(upbeat music) >> Live from San Francisco it's theCUBE. Covering IBM Think 2019. Brought to you by IBM >> Welcome back to theCUBE. I'm Lisa Martin with John Furrier and we are on a rainy San Francisco day. Day three of theCUBE's coverage of IBM Think 2019 here to talk shopping. One of my favorite topics. We have Katie Dunlap VP of Global Unified rather Commerce and Marketing for Bluewolf part of IBM. Katie welcome to theCUBE. >> Welcome, thank you. >> And from Salesforce we have Ben Cesare Senior Director of Global Industry Retail Solutions. Ben it's great to have you on our program. >> How are you? >> Excellent. >> Good. >> Even though we are at the rejuvenated Moscone Center which is fantastic and I think all of the hybrid multi cloud have opened upon San Francisco. >> Right. >> It's a very soggy day. So Katie IBM announced a partnership with Salesforce a couple of years ago. >> Right. >> Just yesterday John and I were chatting. We heard Ginni Rometty your CEO talk about IBM is number one implementer of Salesforce. Talk to us a little bit about the partnership before we get into some specific examples with that. >> So we know that part of that partnership it's really to leverage the best of the technology from Salesforce as well as IBM and ways that we together married together create opportunities for the industry and specifically here today we're talking about retail. >> So on the retail side Salesforce as a great SAS company they keep on blowing the records on the numbers performance wise. SAS business has proven it's a cloud business but retails is a data business. >> Yes. >> So how does IBM look at that? What's the relationship with retail? What's the solution? >> Yeah. >> And what are people looking at Salesforce for retail. >> Yeah, I think it's really important to understand where our strengths are and I think when you talk about Salesforce you talk about Marketing Cloud and Commerce Cloud, Service Cloud. We call that the engagement layer. That's how we can really interact with our consumers with our shoppers. But at the same time to really have a great connection with consumers you need to have great data. You need have great insights. You need to understand what's happening with all the information that drives choices for retailers and that's why the relationship with IBM is absolutely so strong and it is a data driven relationship. Together I guess you can see the customers in the middle. So we have our engagement layer and a data layer. Together we satisfy the customer. >> Lisa what's the solution specifically because obviously you guys going to market together to explain the tactical relationship. You guys join sale, is it an integration? >> Sure. So what we have done given the disruption that's happening right now in the retail space and with the customer at the center of that conversation we've been looking at ways that what the native functionality for Salesforce is Einstein as an intelligent layer and for IBM it's Watson. So where do they complement one another? And so looking at retail with commerce and marketing and service as the center of that conversation and engagement layer. How are we activating and working with a customer from a collection of data information standpoint and activating that data all through supply chain. So the experience is not just the front experience that you and I have when we go to a site it's actually how and when is that delivered to me. If I have an issue how am I going to return that. So we've looked at the entire customer journey and looked at ways that we can support and engage along the way. So for us, we're looking at as you see retail and the way it's evolving is that we're no longer just talking about that one experience where you're actually adding to your cart and your buying. It goes all the way through servicing that customer returning and making sure that information that's specific to me. And if I can choose how I'm going to have that inventory sent to me and those products sent to me. That's exactly what we're looking to do. >> So then the retailer like a big clothing store is much more empowered than they've ever been. Probably really demanded by us consumers who want to be able to do any transaction anywhere started on my phone finish on a tablet, etc. So I can imagine maybe Ben is this like a Watson and Einstein working together to say take external data. Maybe it's weather data for example and combine those external data sources with what a retailer has within their customer database and Salesforce to create very personalized experiences for us shoppers as consumers. >> Right, and where retailers really can grow in terms of the future is really accessing all that data. I think if you look at some of the statistics retailers have up to 29 different systems of records and that's why some of our experiences are very good some of our experiences are not very good. So together if we can collapse that data in a uniform way that really drives personalization, contextual selling so you can actually see what you're buying why you're buying it, why it's just for me. That's the next level and I must say with all the changes in the industry there's some things that will never change and that is consumers want the right product, the right price, the right place and the right time. All enveloped in a great customer experience. That will never change but today we have data that can inform that strategy and when I was a senior merchant at Macy's years ago, I had no data. I had to do a lot of guessing and when mistakes are made that's when retailers have a problem. So if retailers are using data to it's benefit it just make sure that the customer experiences exceptional. And that's what we strive to do together. >> And I can build on that if we're thinking like specifically how we're engaged from a technology perspective. If I'm a merchandiser and I decide I want to run a promotion for New York and I want to make sure before I run that promotion that I have the right inventory and that I not only I'm I creating the right message but I have the information that I need in order to make that successful. One of the things that we partner with Salesforce on is the engagement layer being Salesforce. But in the back end we have access to something called Watson Embedded Business Agent and that business agent actually goes out and talks to all the disparate systems. So it doesn't have to be solutions that are necessarily a homegrown by IBM or Salesforce Watson could actually integrate directly with them and sits on top. So as a merchandiser I can ask the question and receive information back from supply chain. Yes there's enough product in New York for you to run this promotion. It can go out and check to see if there's any disruption that's expected and check in with weather so that as on the back end from an operation standpoint I'm empowered or the right data in order to run those promotions and be successful. >> It's interesting one of the things that comes up with her this expression from IBM. There's no AI without IA information architecture. You talk about systems of record all this silo databases. There's low latency you need to be real time in retail. So this is a data problem, right? So this is where AI really could fit in. I see that happening. The question that I have as a consumer is what's in it for me? Right? So Ben, tell us about the changes in retail because certainly online buying mobile is happening. But what are some of the new experiences that end users and consumers are seeing that are becoming new expectations? What's the big trend in retail? >> Well there's two paths they're your expectations as a consumer, then there's the retailer path and how they can meet your expectations. So let's talk about you first. So what you always want is a great customer experience. That's what you want. And what defines that is are they serving me the products I want when I want them? Are they delivering them on time? Do the products work? If I have a problem, how am I treated? How am I served? And these are all the things that we address with the Salesforce solutions. Now let's talk about the retailer. What's important to the retailer is next retailer myself. It was important that I understood what is my right assortment? And that's hard because you have a broad audience of consumers, you have regional or local requirements. So you want to understand what's the right assortment and working with IBM with their (mumbles) optimizer that helps us out in terms how we promote through our engagement later. That's number one. Number two, how about managing markdowns. This year there were over $300 billion in markdown through retailers. Half of those markdowns 150 billion were unplanned markdowns and that goes right to your P&L. So we want to make sure that the things we do satisfy the consumer but not at the expense of the retailer. The retailer has to succeed. So by using IBM supply chain data information we can properly service you. >> It's interesting we see the trend in retail I mean financial services for early on. >> Yeah. >> High-frequency trading, use of data. That kind of mindset is coming to retail where if you're not a data driven or data architecturally thinking about it. >> Yeah. >> The profit will drop. >> Yeah. >> Unplanned markdowns and other things and inventory variety of things. This is a critical new way to really reimagine retail. >> Yeah retail has become such a ubiquitous term there's retail banking, there's retail in every parts of our life. It's not just the store or online but it's retail everywhere and someone is selling their services to you. So I think the holy grail is really understanding you specifically. And it's not just about historical transact which you bought but behavioral data. What interests you. What are the trends and data has become a much broader term. It's just not numbers. Data is what are your trends? What are you saying on social media? What are you tweeting out? What are you reading.? What videos are you viewing? All that together really gives a retailer information to better serve you. So data is really become exponential in it's use and in it's form. >> So I'm curious what you guys see this retails it's very robust retail use case as driving in the future. We just heard yesterday one of the announcements Watson anywhere. I'm curious leveraging retail as an example and the consumerization of almost any industry because we expect to have things so readily and as you both point out data is commerce. Where do you think this will go from here with Watson Einstein and some of the other technologies? What's the next prime industry that really can benefit from what you're doing in retail? >> I think that I'll start and probably you can add that in as well. But I think that it's going to bleed into everything. So health and life sciences, consumer goods, product goods. We've talked about retail being all different kinds of things right now. Well CPG organizations are actually looking at ways to engage the customer directly and so having access utilizing Watson as a way of engaging and activating data to create insights that you've never thought of before. And so being able to stay a step ahead anticipate the needs stay on the bleeding edge of that interaction so that you're engaging customers in a whole new way is what we see and it's going to be proliferated into all kinds of different industries. >> Yes, every merchant every buyer wants to be able to predict. I mean won't that be incredible be able to see around the corner a bit and and while technologies don't give you the entire answer they can sure get you along the way to make better decisions. And I think with Watson and Einstein it does exactly that. It allows you to really predict what the customers want and that's very powerful. >> I want to get you guys perspective on some trend that we're seeing. We hear Ginni Rometty talk about chapter two of the cloud, you almost say there's a chapter two in retail, if you look at the certainly progressive way out front, doing all the new things. People doing the basics, getting an online presence, doing some basic things with mobile kind of setting the table a foundations, but they stare at the data problem. They almost like so it's a big problem. I know all this systems of record. How do I integrate it all in? So take us through a use case of how someone would attack that problem. Talking about an example a customer or a situation or use case that says okay guys help me. I'm staring at this data problem, I got the foundation set, I want to be a retail have to be efficient and innovative in retail, what do I do? Do I call IBM up, do I call Salesforce? How does that work? Take us through an example. >> So I think the first example that comes to mind is I think about Sally Beauty and how they're trying to approach the market and looking at who they are and many retailers right now because there's such a desire to understand data. Make sure that your cap. Everyone has enough data. But what is the right data to activate and use in that experience. So they came to us to kind of look at are we in the right space because right now everyone's trying to be everything to all people. So how do I pick the right place that I should be and am I in the right place with hair care and hair color? And the answer came back yes. You are in the right space. You need to just dive deeper into that and make sure that that experience online so they used a lot of information from their research on users to understand who their customers are, what they're expecting. And since they sell haircare product that is professional grade. How do I make sure that the customers are getting using it in the proper way. So they've actually created an entire infused way of deciding what exactly hair color you need and for me as a consumer, am I actually buying the right grade level from me and am I using that appropriately. And that data all came from doing the research because they are about to expand out and add in all kinds of things like (mumbles) where you're going into the makeup area but really helping them stay laser focused on what they need to do in order to be successful. >> Because you guys come and do like an audit engage with them on a professional service level. >> Yes, we went end-to-end >> And the buying SAS AI and then they plug in Salesforce. >> Yes, so they actually already had Salesforce. So they had the commerce solution marketing and service. They were fairly siloed so we go back to that whole conversation around data being held individually but not leveraging that as a unit in order to activate that experience for the consumer. What they have decided as a result of our work with them. So we came in and did a digital strategy. We're been involved as an agency of record to support them and how that entire brand strategy should be from an omnichannel perspective in the store, as well as that digital experience and then they actually just decided to go with IBM (mumbles) and use that as a way of activating from an omnichannel order orchestration standpoint. So all the way through that lifecycle we've been engaging them and supporting them and Watson obviously native to Salesforce's Einstein and they're leveraging that but they will be infusing Watson as part of their experience. >> So another benefit that Sally Beauty and imagine other retailers and other companies and other industries, we get is optimizing the use of Salesforce. It's a very ubiquitous tool but you mentioned, I think you mentioned Ben that in the previous days of many, many, many systems of records. So I imagine for Sally Beauty also not just to be able to deliver that personalized customer experience, track inventory but it's also optimizing their internal workforce productivity. But I'm curious-- >> Yes. >> For an organization of that size. What's the time to impact? They come in you guys do the joint implementation, go to market, the consulting, identify the phases of the project, how quickly did Sally Beauty start to see a positive impact on their business? >> I think they... Well there's immediate benefits, right? Because they are already Salesforce clients and so our team our IBM team was able to come in and infuse some best practices and their current existing site. So they've been able to leverage that and see that benefit through all the way through Black Friday and last holiday season. And now what they're seeing is they're on the verge of launching and relaunching their site in the next month and then implementing (mumbles) is a part of that. So they're still on the path in the journey to that success but they've already seen success based on the support that we've provided them. >> And what are some of the learnings you guys have seen with this? Obviously you got existing accounts. They take advantage of this, what are some of the learnings around this new engagement layer and with the data intelligence around AI? What's the learnings have you guys seen? >> Yeah I think the leading thing that I've learned is the power of personalization. It's incredibly powerful. And a good example is one of my favorite grocers and that's Kroger. If we really understand what Kroger has done, I'll talk about their business a bit. I'll talk about what they've been able to do. If you look at someone's shopper basket there's an amazing amount of things you can learn about that. You can learn if they're trying to be fit if they're on a diet. You can learn if their birthdays coming. You can learn if they just had a baby. You can learn so many different things. So with shopper basket analysis, you can understand exactly what coupons you send them. So when I get coupons digital or in my home they're all exactly what I buy. But to do that for 25-30 million top customers is a very difficult thing to do. So the ability to analyze the data, segment it and personalize it to you is extremely powerful and I think that's something that retailers and CPG organizations how they continue to try to do. We're not all the way there. Were probably 30% there I would say but personalization it's going to drive customer for life. That's what it's going to do and that's a massive learning for us. >> And the other thing too Ginni mentioned it in her keynote is the reasoning around the data. So it's knowing that the interest and around the personas, etc. But it's also those surprises. Knowing kind of in advance, maybe what someone might like given their situation-- >> Anticipating. >> And we were talking about this morning. Actually, we're talking about behavioral data and data has taken a different term. >> Data is again what are you doing online what are you talking about, what did you view. What video did you look at. For organizations that have access to that data tells me so much more about your interest right now today. And it's not just about a product but it's about a lifestyle. And if retails could understand your lifestyle that opens the door to so many products and services. So I think that's really what retailers are really into. >> My final question for you guys both of you get the answer. Answer will be great is what's the biggest thing that is going to happen in retail that people may not see coming that's going to be empowering and changing people's lives? What do you guys see as a trend that's knocking on the door or soon to be here and changing lives and empowering people and making them better in life. >> Yeah, I'll jump in on one real quick and I think it's already started but it's really phenomenon of commerce anywhere. Commerce used to be a very linear thing. You'd see an ad some would reach out to you and you buy something. The commerce now is happening wherever you are. You could be tweeting something on Instagram, you could be walking in an airport. You could be anywhere and you can actually execute a transaction. So I think the distance between media and commerce has totally collapsed. It's become real time and traditional media TV, print and radio is still a big part of media. A big part but there's distance. So I think it's the immediacy of media and a transaction. That's really going to take retailers and CPG customers by surprise. >> It changes the direct-to-consumer equation. >> It changes it. It does. >> And I think I would just build on that to say that people have relationships with their brands and the way that you can extend that in this and commerce anywhere is that people don't necessarily need to know they're in that commerce experience. They're actually having a relationship with that individual brand. They're seen for who they are as an individual not a segment. I don't fall into a segment that I'm kind of like this but I'm actually who I am and they're engaging. So the way that I think we're going to see things go as people thinking at more and more out of the box about how to make it more convenient for me and to not hide that it's a commerce experience but to make that more of an engagement conversation that-- >> People centric not person in a database. >> Exactly. >> That's right. >> Moving away from marketing from segmentation and more to individual conversations. >> Yeah I think you said it Ben it's the power of personalization. >> Power of personalization. >> Katie, Ben thanks so much for joining. >> Thank you. >> Talking about what you guys IBM and Salesforce are doing together and we're excited to see where that continues to go. >> Great. >> Thanks so much. >> Our pleasure, thank you. >> We want to thank you for watching theCUBE live from IBM Think 19 I'm Lisa Martin for John Furrier stick around on Express. We'll be joining us shortly. (upbeat music)

>> Announcer: Live from Las Vegas, it's the CUBE, covering IBM Think 2018. Brought to you by IBM. (upbeat music) >> Hello, and welcome back to the CUBE here at IBM Think 2018. We're at the Mandalay Bay at the CUBE Studios where IBM Think 2018. I'm John Furrier, your host. Our next guest, Brian Reagan, Chief Marketing Officer, Actifio, and Mike Errity, VP North America, IBM Resiliency Services, guys welcome to the CUBE, Brian, good to see you. (mumbles) >> Good to see you John, yes. >> Great stuff here at IBM Think, big show, six in one. Six shows brought down to one. A lot of customers here, but the message, you're starting to see a clear line of sight, for customers seeing the innovation formula. Cloud Multi-Cloud Services, On-Prem through private Cloud as Oogie Mound reported, but really A.I. and Blockchain are infrastructure powering data. Data's at the center of the value proposition. You guys are partnering with IBM. What's the story here, what's the relationship? >> Well, yeah, I mean you nailed it John. I mean, data is really at the center of everything, right? I mean we're in, we're in the midst of a massive digital transformation on like anything we've seen in I.T. for 30 years. And, you know, every business is a data business. Whether they know it or not. And with that comes great rewards, but it also brings a lot of risks as well, and you know, we've been a strong partner of IBM's for as long as Actifio's been in business. We're a data company, or about managing data. And now, I think with this rising tide of data threats, you know, partnering with the world's leader in resiliency just makes all the sense in the world. >> (mumbles) Betting your business on data was a good call, don't you think? (loud laughter) >> I think so, yes, absolutely. >> Hey, with Watson and AM Mike, you guys are pioneering, obviously, and we've seen this evolve from the R&D and the, and the modern infrastructure of the systems level, infrastructure is code. But the real applications out there have to drive a lot of money opportunities, success for your clients, security's the biggest risk. It's a lot of industries out there for profit around security, the threats are endless. >> Mike: Yeah. >> There's new threats everyday. >> Mike: Yeah. >> This is hard business, data's the key. What's your reaction and vision around the current state of security? >> What I done in our, our decades of experience working with clients has proven that, at, at the start of a resiliency initiative, it starts with the data element. We've had the opportunity to work with thousands of clients. Everyday, we're helping them test their ability to be able to recover from some unplanned event. Something that could cause them damage. And, and that experience has been evolving over the past, I would say 18 months. We're on warp speed to help clients achieve literally an always on environment, and it starts with data. >> John: Yeah. >> The, the, the point about data that I think is important is that clients recognize that if there's any damage at all to their data repository, it will cause them severe damage, so protecting it and making sure that it's recoverable to a point in time, is what we're working with everyday. >> I'd like you to talk about for a minute, resiliency services, because security's broad, but everyone thinks, command center, killing the bad guys, offense, defense, blue teams, red teams. There's a, the trend of I.T.s, securities kind of moving into the direct line to the sea sweet. >> Mike: Yeah. >> 'Cause it's so important, the risk management alone, on securities, so you're seeing that trend. What do you guys do? What is resilience service? Take a minute to explain specifically what that is, in context of security. >> That is what's so exciting about being here at Think, because we're, we have a total interconnection between our security assets and our security domain, and our resiliency team, and so resiliency's about resuming business operations to the point before you had the event. Being back to that normal state of affairs. Resiliency for us has been about helping clients create a plan after assessing the risk, and designing and implementing that plan to, to return to a point in time where there know that they're safe, that their applications are back up and running. And what we're finding in the security domain, which is the reason why cyber resiliency includes security, networking, and the resiliency methodologies of getting back to normal. >> John: Yeah. >> Is that you have to combine all three of those categories. >> John: Yeah. >> To create a solution to return to that normal state at a safe point in time. >> Talk about the importance of the proactive front-end work that's involved, I mean, back up in recovery in the old tradition, oh yeah, it's at the end of the, probably just throws it back up at it, and then people have been bitten in the butt on that. They've gotten really impacted. How much work is involved? What is the playbook on the front-end to prepare? And give me an example where what's the consequences? >> Mike: Oh well the, >> Of not doing it? >> You used the right term, it is a playbook, and it's one that needs to be well scripted and well tested. The work on the upfront is to design the right solution. Technologically, to ensure that you have a, a solution that moves data from a place that could be harmed to a safe point, and create the environment, create the solution, and then figure out the right team and the right skill and the right investment to constantly test it, test it so that you have the ability, and that's the work that we're doing with Actifio. Actifio has the expertise to help us create the right copy-data management solution to enable a snap-snap-snap-snap copy to be able to then travel back in time to be able to find that right, clean point in the event of a cyber incident that has pervasively impacted a data center environment. >> What's the role of Actifio as an ingredient in that plan? Are they in the insurance policy? Are they in the front end? When are they invoked, and with, where, where are they in the process? >> Well they're the, I mean to be, to use a, an analogy that I'm comfortable with, we, we trust Actifio to provide us the brains of the solution, to be able to move the data constantly. Move it to a point where we can then create a service to be able to, as I said, go back in time, and Brian, you might want to comment on that a little more. >> Brian, talk about the relation to IBM in that context, because you know, covering IBM for so many years, you know, they're the big, the big ship, right? They move at, at a pace, with a huge customer base, you know, how do you guys integrate in? What are you guys providing? >> Brian: Sure. >> And what's the value proposition that you guys are fighting IBM? >> Well I mean the, the you know, because we've been partnering with IBM for so long, I mean literally since the inception of the company, we have a very common user-base, right? We, we serve the mid and large enterprise in global enterprises worldwide. We have, you know, 3000 customers from Actifio and, and almost all of them are IBM accounts as well. One of the things that, you know, just to kind of piggyback on, on, on Mike's discussion, you know one of the, and, and to speak specifically to a customer base, you know, global financials right now are not only worried about cyber threats to production, but increasingly they're worried about cyber-threats to their backup sets. And in fact, there is regulations, you know FISMA regulations in North America that talk about, you need air-gap protection between, you know, one backup set and another, because they're under attack now. So literally these threats have started to creep beyond just the normal production data sets. Actifio, plus, you know, resiliency services equals, you know, a technology that can provide that air-gap, provide the immutability, provide all the, you know, the insurance protection of the data, and provide the, the wear with all the knowledge to really get that playbook to resume business operations as fast as possible. >> You guys need to stay on top of the big trends too, because Blockchain's right around the corner. >> Brian: Yeah. >> That's immutable, that could be an opportunity. >> Brian: Absolutely. >> Thoughts on Blockchain? >> Blockchain is, you know, it is a fascinating technology. Apart from Discripto, right? (laughs) And, and what a better, we talk about, you know, every business is digital, literally Blockchain is turning every business into a digital business. And it is the next generation in terms of securing closed contracts, and securing really immutability and, and reference ability of data. I think it has a huge play with IBM obviously. Around GDPR and privacy. So, we, you know, we see that as absolutely the next frontier. >> Well there's a lot of these supply, IBM's been in the supply chain business for years, running technology for companies. And that's always been kind of the big monolithic systems, mainframe minis, lands, you know, CRM systems, ARPs, whatever you want to call it. Now you have agile cloud coming in. You got the plan, a resiliency plan. While there's a lot of business reconstruction going on at the business model level. >> Mike: That's right. >> So, are clients like banging their head against the wall? What's some of the conversations, like with the clients? So, I mean they got to be proactive. At the same time, they got a lot of stuff on their plate. >> Well they, we're sort of humbled by the role that we're in right now, because for years, we've been working with so many clients, to help them build programs, we, we've got ourselves into a very, you know, into a comfort zone of helping them recover from the environment you're talking about, just standard, legacy, data center recovery. We can accomplish the recovery time in, in minutes, nearly instantly, with no, no data loss. But suddenly, the humbling point is, our phone's ringing off the hook, asking, apply those same methodologies to the, to the risks that I'm seeing as my business is being digitized. And help me evolve, and to us it's all about orchestrating a recovery, creating a softer, defined solution to enable data that's recovered and systems that are automated. >> From quality partners, and you're happy with Actifio? >> Oh absolutely, yeah. >> It's like changing an airplane engine out 30,000 feet. You just, what you just talked about. I'm like, that sounds so hard. I got my business moving so fast, I'm modernizing, and I got to do all this work. >> And the devil's in the details, and whenever we're engaging with Actifio, they have the architects to assist us with those details. >> What about regulatory concerns? Obviously, that's come up a lot. We know GDPR's out there, that be we don't want to beat that dead horse, but you know, when you get into things like Cripto, Blockchain, regions of, of data centers where its cloud is deployed, you got regulatories, it's going to be a constant issue. >> Brian: Absolutely. >> Your thoughts on that? >> It is a constant issue, and part of the challenge with regulations, is they're very ambiguously worded. So, the interpretation of regulations is as challenging as actually delivering solutions to, to meet them. I think that it really does come down to, and in most regulations, good hygiene is protect the data, make sure that there is, you know, increasingly air-gaps around the sensitive data, both production as well as non-production, and make sure that you can resume business operations, you know, where and when you need to, and having the flexibility to do that on Prem, in the IBM cloud, you know, that's, that's what IBM does. >> And, and if I can just add a point to that Brian, the, the driver of the conversations that we're seeing are, is, is predominantly in a compliance area, so businesses are concerned, enterprises are concerned about, am I compliant, am I audit-worthy? And can I prove that not so much at time of recovery, but really a time of test. Can I go prove to the market place that I'm ready? >> No more lip service. >> Mike: None at all. >> You've got an actual plan. >> And, and, and, >> Not just for your own reasons, there's actually filings. >> And have documented proof of it. >> Yeah, IBM Actifio, all about the resiliency in global economy, you got Blockchain, you got A.I. At the heart of it is data. You don't have a plan, you better get one. (mumbles) Congratulations on your relationship. >> Oh thank you. >> John Furrier here inside the CUBE. IBM Think 2018, CUBE studios will be back with more coverage after this short break. (upbeat music)

(lively music) >> Narrator: Live from Las Vegas, it's the CUBE covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. >> Okay, welcome back everyone. We are here live in Las Vegas, the Mandalay Bay for Oracle's Modern CX conference, hashtag Modern CX. This is the CUBE. I'm John Furrier Silicone Angle. My cost, Peter Burch with us for two days. Our next guest is Ron Corbisier. Owner and CEO of Relationship One. Back again, from last year. It was one of my memorable interviews last year. Welcome back-- >> Ron: Thank you for having me. >> to the cube. We went down and dirty last year. I remember we were having a great conversation about ad tech. If you've taken that video, it's on YouTube and look at it, I guarantee you, it's going to play right into what happened this year. Again, we predicted it. We didn't say AI but we did say we're going to see data really driving. That's what Oracle ended up locking in on daily. >> Yeah, absolutely. Data is going to be the underlying conversation for the next few years. We spoke, a lot, last year about martech stack. Actually, martech and ad tech colliding, coming together. All of that is being fueled by the mass quantities of data that we have as sales and marketing folks out there, to leverage and how do you use it. It's never about, do I have enough data? A lot of times you feel you, almost, have too much. But it's, now how can you use it appropriately? >> We were talking, before we came on camera here about that dynamic of ad tech and marteh collision which we talked about last year. It's interesting. If you just say digital, end-to-end, as a fabric, then you can still talk about these pillars of solutions but they're not silos. If you look at the holistic data approach and say, hey, if we're going to have horizontally scalable data which we want, frictionless less than 150 milliseconds responses they want to promote. You can still do your pillars but be open to data sharing versus here's my silent stack. I do this, I do this, that's shifted and that's what Oracle's main news is here. >> Yeah, absolutely. I think what you're seeing, even in, not only Oracle, that organizational level, people are taking a more holistic view of data that they own and data that they can enrich with external information, right? How does that information, then, fuel all of these other areas within customer experience within the CX world? How do you use that to provide better service? How do you use that information to optimize your sales efforts and from a marketing standpoint, obviously, my background, it's how do we leverage that to optimize our spend, optimize our communication, our orchestration, all of those pieces. It all comes down to that common language of data that we have access to. >> Tell me about the real time aspect cause we teased on it last time and we did talk about how to leverage some of the advertising opportunities and the role of data in real time. That's been a message here from batch to real time. The consumer's in motion all the time depending upon their context. How does real time fit into this? >> Yeah, this is the evolution of what we're seeing in the technology, right? Historically, you've built a campaign. You've, maybe, created some type of journey or persona. You're building content around very specific elements within a life cycle structure. Life cycles are not linear any longer. They never really were but they're, definitely, not now and you have to adapt very quickly. Leverage technology, to say, one of my saying, communicating and what channel but in more in a real time thing. You have to look at what was the last thing that individual did, the activity, all of that. Historically, you haven't had that depth or degree of real time lists. It's been more of a structured candance. That doesn't exist, right? That's not going to exist going forward. That's where things like AI which I always hesitate to use that term because it's the buzz word now of today. But tools that are more of that augmentation of how we do things. Leveraging the power of technology. That's going to change how we orchestrate things and how we communicate. >> I'm just looking at your tweet here. I want to bring this up because you mentioned AI and we were talking about it. Thanks to all who stopped by my MME 17 Modern Marketing Experience 17. A little bit of a jab at the messaging that's cool like that. Session on artificial intelligence. Loved all the support from my fellow modern marketers. What do you mean by that? You make a bold statement. Did you have courage? Did you stand tall? Did you call out AI? What was the conversation there? >> Well I called out the silliness of the term AI. I picked on that the marketers but I picked on the term We, as marketers, I call them the squirell moments that, as marketers, we're on to the next thing. I reviewed the past eight some years of these conferences and what were the topics, right? There were some topics that were transformational topics like how does marketing automation or organizational change or those type of things. Those are things that stick with you. There is things that are more timely things. Like predictive scoring and their tactics. There more things that I use as a marketer or sales person. What I was picking on with AI is that it's the buzz word. It gets you funding. It gets you people in a room for a conference, that's great. But it doesn't do anything by itself. It's really an enabler. It's a pervasive thing that combines machine cycle and data but you have to teach it, you have to incorporate it into your applications. As marketers, ultimately, it's going to change our tool set to make it better. It's more poking fun at the term-- >> We always say AI. I've said it on the CUBE, AI's BS. Although, I'm a software guy. I love AI because it really promotes software that has been very nuance. So, IOT, machine learning, this is very geeky computer science stuff that's super cool. Anything that can take that mainstream in the software world, I'm a big fan of. That being said, I think the augmentation is the real message which is, you can use machine learning, you can use software, use some technical things, to make things better. You said it on our earlier segment this morning which is there's a variety of things that you can automate away. >> The thing that's, and you mentioned earlier, it's the ability that we now have the ability to collect an enormous amount of data, that's relevant and important. And we now have the technology to, actually, do something with that data. But we still have to apply it and there's a lot of change that has to happen. The way AI is different from other systems is that, historically, financial systems, software would deliver and answer. It was highly stylized. It was rarely, a clear correspondence with the real world. We closed the books. How much money did we make? There was an answer and it came from some data structures that were defined within the system. Now we're trying to bring in the real world and have these technologies focus on the real world. And they're giving ranges of possible options. That is new. It's good and it's useful but it does not take the requirement for discretion out of the system. That's why it's the augmentation. >> Ron and I were talking last year about this, Peter and I. I think you're getting a trajectory that, I've been saying for a while and this is developing in real time here on the CUBE and also some of our commentary is the role of software development and DevOps that we've seen in Cloud, is moving into the front lines of business. Meaning their techniques. You're seeing Agile, already, being talked about. You're seeing standing up campaigns. Language, you can go to the Cloud stack and say, building blocks, EC2, S3, Cooper Netties, containers, micro services and apply that to marketing because there's a lot of parallels going on to the characteristics of the infrastructure. Certainly critical infrastructure to enabling infrastructure. It's interesting that you're seeing marketers being more savvy and inadative. What's your thoughts on that, a reaction? >> Yeah, it's the evolution though. If you go back to, we as marketers have been using rules engines, we've been using tools like collaborative filtering. You go back to late 90's, early 2000's when we were building recommendation engines in simple. That's algorithmic stuff, right? No different than we're doing today with pricing rules and all that stuff. The difference it that you now have more power to do it. You have the ability to do it more real time and on the fly. You use far more data. More computing power and more data. Not only your data that you own but data that you leverage from third party to really understand people. You have a wider lens. Historically, you're making recommendations based on what you had in a cart or some other things that people have bought that also had that in the cart, that's different now, right? With this type of technology, this enabling kind of world, you an look at a lot more data points to give you that. The problem is that anything around AI requires a couple of things. It is a dumb system so AI. (laughs) >> Still a computer. >> It's still a computer. Everyone forgets that for it to work, it has to learn. I have some friends who have built marketing tools on top of Watson, for example. It takes hundreds and hundreds of hours for it to start doing something. You have to train it. You have to, not only, give it the data, you have to train it. >> Even the word learning and training is misleading in may respects. At the end of the day it's software but what is new is it's being applied in richer, more complex domains. The recommendation engine used to be just for recommendation. Now we're using those same models and we're combining them and applying them to richer more complex domains. Yet, ideally, the software's not getting more difficult to use. And I think what really makes this compelling, as a software engineer, is that we're doing all this more complexity but we're packing it and making it simpler. >> I think that's the point of where Oracle's going and why they don't call it AI. They're using it more the adaptive. Because they're thinking of it at the micro service level. They're thinking of how can they make these widgets of functionality to better the tools we have. To incorporate it into not make it so a jump forward in our tool set. It's just now, an augmented component of what we do today. >> It's, almost, a stack approach. You got foundational building blocks and at the top is high velocity, highly dynamic apps and you could argue, we were talking that the CMO's going to be an app shop, some day. This banks the question and I'd like to get both of you guys to weigh in on this. Because this is a question that I'd like to get on the record. What is modern marketing these days? Define modern marketing because what we're getting at here is, to your point of the evolution is we've seen this movie before. Is it a replatforming? Is it a building block approach? What is a modern marketer? What is a modern marketer mean? How do you execute that? >> I think it's quick and nimble and adaptive. The whole point of modern marketing is that you're always looking at how you can rethink, how you can optimize, how you can leverage technology to do things. It's not about replacing head count with a machine or a tool or a tech. It's really about how do you leverage that head count more effectively? How can you focus on optimization using those technologies. Modern marketing is, again, probably another buzz word but just like modern sales, modern commerce, all of that. It's really about how do you enable it with that stack do better. >> So, is it fashion or is it like hey, there's a modern marketer over there, look at what he or she is wearing. Or is it more technology based that's got some fundamental foundational shifts that are being worked on or both? >> It's leveraging technology and it's leveraging data more effectively and creatively. It's not being stuck with a prescriptive approach on campaign and orchestration and building. It still requires strategy and all of that but it's really how you approach it. >> So, how you think of it. What's your angle on this? >> That's a great question. And that's why I giggled about it. I think you gave a great answer. The three key precepts of Agile are, iterative, opportunistic and empirical and it's nimble quick and you change. But to me, I'll answer the question this way. Modern marketing focuses on delivering value to the customer not back into the business. It used to be that you would deliver into the business. He'd say, oh, we give you a whole bunch of new leads. We give you a whole bunch of this. If along the way, it created value for the customer, that's okay. But more often that not, it was annoying. As customer's can share their experience and share information about how (mumbles) engaged them, that's amplified. Annoying gets amplified. I think if you focus on are you creating value for the customer, you also end up with the derivative element that you're accelerating leads, they are in the process and where they are in the journey. The way I'd answer it. It's not distinct from yours but the idea of modern marketing focuses on creating value for the customer. The only way you, consisting do that is by being nimble and blah-blah-blah-blah-blah. >> I agree, in the same thing though. A core tenant, if you will, of modern marketing is absolutely. It is the value proposition. It's also making sure you understand the impact of the value of proposition The velocity of the pipeline, the impact on revenue, all of those things right? Because it's all about that value which it has to be, from a customers perspective but you're not doing all of the other pieces. You're not going to justify the spend. You're not going to get all of those together. >> Let me see if I can thread the two points together. Cause what I'm seeing, by listening is, you mentioned, the main thing in my mind was the data. That's different right? You're saying okay, thing differently, talk to the customer and the value to the enterprise value is being created through a different mechanism versus just serving it. >> Not really, not really. The fundamental focus, historically, of marketing has been what are we doing for the business? What are we doing for sales? Now, if we focus on, now you say well no. We have to created value for the customer in every thing we do, then we get permission to do things differently. We get more data out of the customer because the trust is there. We're allowed to bias the customer to the next, best option. >> I'm trying to answer my questions. I can see your point. My point is this, the modern marketer is defined by doing it. The business practices it a little bit differently to achieve the same thing. >> By focusing or creating value they have to do things differently and now they can because technology allows them to do it. >> We saw Time Warner, they weren't using data prior. That's a little different. If you go outward to go in, it's a great value while doing the table stake stuff. >> It's changing strategically thinking different of how you do it. Creating that value proposition's very different and also being able to measure and optimize are you doing it correctly? Is it having impact on the business? Most of my customers are not for profits They, actually, have to show, bottom line an impact. All of that requires data and speed and velocity in which we have to run requires tech. >> They got gestures in the market with customers. They have that touch point. They can leverage that. >> Here's (mumbles) modern marketing is not speeding up and increasing the rate and lowering the cost of doing bad marketing. >> No, no, I mean that's exactly. >> It was marketers point. >> That's right. (laughing) You can spend a lot of money to do bad marketing. >> Let's double down on our bad marketing. Ron, thanks so much for coming on the CUBE again. Thanks for sharing the insights. It's always a pleasure to get down and dirty and peel back the onion on some of these things. Final question for you. What do you expect for the evolution for this next year. >> I think AI's going to be with us for awhile just because it's the new buzz word. We've got a couple cycles on that. >> John: It reminds me of Web 2.0, what is it? >> And that lasted for a few years as well. I think over the next year or so, we're going to see the benefits of that augmentation. We're going to, actually, see some of these micro services as people start fueling some of the tools that we already have. You're also going to see some of that further collision of ad tech and mertech. Cause everything's digital and the impact of what that means for us as marketers. >> I can't wait of the hashtag, marketing native. Cause Cloud Native is coming. Someone's going to make it up, I hope not. >> Peter: You did. >> Ron: You just did. >> Okay, Marketing Native. What does that mean? We'll do a whole segment on that. We'll get Ron to come in. Hey, thanks for coming on the CUBE. >> Thanks for having me. >> Great to see you. I'm John Furrier. Peter Burris here inside the CUBE getting all the action. Straight from the data and sharing it with you. Thank you Ron, for coming on again twice in a row, two years in a row. This is the CUBE. We'll be back with more after this short break. (lively music) >> Narrator: Robert Herjavec. >> People, obviously, know you from Shark Tank. But the Herjavec group has been, really, laser folks in cyber security. >> Cause I, actually, helped bring a product called Check Point to Canada, firewalls, URI filtering, that kind of stuff. >> But you're also an entrepreneur? And you know the business. You've been in software, in the tech business. (mumbles) you get a lot of pitches as entertainment meets business. >> On our show, we're a bubble. We don't get to do a lot of tech.

>> Announcer: Live from Las Vegas, it's theCUBE, covering InterConnect 2017. Brought to you by IBM. >> Okay, welcome back, everyone. We're live here at the Mandalay Bay in Las Vegas for the wrap-up of IBM InterConnect 2017. I'm John Furrier. My co-host this week, my partner in crime, co-CEO, co-founder of SiliconANGLE Media Inc. with myself, Dave Vellante. Dave, it's been a great week. I just feel like I have been Watsonized and Blockchained and cloud all week. As we wrap up InterConnect, I want to get your thoughts on IBM, the cloud business, the big data marketplace, some of the things that we're seeing at the 100 of events we go to. We've got our events coming up, we're going to be in Munich next month, we got DockerCon, but a lot of developer events coming up, but in general, we get to see the landscape, in some cases, that others don't see. Let's talk about that, so before we get into the landscape, let's about IBM, IBM's prospects. This show, just quick stat, almost double the online traffic we're seeing on IBMGO than World of Watson, which was the biggest show we've ever done with theCUBE that we've seen. So, an interest, it's a data point. Unpack the data, you can see that there's a lot of global interest in what IBM is doing right now with the cloud and with Watson, and certainly with Blockchain you add another disruptive enabler potentially to what will either be a brilliant IBM strategy or a complete crash and burn. I think this is an IBM go big or go home moment with Ginni Rometty. I love her messaging, I love her three pillars, enterprise strong, data first, cognitive to the core. That is solid messaging, all three pillars. To me, it's clear. IBM is at a reinvention moment, it's all coming together, but it's a go big or go home moment for them. >> Well, you know, John, I mean, Ginni when she took over, sorry, she was running strategy before she became CEO, I mean, IBM had a choice, they could go double down on infrastructure and go knock it out with Dell and EMC and HP, or they could go up the value chain. And my ongoing joke is Dell bought EMC, IBM buys some other company, and that to me underscores the differentiation in thinking. Oracle, I think, is a little different, but Oracle and IBM are somewhat similar, I think you'd agree, in that they've got a big SaaS portfolio, they're trying to vertically integrate, they're trying to drive high value margin businesses. The difference is IBM's much more services oriented than, say, an Oracle, and that's still, as I say, a big challenge for IBM. But I'm more, I'm a bull on IBM. >> Why is that? >> I think the strategy is, number one, they're relevant. We talked for years about how we weren't that excited about Microsoft because they weren't relevant. Satya Nadella came in, all of a sudden, they're relevant again. I think IBM is highly relevant in the minds of CEOs, CIOs, CCOs, CDOs, all the C-suite, IBM is super relevant there, just as are Accenture and Ernie Young and all the big SIs. But IBM's got tons of products beneath it, number one. Number two, despite the fact that, you called it out several years ago, they bought software for 2.4 billion, it was a bare metal hosting company, alright, but IBM's turning that into >> Bluemix. >> a cloud business with Bluemix, right. And they're building, bringing in acquisitions like Cleversafe, like Aspera, like Ustream, and others, where they're bringing services that are differentiated. You can only get Watson on IBM's cloud, you can only get IBM's Blockchain on IBM's cloud, so they're bringing in value-added services, and there's only one place you can get them, and I think that's a viable strategy that's going to throw off a lot of cash, and it's going to lead to success. >> And by the way, they're also continuing to invest in open source. So, again, that's-- >> That's the other piece. I wanted to talk to you, and this is your wheelhouse. IBM's open source mojo is not just lip service, alright. They have deep-rooted DNA in open source and their strategy around it, and they've proven that they can monetize open source. What's their model, I mean, explain the model because I think it's instructive. >> I mean, open source, there's a lot of different models. Red Hat-- >> For IBM, I mean. >> IBM's model of open source is very clear. If you look at what they've done with just Blockchain as a great example, they have mobilized their company, and they did it with Bluemix as well with the cloud, once they said, "We want to get in the cloud game," once, "We want to do Blockchain," they go open source at the core, then they get their entire brain trust workin' on it. It's not just a hand wave, some division, they're kind of reorganizing on the fly, they're kind of agile organization, which some may read as chaotic, but to me, I think that's just good management practice in this day and age. They get an open source project, and they drive that home, and they have people contributing and giving that to the community, and then adding value on top and differentiating. It's just classic 101, create some value, and create some differentiation with your products, and by the way, if you don't want to use our products, build your own, or hey, use the open source code. That's pretty much an over-simplified version of open source. >> But Blockchain's a great example of this, right? So, they see the leverage in open source project, they put all these resources in, and they say, okay, now let's build our product on top of that, let's get the open source community leverage and this is, let me ask you this, does IBM, so several years ago when IBM announced Bluemix, you were pretty critical. >> John: I was very critical. >> IBM has to win the developer audience or it's cooked in this game. >> That's what I said. >> How is it done, how would you grade them? >> I think they're doing very well. I think IBM is, again, to use your word, they're not putting lip service in it. So, I was joking with Meg Swanson last night, I saw Adam Gunther when they interviewed on theCUBE, and I was critical. I didn't say that their cloud was bad, I was just saying it's just not as, just got a lot of work to do, Amazon's kickin' ass, which we now know that happened, right. But they've done well. They've done well, they've ran hard, they've gone the table stakes on the enterprise. I still think they got some more work to do, we can analyze, I'm putting out my cloud ratings matrix, I'm going to put IBM on that list, I have Google and Amazon done. I'm going to add Microsoft Azure and IBM onto the mix in the comparison matrix. But IBM has done good with the developers. They've just invested 10 million in this announcement, and they're ramping up. I wouldn't say they're throwing just money at it, they got people, so I would give them, I'd give them a B-plus, A-minus score because they're hustlin', they're doing it. Are they totally blowing it out of the water? No, I don't think they're pushing hard enough there. I think they could give it some more gas, I think they could do more with it, personally thinking. But you know, Dr. Angel Diaz was on earlier today. They're going at their own pace. >> But you agree they're in the game. >> Oh, totally. >> Making good progress. >> They're totally, IBM is totally in the cloud game, and they don't get a lot of credit for it. Either does Oracle, by the way. Somehow, people seem to talk about Azure and Google. Google is so far behind, in my opinion, they're not even close. I think it's Amazon, Azure, IBM and Oracle and Google all kind of in that-- >> Juxtapose Oracle's developer cred, even though it owns Java, with IBM's. How would you compare the two? >> Very similar, I think. Different approaches, but again, to your point, IBM's relevant, Oracle's relevant. We had this question about VMware when they did the deal with AWS. They have customers and they have cash, so they're not going anywhere. It's not like IBM's a sinking ship. It's not like Oracle's a sinking ship. Now, that being said, there's a huge shift in the business, and I would say in that scenario, Google is in a very good position, so I've been very critical on Google only because they're trying to be acting like they're an enterprise flag. They're not, I mean, Google's got great tech, TensorFlow, machine learning. Google has great cloud tech, but in that game, they're up in the number one, two spot. But in the enterprise side, they're not close. They're workin' on that. So, that's my critique of Google. Microsoft has got the DNA for the enterprise, so Microsoft and Oracle to me are more similar than comparing IBM and Oracle. I'd say IBM is a lot more like Google and Amazon, kind of in-between, but Oracle and Microsoft look the same to me. Big install base, highly differentiated, stacks aren't perfect, but it looks good on paper, and they're gettin' business. And Oracle's earnings, by the way, were very explosive due to the cloud growth. >> Another question I like to ask sometimes is, okay, what would you have done differently if you had a choice? Like when Gerstner was running IBM, he chose to consolidate the company, essentially, not consolidate, but focus on services, one throat to choke, single-faced IBM. Great customer service and build the services business, buy-in, PWC, et cetera, that was the key. What could you have done differently that could've said, well-- >> John: For IBM? >> Yeah, at the time, you could have said, "We're spin out different product groups. "We're going to be the best at microprocessors, "or disk drives, or database, or software." >> I think IBM moved too slow. >> That's a historical example. Given what IBM's doing today, what would you have done differently if you were Ginni Rometty five or six years ago? >> I would've done what they're doing now three years ago. We were, when we started working with them with CUBE, IOD events, and Pulse. >> Dave: Information on Demand. >> You had a lot of silence. I think, if I had to go back and get a mulligan, if I was Ginni Rometty, I would've moved faster. >> Dave: Done that faster. >> Hindsight's 20-20 on that, but it wasn't that clear. But again, it's the big aircraft carrier, it can only move so fast. I think what they're doing now is good strategy, and they're price strong, data force, cognitive to the core is a good strategy. Now, cognitive is words for AI, and that's their word, cognitive 'cause of Watson, but essentially, machine learning and AI is going to be a big pillar there, and then, the data first is more of an architectural component that's very good. But in general, Dave, the cloud is, this is what's going on in my find. It's so obvious to me. The big data marketplace that was we defined by Cloudera and Hadoop and Hortonworks just never panned out. It morphed into a bigger picture, and so, Hadoop is part of, now, a bigger ecosystem. Cloud was growing very fast. Those two worlds are coming together and growing very rapidly independent with big data, with machine learning, AI, and IOT. They're coming together. The intersection of the big data and the cloud. >> Cloud-mapping data. That was Yuri Burton from 2005. >> But it's coming together really fast, and the IOT is the real business driver. I know there's not a lot of stuff shipping yet in the sim stuff out there, but merging IOT into IT into business process and into developer mindset, whether it's an Indiegogo up to full-on developers is the accelerant that's going to fuel the AI value. To me, that's the intersection point of big data and cloud, and that is the home run, that's the holy grail, and that's going to be disrupting some preexisting decisions by big vendors who made bets, and I'm talkin' about bets made in the past five years, not like bets made 20 years ago or 10 years ago. I think the IOT is going to really shape the game. The other thing I worry about now, in my opinion, is a lot of AI-washing. People say, "Oh, AI." You see people on the stage, "Oh, we did this with AI." There's no AI, it's augmented intelligence, which is basically predictive analytics. You know, true AI is not yet here, it's a little bit hyped up, not that I mind that. I think that the machine learning is the real meat on the bone right now, I think that's the core enabler. Machine learning is by far the most important trend in the computer science world today as it relates to integrating that capability into cloud native, microservices, and overall application. >> I agree, I mean, AI is still a heavy lift, but to me, the key, I go back to something you were saying, is developers. That's the lever that's going to give you the ability to move large mountains. If you don't have that developer community, and you don't have open source chops, you're going to struggle a little bit. You're going to be either in a swim lane like Oracle with its database and its red stack, and maybe you can break out of that, but I'm not sure it wants to. Or you're going to be stuck in infrastructure lane. >> Yeah, but the developers are driving all the action right now. My point about machine learning, if you look at the shows just recently, and certainly we have the history of the past year, machine learning is the sexiest trend in every show. Last show was Google Next, machine learning with TensorFlow, both open source. Machine learning's not new, it's just now accelerating the developer. The developers want to move faster, and I think things like machine learning, things like cognitive that IBM puts out there, are great catalysts. That's going to be a big thing we're going to watch, obviously, we have a big developer community at SiliconANGLE, so something to watch. >> What's next? We've got a chief data scientist summit next week in Silicon Valley, we're going to be at the-- >> Let's look at my Friday show this week. Every Friday I do the Silicon Valley Friday show with me and guests, we got that goin' on, so always check that out on soundcloud.com/johnfurrier, or check out my Facebook feed, facebook.com/johnfurrier. But in terms of CUBE events, we've got DataWorks in Munich on April 2nd, DockerCon in Austin, Oracle Marketing Sum Experience, Red Hat, Dell EMC World, Service Now, Open Stack, Big Data in London. >> It's going to be a busy spring. >> Lot of stuff going on. Great stuff. >> Deb, we'll see you in July. >> In bumper sticker, Dave, this show, encapsulate your thoughts. >> Well, I think it's all about cloud, data, and cognitive coming together in a way that allows business value and differentiation through the end customer. That's what this show is about to me. It's not about infrastructure, cloud and infrastructure, that's kind of table stakes. It's all about differentiation up the stack, creating, enabling new business models. >> My encapsulation is the enterprise strong, data first, cognitive to the core message that Ginni said, that translates into IBM's shoring up their base products and putting an innovation strategy around Blockchain and soon to be cognitive computing at a whole 'nother level, and I think they're going to have a real innovation strategy and continue to use what they did with Watson, the winning formula. Put something out there that's a guiding principle and draft the company behind it. I think that, to me, is my big walk away, and I think Blockchain will potentially level, has game-changing capabilities, and if that plays out like Watson's playing out, then IBM could be in great shape on both shoring up the base in cloud and their business and having an innovation strategy that extends them out. That to me is the reason why I'm bullish on them. So, great show, Dave Vellante. Thanks to the guys, thanks for everyone watching. That's it for us here in theCUBE. I'm John Furrier, Dave Vellante wrapping up IBM InterConnect 2017. Thanks for watching, stay with us, and follow us at theCUBE on Twitter and siliconangle.tv on the web. Thanks for watching. (electronic keyboard music)

>> Voiceover: Live from Las Vegas, it's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Okay, welcome back, everyone. We are live in Las Vegas for IBM InterConnect 2017. This is IBM's Cloud show and, now, data show. This is theCUBE's coverage. I'm John Furrier with my cohost, Dave Vellante. Our next guest is Meg Swanson, VP of Marketing for Bluemix, the whole kit and caboodle, SoftLayer of Bluemix. Now you get to watch some data platform, IOT. The Cloud's growing up. How you doing? Good to see you again. >> It's good. Good to see you guys. Every time we get together, it's just huge growth. Every time, every month to month. Under Bluemix, we've pulled together infrastructure. The area that was called SoftLayer. And because we had developers that absolutely you need a provision down to bare metal servers, all the way up to applications. So we pulled the infrastructure together with the developer services, together with our VMware partnership, all in a single console. Continuing to work on, with clients, on just having a unified experience. That's why we have it under the Bluemix brand. >> You knew us when we were just getting theCUBE started. We knew you when you were kicking off the developer program, with Bluemix, was announced here in theCUBE. Seems like 10 dog years ago, which is about 50 years, no, that was, what, four years ago now? Are you four years in? >> I think so. Yeah, 'cause I remember running from the Hakkasan club, we had just ended a virtual reality session, and I had to run, and then I sat down, and we started immediately talking about Bluemix 'cause we just launched it. >> So here's the update. You guys have been making a lot of progress, and we've been watching you. It's been fantastic, 'cause you really had to run fast and get this stuff built out, 'cause Cloud Native, it wasn't called Cloud Native back then, it was just called Cloud. But, essentially, it was the Cloud Native vision. Services, microservices, APIs, things, we've talked about that. What's the progress? Give us the update and the status, and where are you? >> Yeah, obviously just massive growth in services and our partners. When you look at, we had Twitter up with us today, we've had continual growth in the technology partners that we bring to bear, and then also definitely Cloud Native. But then also helping clients that have existing workloads and how to migrate. So, massive partnerships with VMware. We also just announced partnership with Intel HyTrust on secure cloud optimization. When we first met, we talked so much about you're going to win this with an ecosystem. And the coolest thing is seeing that pay off every day with the number of partners that we've been so blessed to have coming to us and working together with us to build out this ecosystem for our clients. >> And what's the differentiator, because what's happening now is you're starting to see the clear line of sight from the big cloud players. You have you guys, you have Oracle, you see Microsoft, you see SAP, you all got the version of the cloud. And it's not a winner-take-all market, it's a multi-cloud world, as we're seeing. Certainly open-source is driving that. How do you guys differentiate, and is it the same message? What's new in terms of IBM's differentiators? What's the key message? >> That we're absolutely staying core to the reason we went into this business. We are looking at, what are the challenges that our clients are looking to solve? How do we build out the right solutions for them? And look at the technologies they're using today, and not have them just forklift everything to a public cloud, but walk with them every step of the way. It's absolutely been about uncovering the partnerships between on-premises and the Cloud, how you make that seamless, how you make those migrations in minutes versus hours and days. The growth that we've seen is around helping clients get to that journey faster, or, if they're not meant to go fully public Cloud, that's okay, too. We've been absolutely expanding our data centers, making sure we have everything lined up from a compliance standpoint. Because country to country, we have so many regulations that we need to make sure we're protecting our clients in. >> I want to ask you, and David Kenny referenced it a little bit today, talked about we built this for the enterprise, it didn't stem out of a retailer or a search. I don't know who he was talking about, but Martin Schroeter, on the IBM earnings call, said something that I want to get your comment on, and if we can unpack a little bit. He said, "Importantly, we've designed Watson "on the IBM Cloud to allow our clients "to retain control of their data and their insights, "rather than using client data "to educate a central knowledge graph." That's a nuance, but it's a really big statement. And what's behind that, if I can infer, is use the data to inform the model, but we're not going to take your data IP and give it to your competitors. Can you explain that a little bit, and what the philosophy is there? >> Yeah, absolutely. That is a core tenet of what we do. It's all about clients will bring their data to us to learn, to go to school, but then it goes home. We don't keep client data, that's critical to us that everything is completely within the client's infrastructure, within their data privacy and protection. We are simply applying our cognitive, artificial intelligence machine learning to help them advance faster. It's not about taking their insights in learning and fueling them into our Cloud to then resell to other teams. That, absolutely, it's great that you bring up that very nuanced point, but that's really important. In today's day and age, your data is your lifeblood as a company, and you have to trust where it's going, you have to know where it's going, and you have to trust that those machine learnings aren't going to be helping other clients that are possibly on the same cloud. >> Is it your contention that others don't make that promise, or you don't know, or you're just making that promise? >> We're making that promise. It's our contention that the data is the client's data. You look at the partnerships that we've made throughout Cloud, throughout Watson, it's really companies that have come to us to solve problems. You look at the healthcare industry, you look at all these partnerships that we have. Everything that we've built out on the IBM Cloud and within Watson has been to help advance client cases. You rarely see us launching something that's completely unique to IBM that hasn't been built together with a client, with a partner. Versus, there are other companies out there in this market where they're constantly providing infrastructure to run their own business, maybe their own retail store, and their own search engine. And they will continue to do that, and they absolutely should, but at the end of the day, when you're a client, what do you want to do? Are you trying to build somebody else's business, or do you want someone who's going to be all in on your business and helping you advance everything that you need to do. >> Well, it seems like the market has glombed on to public data plus automation. But you're trying to solve a harder problem. Explain that. >> When you look at the clients that we're working with and the data that we're working with, it's not just information that's out there to work in a sandbox environment and it's available to anyone, baseball statistics or something that's just out there in the wild. Every client engagement we're in, this is their critical data. You look at financial services. We just launched the great financial services solutions for developers. You look at those areas, and, oh my word, you cannot share that data, yet those clients, you look at the work we're doing with H&R Block, you have to look at, that is absolutely proprietary data, but how do we send in cognitive to help us learn, to help teach it, help teach them alongside, for the H&R Block example, the tax advisor. So we're helping them make their business better. It's not as if we ingested all of the tax data to then run a tax solution service from IBM. It's a nuance, but it's an important nuance of how we run this company. >> So seven years ago, I met this guy, and he said, the 2010 John, you said, "Data is the new development kit." And I was like, "What are you talking about?" But now we see this persona of data scientist and data engineer and the developer persona evolving. How are you redefining the developer? >> Yeah, it's a great point, because we see cognitive artificial intelligence machine learning development in developers really emerging strong as a career path. We see data scientists, especially where as you're building out any application, any solution, data is at the core. So, you had it 10 years ago, right? (laughs) >> (mumbles) But I did pitch it to Dave when I first met him in 2010. No, but this is the premise, right? Back then, web infrastructure, web scale guys were doing their own stuff. The data needs to be programmable. We've been riffing on this concept, and I want to get your thoughts on this. What DevOps was for infrastructurous code, we see a vision in our research at Wikibon that data as code, meaning developers just want to program and get data. They don't want to deal with all the under-the-hood production, complicated stuff like datasets, the databases. Maybe the wrangling could be done by another process. There's all this production heavy lifting that goes on. And then there's the creativity and coolness of building apps. So now you have those worlds starting to stabilize a bit. Your thoughts and commentary on that vision? >> Yeah, that's absolutely where it has been heading and is continuing to head. And as you look at all the platforms that developers get to work in right now. So you have augmented reality, virtual reality are not just being segmented off into a gaming environment, but it's absolutely mainstream. So you see where developers absolutely are looking for. What is a low-code environment for? I'd say more the productivity. How do I make this app more productive? But when it comes to innovation, that's where you see, that's where the data scientist is emerging more and more every day in a role. You see those cognitive developers emerging more and more because that's where you want to spend all your time. My developers have spent the weekend, came back on Monday, and I said, "What'd you do?" "I wrote this whole Getting Started guide "for this Watson cognitive service." "That's not your job." "Yeah, but it's fun." >> Yeah, they're geeking out on the weekends, having some beer and doing some hackathons. >> It's so exciting to see. That's where, that innovation side, that's where we're seeing, absolutely, the growth. One of the partnerships that we announced earlier today is around our investment in just that training and learning. With Galvanize. >> What was the number? How much? >> 10 million dollars. >> Evangelizing and getting, soften the ground up, getting people trained on cognitive AI. >> Yeah, so it's really about making an impactful investment in the work that we started, actually a couple years ago when we were talking, we started building out these Garages. The concept was, we have startup companies, we starting partnering with Galvanize, who has an incredible footprint across the globe. And when you look at what they were building, we started embedding our developers in those offices, calling them Garages because that is your workshop. That's where you bring in companies that want to start building applications quickly. And you saw a number of the clients we had on stage today consistently, started in the Garage, started in the Garage, started in the Garage. >> Yeah, we had one just on theCUBE earlier. >> Yeah, exactly, so they start with us in the Garage. And then we wanted to make sure we're continuing to fuel that environment because it's been so successful for our clients. We're pouring into Galvanize and companies in training, and making sure these areas that are really in their pioneering stages, like artificial intelligence, cognitive, machine learning. >> On that point, you bring up startups and Garage, two-prong question. We're putting together, I'm putting together an enterprise-readiness matrix. So you have startups who are building on the Cloud, who want to sell to the enterprise. And then you have enterprises themselves who are adopting Hybrid Cloud or a combination of public, private. What does enterprise-readiness mean to you guys? 'Cause you guys have a lot of experience. Google next, they said, "We're enterprising." They're really not. They're not ready yet, but they're going that way. You guys are there. What is enterprise-readiness? >> Yeah, and I see a lot of companies have ambitions to do that, which is what we need them to do. 'Cause as you mentioned, it's a multi-cloud environment for clients, and so we need clouds to be enterprise-ready. And that really comes down to security, compliance, scalability, multiple zones. It comes down to making sure you don't have just five developers that can work on something, but how do you scale that to 500? How do you scale that to 500,000? You've got these companies that you have to be able to ensure that developers can immediately interact with each other. You need to make sure that you've got the right compliance by that country, the data leaving that country. And it's why you see such a focus from us on industry. Because enterprise-grade is one thing. Understanding an industry top to bottom, when it comes to cloud compliance is a whole other level. And that's where we're at. >> It's really hard. Most people oversimplify Cloud, but it's extremely difficult. >> It is, 'cause it's not just announcing a healthcare practice for Cloud doesn't mean you just put everybody in lab coats and send out new digital material. It is you have to make sure you've got partnerships with the right companies, you understand all the compliance regulations, and you've built everything and designed it for them. And then you've brought in all the partner services that they need, and you've built that in a private and a public cloud environment. And that's what we've done in healthcare, that's what we're doing in finance, you see all the work we're doing with Blockchain. We are just going industry by industry and making sure that when a company comes to us in an industry like retail, or you saw American Airlines on stage with us today. We're so proud to be working with them. And looking at everything that they need to cover, from regulation, uptime, maintenance, and ensuring that we know and understand that industry and can help, guide, and work alongside of them. >> In healthcare and financial services, the number of permutations are mind-boggling. So, what are you doing? You're pointing Watson to help solve those problems, and you're codifying that and automating that and running that on the Cloud? >> That's a part of it. A part of it is absolutely learning. The whole data comes to school with us to learn, and then it goes back home. That's absolutely part of it, is the cognitive learning. The other part of it is ensuring you understand the infrastructure. What are the on-premises, servers that that industry has? How many transactions per second, per nanosecond, are happening? What's the uptime around that? How do you make sure that what points you're exposing? What's the security baked into all of that? So, it's absolutely, cognitive is a massive part of it, but it is walking all the way through every part of their IT environment. >> Well, Meg, thanks for spending the time and coming on theCUBE and giving us the update. We'll certainly see you out in the field as we cover more and more developer events. We're going to be doing most, if not all, of the Linux foundation stuff. Working a lot with Intel and a bunch of other folks that you're partnering with. So, we'll see you guys out at all the events. DockerCon, you name it, they're all there. >> We'll be there, too, right with them. >> Microservices, we didn't even get to Kubernetes, we could have another session on containers and microservices. Meg Swanson, here inside theCUBE, Vice President of Bluemix Marketing. It's theCUBE, with more coverage after this short break. Stay with us, more coverage from Las Vegas. (techno music)

>> Narrator: Live from San Jose, California it's the Cube, covering Big Data Silicon Valley 2017. (upbeat music) >> Big Data SV 2016, day two of our wall to wall coverage of Strata Hadoob Conference, Big Data SV, really what we call Big Data Week because this is where all the action is going on down in San Jose. We're at the historic Pagoda Lounge in the back of the Faramount, come on by and say hello, we've got a really cool space and we're excited and never been in this space before, so we're excited to be here. So we got George Gilbert here from Wiki, we're really excited to have our next guest, he's Fred Rice, he's the chief architect at IBM Spark Technology Center in San Francisco. Fred, great to see you. >> Thank you, Jeff. >> So I remember when Rob Thomas, we went up and met with him in San Francisco when you guys first opened the Spark Technology Center a couple of years now. Give us an update on what's going on there, I know IBM's putting a lot of investment in this Spark Technology Center in the San Francisco office specifically. Give us kind of an update of what's going on. >> That's right, Jeff. Now we're in the new Watson West building in San Francisco on 505 Howard Street, colocated, we have about a 50 person development organization. Right next to us we have about 25 designers and on the same floor a lot of developers from Watson doing a lot of data science, from the weather underground, doing weather and data analysis, so it's a really exciting place to be, lots of interesting work in data science going on there. >> And it's really great to see how IBM is taking the core Watson, obviously enabled by Spark and other core open source technology and now applying it, we're seeing Watson for Health, Watson for Thomas Vehicles, Watson for Marketing, Watson for this, and really bringing that type of machine learning power to all the various verticals in which you guys play. >> Absolutely, that's been what Watson has been about from the very beginning, bringing the power of machine learning, the power of artificial intelligence to real world applications. >> Jeff: Excellent. >> So let's tie it back to the Spark community. Most folks understand how data bricks builds out the core or does most of the core work for, like, the sequel workload the streaming and machine learning and I guess graph is still immature. We were talking earlier about IBM's contributions in helping to build up the machine learning side. Help us understand what the data bricks core technology for machine learning is and how IBM is building beyond that. >> So the core technology for machine learning in Apache Spark comes out, actually, of the machine learning department at UC Berkeley as well as a lot of different memories from the community. Some of those community members also work for data bricks. We actually at the IBM Spark Technology Center have made a number of contributions to the core Apache Spark and the libraries, for example recent contributions in neural nets. In addition to that, we also work on a project called Apache System ML, which used to be proprietary IBM technology, but the IBM Spark Technology Center has turned System ML into Apache System ML, it's now an open Apache incubating project that's been moving forward out in the open. You can now download the latest release online and that provides a piece that we saw was missing from Spark and a lot of other similar environments and optimizer for machine learning algorithms. So in Spark, you have the catalyst optimizer for data analysis, data frames, sequel, you write your queries in terms of those high level APIs and catalyst figures out how to make them go fast. In System ML, we have an optimizer for high level languages like Spark and Python where you can write algorithms in terms of linear algebra, in terms of high level operations on matrices and vectors and have the optimizer take care of making those algorithms run in parallel, run in scale, taking account of the data characteristics. Does the data fit in memory, and if so, keep it in memory. Does the data not fit in memory? Stream it from desk. >> Okay, so there was a ton of stuff in there. >> Fred: Yep. >> And if I were to refer to that as so densely packed as to be a black hole, that might come across wrong, so I won't refer to that as a black hole. But let's unpack that, so the, and I meant that in a good way, like high bandwidth, you know. >> Fred: Thanks, George. >> Um, so the traditional Spark, the machine learning that comes with Spark's ML lib, one of it's distinguishing characteristics is that the models, the algorithms that are in there, have been built to run on a cluster. >> Fred: That's right. >> And very few have, very few others have built machine learning algorithms to run on a cluster, but as you were saying, you don't really have an optimizer for finding something where a couple of the algorithms would be fit optimally to solve a problem. Help us understand, then, how System ML solves a more general problem for, say, ensemble models and for scale out, I guess I'm, help us understand how System ML fits relative to Sparks ML lib and the more general problems it can solve. >> So, ML Live and a lot of other packages such as Sparking Water from H20, for example, provide you with a toolbox of algorithms and each of those algorithms has been hand tuned for a particular range of problem sizes and problem characteristics. This works great as long as the particular problem you're facing as a data scientist is a good match to that implementation that you have in your toolbox. What System ML provides is less like having a toolbox and more like having a machine shop. You can, you have a lot more flexibility, you have a lot more power, you can write down an algorithm as you would write it down if you were implementing it just to run on your laptop and then let the System ML optimizer take care of producing a parallel version of that algorithm that is customized to the characteristics of your cluster, customized to the characteristics of your data. >> So let me stop you right there, because I want to use an analogy that others might find easy to relate to for all the people who understand sequel and scale out sequel. So, the way you were describing it, it sounds like oh, if I were a sequel developer and I wanted to get at some data on my laptop, I would find it pretty easy to write the sequel to do that. Now, let's say I had a bunch of servers, each with it's own database, and I wanted to get data from each database. If I didn't have a scale out database, I would have to figure out physically how to go to each server in the cluster to get it. What I'm hearing for System ML is it will take that query that I might have written on my one server and it will transparently figure out how to scale that out, although in this case not queries, machine learning algorithms. >> The database analogy is very apt. Just like sequel and query optimization by allowing you to separate that logical description of what you're looking for from the physical description of how to get at it. Lets you have a parallel database with the exact same language as a single machine database. In System ML, because we have an optimizer that separates that logical description of the machine learning algorithm from the physical implementation, we can target a lot of parallel systems, we can also target a large server and the code, the code that implements the algorithm stays the same. >> Okay, now let's take that a step further. You refer to matrix math and I think linear algebra and a whole lot of other things that I never quite made it to since I was a humanities major but when we're talking about those things, my understanding is that those are primitives that Spark doesn't really implement so that if you wanted to do neural nets, which relies on some of those constructs for high performance, >> Fred: Yes. >> Then, um, that's not built into Spark. Can you get to that capability using System ML? >> Yes. System ML edits core, provides you with a library, provides you as a user with a library of machine, rather, linear algebra primitives, just like a language like r or a library like Mumpai gives you matrices and vectors and all of the operations you can do on top of those primitives. And just to be clear, linear algebra really is the language of machine learning. If you pick up a paper about an advanced machine learning algorithm, chances are the specification for what that algorithm does and how that algorithm works is going to be written in the paper literally in linear algebra and the implementation that was used in that paper is probably written in the language where linear algebra is built in, like r, like Mumpai. >> So it sounds to me like Spark has done the work of sort of the blocking and tackling of machine learning to run in parallel. And that's I mean, to be clear, since we haven't really talked about it, that's important when you're handling data at scale and you want to train, you know, models on very, very large data sets. But it sounds like when we want to go to some of the more advanced machine learning capabilities, the ones that today are making all the noise with, you know, speech to text, text to speech, natural language, understanding those neural network based capabilities are not built into the core Spark ML lib, that, would it be fair to say you could start getting at them through System ML? >> Yes, System ML is a much better way to do scalable linear algebra on top of Spark than the very limited linear algebra that's built into Spark. >> So alright, let's take the next step. Can System ML be grafted onto Spark in some way or would it have to be in an entirely new API that doesn't take, integrate with all the other Spark APIs? In a way, that has differentiated Spark, where each API is sort of accessible from every other. Can you tie System ML in or do the Spark guys have to build more primitives into their own sort of engine first? >> A lot of the work that we've done with the Spark Technology Center as part of bringing System ML into the Apache ecosystem has been to build a nice, tight integration with Apache Spark so you can pass Spark data frames directly into System ML you can get data frames back. Your System ML algorithm, once you've written it, in terms of one of System ML's main systematic languages it just plugs into Spark like all the algorithms that are built into Spark. >> Okay, so that's, that would keep Spark competitive with more advanced machine learning frameworks for a longer period of time, in other words, it wouldn't hit the wall the way if would if it encountered tensor flow from Google for Google's way of doing deep learning, Spark wouldn't hit the wall once it needed, like, a tensor flow as long as it had System ML so deeply integrated the way you're doing it. >> Right, with a system like System ML, you can quickly move into new domains of machine learning. So for example, this afternoon I'm going to give a talk with one of our machine learning developers, Mike Dusenberry, about our recent efforts to implement deep learning in System ML, like full scale, convolutional neural nets running on a cluster in parallel processing many gigabytes of images, and we implemented that with very little effort because we have this optimizer underneath that takes care of a lot of the details of how you get that data into the processing, how you get the data spread across the cluster, how you get the processing moved to the data or vice versa. All those decisions are taken care of in the optimizer, you just write down the linear algebra parts and let the system take care of it. That let us implement deep learning much more quickly than we would have if we had done it from scratch. >> So it's just this ongoing cadence of basically removing the infrastructure gut management from the data scientists and enabling them to concentrate really where their value is is on the algorithms themselves, so they don't have to worry about how many clusters it's running on, and that configuration kind of typical dev ops that we see on the regular development side, but now you're really bringing that into the machine learning space. >> That's right, Jeff. Personally, I find all the minutia of making a parallel algorithm worked really fascinating but a lot of people working in data science really see parallelism as a tool. They want to solve the data science problem and System ML lets you focus on solving the data science problem because the system takes care of the parallelism. >> You guys could go on in the weeds for probably three hours but we don't have enough coffee and we're going to set up a follow up time because you're both in San Francisco. But before we let you go, Fred, as you look forward into 2017, kind of the advances that you guys have done there at the IBM Spark Center in the city, what's kind of the next couple great hurdles that you're looking to cross, new challenges that are getting you up every morning that you're excited to come back a year from now and be able to say wow, these are the one or two things that we were able to take down in 2017? >> We're moving forward on several different fronts this year. On one front, we're helping to get the notebook experience with Spark notebooks consistent across the entire IBM product portfolio. We helped a lot with the rollout of notebooks on data science experience on z, for example, and we're working actively with the data science experience and with the Watson data platform. On the other hand, we're contributing to Spark 2.2. There are some exciting features, particularly in sequel that we're hoping to get into that release as well as some new improvements to ML Live. We're moving forward with Apache System ML, we just cut Version 0.13 of that. We're talking right now on the mailing list about getting System ML out of incubation, making it a full, top level project. And we're also continuing to help with the adoption of Apache Spark technology in the enterprise. Our latest focus has been on deep learning on Spark. >> Well, I think we found him! Smartest guy in the room. (laughter) Thanks for stopping by and good luck on your talk this afternoon. >> Thank you, Jeff. >> Absolutely. Alright, he's Fred Rice, he's George Gilbert, and I'm Jeff Rick, you're watching the Cube from Big Data SV, part of Big Data Week in San Jose, California. (upbeat music) (mellow music) >> Hi, I'm John Furrier, the cofounder of SiliconANGLE Media cohost of the Cube. I've been in the tech business since I was 19, first programming on mini computers.