revenue because of outages in it system. That is a staggering number when you think about it. And so then you say, okay, so how do you break down and attack that problem? Well, do you have to get better at fixing problems or you have to get better at avoiding problems altogether. And as you may expect, a little bit of both. You, you want to avoid problems obviously, but in an uncertain world, you're always going to deal with unforeseen challenges. >>So the also the question becomes how fast can you respond and there's no better use of AI. And then to do, I hope you like those tasks, which is understanding your environment, understanding what the systems are saying through their data and identifying issues become before they become outages. And once there is an outage, how do you quickly triage data across all your systems to figure out where is the problem and how you can quickly address it. So we are announcing Watson AI ops, which is the nervous system for a CIO, the manager, all of their systems. What we do is we just collect data, log data from every source system and we build a semantic layer on top that. So Watson understands the systems, understands the normal behavior, understands the acceptable ranges, and then anytime something's not going like it should, Watson raises his hand and says, Hey, you should probably look at this before it becomes a problem. >>We've partnered with companies like Slack, so the UI for Watson AI ops, it's actually in Slack so that companies can use and employees can use a common collaboration tool too. Troubleshoot or look at either systems. It's, it's really powerful. So that we're really proud of. Well I just kind of leads me to my next question, which I mean, IBM got the religion 20 years ago on openness. I mean I can trace it back to the investment you made and Lennox way back when. Um, and of course it's a huge investment last year in red hat, but you know, open source company. So you just mentioned Slack. Talk about open ecosystems and how that it fits into your AI and data strategy. Well, if you think about it, if we're going to take on a challenge this grand, which is AI for all of your it by definition you're going to be dealing with full ecosystem of different providers because every organization has a broad set of capabilities we identified early on. >>That means that our ability to provide open ecosystem interoperability was going to be critical. So we're launching this product with Slack. I mentioned with box, we've got integrations into things like PagerDuty service now really all of the tools of modern it architecture where we can understand the data and help clients better manage those environments. So this is all about an open ecosystem and that's how we've been approaching it. Let's start, it's really about data, applying machine intelligence or AI to that data and about cloud for scale. So I wonder what you're seeing just in terms of that sort of innovation engine. I mean obviously it's gotta be secure. It's, it seems like those are the pillars of innovation for the next 10 plus years. I think you're right. And I would say this whole situation that we're dealing with has emphasized the importance of hybrid deployment because companies have it capabilities on public clouds, on private clouds, really everywhere. >>And so being able to operate that as a single architecture, it's becoming very important. You can use AI to automate tasks across that whole infrastructure that makes a big difference. And to your point, I think we're going to see a massive acceleration hybrid cloud deployments using AI. And this will be a catalyst for that. And so that's something we're trying to help clients with all around the world. You know, you wrote in your book that O'Reilly published that AI is the new electricity and you talked about problems. Okay. Not enough data. If your data is you know, on prem and you're only in the cloud, well that's a problem or too much data. How you deal with all that data, data quality. So maybe we could close on some of the things that you know, you, you talked about in that book, you know, maybe how people can get ahold of it or any other, you know, so the actions you think people should take to get smart on this topic. >>Yeah, so look, really, really excited about this. Paul's the capitalists, a friend of mine and a colleague, we've published this book working with a Riley called the a ladder and it's all the concepts we talked about in terms of how companies can climb this ladder to AI. And we go through a lot of different use cases, scenarios, I think. Yeah. Anybody reading this is going to see their company in one of these examples, our whole ambition was to hopefully plant some seeds of ideas for how you can start to accelerate your journey to AI in any industry right now. Well, Rob, it's always great having you on the cube, uh, your insights over the years and you've been a good friend of ours, so really appreciate you coming on and, uh, and best of luck to you, your family or wider community. I really appreciate it. Thanks Dave. Great to be here and again, wish you and the whole cube team the best and to all of our clients out there around the world. We wish you the best as well. All right. You're watching the cubes coverage of IBM think 20, 20 digital, the vent. We'll be right back right after this short break. This is Dave Volante.

>>live from Miami, Florida. It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, Everybody. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Okay. Welcome back, everyone. He live in San Francisco. Here on Mosconi St for the cubes. Exclusive coverage of IBM. Think twenty nineteen. I'm Jeffrey David Long. Four days of coverage bringing on all the action talking. The top executives, entrepreneurs, ecosystem partners and everyone who can bring the signal from the noise here on the Q and excuses. Rob Thomas, general manager, IBM Data and a I with an IBM Cube Alumni. Great to see you again. >> Great. There you go. >> You read a >> book yet? This year we've written ten books on a data. Your general manager. There's >> too much work. Not enough time >> for that's. Good sign. It means you're working hard. Okay. Give us give us the data here because a I anywhere in the center of the announcements we have a story up on. Slick earnings have been reported on CNBC. John Ford was here earlier talking to Ginny. This is a course centerpiece of it. Aye, aye. On any cloud. This highlights the data conversation you've been part of. Now, I think what seven years seems like more. But this is now happening. Give us your thoughts. >> Go back to basics. I've shared this with you before. There's no AI without IA, meaning you need an information architecture to support what you want to do in AI. We started looking into that. Our thesis became so clients are buying into that idea. The problem is their data is everywhere onpremise, private cloud, multiple public clouds. So our thesis became very simple. If we can bring AI to the data, it will make Watson the leading AI platform. So what we announced wtih Watson Anywhere is you could now have it wherever your data is public, private, any public cloud, build the models, run them where you want. I think it's gonna be amazing >> data everywhere and anywhere. So containers are big role in This is a little bit of a deb ops. The world you've been living in convergence of data cloud. How does that set for clients up? What are they need to know about this announcement? Was the impact of them if any >> way that we enable Multi Cloud and Watson anywhere is through IBM cloud private for data? That's our data Micro services architectural writing on Cooper Netease that gives you the portability so that it can run anywhere because, in addition Teo, I'd say, Aye, aye, ambitions. The other big client ambition is around how we modernize to cloud native architectures. Mohr compose herbal services, so the combination gets delivered. Is part of this. >> So this notion of you can't have a eye without a it's It's obviously a great tagline. You use it a lot, but it's super important because there's a gap between those who sort of have a I chops and those who don't. And if I understand what you're doing is you're closing that gap by allowing you to bring you call that a eye to the data is it's sort of a silo buster in regard. Er yeah, >> the model we use. I called the eye ladder. So they give it as all the levels of sophistication an organization needs to think about. From how you collect data, how you organize data, analyze data and then infused data with a I. That's kind of the model that we used to talk about. Talk to clients about that. What we're able to do here is same. You don't have to move your data. The biggest problem Modi projects is the first task is OK move a bunch of data that takes a lot of time. That takes a lot of money. We say you don't need to do that. Leave your data wherever it is. With Cloud private for data, we can virtualized data from any source. That's kind of the ah ha moment people have when they see that. So we're making that piece really >> easy. What's the impact this year and IBM? Think to the part product portfolio. You You had data products in the past. Now you got a eye products. Any changes? How should people live in the latter schism? A kind of a rubric or a view of where they fit into it? But what's up with the products and he changes? People should know about? >> Well, we've brought together the analytics and I units and IBM into this new organization we call Dayton ay, ay, that's a reflection of us. Seen that as two sides of the same coin. I really couldn't really keep them separate. We've really simplified how we're going to market with the Watson products. It's about how you build run Manager II watching studio Watson Machine Learning Watson Open scale. That's for clients that want to build their own. Aye, aye. For clients that wants something out of the box. They want an application. We've got Watson assistant for customer service. Watson Discovery, Watson Health Outset. So we've made it really easy to consume Watson. Whether you want to build your own or you want an application designed for the line of business and then up and down the data, stack a bunch of different announcements. We're bringing out big sequel on Cloudera as part of our evolving partnership with the new Cloudera Horn Works entity. Virtual Data Pipeline is a partnership that we've built with active fio, so we're doing things at all layers of the last. >> You're simplifying the consumption from a client, your customer perspective. It's all data. It's all Watson's, the umbrella for brand for everything underneath that from a tizzy, right? >> Yeah, Watson is the Aye, aye, brand. It is a technology that's having an impact. We have amazing clients on stage with this this week talking about, Hey, Eyes No longer. I'd like to say I was not magic. It's no longer this mystical thing. We have clients that are getting real outcomes. Who they II today we've got Rollback of Scotland talking about how they've automated and augmented forty percent of their customer service with watching the system. So we've got great clients talking about other using >> I today. You seen any patterns, rob in terms of those customers you mentioned, some customers want to do their own. Aye, aye. Some customers wanted out of the box. What? The patterns that you're seeing in terms of who wants to do their own. Aye. Aye. Why do they want to do their own, eh? I do. They get some kind of competitive advantage. So they have additional skill sets that they need. >> It's a >> It's a maker's mark. It is how I would describe it. There's a lot of people that want to make their own and try their own. Ugh. I think most organizations, they're gonna end up with hundreds of different tools for building for running. This is why we introduced Watson Open Scale at the end of last year. That's How would you manage all of your A II environments? What did they come from? IBM or not? Because you got the and the organization has to have this manageable. Understandable, regardless of which tool they're using. I would say the biggest impact that we see is when we pick a customer problem. That is widespread, and the number one right now is customer service. Every organization, regardless of industry, wants to do a better job of serving clients. That's why Watson assistant is taking off >> this's. Where? Data The value of real time data. Historical data kind of horizontally. Scaleable data, not silo data. We've talked us in the past. How important is to date a quality piece of this? Because you have real time and you have a historical date and everything in between that you had to bring to bear at low ladened psi applications. Now we're gonna have data embedded in them as a feature. Right. How does this change? The workloads? The makeup of you? Major customer services? One piece, the low hanging fruit. I get that. But this is a key thing. The data architecture more than anything, isn't it? >> It is. Now remember, there's there's two rungs at the bottom of the ladder on data collection. We have to build a collect data in any form in any type. That's why you've seen us do relationships with Mongo. D B. Were they ship? Obviously with Claude Era? We've got her own data warehouse, so we integrate all of that through our sequel engine. That thing gets to your point around. Are you gonna organize the data? How are you going to curate it? We've got data catalogue. Every client will have a data catalogue for many dollar data across. Clouds were now doing automated metadata creation using a I and machine learning So the organization peace. Once you've collected it than the organization, peace become most important. Certainly, if you want to get to self service analytics, you want to make data available to data scientists around the organization. You have to have those governance pieces. >> Talk about the ecosystem. One of the things that's been impressive IBM of the years is your partnerships. You've done good partners. Partnership of relationships now in an ecosystem is a lot of building blocks. There's more complexity requires software to distract him away. We get that. What's opportunities for you to create new relationships? Where are the upper opportunities for someone a developer or accompanied to engage with you guys? Where's the white spaces? Where is someone? Take advantage of your momentum and you're you're a vision. >> I am dying for partners that air doing domain specific industry specific applications to come have them run on IBM cloud private for data, which unleashes all the data they need to be a valuable application. We've already got a few of those data mirrors. One sensing is another one that air running now as industry applications on top of IBM Club private for data. I'd like to have a thousand of these. So all comers there. We announced a partnership with Red Hat back in May. Eventually, that became more than just a partnership. But that was about enabling Cloud Private, for data on red had open shift, So we're partnered at all layers of the stack. But the greatest customer need is give me an industry solution, leveraging the best of my data. That's why I'm really looking for Eyes V. Partners to run on Ivan clubs. >> What's your pitch to those guys? Why, why I should be going. >> There is no other data platform that will connect to all your data sources, whether they're on eight of us as your Google Cloud on premise. So if you believe data is important to your application. There's simply no better place to run than IBM. Claude Private for data >> in terms of functionality, breath o r. Everything >> well, integrating with all your data. Normally they have to have the application in five different places. We integrate with all the data we build the data catalogue. So the data's organized. So the ingestion of the data becomes very easy for the Iast V. And by the way, thirdly, IBM has got a pretty good reach. Globally, one hundred seventy countries, business partners, resellers all over the world, sales people all over the world. We will help you get your product to market. That's a pretty good value >> today. We talk about this in the Cube all the time. When the cloud came, one of the best things about the cloud wasn't allowed. People to put applications go there really quickly. Stand them up. Startups did that. But now, in this domain world of of data with the clouds scale, I think you're right. I think domain X expertise is the top of the stack where you need specially special ism expertise and you don't build the bottom half out. What you're getting at is of Europe. If you know how to create innovation in the business model, you could come in and innovate quickly >> and vertical APS don't scale enough for me. So that's why focus on horizontal things like customer service. But if you go talk to a bank, sometimes customer service is not in office. I want to do something in loan origination or you're in insurance company. I want to use their own underwriting those air, the solutions that will get a lot of value out of running on an integrated data start >> a thousand flowers. Bloom is kind of ecosystem opportunity. Looking forward to checking in on that. Thoughts on on gaps. For that you guys want to make you want to do em in a on or areas that you think you want to double down on. That might need some help, either organic innovation or emanate what areas you looking at. Can you share a little bit of direction on that? >> We have, >> ah, a unique benefit. And IBM because we have IBM research. One of their big announcement this week is what we call Auto Way I, which is basically automating the process of feature engineering algorithm selection, bringing that into Watson Studio and Watson Machine learning. I am spending most of my time figure out howto I continue to bring great technology out of IBM research and put in the hand of clients through our products. You guys solve the debaters stuff yesterday. We're just getting started with that. We've got some pretty exciting organic innovation happen in IBM. >> It's awesome. Great news for startups. Final question for you. For the folks watching who aren't here in San Francisco, what's the big story here? And IBM think here in San Francisco. Big event closing down the streets here in Howard Street. It's huge. What's the big story? What's the most important things happening? >> The most important thing to me and the customer stories >> here >> are unbelievable. I think we've gotten past this point of a eyes, some idea for the future we have. Hundreds of clients were talking about how they did an A I project, and here's the outcome they got. It's really encouraging to see what I encourage. All clients, though, is so build your strategy off of one big guy. Project company should be doing hundreds of Aye, aye projects. So in twenty nineteen do one hundred projects. Half of them will probably fail. That's okay. The one's that work will more than make up for the ones that don't work. So we're really encouraging mass experimentation. And I think the clients that air here are, you know, creating an aspirational thing for things >> just anecdotally you mentioned earlier. Customer service is a low hanging fruit. Other use cases that are great low hanging fruit opportunities for a >> data discovery data curation these air really hard manual task. Today you can start to automate some of that. That has a really big impact. >> Rob Thomas, general manager of the data and a I groupie with an IBM now part of a bigger portfolio. Watson Rob. Great to see you conventionally on all your success. But following you from the beginning. Great momentum on the right way. Thanks. Gradually. More cute coverage here. Live in San Francisco from Mosconi North. I'm John for Dave A lot. They stay with us for more coverage after this short break

(digital music) >> From Yorktown Heights, New York It's theCUBE! Covering IBM Cloud Innovation Day. Brought to you by IBM. >> Hi, it's Wikibon's Peter Burris again. We're broadcasting on The Cube from IBM Innovation Day at the Thomas J Watson Research Laboratory in Yorktown Heights, New York. Have a number of great conversations, and we got a great one right now. Rob Thomas, who's the General Manager of IBM Analytics, welcome back to theCUBE. >> Thanks Peter, great to see you. Thanks for coming out here to the woods. >> Oh, well it's not that bad. I actually live not to far from here. Interesting Rob, I was driving up the Taconic Parkway and I realized I hadn't been on it in 40 years, so. >> Is that right? (laugh) >> Very exciting. So Rob let's talk IBM analytics and some of the changes that are taking place. Specifically, how are customers thinking about achieving their AI outcomes. What's that ladder look like? >> Yeah. We call it the AI ladder. Which is basically all the steps that a client has to take to get to get to an AI future, is the best way I would describe it. From how you collect data, to how you organize your data. How you analyze your data, start to put machine learning into motion. How you infuse your data, meaning you can take any insights, infuse it into other applications. Those are the basic building blocks of this laddered AI. 81 percent of clients that start to do something with AI, they realize their first issue is a data issue. They can't find the data, they don't have the data. The AI ladder's about taking care of the data problem so you can focus on where the value is, the AI pieces. >> So, AI is a pretty broad, hairy topic today. What are customers learning about AI? What kind of experience are they gaining? How is it sharpening their thoughts and their pencils, as they think about what kind of outcomes they want to achieve? >> You know, its... For some reason, it's a bit of a mystical topic, but to me AI is actually quite simple. I'd like to say AI is not magic. Some people think it's a magical black box. You just, you know, put a few inputs in, you sit around and magic happens. It's not that, it's real work, it's real computer science. It's about how do I put, you know, how do I build models? Put models into production? Most models, when they go into production, are not that good, so how do I continually train and retrain those models? Then the AI aspect is about how do I bring human features to that? How do I integrate that with natural language, or with speech recognition, or with image recognition. So, when you get under the covers, it's actually not that mystical. It's about basic building blocks that help you start to achieve business outcomes. >> It's got to be very practical, otherwise the business has a hard time ultimately adopting it, but you mentioned a number of different... I especially like the 'add the human features' to it of the natural language. It also suggests that the skill set of AI starts to evolve as companies mature up this ladder. How is that starting to change? >> That's still one of the biggest gaps, I would say. Skill sets around the modern languages of data science that lead to AI: Python, AR, Scala, as an example of a few. That's still a bit of a gap. Our focus has been how do we make tools that anybody can use. So if you've grown up doing SPSS or SaaS, something like that, how do you adopt those skills for the open world of data science? That can make a big difference. On the human features point, we've actually built applications to try to make that piece easy. Great example is with Royal Bank of Scotland where we've created a solution called Watson Assistant which is basically how do we arm their call center representatives to be much more intelligent and engaging with clients, predicting what clients may do. Those types of applications package up the human features and the components I talked about, makes it really easy to get AI into production. >> Now many years ago, the genius Turing, noted the notion of the Turing machine where you couldn't tell the difference between the human and a machine from an engagement standpoint. We're actually starting to see that happen in some important ways. You mentioned the call center. >> Yep. >> How are technologies and agency coming together? By that I mean, the rate at which businesses are actually applying AI to act as an agent for them in front of customers? >> I think it's slow. What I encourage clients to do is, you have to do a massive number of experiments. So don't talk to me about the one or two AI projects you're doing, I'm thinking like hundreds. I was with a bank last week in Japan, and they're comment was in the last year they've done a hundred different AI projects. These are not one year long projects with hundreds of people. It's like, let's do a bunch of small experiments. You have to be comfortable that probably half of your experiments are going to fail, that's okay. The goal is how do you increase your win rate. Do you learn from the ones that work, and from the ones that don't work, so that you can apply those. This is all, to me at this stage, is about experimentation. Any enterprise right now, has to be thinking in terms of hundreds of experiments, not one, not two or 'Hey, should we do that project?' Think in terms of hundreds of experiments. You're going to learn a lot when you do that. >> But as you said earlier, AI is not magic and it's grounded in something, and it's increasingly obvious that it's grounded in analytics. So what is the relationship between AI analytics, and what types of analytics are capable of creating value independent of AI? >> So if you think about how I kind of decomposed AI, talked about human features, I talked about, it kind of starts with a model, you train the model. The model is only as good as the data that you feed it. So, that assumes that one, that your data's not locked into a bunch of different silos. It assumes that your data is actually governed. You have a data catalog or that type of capability. If you have those basics in place, once you have a single instantiation of your data, it becomes very easy to train models, and you can find that the more that you feed it, the better the model's going to get, the better your business outcomes are going to get. That's our whole strategy around IBM Cloud Private for Data. Basically, one environment, a console for all your data, build a model here, train it in all your data, no matter where it is, it's pretty powerful. >> Let me pick up on that where it is, 'cause it's becoming increasingly obvious, at least to us and our clients, that the world is not going to move all the data over to a central location. The data is going to be increasingly distributed closer to the sources, closer to where the action is. How does AI and that notion of increasing distributed data going to work together for clients. >> So we've just released what's called IBM Data Virtualization this month, and it is a leapfrog in terms of data virtualization technology. So the idea is leave your data where ever it is, it could be in a data center, it could be on a different data center, it could be on an automobile if you're an automobile manufacturer. We can federate data from anywhere, take advantage of processing power on the edge. So we're breaking down that problem. Which is, the initial analytics problem was before I do this I've got to bring all my data to one place. It's not a good use of money. It's a lot of time and it's a lot of money. So we're saying leave your data where it is, we will virtualize your data from wherever it may be. >> That's really cool. What was it called again? >> IBM Data Virtualization and it's part of IBM Cloud Private for Data. It's a feature in that. >> Excellent, so one last question Rob. February's coming up, IBM Think San Francisco thirty plus thousand people, what kind of conversations do you anticipate having with you customers, your partners, as they try to learn, experiment, take away actions that they can take to achieve their outcomes? >> I want to have this AI experimentation discussion. I will be encouraging every client, let's talk about hundreds of experiments not 5. Let's talk about what we can get started on now. Technology's incredibly cheap to get started and do something, and it's all about rate and pace, and trying a bunch of things. That's what I'm going to be encouraging. The clients that you're going to see on stage there are the ones that have adopted this mentality in the last year and they've got some great successes to show. >> Rob Thomas, general manager IBM Analytics, thanks again for being on theCUBE. >> Thanks Peter. >> Once again this is Peter Buriss of Wikibon, from IBM Innovation Day, Thomas J Watson Research Center. We'll be back in a moment. (techno beat)

>> [Announcer] Live from Times Square in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to theCUBE's special presentation. We're covering IBM's announcements today around AI. IBM, as theCUBE does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on theCUBE, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching theCUBE.

>> Live from Times Square in New York City, it's The Cube covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to The Cube's special presentation. We're covering IBM's announcements today around AI. IBM, as The Cube does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on The Cube, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching The Cube.

>> Announcer: Live from Las Vegas. It's the Cube. Covering IBM Think 2018, brought to you by IBM. >> Hello everyone, I'm John Furrier. We are here in the Cube at IBM Think 2018. Great conversations here in the Mandalay Bay in Las Vegas for IMB Think, which is six shows wrapped into one, all combined into a big tent event. Good call by IBM, great branding. Our next guest is Rob Thomas. Cube alumni, general manager of IBM Analytics. Great to see you. >> John, great to see you. Thanks for being here. >> We love having you on, Cube alumni many times. I mean, you've seen the journey. I can remember when I talked to you, it was almost five, four or five years ago. Data, Hadoop, big data analytics, data lakes evolved significantly now where Jenny's major keynote speech has data at the center of the value proposition. I mean, we've said that before. >> Yes, we have. >> The data is the center of the value proposition. >> Every company is finally waking up. >> And then I had coined the term "the innovation sandwich." Blockchain on one side of the data, and you got AI on the other side, it's actually software. This is super important with multi-cloud. You've got multiple perspectives. You've got regions all around the world, GDPR, which everyone's been talking about, you guys have been doing lately, but the bigger question is: the technical stacks are changing. 30 years of stacks evolving, technology under the hood is changing, but the business models are also changing. This puts data as the number one conversation. That's your division. Your keynote here, what are you guys talking about? Are you hitting that note as well? >> So, number one is, think of this ladder to AI. We've talked about that before. Every client's on a journey towards AI, and there's a set of building blocks everybody needs to get there. We used the phrase once before, "There's no AI without IA," meaning if you want to get to that end point, you have to have the right information architecture. We're going to focus a lot on that. We've got a new product we've released called IBM Cloud Private for Data, which takes all of the assembly out of the data process. A really elegant solution to see all your enterprise data. That's going to be the focus for me this week. >> I want to get into that, but I also heard Scott, your VP of marketing now, talk about bad data can cripple you. So, I want to explain what that actually means. Because it's always been dirty data, it's been kind of a data science word, data warehouse word, clean data, you know, data cleanliness, but if you're going to use AI as a real strategic thing, you need high quality data. >> You do. >> John: Your thoughts? >> Think backwards from the shiny object, 'cause everybody loves the shiny object, which is some type of AI outcome, customer centricity, making you feel like a celebrity. There's two things that have to happen before that, or really three. One is you need some type of inferencing, a model layer where you're actually automating a lot of the predictive process. Before that, you need to actually understand what the data is. That's the data governance, the data integration. And before that, you need to actually have access to the data, meaning know where it's stored. Without those things, you just have a shiny object and not necessarily an outcome. That's why these building blocks are fundamental. And the clients, they get to this point, and they're the ones who try to jump to the shiny object and they don't have the data to support that. >> And then you've got companies going on digital transformation, which is basically saying all their data legacy, trying to modernize it. The modern companies like Uber, and we saw the first fatality of an Uber car this week, again, points out the reality that realtime is realtime, and the importance of having data, whether it's sensing data. We're not, it's coming there, you can start to see it happening. Realtime data is key. That means data mobility is critical, and you mentioned private, public. Storing the data and moving the data around, having data intelligence, is the most important thing. Realtime data in motion, intelligence, you know, where are we? Is that a setback with the Uber incident? Is it a step forward, is it learning? What's your view of the data quality of movement in realtime? >> I think data ingestion is one of the least talked about topics that is one of the most important. With IBM Cloud Private for data, we can ingest 250 billion events a day. Let me give you some context for that. 2016, the entire credit card industry, everywhere in the world, did 250 billion transactions. So what credit cards do in a year, we can do in a day. Biggest stock trading day ever on the New York Stock Exchange, what got done in that entire day, we can do in the first 40 minutes of trading. But that value there is, how fast can you bring data in to be analyzed, and can you do a decent bit of that pre-processing, or analytics, on the way in? That's how you start to solve some of the problems that you're describing, because it's instant >> John: Yeah. >> And it's unsurpassed amounts of data. >> So ingestion's a key part of the value chain, if you will, on data management. The new kind of data management. Ingesting it, understanding context, then is that where AI kicks in? Where does the AI kick in? Because the ingestion speaks to the information architecture, IA. >> Rob: Yes. >> Now I got to put AI on top of that data, so is the data different? Talk about the dynamic between, okay I'm ingesting data for the sake of ingesting, where does the AI connect? >> So you got the data, yep. So you go the data, AI starts where you're saying, all right, now we want to automate this. We're going to build models, we're going to use the data that we've got in here to train those models. As we get more data, the models are going to get better. Now we're going to connect it to how humans want to interact. Maybe it's natural language processing, maybe it's visualizing data. That's the whole lineage of how somebody gets toward this AI idea. >> What are some of the conversations you're having with customers, and how have they changed? And give some color, I mean, only a few years ago we're talking about data lakes. >> Right. >> Okay, what is the conversation now, and give some context of how far that conversation has gone down the road toward advancement. >> I think we're going from data lakes to an idea of a fluid data layer, which is all your data assets managed as a single system, even if they sit in different architectures. Because there's no one, we all know this. We've been around this industry forever. There's no one way to support or manage data that's going to support every use case. So this idea of a fluid data layer becomes critical for every organization. That's one big change. Other big change is containers. What we're doing with Cloud Private for Data is based on Kubernetes, that's how people want to consume applications, but nobody's really solved that for data. I think we're solving that for data. >> Let's dig into that. It was one of my topics I wanted to drill down on. Containers have been great for moving workloads around, certainly Kubernetes has been a great orchestration tool. How does that fit for data? I'm just putting a container on data sets? Who's addressing the envelope of that container? How is that addressable? I mean, how does it work? >> Let me give you an analogy. So you go back to the year 1955. There is no standards in any shipping port around the world. Everybody is literally building their own containers, building their own ships, building their own trucks. It's incredibly expensive and takes forever to get cargo to move from one place to the next. 1956, a guy named Malcom McClean, he invents the first intermodal shipping container, patents it. It becomes the standard. So now, every port, every container looks identical. What's the benefit? Sure, it made more flexibility. Saved lot of money, 90% of the cost came out of shipping a container. But the biggest thing is it changed commerce. So, you look at GDP at that time, it took off. All because of the standardization around a form factor that made it accessible to everybody. Now, let's put that in the IT world. We got containers for the application world. Made it much easier to deploy, a standard, again. >> Yeah, and program around. >> More cost-effective, more-- Yep, exactly. What's the cargo in IT? It's data. Data is the cargo, that's what's sitting inside the container. Now you have to say, how do we actually take the same concepts that we did for applications, make that available for data so that my data can fit anywhere? That's what we're doing. >> How does that work and what's the impact to the customer? Is it IBM software that you're doing? Is it Kubernetes open source software? Just tie that together for me. >> So IBM Cloud Private is our Kubernetes distribution, with some different pieces we put on it. When you add the Cloud Private for Data, it's got a Spark Engine, like everything we do it's based on open source to start with. And then we have an experience for a data scientist, an experience for a data analyst. It's your view to your enterprise data. You'll love the UI when you see it. First, above the fold, all my machine learning models in the organization, what's working, what's not working. Below the fold, what's my data? Structured or unstructured? Sensitive, non-sensitive? I click it on, I can see all of my data. Hadoop, Cloud-A, Cloud-B, Cloud-C, on-premise system. It's get a view to all of your data. >> So is the purpose to move the data around? >> No, the purpose is actually the exact opposite. Leave the data in place, but be able to treat it as a single data environment. We're doing a lot of work with Federation, our SQL technology which historically, as we all know, Federation hasn't really performed. We have it performing. >> Okay, so I'm just, in the use case in my head, so I store the data on my private, secure, comfortable, feeling good about it, but I have a public cloud app. How does that work? Is it a replica of the data? Is it just the container that makes it addressable? How does that move across? >> So, click a button, move the data. If you want it to be a replica, click a button and say "replicate." If you want to just move it, just click a button and move it. It's literally that easy. >> And so the customers can choose where to put the data. >> Yes. >> Can they do a public version of this, or only private? >> Both, it connects to public as well. >> Okay, so that was Jenny's mention, okay cool. What's the most exciting thing for you this week going on in your world? Obviously, center of the value proposition, and Jenny used your lines so I'm sure you fed her some good sound bytes there, because she was basically taking your pitch as the headline for the keynote. Is that the highlight, or is it customer activity? >> I think the exciting thing, and Jenny did talk about it, is connecting data to AI. I'd say many clients have kind of thought of those as two different topics. We do that in three ways. We say common machine learning fabric. You can build a model in Watson, you can deploy it where your enterprise data is or vice versa. We do that with the metadata. You create business or technical metadata on-premise, you can push that to Watson or vice versa. And like we just talked about, we make the data movement incredibly easy. So we're uniting these two worlds of data and AI that have tended to be different parts of an organization in many clients. We're uniting that, I think that's pretty interesting. >> All right, so final question, I've got to ask the tough one, which is, okay, Rob I love it, but I'm really not paying attention to the data because I've got my hands full in my IT transformation and we're making critical decisions on cloud globally, I've got multiple regions to deal with, I got different issues outside in each digital nation, but I'm going to get the data after. What's in it for me, your whole pitch? I'm dealing with cloud right now, so why should I be cross-connecting with the cloud decision and the cloud conversations that relate to the benefit of what you're doing? >> If you're not paying attention to the data, you're not going to be around. So your cloud decisions are kind of worthless, because you're not going to be around if you're not paying attention to the data. >> So I can make a bad cloud decision if I don't factor in what? >> I believe you have to think about your data strategy. Look, every organization is going to be multi-cloud, but you have to have a single data strategy regardless of what your cloud strategy is. You've got to think about all those building blocks I talked about. Manage data, collect data, govern data, analyze data. That has to be one strategy regardless of cloud. If you're not thinking about that, you're in trouble. >> Or making sure that I have Kubernetes? Is that a good decision? >> That is a great decision. >> (laughs) >> Makes it really easy, seamless to deploy applications, to deploy data, to move it around clouds. Makes it really easy. >> And what's the business model for containers? Kind of shifts to being a commodity? >> I think over time, yes, but there's so much to do around containers because containers, again, go back to the analogy. It's just the crate. >> John: Makes things easy. >> It's not the cargo, it's not the ship. It's just the crate, it's one piece. >> Yeah, and there's no, a lot of choice there, too. Clients can do whatever they want. >> Yeah. >> All right, we love Kubernetes. We'll be at KubeCon in Copenhagen next month, so keep a lookout there for us. This is Rob Thomas, here inside the Cube, here at IBM Think, breaking down all the action in the data science world, data world. It's the center of the value proposition. Main story here at IBM Think is data at the center of the value proposition for the modern enterprise. I'm John Furrier inside the Cube. We'll be back with more after this short break. (light electronic music)

>> Announcer: Live from New York, it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI, brought to you by IBM. >> Welcome back to New York City. theCUBE continue our coverage here at IBM's event, Machine Learning Everywhere: Build Your Ladder to AI. And with us now is Rob Thomas, who is the vice president of, or general manager, rather, of IBM analytics. Sorry about that, Rob. Good to have you with us this morning. Good to see you, sir. >> Great to see you John. Dave, great to see you as well. >> Great to see you. >> Well let's just talk about the event first. Great lineup of guests. We're looking forward to visiting with several of them here on theCUBE today. But let's talk about, first off, general theme with what you're trying to communicate and where you sit in terms of that ladder to success in the AI world. >> So, maybe start by stepping back to, we saw you guys a few times last year. Once in Munich, I recall, another one in New York, and the theme of both of those events was, data science renaissance. We started to see data science picking up steam in organizations. We also talked about machine learning. The great news is that, in that timeframe, machine learning has really become a real thing in terms of actually being implemented into organizations, and changing how companies run. And that's what today is about, is basically showcasing a bunch of examples, not only from our clients, but also from within IBM, how we're using machine learning to run our own business. And the thing I always remind clients when I talk to them is, machine learning is not going to replace managers, but I think machine learning, managers that use machine learning will replace managers that do not. And what you see today is a bunch of examples of how that's true because it gives you superpowers. If you've automated a lot of the insight, data collection, decision making, it makes you a more powerful manager, and that's going to change a lot of enterprises. >> It seems like a no-brainer, right? I mean, or a must-have. >> I think there's a, there's always that, sometimes there's a fear factor. There is a culture piece that holds people back. We're trying to make it really simple in terms of how we talk about the day, and the examples that we show, to get people comfortable, to kind of take a step onto that ladder back to the company. >> It's conceptually a no-brainer, but it's a challenge. You wrote a blog and it was really interesting. It was, one of the clients said to you, "I'm so glad I'm not in the technology industry." And you went, "Uh, hello?" (laughs) "I've got news for you, you are in the technology industry." So a lot of customers that I talk to feel like, meh, you know, in our industry, it's really not getting disrupted. That's kind of taxis and retail. We're in banking and, you know, but, digital is disrupting every industry and every industry is going to have to adopt ML, AI, whatever you want to call it. Can traditional companies close that gap? What's your take? >> I think they can, but, I'll go back to the word I used before, it starts with culture. Am I accepting that I'm a technology company, even if traditionally I've made tractors, as an example? Or if traditionally I've just been you know, selling shirts and shoes, have I embraced the role, my role as a technology company? Because if you set that culture from the top, everything else flows from there. It can't be, IT is something that we do on the side. It has to be a culture of, it's fundamental to what we do as a company. There was an MIT study that said, data-driven cultures drive productivity gains of six to 10 percent better than their competition. You can't, that stuff compounds, too. So if your competitors are doing that and you're not, not only do you fall behind in the short term but you fall woefully behind in the medium term. And so, I think companies are starting to get there but it takes a constant push to get them focused on that. >> So if you're a tractor company, you've got human expertise around making tractors and messaging and marketing tractors, and then, and data is kind of there, sort of a bolt-on, because everybody's got to be data-driven, but if you look at the top companies by market cap, you know, we were talking about it earlier. Data is foundational. It's at their core, so, that seems to me to be the hard part, Rob, I'd like you to comment in terms of that cultural shift. How do you go from sort of data in silos and, you know, not having cloud economics and, that are fundamental, to having that dynamic, and how does IBM help? >> You know, I think, to give companies credit, I think most organizations have developed some type of data practice or discipline over the last, call it five years. But most of that's historical, meaning, yeah, we'll take snapshots of history. We'll use that to guide decision making. You fast-forward to what we're talking about today, just so we're on the same page, machine learning is about, you build a model, you train a model with data, and then as new data flows in, your model is constantly updating. So your ability to make decisions improves over time. That's very different from, we're doing historical reporting on data. And so I think it's encouraging that companies have kind of embraced that data discipline in the last five years, but what we're talking about today is a big next step and what we're trying to break it down to what I call the building blocks, so, back to the point on an AI ladder, what I mean by an AI ladder is, you can't do AI without machine learning. You can't do machine learning without analytics. You can't do analytics without the right data architecture. So those become the building blocks of how you get towards a future of AI. And so what I encourage companies is, if you're not ready for that AI leading edge use case, that's okay, but you can be preparing for that future now. That's what the building blocks are about. >> You know, I think we're, I know we're ahead of, you know, Jeremiah Owyang on a little bit later, but I was reading something that he had written about gut and instinct, from the C-Suite, and how, that's how companies were run, right? You had your CEO, your president, they made decisions based on their guts or their instincts. And now, you've got this whole new objective tool out there that's gold, and it's kind of taking some of the gut and instinct out of it, in a way, and maybe there are people who still can't quite grasp that, that maybe their guts and their instincts, you know, what their gut tells them, you know, is one thing, but there's pretty objective data that might indicate something else. >> Moneyball for business. >> A little bit of a clash, I mean, is there a little bit of a clash in that respect? >> I think you'd be surprise by how much decision making is still pure opinion. I mean, I see that everywhere. But we're heading more towards what you described for sure. One of the clients talking here today, AMC Networks, think it's a great example of a company that you wouldn't think of as a technology company, primarily a content producer, they make great shows, but they've kind of gone that extra step to say, we can integrate data sources from third parties, our own data about viewer habits, we can do that to change our relationship with advertisers. Like, that's a company that's really embraced this idea of being a technology company, and you can see it in their results, and so, results are not coincidence in this world anymore. It's about a practice applied to data, leveraging machine learning, on a path towards AI. If companies are doing that, they're going to be successful. >> And we're going to have the tally from AMC on, but so there's a situation where they have embraced it, that they've dealt with that culture, and data has become foundational. Now, I'm interested as to what their journey look like. What are you seeing with clients? How they break this down, the silos of data that have been built up over decades. >> I think, so they get almost like a maturity curve. You've got, and the rule I talk about is 40-40-20, where 40% of organizations are really using data just to optimize costs right now. That's okay, but that's on the lower end of the maturity curve. 40% are saying, all right, I'm starting to get into data science. I'm starting to think about how I extend to new products, new services, using data. And then 20% are on the leading edge. And that's where I'd put AMC Networks, by the way, because they've done unique things with integrating data sets and building models so that they've automated a lot of what used to be painstakingly long processes, internal processes to do it. So you've got this 40-40-20 of organizations in terms of their maturity on this. If you're not on that curve right now, you have a problem. But I'd say most are somewhere on that curve. If you're in the first 40% and you're, right now data for you is just about optimizing cost, you're going to be behind. If you're not right now, you're going to be behind in the next year, that's a problem. So I'd kind of encourage people to think about what it takes to be in the next 40%. Ultimately you want to be in the 20% that's actually leading this transformation. >> So change it to 40-20-40. That's where you want it to go, right? You want to flip that paradigm. >> I want to ask you a question. You've done a lot of M and A in the past. You spent a lot of time in Silicon Valley and Silicon Valley obviously very, very disruptive, you know, cultures and organizations and it's always been a sort of technology disruption. It seems like there's a ... another disruption going on, not just horizontal technologies, you know, cloud or mobile or social, whatever it is, but within industries. Some industries, as we've been talking, radically disrupted. Retail, taxis, certainly advertising, et cetera et cetera. Some have not yet, the client that you talked to. Do you see, technology companies generally, Silicon Valley companies specifically, as being able to pull off a sort of disruption of not only technologies but also industries and where does IBM play there? You've made a sort of, Ginni in particular has made a deal about, hey, we're not going to compete with our customers. So talking about this sort of dual disruption agenda, one on the technology side, one within industries that Apple's getting into financial services and, you know, Amazon getting into grocery, what's your take on that and where does IBM fit in that world? >> So, I mean, IBM has been in Silicon Valley for a long time, I would say probably longer than 99.9% of the companies in Silicon Valley, so, we've got a big lab there. We do a lot of innovation out of there. So love it, I mean, the culture of the valley is great for the world because it's all about being the challenger, it's about innovation, and that's tremendous. >> No fear. >> Yeah, absolutely. So, look, we work with a lot of different partners, some who are, you know, purely based in the valley. I think they challenge us. We can learn from them, and that's great. I think the one, the one misnomer that I see right now, is there's a undertone that innovation is happening in Silicon Valley and only in Silicon Valley. And I think that's a myth. Give you an example, we just, in December, we released something called Event Store which is basically our stab at reinventing the database business that's been pretty much the same for the last 30 to 40 years. And we're now ingesting millions of rows of data a second. We're doing it in a Parquet format using a Spark engine. Like, this is an amazing innovation that will change how any type of IOT use case can manage data. Now ... people don't think of IBM when they think about innovations like that because it's not the only thing we talk about. We don't have, the IBM website isn't dedicated to that single product because IBM is a much bigger company than that. But we're innovating like crazy. A lot of that is out of what we're doing in Silicon Valley and our labs around the world and so, I'm very optimistic on what we're doing in terms of innovation. >> Yeah, in fact, I think, rephrase my question. I was, you know, you're right. I mean people think of IBM as getting disrupted. I wasn't posing it, I think of you as a disruptor. I know that may sound weird to some people but in the sense that you guys made some huge bets with things like Watson on solving some of the biggest, world's problems. And so I see you as disrupting sort of, maybe yourselves. Okay, frame that. But I don't see IBM as saying, okay, we are going to now disrupt healthcare, disrupt financial services, rather we are going to help our, like some of your comp... I don't know if you'd call them competitors. Amazon, as they say, getting into content and buying grocery, you know, food stores. You guys seems to have a different philosophy. That's what I'm trying to get to is, we're going to disrupt ourselves, okay, fine. But we're not going to go hard into healthcare, hard into financial services, other than selling technology and services to those organizations, does that make sense? >> Yeah, I mean, look, our mission is to make our clients ... better at what they do. That's our mission, we want to be essential in terms of their journey to be successful in their industry. So frankly, I love it every time I see an announcement about Amazon entering another vertical space, because all of those companies just became my clients. Because they're not going to work with Amazon when they're competing with them head to head, day in, day out, so I love that. So us working with these companies to make them better through things like Watson Health, what we're doing in healthcare, it's about making companies who have built their business in healthcare, more effective at how they perform, how they drive results, revenue, ROI for their investors. That's what we do, that's what IBM has always done. >> Yeah, so it's an interesting discussion. I mean, I tend to agree. I think Silicon Valley maybe should focus on those technology disruptions. I think that they'll have a hard time pulling off that dual disruption and maybe if you broadly define Silicon Valley as Seattle and so forth, but, but it seems like that formula has worked for decades, and will continue to work. Other thoughts on sort of the progression of ML, how it gets into organizations. You know, where you see this going, again, I was saying earlier, the parlance is changing. Big data is kind of, you know, mm. Okay, Hadoop, well, that's fine. We seem to be entering this new world that's pervasive, it's embedded, it's intelligent, it's autonomous, it's self-healing, it's all these things that, you know, we aspire to. We're now back in the early innings. We're late innings of big data, that's kind of ... But early innings of this new era, what are your thoughts on that? >> You know, I'd say the biggest restriction right now I see, we talked before about somehow, sometimes companies don't have the desire, so we have to help create the desire, create the culture to go do this. Even for the companies that have a burning desire, the issue quickly becomes a skill gap. And so we're doing a lot to try to help bridge that skill gap. Let's take data science as an example. There's two worlds of data science that I would describe. There's clickers, and there's coders. Clickers want to do drag and drop. They will use traditional tools like SPSS, which we're modernizing, that's great. We want to support them if that's how they want to work and build models and deploy models. There's also this world of coders. This is people that want to do all their data science in ML, and Python, and Scala, and R, like, that's what they want to do. And so we're supporting them through things like Data Science Experience, which is built on Apache Jupiter. It's all open source tooling, it'd designed for coders. The reason I think that's important, it goes back to the point on skill sets. There is a skill gap in most companies. So if you walk in and you say, this is the only way to do this thing, you kind of excluded half the companies because they say, I can't play in that world. So we are intentionally going after a strategy that says, there's a segmentation in skill types. In places there's a gap, we can help you fill that gap. That's how we're thinking about them. >> And who does that bode well for? If you say that you were trying to close a gap, does that bode well for, we talked about the Millennial crowd coming in and so they, you know, do they have a different approach or different mental outlook on this, or is it to the mid-range employee, you know, who is open minded, I mean, but, who is the net sweet spot, you think, that say, oh, this is a great opportunity right now? >> So just take data science as an example. The clicker coder comment I made, I would put the clicker audience as mostly people that are 20 years into their career. They've been around a while. The coder audience is all the Millennials. It's all the new audience. I think the greatest beneficiary is the people that find themselves kind of stuck in the middle, which is they're kind of interested in this ... >> That straddle both sides of the line yeah? >> But they've got the skill set and the desire to do some of the new tooling and new approaches. So I think this kind of creates an opportunity for that group in the middle to say, you know, what am I going to adopt as a platform for how I go forward and how I provide leadership in my company? >> So your advice, then, as you're talking to your clients, I mean you're also talking to their workforce. In a sense, then, your advice to them is, you know, join, jump in the wave, right? You've got your, you can't straddle, you've got to go. >> And you've got to experiment, you've got to try things. Ultimately, organizations are going to gravitate to things that they like using in terms of an approach or a methodology or a tool. But that comes with experimentation, so people need to get out there and try something. >> Maybe we could talk about developers a little bit. We were talking to Dinesh earlier and you guys of course have focused on data scientists, data engineers, obviously developers. And Dinesh was saying, look, many, if not most, of the 10 million Java developers out there, they're not, like, focused around the data. That's really the data scientist's job. But then, my colleague John Furrier says, hey, data is the new development kit. You know, somebody said recently, you know, Andreessen's comment, "software is eating the world." Well, data is eating software. So if Furrier is right and that comment is right, it seems like developers increasingly have to become more data aware, fundamentally. Blockchain developers clearly are more data focused. What's your take on the developer community, where they fit into this whole AI, machine learning space? >> I was just in Las Vegas yesterday and I did a session with a bunch of our business partners. ISVs, so software companies, mostly a developer audience, and the discussion I had with them was around, you're doing, you're building great products, you're building great applications. But your product is only as good as the data and the intelligence that you embed in your product. Because you're still putting too much of a burden on the user, as opposed to having everything happen magically, if you will. So that discussion was around, how do you embed data, embed AI, into your products and do that at the forefront versus, you deliver a product and the client has to say, all right, now I need to get my data out of this application and move it somewhere else so I can do the data science that I want to do. That's what I see happening with developers. It's kind of ... getting them to think about data as opposed to just thinking about the application development framework, because that's where most of them tend to focus. >> Mm, right. >> Well, we've talked about, well, earlier on about the governance, so just curious, with Madhu, which I'll, we'll have that interview in just a little bit here. I'm kind of curious about your take on that, is that it's a little kinder, gentler, friendlier than maybe some might look at it nowadays because of some organization that it causes, within your group and some value that's being derived from that, that more efficiency, more contextual information that's, you know, more relevant, whatever. When you talk to your clients about meeting rules, regs, GDPR, all these things, how do you get them to see that it's not a black veil of doom and gloom but it really is, really more of an opportunity for them to cash in? >> You know, my favorite question to ask when I go visit clients is I say, I say, just show of hands, how many people have all the data they need to do their job? To date, nobody has ever raised their hand. >> Not too many hands up. >> The reason I phrased it that way is, that's fundamentally a governance challenge. And so, when you think about governance, I think everybody immediately thinks about compliance, GDPR, types of things you mentioned, and that's great. But there's two use cases for governance. One is compliance, the other one is self service analytics. Because if you've done data governance, then you can make your data available to everybody in the organization because you know you've got the right rules, the right permissions set up. That will change how people do their jobs and I think sometimes governance gets painted into a compliance corner, when organizations need to think about it as, this is about making data accessible to my entire workforce. That's a big change. I don't think anybody has that today. Except for the clients that we're working with, where I think we've made good strides in that. >> What's your sort of number one, two, and three, or pick one, advice for those companies that as you blogged about, don't realize yet that they're in the software business and the technology business? For them to close the ... machine intelligence, machine learning, AI gap, where should they start? >> I do think it can be basic steps. And the reason I say that is, if you go to a company that hasn't really viewed themselves as a technology company, and you start talking about machine intelligence, AI, like, everybody like, runs away scared, like it's not interesting. So I bring it back to building blocks. For a client to be great in data, and to become a technology company, you really need three platforms for how you think about data. You need a platform for how you manage your data, so think of it as data management. You need a platform for unified governance and integration, and you need a platform for data science and business analytics. And to some extent, I don't care where you start, but you've got to start with one of those. And if you do that, you know, you'll start to create a flywheel of momentum where you'll get some small successes. Then you can go in the other area, and so I just encourage everybody, start down that path. Pick one of the three. Or you may already have something going in one of them, so then pick one where you don't have something going. Just start down the path, because, those building blocks, once you have those in place, you'll be able to scale AI and ML in the future in your organization. But without that, you're going to always be limited to kind of a use case at a time. >> Yeah, and I would add, this is, you talked about it a couple times today, is that cultural aspect, that realization that in order to be data driven, you know, buzzword, you have to embrace that and drive that through the culture. Right? >> That starts at the top, right? Which is, it's not, you know, it's not normal to have a culture of, we're going to experiment, we're going to try things, half of them may not work. And so, it starts at the top in terms of how you set the tone and set that culture. >> IBM Think, we're less than a month away. CUBE is going to be there, very excited about that. First time that you guys have done Think. You've consolidated all your big, big events. What can we expect from you guys? >> I think it's going to be an amazing show. To your point, we thought about this for a while, consolidating to a single IBM event. There's no question just based on the response and the enrollment we have so far, that was the right answer. We'll have people from all over the world. A bunch of clients, we've got some great announcements that will come out that week. And for clients that are thinking about coming, honestly the best thing about it is all the education and training. We basically build a curriculum, and think of it as a curriculum around, how do we make our clients more effective at competing with the Amazons of the world, back to the other point. And so I think we build a great curriculum and it will be a great week. >> Well, if I've heard anything today, it's about, don't be afraid to dive in at the deep end, just dive, right? Get after it and, looking forward to the rest of the day. Rob, thank you for joining us here and we'll see you in about a month! >> Sounds great. >> Right around the corner. >> All right, Rob Thomas joining us here from IBM Analytics, the GM at IBM Analytics. Back with more here on theCUBE. (upbeat music)

>> 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)

>> Announcer: Live from Midtown Manhattan, it's theCUBE. Covering Big Data New York City 2017. Brought to you by SiliconANGLE media, and its ecosystem sponsor. >> Okay, welcome back, everyone. We're here live in New York City for BigData NYC, our annual event with SiliconANGLE Media, theCUBE, and Wikibon, in conjunction with Strata Hadoop, which is now called Strata Data as that show evolves. I'm John Furrier, cohost of theCUBE, with Peter Burris, head of research for SiliconANGLE Media, and General Manager of Wikibon. Our next two guests are two legends in the big data industry, Rob Bearden, the CEO of Hortonworks, really one of the founders of the big data movement, you know, got Cloudaire and Hortonworks, really kind of built that out, and Rob Thomas, General Manager of IBM Analytics. Big-time investments have made both of them. Congratulations for your success, guys. Welcome back to theCUBE, great to see you guys! >> Great to see you. >> Great, yeah. >> And got an exciting partnership to talk about, as well. >> So, but let's do a little history, you guys, obviously, I want to get to that, and get clarified on the news in a second, but you guys have been there from the beginning, kind of looking at the market, developing it, almost from the embryonic state to now. I mean, what a changeover. Give a quick comparison of where we've come from and what's the current landscape now, because you have, it evolved into so much more. You got IOT, you got AI, you have a lot of things in the enterprise. You've got cloud computing. A lot of tailwinds for this industry. It's gotten bigger. It's become big and now it's huge. What's your thoughts, guys? >> You know I, so you look at arcs and really all this started with Hadoop, and Rob and I met early in the days of that. You kind of gone from the early few years is about optimizing operations. Hadoop is a great way for a company to become more efficient, take out costs in their data infrastructure, and so that put huge momentum into this area, and now we've kind of fast-forwarded to the point where now it's about, "So how "am I actually going to extract insight?" So instead of just getting operational advantages, how am I going to get competitive advantage, and that's about bringing the world of data science and machine learning, run it natively on Hadoop, that's the next chapter, and that's what Rob and I are working closely together on. >> Rob, your thoughts, too? You know, we've been talking about data in motion. You guys were early on in that, seeing that trend. Real time is still hot. Data is still the core asset people are trying to figure out and move from wrangling to actually enabling that data. >> Right. Well, you know, in the early days of Big Data, it was, to Rob's point, it was very much about bringing operational leverage and efficiency and being able to aggregate very siloed data sets, and unlocking that data and bringing it into a central platform. In the early days in resources, and Hadoop went to making Hadoop an enterprise-viable data platform, with security, governance, operations, management capability, that mirrored any of the proprietary transactional or EDW platforms, and what the lessons learned in that were, is that by bringing all that data together in a central data set, we now can understand what's happening with our customers, and with our other assets pre-transaction, and so they can become very prescriptive in engaging in new business models, and so what we've learned now is the further upstream we can get in the world of IOT and bring that data under management from the point of origination and be able to manage that all the way through its life cycle, we can create new business models with higher velocity of engagement and a lot more rapid value that gets created. It, though, creates a number of new challenges in all the areas of how you secure that data, how you bring governance across that entire life cycle from a common stream set. >> Well, let's talk about the news you guys have. Obviously, the partnership. Partnerships become the new normal in an open source era that we're living in. We're seeing open source software grow really exponentially in the forecast coming in the next five years and ten years and exponential growth in new code. Just new people coming on board, new developers, dev ops is mainstream. Partnerships are key for communities. 90% of the code is going to be open source, 10%, as they say, the Code Sandwich as Jim Zemlin, the executive director of Linux Foundation, wants to, and you're seeing that work. You guys have worked together with Apache Atlas. What's the news, what's the relationship with Hortonworks and IBM? Share the news. >> So, a lot of great work's been happening there, and generally in the open source community, around Apache Atlas, and making sure that we're bringing missing critical governance capabilities across the big data sets and environments. As we then get into the complexity of now multiple data lakes, multiple tiers of data coming from multiple sources, that brings a higher level of requirement in both the security and governance aspects, and that's where the partnership with IBM is continuing to drive Apache Atlas into mission critical enterprise viability, but then when we get into the distributed models and enterprise requirements, the IBM platforms leveraging Atlas and what we're doing together then take that into the mission critical enterprise capability. >> You got the open source, and now you got the enterprise. Rob, we've talked many times about the enterprise as a hard, hard environment to crack for say, a start up, but even now, they're becoming reliant on open source, but yet, they have a lot of operational challenges. How does this relate to the challenge of, you know, CIO and his staff, now new personas coming in, you seeing the data science role, you see it expanding from analytics to dev ops. A day of challenges. >> Look, enterprises are getting better at this. Clearly we've seen progress the last five years on that, but to kind of go back and link the points, there's a phrase I heard I like. It says, "There's no AI without IA," meaning information architecture. Fundamentally, what our partnership is about is delivering the right information architecture. So it's Hadoop federated with whatever you have in terms of warehouses and databases. We partner around IBM common sequel for that. It's meta data for your core governance because without governance you don't have compliance, you can't offer self-service analytics, so we are forming what I would call the fluid data layer for an enterprise that enables them to get to this future of AI, and my view is there's a stop in between, which is data science, machine learning, applications that are ready today that clients can put into production and improve the outcomes they're getting. That's what we're focused on right now is how do we take the information architecture we've been able to establish, and then help clients on this journey? That's what enterprises want, because that's how they're going to build differentiation in their businesses. >> But the definition of an information architecture is closest to applications, and maybe this informs your perspective, it's close to the applications that the business is running on. Goes back to your observation about, "We used to be focusing, optimizing operations." As you move away from those applications, your information architecture becomes increasingly diffuse. It's not as crystal clear. How do you drive that clarity, as the data moves to derived new applications? >> Rob and I have talked about this. I think we're at the dawn of probably a new era in application development. Much more agile, flexible applications that are taking advantage of data wherever it resides. We are really early in that. Right now we are in the let's actually put into practice, machine learning and data science, let's extract value the data we got, that will then inform a new set of applications, which is related to the announcements that Hortonworks made this week around data plane, which is looking at multi-cloud environments and how would you manage applications and data across those? Rob, you can speak to that better than I can, I think. >> Well, the data plan thing, this information architecture, I think you're 100% right on. The data that we're hearing from customers in the enterprise is, they see the IOT buzz, oh, of course they're going to connect with IOT devices down the road, but when they see the security challenges, when they see the operational challenges around hiring people to actually run the dev ops, they have to then re-architect. So there's certainly a conversation we see on what is the architecture for the data, but also a little bit bigger than that, the holistic architecture of, say, cloud. So a lot of people are like, trying to clean up their house, if you will, to be ready for this new era, and I think Wikibon, your private cloud report you guys put out really amplified that by saying, "Yeah, they see these trends, "but they got to kind of get their act together." They got to look at who the staff is, what the data architecture's going to be, what apps are being developed, so doing a lot more retrenching. Given that, if we agree, what does that mean for the data plane, and then your vision of having that data architecture so that this will be a solid foundational transition? >> I think we all hit on the same point, which is it is about enabling a next generation IT architecture, of which, sort of the X and the Y axis or network, and generally what Big Data's been able to do, and Hadoop specifically, was over the last five years, enabling the existing applications architected, and I like the term that's been coined by you, is they were known processes with known technology, and that's how applications in the last 20 years have been enabled. Big Data and Hadoop generally have unlocked that ability to now be able to move all the way out to the edge and incorporate IOT, data at rest, data in motion, on-prem and cloud hybrid architecture. What that's done is said, "Now we know how to build an "application that takes advantage of an event or an "occurrence and then can drive outcome in a variety of ways. "We don't have to wait for a static programming model "to automate a function." >> And in fact, if we are wait, we're going to fail. That's one of the biggest challenges. I mean, IBM, I will tell you guys, or I'll tell you, Rob, that one of the craziest days I've ever spent is I flew from Japan to New York City for the IBM Information Architecture Announcement back in like 1994, and it was the most painful two days I've ever experienced in my entire life. That's a long time ago. It's ancient history. We can't use information architecture as a way of slowing things down. What we need to be able to do is we need to be able to introduce technology that again, allows the clarity of information architecture close to these core applications to move, and that may involve things like machine learning itself being embedded directly into how we envision data being moved, how we envision optimization, how we envision the data plane working. So, as you guys think about this data plane, everybody ends up asking themselves, "Is there a natural place for data to be?" What's going to be centralized, what's going to be decentralized, and I'm asking you, is increasingly the data going to be decentralized but the governance and securities and policies that we put in place going to be centralized and that's what's going to inform the operation of the data plane? What do you guys think? >> It's our view, very specifically from Hortonworks' perspective, that we want to give the ability for the data to exist and reside wherever the physics dictate, whether that be on-prem, whether that be in the cloud, and we want to give the ability to process and take action on an event or an occurrence or drive and outcome as early in the cycle as possible. >> Describe what you mean by "early in the cycle." >> So, as we see conditions emerge. A machine part breaking down. A customer taking an action. A supply chain inventory outage. >> So as close as possible to the event that's generating the data. >> As it's being generated, or as the processes are leading up to the natural outcome and we can maybe disintermediate for a better outcome, and so, that means that we have to be able to engage with the data irrespective of where it is in its cycle, and that's where we've enabled, with data plane, the ability to extract out the requirement of where that data is, and to be able to have a common plane, pun intended, for the operations and managing and provisioning of the environment, for being able to govern that and secure it, which are increasingly becoming intertwined, because you have to deal with it from point of origin through point at rest. >> The new phrase, "The single plane of glass." All joking aside, I want to just get your thoughts on this, Rob, too. "What's in it for me? "I'm the customer. "Right now I have a couple challenges." This is what we hear from the market. "I need data consistency because things are happening in "real time; whatever events are going on with data, we know "more data's going to be coming out from the edge and "everywhere else, faster and more volume, so I need "consistency of my data, and I don't want "to have multiple data silos," and then they got to integrate the data, so on the application developer side, a dev ops-like ethos is emerging where, "Hey, if there's data being done, I need to integrate that "into my app in real time," so those are two challenges. Does the data plane address that concern for customers? That's the question. >> Today it enables the ops world. >> So I can integrate my apps into the data plane. >> My apps and my other data assets, irrespective of where they reside, on-prem, cloud, or out to the edge, and all points in between. >> Rob, for enterprise, is this going to be the single pane of glass for data governance? Is that how the vision that you guys see this, because that's a benefit. If that could happen, that's essentially one step towards the promised land, if you will, for more data flowing through apps and app developers. >> So let me reshape a little bit. There's two main problems that collectively we have to address for enterprises: one is they want to apply machine learning and data science at scale, and they're struggling with that, and two is they want to get the cloud, and it's not talked about nearly enough, but most clients are really struggling with that. Then you fast forward on that one, we are moving to a multi-cloud world, absolutely. I don't think any enterprise is going to standardize on a single cloud, that's pretty clear. So you need things like data plane that acknowledge it's a multi-cloud world, and even as you move to multi clouds, you want a single focus for your data governance, a single strategy for your data governance, and then what we're doing together with IBM Data Science Experience with Hortonworks, let's say, whatever data you have in there, you can now do your machine learning right where that data is. You don't need to move it around. You can if you want, but you don't have to move it around, 'cause it's built in, and it's integrated right into the Hadoop ecosystem. That solves the two main enterprise pain points, which is help me get the cloud, help me apply data science and machine learning. >> Well we'll have to follow up and we'll have to do just a segment just on that. I think multi-cloud is clearly the direction, but what the hell does that mean? If I run 365 on Azure, that's one app. If I run something else on Amazon, that's multiple clouds, not necessarily moving workloads across. So the question I want to ask here is, it's clear from customers they want single code bases that run on all clouds seamlessly so I don't have to scale up on things on Amazon, Azure, and Google. Not all clouds are created equal in how they do things. Storage, through ever, inside the data factories of how they process. That's a challenge. How do you guys see that playing out of, you have on-premise activities that have been bootstrapped. Now you have multiple clouds with different ways of doing things, from pipelining, ingestion and processing, and learning. How do you see that playing out? Clouds just kind of standardizing around data plane? >> There's also the complexity of even within the multi-clouds, you're going to have multiple tiers within the clouds, if you're running in one data center in Asia, versus one in Latin America, maybe a couple across the Americas. >> But as a customer, do I need to know the cloud internals of Amazon, Azure, and Google? >> You do. In a stand-alone world, yes you do. That's where we have to bring and abstract the complexity of that out, and that's the goal with data plane, is to be able to extract, whether it's, which tier it's in, on-prem, or whether it's on, irrespective of which cloud platform. >> But Rob Thomas, I really like the way you put it. There may be some other issues that users have to worry about, certainly there are some that we think, but the two questions of, "Where am I going to run the machine learning," and "How am I going to get that to the cloud appropriately," I really like the way you put that. At the end of the day, what users need to focus on is less where the application code is, and more where the data is, so that they can move the application code or they can move the work to the data. That's fundamentally the perspective. We think that businesses don't take their business to the cloud, they bring the cloud to their business. So, when you think about this notion of increasingly looking at a set of work that needs to be performed, where the data exists, and what acts you're going to take in that data, it does suggest that data is going to become more of a centerpiece asset within the business. How does some of the things that you guys are doing lead customers to start to acknowledge data as an asset so they're making the appropriate investments in their data as their business evolves, and partly in response to data as an asset? What do you think? >> We have to do our job to build to common denominators, and that's what we're doing to make this easy for clients. So today we announced the IBM integrated analytics system. Same code base on private cloud as on a hardware system as on public cloud, all of it federates to Hortonworks through common sequel. That's what clients need, 'cause it solves their problem. Click of a button, they can get the cloud, and by the way, on private cloud it's based on Kubernetes, which is aligned with what we have on public cloud. We're working with Hortonworks to optimize Yarn and Kubernetes working together. These are the meaty issues that if we don't solve it, then clients have to deal with the bag of bolts, and so that's the kind of stuff we're solving together. So think about it: one single code base for managing your data, federates to Hadoop, machine learning is built into the system, and it's based on Kubernetes, that's what clients want. >> And the containers is just great, too. Great cloud-native trend. You guys been great, active in there. Congratulations to both of you guys. Final question, get you guys the last word: How does the relationship between Hortonworks and IBM evolve? How do you guys see this playing out? More of the same? Keep integrating in code? Is there any new thing you see on the horizon that you're going to be knocking down in the future? >> I'll take the first shot. The goal is to continue to make it simple and easy for the customer to get to the cloud, bring those machine learning and data science models to the data, and make it easy for the consumption of the new next generation of applications, and continue to make our customer successful and drive value, but to do it through transparently enabling the technology platforms together, and I think we've acknowledged the things that IBM is extraordinarily good at, the things that Hortworks is good at, and bring those two together with virtually no overlap. >> Rob, you've been very partner-centric. Your thoughts on this partnership? >> Look, it's what clients want. Since we announced this, the results and the response has been fantastic, and I think it's for one simple reason. So, Hortonworks' mission, we all know, is open source, and delivering in the community. They do a fantastic job of that. We also know that sometimes, clients need a little bit more, and so, when you bring those two things together, that's what clients want. That's very different than what other people in the industry do that say, "We're going to create a proprietary wrapper "around your Hadoop environment and lock your data in." That's the opposite of what we're doing. We're saying we're giving you full freedom of open source, but we're enabling you to augment that with machine learning, data science capabilities. This is what clients want. That's why the partnership's working. I think that's why we've gotten the response that we have. >> And you guys have been multiple years into the new operating model of being much more aggressive within the Big Data community, which has now morphed into much larger landscape. You pleased with some of the results you're seeing on the IBM side and more coding, more involvement in these projects on your end? >> Yeah, I mean, look, we were certainly early on Spark, created a lot of momentum there. I think it actually ended up helping both of our interests in the market. We built a huge community of developers at IBM, which is not something IBM had even a few years ago, but it's great to have a relationship like this where we can continue to augment our skills. We make each other better, and I think what you'll see in the future is more on the governance side; I think that's the piece that's still not quite been figured out by most enterprises yet. The need is understood. The implementation is slow, so you'll see more from us collectively there. >> Well, congratulations in the community work you guys have done. I think the community's model's evolving mainstream as well. Open source will continue to grow. Congratulations. Rob Bearden and Rob Thomas here inside theCUBE, more coverage here in Big Data NYC with theCUBE, after this short break.

>> Announcer: Live from Munich, Germany, it's theCUBE. Covering IBM: Fast Track Your Data. Brought to you by IBM. >> Welcome, everybody, to Munich, Germany. This is Fast Track Your Data brought to you by IBM, and this is theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise. My name is Dave Vellante, and I'm here with my co-host Jim Kobielus. Rob Thomas is here, he's the General Manager of IBM Analytics, and longtime CUBE guest, good to see you again, Rob. >> Hey, great to see you. Thanks for being here. >> Dave: You're welcome, thanks for having us. So we're talking about, we missed each other last week at the Hortonworks DataWorks Summit, but you came on theCUBE, you guys had the big announcement there. You're sort of getting out, doing a Hadoop distribution, right? TheCUBE gave up our Hadoop distributions several years ago so. It's good that you joined us. But, um, that's tongue-in-cheek. Talk about what's going on with Hortonworks. You guys are now going to be partnering with them essentially to replace BigInsights, you're going to continue to service those customers. But there's more than that. What's that announcement all about? >> We're really excited about that announcement, that relationship, just to kind of recap for those that didn't see it last week. We are making a huge partnership with Hortonworks, where we're bringing data science and machine learning to the Hadoop community. So IBM will be adopting HDP as our distribution, and that's what we will drive into the market from a Hadoop perspective. Hortonworks is adopting IBM Data Science Experience and IBM machine learning to be a core part of their Hadoop platform. And I'd say this is a recognition. One is, companies should do what they do best. We think we're great at data science and machine learning. Hortonworks is the best at Hadoop. Combine those two things, it'll be great for clients. And, we also talked about extending that to things like Big SQL, where they're partnering with us on Big SQL, around modernizing data environments. And then third, which relates a little bit to what we're here in Munich talking about, is governance, where we're partnering closely with them around unified governance, Apache Atlas, advancing Atlas in the enterprise. And so, it's a lot of dimensions to the relationship, but I can tell you since I was on theCUBE a week ago with Rob Bearden, client response has been amazing. Rob and I have done a number of client visits together, and clients see the value of unlocking insights in their Hadoop data, and they love this, which is great. >> Now, I mean, the Hadoop distro, I mean early on you got into that business, just, you had to do it. You had to be relevant, you want to be part of the community, and a number of folks did that. But it's really sort of best left to a few guys who want to do that, and Apache open source is really, I think, the way to go there. Let's talk about Munich. You guys chose this venue. There's a lot of talk about GDPR, you've got some announcements around unified government, but why Munich? >> So, there's something interesting that I see happening in the market. So first of all, you look at the last five years. There's only 10 companies in the world that have outperformed the S&P 500, in each of those five years. And we started digging into who those companies are and what they do. They are all applying data science and machine learning at scale to drive their business. And so, something's happening in the market. That's what leaders are doing. And I look at what's happening in Europe, and I say, I don't see the European market being that aggressive yet around data science, machine learning, how you apply data for competitive advantage, so we wanted to come do this in Munich. And it's a bit of a wake-up call, almost, to say hey, this is what's happening. We want to encourage clients across Europe to think about how do they start to do something now. >> Yeah, of course, GDPR is also a hook. The European Union and you guys have made some talk about that, you've got some keynotes today, and some breakout sessions that are discussing that, but talk about the two announcements that you guys made. There's one on DB2, there's another one around unified governance, what do those mean for clients? >> Yeah, sure, so first of all on GDPR, it's interesting to me, it's kind of the inverse of Y2K, which is there's very little hype, but there's huge ramifications. And Y2K was kind of the opposite. So look, it's coming, May 2018, clients have to be GDPR-compliant. And there's a misconception in the market that that only impacts companies in Europe. It actually impacts any company that does any type of business in Europe. So, it impacts everybody. So we are announcing a platform for unified governance that makes sure clients are GDPR-compliant. We've integrated software technology across analytics, IBM security, some of the assets from the Promontory acquisition that IBM did last year, and we are delivering the only platform for unified governance. And that's what clients need to be GDPR-compliant. The second piece is data has to become a lot simpler. As you think about my comment, who's leading the market today? Data's hard, and so we're trying to make data dramatically simpler. And so for example, with DB2, what we're announcing is you can download and get started using DB2 in 15 minutes or less, and anybody can do it. Even you can do it, Dave, which is amazing. >> Dave: (laughs) >> For the first time ever, you can-- >> We'll test that, Rob. >> Let's go test that. I would love to see you do it, because I guarantee you can. Even my son can do it. I had my son do it this weekend before I came here, because I wanted to see how simple it was. So that announcement is really about bringing, or introducing a new era of simplicity to data and analytics. We call it Download And Go. We started with SPSS, we did that back in March. Now we're bringing Download And Go to DB2, and to our governance catalog. So the idea is make data really simple for enterprises. >> You had a community edition previous to this, correct? There was-- >> Rob: We did, but it wasn't this easy. >> Wasn't this simple, okay. >> Not anybody could do it, and I want to make it so anybody can do it. >> Is simplicity, the rate of simplicity, the only differentiator of the latest edition, or I believe you have Kubernetes support now with this new addition, can you describe what that involves? >> Yeah, sure, so there's two main things that are new functionally-wise, Jim, to your point. So one is, look, we're big supporters of Kubernetes. And as we are helping clients build out private clouds, the best answer for that in our mind is Kubernetes, and so when we released Data Science Experience for Private Cloud earlier this quarter, that was on Kubernetes, extending that now to other parts of the portfolio. The other thing we're doing with DB2 is we're extending JSON support for DB2. So think of it as, you're working in a relational environment, now just through SQL you can integrate with non-relational environments, JSON, documents, any type of no-SQL environment. So we're finally bringing to fruition this idea of a data fabric, which is I can access all my data from a single interface, and that's pretty powerful for clients. >> Yeah, more cloud data development. Rob, I wonder if you can, we can go back to the machine learning, one of the core focuses of this particular event and the announcements you're making. Back in the fall, IBM made an announcement of Watson machine learning, for IBM Cloud, and World of Watson. In February, you made an announcement of IBM machine learning for the z platform. What are the machine learning announcements at this particular event, and can you sort of connect the dots in terms of where you're going, in terms of what sort of innovations are you driving into your machine learning portfolio going forward? >> I have a fundamental belief that machine learning is best when it's brought to the data. So, we started with, like you said, Watson machine learning on IBM Cloud, and then we said well, what's the next big corpus of data in the world? That's an easy answer, it's the mainframe, that's where all the world's transactional data sits, so we did that. Last week with the Hortonworks announcement, we said we're bringing machine learning to Hadoop, so we've kind of covered all the landscape of where data is. Now, the next step is about how do we bring a community into this? And the way that you do that is we don't dictate a language, we don't dictate a framework. So if you want to work with IBM on machine learning, or in Data Science Experience, you choose your language. Python, great. Scala or Java, you pick whatever language you want. You pick whatever machine learning framework you want, we're not trying to dictate that because there's different preferences in the market, so what we're really talking about here this week in Munich is this idea of an open platform for data science and machine learning. And we think that is going to bring a lot of people to the table. >> And with open, one thing, with open platform in mind, one thing to me that is conspicuously missing from the announcement today, correct me if I'm wrong, is any indication that you're bringing support for the deep learning frameworks like TensorFlow into this overall machine learning environment. Am I wrong? I know you have Power AI. Is there a piece of Power AI in these announcements today? >> So, stay tuned on that. We are, it takes some time to do that right, and we are doing that. But we want to optimize so that you can do machine learning with GPU acceleration on Power AI, so stay tuned on that one. But we are supporting multiple frameworks, so if you want to use TensorFlow, that's great. If you want to use Caffe, that's great. If you want to use Theano, that's great. That is our approach here. We're going to allow you to decide what's the best framework for you. >> So as you look forward, maybe it's a question for you, Jim, but Rob I'd love you to chime in. What does that mean for businesses? I mean, is it just more automation, more capabilities as you evolve that timeline, without divulging any sort of secrets? What do you think, Jim? Or do you want me to ask-- >> What do I think, what do I think you're doing? >> No, you ask about deep learning, like, okay, that's, I don't see that, Rob says okay, stay tuned. What does it mean for a business, that, if like-- >> Yeah. >> If I'm planning my roadmap, what does that mean for me in terms of how I should think about the capabilities going forward? >> Yeah, well what it means for a business, first of all, is what they're going, they're using deep learning for, is doing things like video analytics, and speech analytics and more of the challenges involving convolution of neural networks to do pattern recognition on complex data objects for things like connected cars, and so forth. Those are the kind of things that can be done with deep learning. >> Okay. And so, Rob, you're talking about here in Europe how the uptick in some of the data orientation has been a little bit slower, so I presume from your standpoint you don't want to over-rotate, to some of these things. But what do you think, I mean, it sounds like there is difference between certainly Europe and those top 10 companies in the S&P, outperforming the S&P 500. What's the barrier, is it just an understanding of how to take advantage of data, is it cultural, what's your sense of this? >> So, to some extent, data science is easy, data culture is really hard. And so I do think that culture's a big piece of it. And the reason we're kind of starting with a focus on machine learning, simplistic view, machine learning is a general-purpose framework. And so it invites a lot of experimentation, a lot of engagement, we're trying to make it easier for people to on-board. As you get to things like deep learning as Jim's describing, that's where the market's going, there's no question. Those tend to be very domain-specific, vertical-type use cases and to some extent, what I see clients struggle with, they say well, I don't know what my use case is. So we're saying, look, okay, start with the basics. A general purpose framework, do some tests, do some iteration, do some experiments, and once you find out what's hunting and what's working, then you can go to a deep learning type of approach. And so I think you'll see an evolution towards that over time, it's not either-or. It's more of a question of sequencing. >> One of the things we've talked to you about on theCUBE in the past, you and others, is that IBM obviously is a big services business. This big data is complicated, but great for services, but one of the challenges that IBM and other companies have had is how do you take that service expertise, codify it to software and scale it at large volumes and make it adoptable? I thought the Watson data platform announcement last fall, I think at the time you called it Data Works, and then so the name evolved, was really a strong attempt to do that, to package a lot of expertise that you guys had developed over the years, maybe even some different software modules, but bring them together in a scalable software package. So is that the right interpretation, how's that going, what's the uptake been like? >> So, it's going incredibly well. What's interesting to me is what everybody remembers from that announcement is the Watson Data Platform, which is a decomposable framework for doing these types of use cases on the IBM cloud. But there was another piece of that announcement that is just as critical, which is we introduced something called the Data First method. And that is the recipe book to say to a client, so given where you are, how do you get to this future on the cloud? And that's the part that people, clients, struggle with, is how do I get from step to step? So with Data First, we said, well look. There's different approaches to this. You can start with governance, you can start with data science, you can start with data management, you can start with visualization, there's different entry points. You figure out the right one for you, and then we help clients through that. And we've made Data First method available to all of our business partners so they can go do that. We work closely with our own consulting business on that, GBS. But that to me is actually the thing from that event that has had, I'd say, the biggest impact on the market, is just helping clients map out an approach, a methodology, to getting on this journey. >> So that was a catalyst, so this is not a sequential process, you can start, you can enter, like you said, wherever you want, and then pick up the other pieces from majority model standpoint? Exactly, because everybody is at a different place in their own life cycle, and so we want to make that flexible. >> I have a question about the clients, the customers' use of Watson Data Platform in a DevOps context. So, are more of your customers looking to use Watson Data Platform to automate more of the stages of the machine learning development and the training and deployment pipeline, and do you see, IBM, do you see yourself taking the platform and evolving it into a more full-fledged automated data science release pipelining tool? Or am I misunderstanding that? >> Rob: No, I think that-- >> Your strategy. >> Rob: You got it right, I would just, I would expand a little bit. So, one is it's a very flexible way to manage data. When you look at the Watson Data Platform, we've got relational stores, we've got column stores, we've got in-memory stores, we've got the whole suite of open-source databases under the composed-IO umbrella, we've got cloud in. So we've delivered a very flexible data layer. Now, in terms of how you apply data science, we say, again, choose your model, choose your language, choose your framework, that's up to you, and we allow clients, many clients start by building models on their private cloud, then we say you can deploy those into the Watson Data Platform, so therefore then they're running on the data that you have as part of that data fabric. So, we're continuing to deliver a very fluid data layer which then you can apply data science, apply machine learning there, and there's a lot of data moving into the Watson Data Platform because clients see that flexibility. >> All right, Rob, we're out of time, but I want to kind of set up the day. We're doing CUBE interviews all morning here, and then we cut over to the main tent. You can get all of this on IBMgo.com, you'll see the schedule. Rob, you've got, you're kicking off a session. We've got Hilary Mason, we've got a breakout session on GDPR, maybe set up the main tent for us. >> Yeah, main tent's going to be exciting. We're going to debunk a lot of misconceptions about data and about what's happening. Marc Altshuller has got a great segment on what he calls the death of correlations, so we've got some pretty engaging stuff. Hilary's got a great piece that she was talking to me about this morning. It's going to be interesting. We think it's going to provoke some thought and ultimately provoke action, and that's the intent of this week. >> Excellent, well Rob, thanks again for coming to theCUBE. It's always a pleasure to see you. >> Rob: Thanks, guys, great to see you. >> You're welcome; all right, keep it right there, buddy, We'll be back with our next guest. This is theCUBE, we're live from Munich, Fast Track Your Data, right back. (upbeat electronic music)