(upbeat violin music) >> Hello and welcome to Special CUBE conversations. I'm John Furrier here at theCUBE Studios in Palo Alto. I'm joined with Jeff Jonas who's the co-founder and CEO of a stealth start-up called Senzing. He won't talk about it. I try to wrestle him to the ground to get information launching later. You're in town. Thanks for swinging by. Former IBM fellow, CUBE alumni. Some great videos. Check out Jeff Jonas, search Jeff Jonas theCUBE on Google and check out the videos. We've got great conversations over the years. Last time we saw you at your IBM event, riffing on, you know, the context of data. You're written and recognized by National Geographic as one of the major, the innovator in data space, which is a big honor, congratulations. >> Thank you. >> I appreciate it. Couldn't happen to a better person. >> Lucky, lucky. >> So what's going on? Tell us about the new startup. >> You know, I had a great run at IBM. They were really good to me when they bought my company. They were good to me for 11 and a half years. I think it was the longest-standing founder from an acquired company that IBM ever had. Great run and then they were good to me on an exit. I proposed something last, in 2016 in June. I kind of like it was a red pill, blue pill Matrix kind of move. I went hey, I got some ideas, but it's time to go. I've got to get back to my entrepreneurial spirit. Blue pill, red pill and they were like yeah, but you're a fellow. Go to research and live happily ever after. >> You've made it, you're a fellow. Why would you do anything? Why would you be a lowly entrepreneur? >> And it truly is, of all the things I've done, that I'm like wow, that is crazy to happen in my life. That's actually the single highest. It's over a few other things. >> John: It's a big deal. >> It is a huge deal, so. >> But you're an entrepreneur. You're scratching the itch. So what happened with the blue pill, red pill? >> So one of the options was hey, I've been working on this thing here at IBM called G2. It was my next generation entity engine. Figures out who's who in your data, matches identities. We've been working on it for years, I think nine years and I just said, I'd like to go build a company around that and I'll give you a rev share. You'll make more money than if I stayed. They were like, oh that was a great idea. Let's have a partnership, let's do that. So August of 2016, I spun out the source code. >> John: Who was the main executive at that point? Was it -- >> It was Bob Picciano. >> Bob Picciano. >> Yeah. >> He's very entrepreneurial-friendly. >> Yeah and he had to get in alignment across a whole bunch of IBM to make this happen. Anyways, I was really fortunate and the partnership that I had with IBM even to this day is just extraordinary. >> So did they fund you as well? >> Fund, no. I funded it myself for the first five or six months. I took two, money from two private investors that I've known a long time. Really smart, strategic money. Very active in my business. >> John: And you know them. >> Yeah, I've known them for a long time. One of them was a customer of mine. One I sat on the board with. It was just great. >> So the inner circle, they're in the boat. You've got some good people that you know. >> Yeah. Some people are like how do you manage your investors and I'm like, we don't even talk like that. >> We hang out. >> Yeah, we hang out. They manage me. Like, I go to them and, help me. >> That's how it should be, right? >> It's different. >> You don't have VCs on your board? No, but that's the formula. That's what you want. Entrepreneurs these days get so star-struck on having investors, but it's hard work. You want to get people that you trust and you like. >> Yeah, I learned that in my first company. We had two rounds of venture capitals in my first company. I learned a bunch of things, but they were great investors. It was a great relationship. I learned about VC because I had my own money in four VC funds. I've been able to fund four, five companies, but with all of that in mind, I have a really clean cap table. But anyway, we went off to the races since, since August of 2015. >> John: So that's when you left IBM, last time we checked. >> Yeah. >> Okay. >> And then I went into stealth mode. We've been collecting real customers. We've been iterating on the product. Our calling, if you will. You know, when I left IBM, I sat there with this thing called G2 and I'm like, this is the only thing that makes my team and I special is how to figure out in data, especially big data, who is the same as who across cultures, across languages and scripts and doing it where you don't need a data scientist. You don't need an expert to tune it and I did a survey of about 50 companies out there that are out there in the same business of selling entity resolution and almost all of them say call for a quote because it's all so hard and really, it's hard to find any software that's world class that's less than a quarter of a million and you're going to spend a million and so what we've been doing is working on making it so easy to consume that-- >> You're moving it down from a high ticket item, probably bolted on a ton of professional services to a much more turn key democratized-- >> Yeah, totally. You're absolutely right. Like we don't even have professional services. We're like download it, try it on a subscription license. You pay monthly, we send them the code so no data flows to us and when I, this is kind of funny and it's very private. Oh, I know I'm saying this on your cameras and all, but every team meeting, you know, our mission is smarter entity resolution for everyone everywhere and then I tell my team, what's going to make our company amazing is no one calls us. Everyone loves us and we've been really working on iterating on that. You know, any time somebody has any reason they have to call, that's not a moment of joy. >> You're launching when? This month, right? >> We are launching. >> 'cause there's nothing on the web. >> Yeah, yeah, yeah. Senzing.com is on the web, but at right this split second, it's a holding site. There will be a better, the real site's coming out very, very soon like in the quarter of the next week. >> Total stealth dark mode. >> We're in really dark mode. Although we've been collecting, again, customers and great logos. IBM's a customer. They license G2 from us. >> And so they didn't put money in. >> No, they did not put money in. I put my own money in. >> I guess they bumped my company and then I put my money in so in some sense, you can say if you followed the money. >> Do they own any? >> No, they don't own any of the company. >> But there's a business partnership. >> Absolutely. >> Okay, got it. >> And it's an incredible relationship. We have all kinds of interesting things we're doing with IBM. It's almost as if I've not left. They just don't give me a paycheck anymore. >> Which is why they're like, that guy's a fellow. Why is he doing it? He's going to go start a company? Why would he do that? 'cause you're an entrepreneur. That's why. Well, that's awesome. What are you working on at IBM with the G2 and I know you don't want to talk about the product and I respect that even though I try to dig at it. But what I really want to do 'cause you're going to launch in a couple weeks anyway. Let's get the aperture of what you're looking at. What market are you looking at? What problems out there, you mentioned entity is one piece. What's the key thing that you're looking at? >> You know, the key thing is that organizations have all of this data in all of these piles and they don't, they're having difficulty knowing about the same person at the same company. And I'll give you one of my favorite use cases that's, you know, G2's been in production already for many years, maybe my favorite deployment to date was deployed in 2012? Yeah, 2012, five years ago, six, for a company called ERIC. It's a non-profit. It's run by states. 22 states put their data in there on voter registration data, and it's used to improve the quality of election roles and it's got my privacy by design features baked into it and I'm just so damn proud of this thing. You know, the Democrats like it, the Republicans like it. I share the privacy community. >> No calls and everyone loves you. >> Yeah, no, that's the truth and this system, it's got a quarter of a billion records of about 100 million people and they have one person in IT that runs the entire IT department including G2. Like this is unheard of. So that's been in production for five years. But the range of companies that are having a challenge with who is who in their data is just everywhere. >> And give me an example of what that means. I'm trying to crop that, who is who like across multiple databases or? >> Yeah, I'll give you an example. See, in the voter registration system, you have somebody's registered in two different states, but it's the same person. You've got to get the data together to realize that somebody's registered in two states and that's because they moved. If you've ever moved between states, you may have forgotten to unregister. Most people do. >> Every person does. >> That's illegal. >> Like 1% would actually go through the motions. >> Lawbreaker. >> Tell the state I moved. >> Right. >> As far as the jury knows, I'm getting a new jersey. What's happening? >> Exactly, so you've got these two piles of data, but we combine it, you see that these two are the same and they're registered in both. So now they have to go back to somebody and say do you want to be registered to vote? But now I'll flip and give you an example of companies. There's a, one of our customers does supply chain risk. They take a vendor, some of the biggest global brands, and in their vendor list of all these customers across the world, there's duplicates in there, and then of course these companies reach the same manufacturers and there's duplicates across these lists but this is messy data. Then they scrape the web and look for toxic spills, child labor and other derogatory data about manufacturers in China, the Philippines, India and this is super messy and then they extract the data off the web, with just a crappy as you can see. We, they got our code on a Tuesday. They didn't call us until Thursday and when they called us Thursday they just said, and what they did was they combined all the data so they can go back to a global brand and say hey, this manufacturer is going to cause you risk to your reputation. So they're resolving who is who. >> You're untangling a lot of messy data. >> Yeah. >> And making it insightful. >> We get insights and we got a, this is an example. They got this offer on Tuesday without a call. We got a call on Thursday and said we canceled all of our internal work to try to mess with all this. We're just using your stuff, it's done. And the last we heard from them, they just went, the quality of your matching you're doing, without any tuning or training, it's a special kind of real-time machine learning that we invented, no training, no tuning and they went, the results it's getting are human-quality. >> So how, obviously you don't want to talk about price points, but it's affordable, it sounds like. It sounds like you're mission-driven on this thing so it's not like getting, you've already made some good dough as an entrepreneur. You're not afraid to make more money, but this is a mission-driven opportunity. >> So many organizations are struggling with this. We are going to make it affordable to the smallest companies and I can't quite tell you the price point. >> It's okay, we're at theCUBE. >> Think order of magnitude life in any other option. >> Can you take care of us? >> Oh, I could hook you up. >> We have duplicates all over the place. >> We'll give it to you and you'll get a towel set too. >> That would be great. Question for you. What's your take on crypto block chain because you mentioned, you know, your customer's a great part of anti-money laundering, big part of, you mentioned privacy baked into by design there. This is now a phenomenon. You looked at China with WeChat. They're making real names, real identities be part of that system. So more and more of this potential attention, public data's going to be out there. What's on your take on, you know, your customer and some of these trends that are involved in this? >> You know, on block chain, what it really is, it's calling, I mean I've seen a lot of people use the term block chain around that just ain't it. 'cause it's got a lot of buzz. >> Buzzword. >> But the reality is, it is a tamper-resistance ledger and I've been writing about immutable audit logs and tamper-resistance ledgers in my privacy by design work before block chain came out, which is really distributed form. The value of it to the kinds of work that we do is a tamper-resistance log allows you to connect it to software so that when say, somebody searches for something, you can record it in a tamper-resistance way and why do you want to do that? Well if you've created an index in some central data, you want to make sure it's not being abused. You want to make sure that the person who's searching is not searching out their neighbor or their daughter's new boyfriend. That would be an abuse, right? >> Yeah, yeah. >> Right. So a tamper-resistance auto log would be a great place to put that. That would be a natural thing to do with block chain. >> Awesome. So you got the launch coming. How are you doing and are you doing any of the marathons and triathlons? What are you doing? What's the latest? >> Since I was last on your show here, I became one of three people to do every Iron Man on the world, every Iron Man triathlon. There's one person in Canada. There's one person in Mexico and I'm representing America. >> You're the American representation. All triathlons. >> You know, if you go to the IronMan.com webpage, there's a list of races around the world and I'm one of three that can just look at every single race and say yes, yes, yes. >> Your favorite. >> Austria. >> Why? >> It's beautiful, it's a great course. It was well-run. I had a good time. >> Beautiful weather and people. >> And your worst? The one where you had your bike on a plane and you lost your luggage? >> Oh, I had no, I had a really really dark time this last year at the race in South Korea. And this is how bad it was. It's the only race where I walked across the finish and I sat in the bath tub. This is embarrassing, okay? I sat in this bath tub with the shower thing that you have to hand-hold over my head and I was trying to cry 'cause I was so defeated, but I was too dehydrated to even cry. The level of failure. >> It just knocked you down. >> When you can't even cry. >> Well you know you went from IBM Fellow to lowly entrepreneur, how's it feel? I mean you're back, rolling your sleeves up, getting down and dirty. Fun, having a blast? >> I really love being a benevolent dictator. >> John: How many people on the team? >> We're like about 16 if you count people that are full time or half time or better. I have a few people who are half time or better so yeah, about 16. >> Sounds like fun. >> Great fun. >> Great, Jeff Jonas. We'll be looking forward to your launch Senzing.com. S-E-N-Z-I-N-G.com. Former IBMer, great to see you and we'll keep you in touch. And where are you going to be headquartered out of? What's the location? >> Venice Beach, California, where I live. Although my team is scattered all over the country. We also are licensed in Singapore and we are hoping to launch Senzing Lab's RND activities out of Singapore. >> Alright, so we'll pop down to LA to check you out when you're up and running. Okay, Jeff Jonas stopping by theCUBE here on a great Thought Leader Thursday. I'm John Furrier. Every Thursday, we do the Thought Leader interviews with friends, colleagues, CUBE alumni and more. Always look up to great people. Have to be a thought leader, have to have original content and be an innovator. Thanks for watching. (upbeat violin music)

(upbeat music) >> Welcome back to the Seaport in Boston. This is day two of theCUBES's coverage of Red Hat Summit 2022 different format this year for Red Hat Summit. You know we are used to the eight to 9,000 people big conferences, but this is definitely and a lot of developers this is definitely a smaller, more intimate, more abbreviated keynotes which I love that new style they've really catering to the virtual audience as well as the physical audience, a lot of good stuff going on last night in the Seaport, which a lot of fun Stephanie Chiras is here is the Senior Vice President of Partner Ecosystem Success at Red Hat. >> Yeah. >> On the move again, Stephanie love to see you. >> yeah. Thank you. It's great to be here with you and now in a little different bit of a role. >> Yeah, I'm happy that we're actually in Boston and we can meet face to face. >> Yes. >> We don't have to get in a plane, but you know we'll be on a lot of planes in the next few months. >> Yeah. >> But look, a new role for you in ecosystems. You are interviewing all the partners, which is very cool. So you get a big observation space as my friend Jeff Jonas would like to say. And so, but I'd like to observe the partner ecosystem in this new era is different. >> It's very different. >> I mean just press release is going back it's really deep engineering and really interesting flywheel approaches. How is the cloud and the hybrid cloud ecosystem and partner ecosystem different today? >> I think there's a couple of things, I think first of all cloud accelerating all the innovation, the whole cloud motion pulls in a cloud partner in addition to many of the other partners that you need to deploy a solution. So this makes almost every deployment a multi-partner deployment. So that creates the need not just for one on one partnerships between companies and vendors but really for a multi-partner experience. Right, how does an ISV work with a distributor work with a cloud vendor? How do you pull all of that together and I think at Red Hat, our view of being a platform company, we want to be able to span that and bring all of those folks together. So I see this transition going from a world of partnerships into a world of a networked ecosystem. And the real benefit is when you can pull together one ecosystem with another ecosystem, build that up and it really becomes an ecosystem of ecosystems. >> Well and I'm a fan, you're a multi tool star, so it may kind of makes you dangerous because you can talk tech in your technical roles. You've been a GM so you understand the business and that's really what it takes in the part of ecosystem. It can't be just technology and just engineering integration, it's got to be a business model associated with that. Talk about those two dimensions. >> And I think what we're seeing in the ecosystem is there are partners that you build with there are partners you service with, there are partners you sell with some do all three, some do two out of three. How do you work those relationships at the end of the day every partner in the ecosystem wants to bring their value to the customer. And their real goal is how do you merge those values together and I think as you know, right, I come from the technology and the product space. I love moving into this space where you look for those value and that synergy of value to bring better technology, a better procurement experience is often really important and simplicity of deployment to customers, but partners span everything we do. We develop with them, we build with them, we deploy with them, we service with them and all has to come together. >> So how do you make this simple for customers? I mean you're describing an increasingly complex environment. How do you simplify this? >> So a couple of things one, spot onto your point Paul, I think customer expectations now are more aggressive than they've ever been that the ecosystem has done pre-work before they show up. The customer doesn't want to be the one who's pulling together this from one vendor, this from another vendor and stitching it together themselves. So there's a number of things I think we've stepped in to try and do digital engagement for certification and deployment, the creation of operators on OpenShift is one way that technology from partners can be done and enabled more easily and quickly with Red Hat platforms. I think in addition, you've seen. >> Can you go a little deeper on that? >> Sure. >> Explain that a little bit more what does that mean? Yeah, First off, we have a digital experience where partners can come in, they can certify and test their applications to run it on Red Hat platforms themselves. So it's a bit of a come one, come all. We also have an engineering team and a developer team to work side by side with them to build those into solutions. We've done things again to supplement that with capabilities of what we call validated patterns things we've done in the market with customers, with partners, we pull together a validated pattern, we put it onto GitHub so anyone can get access to it. It becomes kind of a recipe for deployment that's available for partners to come in and augment on top of that or customers can come in and pull it up GitHub and build off of it. So I feel like there's different layers in the sort of build model that we work with partners and you want to be able to on-ramp any partner wherever they want to influence their value. It could be at the base certification level, it could be even with RHEL 9 was a good one, right. RHEL 9 was the first version of RHEL that we deployed based upon the CentOS Stream model. CentOS Stream is an upstream version of RHEL very tightly tied into the development model but it allowed partners to engage with that code prior to deployment everything from hardware partners to ISV partners, it becomes a much more open way for them to collaborate with us, so there's so much we can do. >> What's the pitch to partners. I mean I know hybrid cloud is fundamental to your value proposition. I mean most people want hybrid cloud even though the cloud guys might not admit it, right, but so what's the pitch, how do you approach partners there's got to be a common theme there pitch me. >> I think one of the things when it comes to the Red Hat ecosystem is the ecosystem itself has to bring value. Yes, we at Red Hat want to bring value, we want to come in and make it easy and simple for you to access our technology when want to make it easy and simple to engage side by side in front of a customer. But at the end of the day the value of the Red Hat ecosystem is not only Red Hat, it's our partnerships with others. It's our partnerships with the hyperscalers, it's our partnerships with ISVs, it's our work in open source communities. So it's not about Red Hat being this sort of epicenter of the ecosystem. The value comes from the collective ecosystem as it stands, and I think we've made a number of changes here at the beginning of the year in order to create a end to end team within Red Hat that does everything from the build to the sell with all the way from end to end. And I think that's bringing a new layer of simplicity for our engagement with their partners, and it's allowing us to stitch together and introduce partners to partners. >> But you are a dot connector in a sense. >> Absolutely. >> And you can't do it all, I mean nobody can. >> Yeah. But especially Red Hat your strategy is not to do it all by design, so where's the big white spaces where you feel as though your strengths need to be complimented by the partners? >> Oh, I think you caught it spot on. We don't think we can do it all, we're a platform company, we know the value of hybrid cloud is all about bringing a flexibility of an ecosystem together. I think the places where we're really doubling down on is simplicity. So the Ansible announcement that we did right with Ansible automation platform on Azure. With that announcement, it brings in certified collections of ecosystem partners on that deployment. We do the work with Azure in order to do that deployment of Ansible automation platform, and then it comes with a set of certified collections that have been done with other partners. And I think those are the pieces where we can really double down on bringing simplicity. Right, so if I look at areas of focus, that's a great space, and I think it is all about connecting the dots, right, it's about connecting our work with Azure with our work with other ISV partners to pull that together and show up to a customer with something that's fast time to value. >> With so many partners to manage, how do you make sure you're not playing favorites. I guess how do you treat all partners equally or do you even try? >> We absolutely try. I think any partnership is a relationship, right, so it is what Red Hat brings to the table, it's also what the partner brings to the table. Our goal is to understand what the value is the partner wants to deliver to the customer. We focus on that and bringing that to the forefront of what we deploy. We absolutely in a hybrid world it's about choice and flexibility. Certainly there are partners and we made some announcements of course, this week, right yesterday and today with some we're continued to deepen our partnerships with those folks who are doubling down with us where their strategy is very well aligned with us. But our goal is to bring a broad ecosystem that offers customers choice. That's what hybrid cloud's all about. >> I remember years ago, your colleague Bob Pitino, I went down and met him in his office and he schooled me, he was awesome and we did a white board on alternative processors. >> Yeah. >> You guys were doing combat duty in the power division at the time. But basically he helped me understand the trend that is absolutely come true which is alternative processors. It's not just about the CPU anymore, it's about all the CPU and GPU and NPU and accelerators and all these other connected parts. You guys obviously are in the middle of that, you've got relationships with ARM, NVIDIA, Intel, we saw on stage today. Explain the importance and the trends that you see of these alternative processors and accelerators and what that means for customers in terms of the applications that they're now going to be able to tap. >> Yeah, so you know I love this topic when it comes. So one of the spaces is edge, right, we talked about edge today. Edge to me is the epitome of kind of a white space and an opportunity where ecosystem is essential. Edge is pulling together unique hardware capabilities from an accelerator all the way out to new network capabilities and then to AI applications. I mean the number of ISVs building AI applications is just expanding. So it's really that top to bottom ecosystem story, and our work with the telco comes in, our work with the ARM partners, the NVIDIA of the world, the accelerators of the world comes in edge. And then you pull it up to the applications as well. And then to touch in, we're seeing edge be deployed a lot in industries and industry verticals, right. A lot of edge deployments are tailored for a retail market or for a financial services sector. Again, for us, we rely very much on the ecosystem to go into industry verticals where platform companies. So our goal is to find those key partners in those industry verticals who speak the speak, talk the language, and we partner with them in order to support them and so this whole edge space pulls all of that together I think even out to the go to market with industry alignment. >> It's interesting to partner, so we're talking about Silicon, we could talk about that all day long. >> Yes. >> And then it spans and that we had Accenture on we had Raj yesterday. And it was interesting 'cause you think Accenture's like deep vertical industry expertise which it is but Raj's role is really cross industry, and then to tap into that industry expertise you guys had an announcement yesterday with those guys and obviously the GSIs are a key player. >> Absolutely. >> We saw a bunch of 'em last night out and about. >> Yeah. >> So talk about the importance of those relationships. >> I think we are in the announcement with Accenture is a great one, right. We're really doubling down because customers are looking to them, they're looking to the Accentures of the world to help them move into this hybrid world. It's not simple, it's not simple to deploy and get that value of the flexibility. So Accenture has built a number of tools in order to help customers on that journey which we talked about yesterday it really is a continuum of how customers adopt for their cloud space. And so us partnering with them offers a platform underneath, give them technology capabilities and Accenture is able to help customers and guide them along that journey and add a new layer of simplicity. So I think the GSI are critical in this space. >> Yeah. >> You talked about the number of companies developing AI, new AI tools right now. And it seems like there's just the pace of innovation is amazing, the number of startups is unprecedented. How do you decide who makes it into your partner system? What bars do they have to jump over to become a Red Hat partner? >> I think our whole partner structure is layered out quite honestly a bit in tiering, depending upon how much the partner is moving forward with Red Hat, how strategically we aligned our et cetera. But there is definitely a tier that is a come one come all, get your technology to work with Red Hat. We do that digitally now in the world of digital it's much easier to do that to give accessibility but there is definitely a tier that is a come one come all and participate. And then above that, it comes into tierings. How deeply do we go to do joint building to do co-creation and how do we sort of partner even on things like we have ARO and ROSA as you know which is OpenShift built with AWS with Azure those provide very deep technical engagements to bring that level of simplicity, but I would say it spans all the layers, right. We do have a dedicated engineering team to work with the ecosystem partners. We have a dedicated digital team to reach out and proactively right, invite folks to participate and encourage them through the thing and through the whole path. And we've done some things on enablement, we just made early March, we made enablement free for all our partners in order to learn more and get more skilled in Red Hat. Skills and skill creation is just critical for partners, and we want to start there right. >> So we started this conversation with how cloud ecosystems are different. And I think AWS as the mother of all ecosystems, so does Microsoft too but they've had it for a while. And I got felt like last decade partners were kind of afraid, all right, we're going to partner with a cloud vendor, but they're going to eat our lunch. I noticed last year at Reinvent that whole dynamic is changing and I think the industry's realizing this is not a zero sum game. That there's just so much opportunity especially when you start thinking about the edge. So you guys use the term hybrid, right, and John and I wrote a piece prior to Reinvent last year, we said there's something new brewing, we've got on-prem connecting to the clouds, it's going across clouds. People call that multi-cloud, but multi-cloud has been like multi-vendor. It really hasn't been a sort of strategy or a technical layer. And now you're talking the edge and we see the hyperscaler spending a hundred billion dollars a year on infrastructure. And now we see companies like yours and your ecosystem building on top of that. They're not afraid of it anymore, they're actually looking at it as a gift and so we coined this term called Supercloud which is a abstraction layer, and it rises above highs all the complexity of the underlying primitives and APIs and people kind of wince at the term Ashesh called it Metacloud which I like it's kind of fun. But do you feel like that's happening in the ecosystem? Is that a real trend or is that just my imagination? >> I think it's definitely a real trend and it's coming from customers, right, that's what customers want. So customers want the ability to choose are they going to self-manage their applications within a public cloud. There's much more than just technology in the public cloud too right. There's a procurement experience that they provide a simplicity of our relationship. They may choose one of the hyperscalers. They pick a procurement experience, they deepen that relationship, they leverage the services. And I think now what you're seeing is customers are demanding it. They want to be a part of that, they want to run on multiple clouds. And now we're looking at cloud services you've seen our strategy double down on cloud services. I think that kind of comes back together to a customer wants simplicity. They expect the ecosystem to work together behind the scenes. That's what capabilities like ARO are or OpenShift on Azure and OpenShift on AWS. That's what we can provide. We have an SRV team, we jointly support it with those partners behind the scenes but as you said, it's no longer that fear, right. We've rolled up our sleeves together specifically because we wanted to show up to the customer as one. >> Yeah, and by the way, it's not just traditional technology vendors, it's insurance companies, it's banks, it's manufacturers who are building out these so-called super clouds. And to have a super cloud, you got to have a super PaaS and OpenShift is the supers of all PaaS So Stephanie cheers, thanks so much for coming back to theCUBE, >> Oh it's my pleasure. it great to see you again. >> Thank you for the time. >> All right, and thank you for watching keep it right there this is day two of Red Hat Summit 2022 from the Seaport in Boston. You're watching theCUBE. (upbeat music)

>> Announcer: Live from the Computer History Museum in Mountain View, California. It's theCUBE, covering DevNet Create 2018. Brought to you by Cisco. >> Hey, welcome back, everyone. This is theCUBE, live here in Mountain View, California, heart of Silicon Valley for Cisco's DevNet Create. This is their Cloud developer event. It's not the main Cisco DevNet which is more of the Cisco developer, this is much more Cloud Native DevOps. I'm joined with my cohost, Lauren Cooney and our next guest is Christine Yen, who is co-founder and Chief Product Officer of Honeycomb.io. Welcome to theCUBE. >> Thank you. >> Great to have an entrepreneur and also Chief Product Officer because you blend in the entrepreneurial zeal, but also you got to build the product in the Cloud Native world. You guys done a few ventures before. First, take a minute and talk about what you guys do, what the company is built on, what's the mission? What's your vision? >> Absolutely, Honeycomb was built, we are an observability platform to help people find the unknown unknowns. Our whole thesis is that the world is getting more complicated. We have microservices and containers, and instead of having five application servers that we treated like pets in the past, we now have 500 containers running that are more like cattle and where any one of them might die at any given time. And we need our tools to be able to support us to figure out how and why. And when something happens, what happened and why, and how do we resolve it? We look around at the landscape and we feel like this dichotomy out there of, we have logging tools and we have metrics tools. And those really evolved from the fact that in 1995, we had to choose between grep or counters. And as technology evolved, those evolved to distribute grep or RDS. And then we have distribute grep with fancy UIs and well, fancy RDS with UIs. And Honeycomb, we were started a couple years ago. We really feel like what if you didn't have to choose? What if technology supported the power of having all the context there the way that you do with logs while still being able to provide instant analytics the way that you have with metrics? >> So the problem that you're solving is one, antiquated methodologies from old architectures and stacks if you will, to helping people save time, with the arcane tools. Is that the main premise? >> We want people to be able to debug their production systems. >> All right, so, beyond that now, the developer that you're targeting, can you take us through a day in the life of where you are helping them, vis a vis the old way? >> Absolutely, so I'll tell a story of when myself and my co-founder, Charity, were working together at PaaS. PaaS, for those who aren't familiar, used to be RD, a backend form of mobile apps. You can think of someone who just wants to build an iOS app, doesn't want to deal with data storage, user records, things like that. And PaaS started in 2011, got bought by Facebook in 2013, spun down very beginning of 2016. And in 2013, when the acquisition happened, we were supporting somewhere on the order of 60,000 different mobile apps. Each one of them could be totally different workload, totally different usage pattern, but any one of them might be experiencing problems. And again, in this old world, this pre-Honeycomb world, we had our top level metrics. We had latency, response, overall throughput, error rates, and we were very proud of them. We were very proud of these big dashboards on the wall that were green. And they were great, except when you had a customer write in being like, "Hey, PaaS is down." And we look at our dashboard we'd be like, "Nope, it's not down. "It must be network issues." >> John: That's on your end. >> Yeah, that's on your end. >> John: Not a good answer. >> Not a good answer, and especially not if that customer was Disney, right? When you're dealing with these high level metrics, and you're processing tens or hundreds of thousands of requests per second, when Disney comes in, they've got eight requests a second and they're seeing all of them fail. Even though those are really important, eight requests per second, you can't tease that out of your graphs. You can't figure out why they're failing, what's going on, how to fix it. You've got to dispatch an engineer to go add a bunch of if app ID equals Disney, track it down, figure out what's going on there. And it takes time. And when we got to Facebook, we were exposed to a type of tool that essentially inspired Honeycomb as it is today that let us capture all this data, capture a bunch of information about everything that was happening down to these eight requests per second. And when a customer complained, we could immediately isolate, oh, this one app, okay let's zoom in. For this one customer, this tiny customer, let's look at their throughput, error rates, latency. Oh, okay. Something looks funny there, let's break down by endpoint for this customer. And it's this iterative fast, highly granular investigation, that is where all of us are approaching today. With our systems getting more complicated you need to be able to isolate. Okay, I don't care about the 200s, I only care about the 500s, and within the 500s, then what's going on? What's going on with this server, with that set of containers? >> So this is basically an issue of data, unstructured data or have the ability to take this data in at the same time with your eye on the prize of instrumentation. And then having the ability to make that addressable and discoverable in real time, is that kind of? >> Yeah, we've been using the term observability to describe this feeling of, I need to be able to find unknown unknowns. And instrumentation is absolutely the tactic to observability of the strategy. It is how people will be able to get information out of their systems in a way that is relevant to their business. A common thing that we'll hear or people will ask, "Oh, can you ingest my nginx logs?" "Can you ingest my SQL logs?" Often, that's a great place to start, but really where are the problems in an application? Where are your problems in the system? Usually it's the places that are custom that the engineers wrote. And tools need to be able to support, providing information, providing graphs, providing analytics in a way that makes it easy for the folks who wrote the code to track down the problem and address them. >> It's a haystack of needles. >> Yeah, absolutely. >> They're all relevant but you don't know which needle you're going to need. >> Exactly. >> So, let me just get this. So I'm ducking out, just trying to understand 'cause this is super important because this is really the key to large scale Cloud ops, what we're talking about here. From a developer standpoint, and we just had a great guest on, talking about testing features and production which is really the important, people want to do that. And then, but for one person, but in production scale, huge problem, opportunity as well. So, if most people think of like, "Oh, I'll just ingest with Splunk," but that's a different, is that different? I mean, 'cause people think of Splunk and they think of Redshift and Kinesis on Amazon, they go, "Okay." Is that the solution? Are you guys different? Are you a tool? How do I understand you guys' context to those known solutions? >> First of all, explain the difference between ourselves and the Redshifts and big queries of the world, and then I'll talk about Splunk. We really view those tools as primarily things built for data scientists. They're in the big data realm, but they are very concerned with being 100% correct. They're concerned with fitting into big data tools and they often have an unfortunate delay in getting data in and making it acquirable. Honeycomb is 100% built for engineers. Engineers of people, the folks who are going to be on the hook for, "Hey, there's downtime, what's going on?" And in-- >> So once business benefits, more data warehouse like. >> Yeah. And what that means is that for Honeycomb, everything is real time. It's real time. We believe in recent data. If you're looking to get query data from a year ago we're not really the thing, but instead of waiting 20 minutes for a query over a huge volume of data, you wait 10 seconds, or it's 3:00 AM and you need to figure out what's happening right now, you can go from query to query, to query, to query, as you come up with hypotheses, validate them or invalidate them, and continue on your investigation path. So that's... >> That makes sense. >> Yeah. >> So data wrangling, doing queries, business intelligence, insights as a service, that's all that? >> Yeah. We almost, we played with and tossed the tagline BI for systems because we want that BI mentality of what's going on, let me investigate. But for the folks who need answers now, an approximate answer now is miles better than a perfect one-- >> And you can't keep large customers waiting, right? At the end of the day, you can't keep the large customers waiting. >> Well, it's also so complicated. The edge is very robust and diverse now. I mean, no-js is a lot of IO going on for instance. So let's just take an example. I had developer talking the other day with me about no-js. It's like, oh, someone's complaining but they're using Firefox. It's like, okay, different memory configuration. So the developer had to debug because the complaints were coming in. Everyone else was fine, but the one guy is complaining because he's on Firefox. Well, how many tabs does he have open? What's the memory look like? So like, this a weird thing, I mean, that's just a weird example, but that's just the kinds of diverse things that developers have to get on. And then where do they start? I mean. >> Absolutely. So, there's something we ran into or we saw our developers run into all the time at PaaS, right? These are mobile developers. They have to worry about not only which version of the app it is, they have to worry about which version of the app, using which version of RSDK on which version of the operating system, where any kind of strange combination of these could result in some terrible user experience. And these are things that don't really work well if you're relying on pre-aggregated 10 series system, like the evolution of the RDS, I mentioned. And for folks who are trying to address this, something like Splunk, these logging tools, frankly, a lot of these tools are built on storage engines that are intended for full text search. They're unstructured text, you're grepping over them, and then you're build indices and structure on top of that. >> There's some lag involved too in that. >> There's so much lag involved. And there's almost this negative feedback loop built in where if you want to add more data, if on each log line you want to start tracking browser user agent, you're going to incur not only extra storage costs, you're going to incur extra read time costs because you're reading that more data, even if you're don't even care about that on those queries. And you're probably incurring cost on the right time to maintain these indices. Honeycomb, we're a column store through and through. We do not care about your unstructured text logs, we really don't want them. We want you to structure your data-- >> John: Did you guys write your own column store or is that? >> We did write our own column store because ultimately there's nothing off the shelf that gave us the speed that we wanted. We wanted to be able to, Hey, sending us data blogs with 20, 50, 200 keys. But if you're running analysis and all you care about is a simple filter and account, you shouldn't have to pull in all this-- >> To become sort of like Ferrari, if you customize, it's really purpose built, is that what you guys did? >> That is. >> So talk about the dynamic, because now you're dealing with things like, I mean, I had a conversation with someone who's looking at say blockchain, where there's some costs involved, obviously writing to the blockchain. And this is not like a crypto thing it's more of a supply chain thing. They want visibility into latency and things of that nature. Does this sounds like you would fit there as a potential use case? Is that something that you guys thought of at all? >> It could absolutely be. I'm actually not super familiar with the blockchain or blockchain based applications but ultimately Honeycomb is intended for you to be able to answer questions about your system in a way that tends to stymie existing tools. So we see lots of people come to us from strange use cases who just want to be able to instrument, "Hey I have this custom logic. "I want to be able to look at what it's doing." And when a customer complains and my graphs are fine or when my graphs are complaining, being able to go in and figure out why. >> Take a minute to talk about the company you founded. How many employees funding, if you can talk about it. And use case customers you have now. And how do you guys engage? The service, is it, do I download code? Is it SaaS? I mean, you got all this great tech. What's the value proposition? >> I think I'll answer this-- >> John: Company first. >> All right. >> John: Status of the company. >> Sure. Honeycomb is about 25 people, 30 people. We raised a series A in January. We are about two and a half years old and we are very much SaaS of the future. We're very opinionated about a number of things and how we want customers to interact with us. So, we are SaaS only. We do offer a secure proxy option for folks who have PII concerns. We only take structured data. So, at our API, you can use whatever you want to slurp data from your system. But at our API, we want JSON. We do offer a wide variety of integrations, connectors, SDKs, to help you structure that data. But ultimately-- >> Do you provide SDKs to your customers? >> We do. So that if they want to instrument their application, we just have the niceties around like batching and doing things asynchronously so it doesn't block their application. But ultimately, so we try to meet folks where they're at, but it's 2016, it was 2017, 2018-- >> You have a hardened API, API pretty much defines your service from an inbound standpoint. Prices, cost, how does someone engage with you guys? When does someone know to engage? Where's the smoke signals? When is the house on fire? Is it like people are standing around? What's the problem? When does someone know to call you guys up at? >> People know to call us when they're having production problems that they can't solve. When it takes them way too long to go from there's an alert that went off or a customer complaint, to, "Oh, I found the problem, I can address it." We price based on storage. So we are a bunch of engineers, we try to keep the business side as simple as possible for better, for worse. And so, the more data you send us, the more it'll cost. If you want a lot of data, but stored for a short period of time, that will cost less than a lot of data stored for a long period of time. One of the things that we, another one of the approaches that is possibly more common in the big data world and less in the monitoring world is we talk a lot about sampling. Sampling as a way to control those costs. Say you are, Facebook, again, I'll return to that example. Facebook knew that in this world where lots and lots of things can go wrong at any point in time, you need to be able to store the actual context of a given event happening. Some unit of work, you want to keep track of all the pieces of metadata that make that piece of work unique. But at Facebook scale, you can't store every single one of them. So, all right, you start to develop these heuristics. What things are more interesting than others? Errors are probably more interesting than 200 okays. Okay. So we'll keep track of most errors, we'll store 1% of successful requests. Okay. Well, within that, what about errors? Okay. Well, things that time out are maybe more interesting than things that are permissioning errors. And you start to develop this sampling scheme that essentially maps to the interesting ness of the traffic that's flowing through your system. To throw out some numbers, I think-- >> Machine learning is perfect for that too. They can then use the sampling. >> Yeah. There's definitely some learning that can happen to determine what things should be dropped on the ground, what requests are perfectly representative of a large swath of things. And Instagram, used a tool like this inside Facebook. They stored something like 1/10 of a percent or a 1/100 of a percent of their requests. 'Cause simply, that was enough to give them a sketch of what representative traffic, what's going wrong, or what's weird that, and is worth digging into. >> Final question. What's your priorities for the product roadmap? What are you guys focused on now? Get some fresh funding, that's great. So expand the team, hiring probably. Like product, what's the focus on the product? >> Focus on the product is making this mindset of observability accessible to software engineers. Right, we're entering this world where more and more, it's the software engineers deploying their code, pushing things out in containers. And they're going to need to also develop this sense of, "Okay, well, how do I make sure "something's working in production? "How do I make sure something keeps working? "And how do I think about correctness "in this world where it's not just my component, "it's my component talking to these other folks' pieces?" We believe really strongly that the era of this single person in a room keeping everything up, is outdated. It's teams now, it's on call rotations. It's handing off the baton and sharing knowledge. One of the things that we're really trying to build into the product, that we're hoping that this is the year that we can really deliver on this, is this feeling of, I might not be the best debugger on the team or I might not be the best person, best constructor of graphs on the team, and John, you might be. But how can a tool help me as a new person on a team, learn from what you've done? How can a tool help me be like, Oh man, last week when John was on call, he ran into something around my SQL also. History doesn't repeat, but it rhymes. So how can I learn from the sequence of those things-- >> John: Something an expert system. >> Yeah. Like how can we help build experts? How can we raise entire teams to the level of the best debugger? >> And that's the beautiful thing with metadata, metadata is a wonderful thing. 'Cause Jeff Jonas said on the, he was a Cube alumni, entrepreneur, famous data entrepreneur, observation space is super critical for understanding how to make AI work. And that's to your point, having observation data, super important. And of course our observation space is all things. Here at DevNet Create, Christine, thanks for coming on theCUBE, spending the time. >> Thank you. >> Fascinating story, great new venture. Congratulations. >> Christine: Thank you. >> And tackling the world of making developers more productive in real time in production. Really making an impact to coders and sharing and learning. Here in theCUBE, we're doing our share, live coverage here in Mountain View, DevNet Create. We'll be back with more after this short break. (gentle music)

I from Las Vegas Nevada it's the cube covering IBM world of Watson 2016 brought to you by IBM now here are your hosts John furrier and Dave vellante hey welcome back everyone we're here live in Las Vegas for IBM's world of Watson at the mandalay bay here this is the cube SiliconANGLE media's flagship program we go out to the events and extract the signal-to-noise I'm John Ford SiliconANGLE i'm here with dave vellante my co-host chief researcher red Wikibon calm and our next guest is inderpal bhandari who's the chief global chief data officer for IBM welcome to the cube welcome back thank you thank you meet you you have in common with Dave at the last event 10 years Papa John was just honest we just talked about the ten year anniversary of I OD information on demand and Dave's joke why thought was telling we'll set up the says that ten years ago different data conversation how do you get rid of it is I don't want the compliance and liability now it shifted to a much more organic innovative exciting yeah I need a value add what's the shift what's the big change in 10 years what besides the obvious of the Watson vision how did what it move so fast or too slow what's your take on this ya know so David used to be viewed as exhaust right the tribe is something to get rid of like you pointed out and now it's much more to an asset and in fact you know people are even talking about about quantifying it as an asset so that you can reflect it on the balance sheet and stuff like that so it certainly moved a long long way and I think part of it has to do with the fact that we are inundated with data and data does contain valuable information and to the extent that you're able to glean it and act on it efficiently and quickly and accurately it leads to a competitive advantage what's the landscape for architects out there because a lot of things that we hear is that ok i buy the day they I got a digital transformation ok but now I got to get put the data to work so I need to have it all categorized what's the setup is there a general architecture philosophy that you could share with companies that are trying to set themselves up for some baseline foundational sets of building blocks I mean I think they buy the Watson dream that's a little Headroom I just want to start in kindergarten or in little league or whatever metaphor we want to use any to baseline what's today what's the building blocks approach the building blocks approach I mean from a if you're talking about a pure technical architectural that kind of approach that's one thing if you're really going after a methodology that's going to allow you to create value from data I would back you up further I would say that you want to start with the business itself and gaining an understanding of how the business is going to go about monetizing itself not its data but you know what is the businesses monetization strategy how does the business plan to make money over the next few years not how it makes money today but over the next few years how it plans to make money that's the right starting point once you've understood that then it's basically reflecting on how data is best used in service of that and then that leads you down to the architecture the technologies the people you need the skills makes the process Tanner intuitive the way it used to be the ivory tower or we would convene and dictate policy and schemas on databases and say this is how you do it you're saying the opposite business you is going to go in and own the road map if you will the business it's a business roadmap and then figure it out yeah go back then go back well that's that's really the better way to address it than my way so the framework that we talked about in in Boston and now and just you're like the professor I'm the student so and I've been out speaking to other cheap date officers about it it's spot on this framework so let me briefly summarize it and we can I heard you not rebuilding it to me babe I'm saying this is Allah Falls framework I've stolen it but with no shame no kidding and so again we're doing a live TV it's you know he can source your head I will give him credit so but you have said they're there are two parallel and three sequential activities that have to take place for data opposite of chief data officer the two parallel our partnership with the line of business and get the skill sets right the three sequential are the thing you just mentioned how you going to monetize data access to data data sources and Trust trust the data okay so great framework and I'd say I've tested it some CEOs have said to me well I geeza that's actually better than the framework I had so they've sort of evolved as I said you're welcome and oh okay but now so let's drill into that a little bit maybe starting with the monetization piece in the early days Jonna when people are talking about Big Data it was the the mistake people made was I got to sell the data monetize the data itself not necessarily it's what you're saying yes yes I think that's the common pitfall with that when you start thinking about monetization and you're the chief data officer your brain naturally goes to well how do I monetize the data that's the wrong question the question really is how is the business planning to monetize itself what is the monetization strategy for the overall business and once you understand that then you kind of back into what data is needed to support it and that's really kind of the sets the staff the strategy in place and then the next two steps off well then how do you govern that data so it's fit for the purpose of that business lead that you just identified and finally what data is so critical that you want to centralize it and make sure that it's completely trusted so you back into those three those three steps so thinking about data sources you know people always say well should you start with internal should you start with external and the answer presumably is it depends it depends on the business so how do you how do you actually go through that decision tree what's that process like yeah I mean if you know you start with the monetization strategy of the company so for example I'll use IBM a banana and the case of IBM took me the first few months to understand that our monetization strategy was around cognitive business specifically making enterprises into cognitive businesses and so then the strategy that we have internally for IBM's data is to enable cognition within within IBM the enterprise and move forward with that and then that becomes a showcase for our customers because it is after all such a good example of a complex enterprise and so backing you know backing in from that strategy it becomes clear what are some of the critical data elements that you need to master that you need to trust that you need to centralize and you need to govern very very rigorously so that's basically how I approached it did I answer your question daivam do you get so so you touched on the on the second part I want to drill into the the third sequential activities which which is sources so i did so you did we just talk about this well the sources i mean if you had something add to that yes in terms of the i think you mentioned the internal versus external so one thing else i'll mention especially if you kind of take that 10-year outlook that we were talking about 10 years ago serials had very internal outlook in terms of the data was all internal business data today it's much more external as well there's a lot more exogenous data that we have to handle and validity and that's because we're making use of a lot more unstructured data so things like news feeds press releases articles that have just been written all our fair game to amplify the view that you have about some entity so for example if we're dealing with a new supplier you know previously we might gather some information by talking with them now we'd also be able to look at essentially everything that's out there about them and factor that in so it is a there's an element of the exogenous data that's brought to bear and then that obviously becomes part of the realm of the CDO as well to make sure that that data is available and you unusable by the business is John Kelly said something go ahead sorry well Jeff Jonas would say that's the observation space right that you want to have the news feeds it's extra metadata that could change the alchemy if you will of whatever the mix of the data is that kind of well yeah I would say you might even go further than just metadata i would say that in some some sense it's part of your intrinsic data set because you know it gives you additional information about the entities that you're collecting data on and that measuring the John Kelly in the keynote this morning he made two statements he said one is in three to five years every health care practitioners going to going to want to consult Watson and then he also said same thing for MA because watch is going to know every public piece of data about every single company right so it's would seem that within the three to five year time frame that the shift is going to be increasingly toward external data sources not necessarily the value in the lever points but in terms of the volume certainly of data is that fair I think it's a it's a fair statement I mean I think if you think of it in the healthcare context if you know a patient comes in and there's a doctor or a practitioner that's examining the patient right there they're generating some data based on their interaction but then if you think about the exogenous data that's relevant and pertinent to that case that could involve you know thousands of journals and articles and so you know your example of essentially saying that the external data could be far greater than the internal data out say we're already there okay and then the third sequential piece is trust are you gonna be able to trust the trust we talk a lot about we were down to Big Data NYC the same week you guys made your big announcement the data works everybody talks about data Lakes we joke gets the data swamp and can't really trust the data yeah we further away from a single version of the truth than we ever were so how are you dealing with that problem internally at IBM and what's the focus is it more on reporting is it more on supporting lines of business in product yeah the focus internal within IBM is in terms of driving cognition at the way I would describe it is at points where today we have significant human judgment being exercised to make decisions and that's you know thousands of points in our enterprise or complicated enterprise like IBM's and each of those decision points is actually an opportunity to inject cognitive technology and play and then bring to bear and augmented intelligence to those decisions that you know a factors in the exogenous data so leaving a much better informed decision but also them a much more accurate decision okay the two parallel activities let's start with the first one line of business you know relationships sounds like bromide why is it not just sort of a trite throwaway statement what where's the detail behind that so the detail behind that if you go back to the very first and the most important step and this whole thing with regard to the monetization strategy of the company understanding that if you don't have those deep relationships with the lines of business there's no way that you'll be able to understand the monetization strategy of the business so that's why that's a concurrent activity that has to start on day one otherwise you won't even get past the you know that that very first first base in terms of understanding what the monetization strategies are for the business and that can only really come by working directly with the business units meeting with their leadership understanding their business so you have to do that due diligence and that's where that partnership becomes critical then as you move on as you progress to that sequence you need them again so for instance once you understood the strategy and now you understood what data you need to follow that strategy and to govern it you need their help in governing the business because in many cases the businesses may be the ones collecting the data or at least controlling the source systems for that data so that partnership then just gets deeper and deeper and deeper as you move forward in that program I love the conscience of monetizing earlier and this some tweets going around you know what's holding it back cost of building it obviously and manageability but I want to bring that back and bring a developer perspective here because a lot of emphasis is on developing apps where the data is now part of the development process I wrote a blog post in 2008 saying that dated some new development kit radical at the time but reality it came out to be true and that they're looking at data as library of value to tap into so if stuffs annandale they could be sitting there for years but I could pull something out and be very relevant in context in real time and change the game on some insight and the insight economy is bob was saying so what is your strategy for IBM 21 on board more developer goodness and to how do you talk to customers were really trying to figure out a developer strategy so they can build apps and not to go back and rewrite it make it certainly mobile first etc but what's how does a date of first appt get built and I should developers be programming with you I'll give you a way to think about it right i mean and going back again to that ten-year paradigm shift right so ten years ago if somebody wanted to write an application and put it on the internet and it was based on data the hardest part was getting hold of the data because it was just very very difficult for them to get all of it to access the data and then those who did manage to get all of the data they were very successful in being able to utilize it so now with the the paradigm shift that's happened now is the approaches that you make the data available to developers and so they don't have to go through that work both in terms of accessing collecting finding that data then cleaning it it's also significant and so time consuming that it could put put back there their whole process of eventually getting to the app so to the extent that you have large stores of data that are ready to go and you can then make that available to a body of developers it just unleashes it's like having a library of code available is it all the hard work and I think that's a good way to look at it I mean that's think that's a very good way to look at it because you've also got technologies like the deep learning technologies where you can essentially train them with data so you don't need to write the code they get trained to later so I see a DevOps of data means like an agile meets I'm again you're right a lot of the cleaning and this is where you no more noise we all know that problem or data creates more noise better cleaning tools so however you can automate that yes seems to be the secret differentiator it's an accelerator it's amazing accelerator for development if you have good sets of data that are available for them to used so I want to round out my my little framework here your frame working with my my learnings for the fifth one being skills yes so this is complicated because it involves organization skills changes as pepper going through the lava here we try to get her on the cube Dave home to think the pamper okay babe yeah so should I take over pepper you want to go see pepper I want to see pepper on the cube hey sorry exact dress but so a lot of issues there there's reporting structures so what do you mean when you talk about sort of the skill sets and rescaling so and I'll describe to you a little bit about the organization that I have at IBM as an example some of that carries over and some of that doesn't the reason I say that is again I mean the skills piece there are some generic skill sets that you need for to be achieved data officer to be a successful chief data officer in an enterprise there is one pillar that I have in my organization is around data science data engineering DevOps deep learning and these are the folks who are adept at those technologies and approaches and methodologies and they can take those and apply them to the enterprise so in a sense these are the more technical people then another pillar that's again pretty generic and you have to have it is the information and data governance pillow so that anything that's flowing any data that's flowing through the data platform that I spoke off in the first pillar that those that that data is governed and fit for purpose so they have to worry about that as soon as any data is you even think of introducing that into the platform these folks have to be on that and they're essentially governing it making sure that people have the right access security the quality is good its improving there's a path to improving it and so forth I think those are some fairly generic you know skill sets that we have to get in the case of the first pillar what's difficult is that there aren't that many people with those skills and so it's hard to find that talent and so the sooner you get on it so that would that's the biggest barrier in the case of the second pillar what's the most difficult piece there is you need people who can walk the balance between monetization and governance too much governance and you essentially slow everything down and nothing moved a cuff and you're handcuffed and then you know if it's too much monetization you might run aground because you you ignored some major regulation so walking that loss of market value yeah that's what you have to really get ahead of your skis as they say and have a faceplant you'll try too hard to live boost mobile web startups like Twitter that's big cock rock concert with Twitter Facebook if you try to monetize too early yes you lose the flywheel effect of value absolutely so walking that balance is critical so that's that that's really finding the skill set to be able to do that that's that's what what's at play in that second or the third one is if you are applying it to an enterprise you have to integrate these you know this platform into the workflow off the enterprise itself otherwise you're not going to create any impact because that's where the impact gets created right that's basically where the data is that the tip of the spear to so to speak so you it's going to create value and in a large enterprise which has legacy systems which are silos which is acquiring companies and so on and so forth that's enough itself a significant job and that skill set is that's a handicapped because if you have that kind of siloed mentality you don't get the benefits of the data sharing right so what's that what's said how much how much effort would it take I'm just kind of painting that picture kind of like out there like well a lot of massively hard ya know that that's you know a lot of you know a lot of people think that data mining is all about my data you know this is my data I'm not going to give it to you the one of the functions of the chief data office is to change that mindset yeah and to stop making use of the data in a broader context than just a departmental siloed type of approach and now some data can legitimately be used only departmentally but the moment you need two or more department start using that data I mean it's essentially corporate data so are those roles a shared service everybody see that works it maybe varies but is it a shared service that reports into the chief data officer or is it embedded into the business those those skill sets that you talked about I think those skill sets are definitely part of the chief data officer you know organization now it's interesting you mentioned that about embedding them and the business units now in a in a large enterprise a complicated enterprise like IBM the different business units and that potentially have different business objectives and so forth you know you you do need a chief data officer role for each of these business units and that's something that I've been advocating that's my fault pillar and we are setting that up and then within the context of IBM so that they serve the business unit but they essentially reporting to me so that they can make use of the overall corporate structure you do their performance review the performance review is done by the business unit it is ok but the functional direction is given by me ok so I get back to still go either way oh yes that's a balance loon yeah absolutely under a lot of time for sure i'll get back to this data mining because you bring up a good point we can maybe continue on our next time we talk but data monies were all the cutting edge kind of best practices are were arsed work what we're relations are still there technically if you're here but that the dynamic of data mining is is that you're assuming no new data so with if you have a lot of data coming in most of the best data mining techniques are like a corpus you attack it and learned but if the pile of data is getting bigger faster that you could date a mine it what good is against or initial circular hole I'm going to again you know just take you back 10 years from now and now right and the differences between the two so it's very interesting points that you bring up I'll give you an example from 10 years ago this data mining example not ten years ago actually my first go-around at IBM so it's like 94 yeah one of the things I've done was we had a program a computer program that every team in the National Basketball Association started using and this was a classic data mining program it would look at the data and find insights and present them and one of the insights that it came up with and this was for a critical playoff game it told the coach you got to play your backup point guard and your backup forward now think about that which same coach would actually go with that so it's very hard for them to believe that they don't know if it's right or wrong in my own insurance and the way we got around that was we essentially pointed back to the snippets of video where those circumstances occurred and now the coach could see what is going on make a you know an informed decision flash forward to now the systems we have now can actually look at all that context all at once what's happening in the video what's happening in the audio also the data can piece together the context so data mining is very different today than what it was them now it's all about weaving the context and the story together and serving it up yeah what happened what's happening and what's going to happen kinda is the theaters of yes there are in sight writing what happened it's easy just yeah look at the data and spit out some insight what's happening now is a bit harder in memory I think that's the difference between cognition as it away versus data mining as you know we understood a few years ago great cartridge we can go for another hour but do we ever get enough love to follow up on some of the deep learning maybe come down to armonk next time we're in this certainly on the sports data we have a whole program on sports data so we love the sports with the ESPN of tech and bringing you all the action right here yes I did Doug before Moneyball you know my mistake was letting right yeah yeah right the next algorithm but that's okay you know we put a little foot mark on the cube notes for that thank you very much thank you appreciate okay live in Mandalay Bay we're right back with more live coverage I'm Sean for a table on thing great back today I am helping people

>>Live from the Mandalay convention center in Las Vegas, Nevada it's doc cube at IBM insight, 2014. Here are your hosts, John furrier and Dave Volante. >>Okay. Welcome back everyone. We are here. Live in Las Vegas for IBM impact. This is the cube special presentation at IBM insight inside the digital experience. IBM insight go. Social media lounge. Uh, the social media gurus are here. John furry with David. Um, that's playing off the joke. We're just sharing on Twitter, but seriously, we're here. If I didn't see this on the noise, my coast, Dave latte, next guest Marsha Cola year. Who's the managing director of impact ingenuity at Marsha Marsha. Yes, that's your Twitter handle is awesome. Welcome. Welcome back. Welcome back. >>Well, thanks. It's thrilled to be here. >>So we were just joking about Halloween and we're going to be a social media guru. It's a little bit of a meme going around the internet. I mean, there is no social media guru. I mean, you can't really be a guru with developing technology. You can be a practitioner. I mean, I mean, guru, what is a social media? What is a social media guru? This, >>This is where, because I offered that. I would answer any question you ask me, you can ask me those things. Sure. Well, I think that's the problem. I think that's why it'd be a fabulous Halloween costume. I'm going to think about doing that one too, because people seem to be know to these folks. So following them to the ends of the earth, because of something that they sit on, social media, I mean that, that's a kind of a scary concept, but Google glass >>As well. I mean, I mean, I'm not going to go there. Um, but let's talk, let's go in into that, that theme. I mean, honestly, you know, Jeff Jonas was just on he's awesome. We always get in the weeds. He's a fun character to talk to, but he's super smart as we're on this G2 thing, observation space, but we're all internet of things, right? I mean, it reminds me of that book is to read to my kids thing one and thing two, you know, we, all things we're all in another thing. So what do you see as that impact to, uh, this digital transformation where not only are the humans connected to the machines, the data that they're exhausting or sharing or streaming, but the machines are connected and collecting as well. How is that going to change? What's your view on all this? >>While I have been in the technology sector, most of my, uh, most of my life, uh, and I appreciate and enjoy the technology. I never lose sight of the fact that this is about the people it's about us actually working together of actually learning together, doing whatever the hell it is we're needing to do. So if all of my appliances are actually then taking care of the mundane, if my water softener system is actually getting the water put in and getting delivered on the right day, you know, all, all the better. If the, if the toaster is alerting me to some sort of news, I'm thrilled. I love the idea of the technology. Actually being able to take care of all that stuff that we never wanted to do in the first place, but the technology has been so lousy over the last couple of years, actually forever, uh, that we've had to do this stuff because the technology isn't doing it for us. >>Sure. I was a patient out in the customer space because that's, you know, that's more of the home example, but even business now seems to be early innings. I mean, people are kicking the tires. You know, we've talked to all the gurus coming up here who are the tech side, IBM and customers. And the reality is we're all pro data, which we all kind of see that obvious social data and, you know, big data analytics, certainly helpful, but this transformation people are now really changing how to operate, operationalize their business with it. It's a huge daunting task and it's scary. Um, some people are like, whoa, I don't want to do it. Or, Hey, I'm jumping in. I'm cool. Is there a cool factor? Is there a scared factor? What's your, what's your observation from mountain talking to everyone out in the, in the marketplace? >>Well, first I would, I'll totally bash the, the idea that this is only a consumer play or that it doesn't apply to businesses. Think of all the, uh, the mundane and ridiculous things we have to do at work because they're not being taken care of us. We aren't taken care of for us by our desks. If you want to look at that way or our computers, I loved hearing about the, the new, uh, uh, pairing of, uh Wayblazer and, you know, Watson and the idea of the travel being taken care of us, what we discover because of the data that we're putting off each and every moment is their systems around us all the time that actually know our preferences, know how we would be handling this, but yet they don't do anything about it. So the idea that we can actually move forward in that way should be just as applicable to our business. Uh, a manager should not have to actually be asking some of the questions that they're asking the HR department is need to be asking how you're doing. It's evident by all the things that you put out into the world. And by just actually attending to what's going on, we have a huge opportunity to get back all that time that we've been wasting all these years. I'm just a stupid >>And just to what's. So what's the bottleneck is a fear security, oh, we don't want privacy. Marcia will get offended. If we tweet her, she knows that we know that she tweeted that. I mean, that's, that's a concern. People have, it seems to be, is it? Yeah. Well, look, go back up, >>But why is it a concern? It's because the people who've been doing it early are doing it horribly. I mean, they're doing it in not respectful ways. There isn't actually a real thought about how would I be okay with this doing? And then those are we're. So ahead of the curve, maybe because of the guru status, some of these social media, maybe that maybe that's the reason, >>Just look at the government, they were big data gurus and they screwed up that that whole Snowden thing was all like, Hey, just ask us, we'll give you our email addresses. You can search my email, have a nice day. >>It's a very different message. It's a very different conversation. It's a very different question. It's a very different level of respect that we have from one person working with another. I'm actually talking with people as opposed to at them. And instead of just making assumptions of actually participating, I mean, the idea that engagement is goal just implies that we haven't been engaged all these years. We haven't been thinking we haven't been doing, I haven't met. I personally, haven't met a really dumb person. It, you know, and years, and yet everything I do at would imply that we're, we're too stupid to be able to really think and act and, and be thoughtful about it. >>So you're an influencer. Um, you're out here in the digital sphere and you are, you're hearing influencer. Um, I mean, whatever you define it. Well, it's, I guess if they say so, if you are a VIP influencer, we'll go with that. Um, >>Digging on your Twitter stream here. Fantastic. >>Working on it. So share this law, you know, we'd love, we'd love to hear your stories cause you last year you were awesome with the cube. We'd love, love JV. Give us the update. What's going on with, sorry. We started together Ted at IBM conference. You super busy. Um, what's going on share with the folks out there. Some of the things you've been even into what your what's working show some, you know, some stuff that didn't work, what's going on, what's happening? What are you, what are you doing? What are you worried? All right, >>John, if you're going to ask them, I'm telling you you're really, if you're really ready, Don Damian, probably a little after I saw you last time after I was visiting here that, uh, our world's falling apart. And if all of us actually don't get on that. If we don't actually start figuring out how to use the precious time we have the, the precious money we have, the, the roles we have in our organizations, the resources at our disposal, our brains for good, not evil. I'm not so sure about the world that my son is going to be inheriting for example. And, uh, I'm, I'm at a point in my life where I realize, I, I know a heck of a lot in the world. I have a lot of skills, everybody. I know. I look at these people around me having tremendous skills. And instead of us just sort of churning out the butter one more year, uh, we best, we best be thinking about what can I do given what I have of my time and my resources, my skills, or whatever that is and apply that to what I have influence over and be able to make as much difference. >>Are we talking about God's last offer here, the sustainable world, or what's actually on all? >>Oh, you're not at the time that the timing is perfect too. If you think about it, don't seriously. >>What are we talking about? The deterioration of our planet? We're talking about social condition. Yes, >>I, well, I mean, I can go on and >>On about money return. I can, I can entertain for hours. You just made. The comment >>I made is that no matter where we look, that that scientists have pointed out that we're past the point of no return with our climate. We, uh, we look at the, uh, at the deterioration of the planet around us. I happen to live in the woods and I mean, deep in the woods and you can, you can see the change of how much rain is coming down. That didn't, I mean, I, I'm not, my intent here is not to talk about all the, that the problems around us. We all actually feel them, even if we're not acknowledging them, what I see is the wasted opportunity of us, not actually, re-examining what we're choosing to do and figure out how, whatever it is we're capable of doing could actually be helping instead of bringing it up. So how should people, let's say, people want to know that's good, but I just wanted to frame it. So let's >>Take people want to, so let's say that resonates to somebody in the audience. What should they do? How should they start pick a passion? And they >>Have, um, I mean, I, my, my approach to all the change work I do and have been doing with corporations for the last 20 years is actually not additive. It's not asking the question. What more could I do? Because that's usually what keeps people from doing it. I asked the question, what's keeping me from doing what I've always known needed to be done. So in, in our communities, you know, my experience is everybody knows who it is that could use some assistance, not in a handout sort of way in a reaching out and caring way of asking of, of having a conversation, a participating, and to be able to step back and ask that question. What's keeping me from doing that. We know what needs to be done, but we're not doing it. So how can I say, oh, well, what's keeping me from doing it. I don't have time to do it. Okay. Well, what can I do to actually just get a little bit more time to do something that matters in the world? So that that's the most, very, >>Very basic level. It could be slowly be that it's, >>It's less Twitter. It could also be a re-evaluating how much time I'm spending at work on stuff that could be automated. I mean, going back to this whole conversation about automation, it is to ask those questions. What I can do. That's just about time. Um, >>I, yeah, that is one of the biggest objections I don't have time. Right? >>Yeah. So what I find is when I talk about, uh, global health actually, is that when we look at the idea of health, not as in just exercising more or just eating, right, we're talking about fiscal health, we're talking about, uh, creating a world that is just, uh, a healthier place. When I ask people those questions, most of them can say, well, yeah, this isn't, this is important to me, but I don't know what to do about it. So one is, as you absolutely said, is finding, finding those passions and be able to figure out what you're going to do. But more importantly, to ask yourself that question, when am I going to do this? If not now, I feel like I'm, I'm falling. Like I, uh, I'm Mike is falling out. Let me, let me get that. >>Well, we chit chat a lot of hair. Yeah. Yeah. So I think, okay. So we're talking about different ways to find time. >>Um, Dave, I mean, I think it's a great time. I mean, the passionate thing, passionate thing is where the keyword is contributing, right? So like, I think it's a good time because I have, we, I, we both Dave and I both have four kids. So we see the new generation in their minds all the time because we're driving around, but they're impressionable right now is the old expression is you can grab the play though, and you can shape it. You can act, we can actually, as leaders and mature experience, instant people that have some skills in computing, we can influence like stem. We can influence women in tech. We can influence computer science curriculums or get influenced modern society because the new generation is coming in and they're natives, they're adopting and they're thirsty for leadership, but I don't think that they're seeing it. So I think there's really a good time. You've seen the Kickstarter crowdsourcing stuff is really becoming a part of this new tribe. So I believe the gravity around making things happen is participation, collaboration and data. Data is knowledge, endorsement, social proof. These are concepts that are easily transferable. If you can just, if you just wake up and do it. So I think, you know, >>If you just wake up and do it everywhere about, so Y Y if you wake up every day, why aren't you doing it today? >>We have Craig brown on earlier, he's doing $25,000 investments for kids to start companies, you know, whether the inner city kids. And that's pretty cool. I mean, so, you know, this is, this is the democratization piece, but in a connected network, it's frictionless communication. I mean, hell Twitter, overthrew governments. So you can have solidarity, peaceful solidarity as well as other rev revolution. So I think that's a very doable thing versus just checking the Basel. I volunteer to do something. And I think that has been more of like a peace Corps. I helped people. >>Uh, and I'm personally, I asked this question of everybody that I asked her, actually asked two questions of everybody I work with now. Uh, one of them is what can you not do? What can you not, not do actually. So if you, if you think to yourself, if I look back on my life, if I look back on my life, what is it that I thought to myself, oh, I didn't have time for that. Or I couldn't do it. You we've all heard that, you know, what do you want on your tombstone? However, that works. But I find that everybody, I know, think it has a burning need to be doing something useful in their lives. It's not just mission driven. It absolutely. It's a purpose. It's a connecting with, with connecting with people who are helping to move the world forward. And I just stopped. And I said, even in a business context, I say, you know, now it's time. We're kind of out of time. Get on with it, >>Please. The clock is ticking. Well, Jeff Jones was talking about the asteroid thing to geospatial smart geeky conversation. But the key thing out of that was better focus of finite resources. And that really comes down to the fundamental better decision-making. I mean, we, my wife says, so our kids will make better decisions. I mean, that's a mother talking to the kids, but that's our life now. So like, if we can make better decisions, that ultimately is the big data opportunity from social change to play to business. >>And then the second question absolutely, absolutely agree. Everything you said. I, the next big question I asked is what are you doing to improve the world? Now? I would say 50% of the people I say, just give me this completely deer in the headlights. Look, what do you mean to save the world or to improve the world, to change world? However you want to frame that. But I haven't met anybody in years that isn't interested in truly contributing, leaving the world a better place than they came into. And that's no matter what their, their demographic makeup is. That's no matter the community they live in, no matter what they're doing, people have a fundamental desire to do better. And so I asked that of every business person, every corporation I work with. And that's one of the things I love about this whole idea of, you know, building a smarter planet that should tie to every single thing we do. And, and when we lose sight of that, we see that, no, I think >>This is a really great conversation to have because it's, it's something that's emerging. And, you know, again, there's some obvious examples, oh, pebble watch crowdfunding. But if you look at really impactful things like open source software, you are seeing the playbook. I mean, the playbook is, you know, people can participate at any level. So the, the fear of getting this kind of group going is that I'm too busy or, you know, you can, the contribution doesn't have to be game changing for an individual could be one small piece of the puzzle. It could be small contribution. Someone might do more heavy lifting than the other. That's an open source concept. We've seen that work huge. A lot of leverage, a lot of participation. Um, so I think that's something that I really haven't seen get applied to at a large scale. I mean, you see the protest in Hong Kong are interesting. That's an indicator. What does that mean? Right. So what's your take on all? What do you think needs to happen to get more people tied into these shared missions? >>It's a little little over there off >>The ranch. A little bit more honesty. More honesty. Yeah. Yeah. I mean, not, not something that we talk about these sorts of events is that I I've gotten to the point where I do these large talks in front of thousands of people. And I ask everybody to turn to the person next to them and introduce themselves, honestly, like, why are you here? And why do you care? We've all gotten so wrapped up in the >>Who we are as well. And that's why I say, I love the idea of you being >>A social media guru for Halloween. It's just become, so it's so about the role that we've lost the connection with our humanity. And so I just, I asked people just to step back. So it's as simple. So yeah, I am all for the large initiatives. >>Yes. Self-aware is a really interesting concept. And that really what you're talking about here is, I mean, I make fun of myself. I put that out there. Probably gonna get some hate mail for that tweet, but no, it is what it is. I mean, I'm making fun of myself and us because we have to, because it's really not moving fast enough in the writer in my mind, at least I think, I mean, I think social media is a real, real game changer. I'm pro pro social media, but I mean, come on, if you can't make fun of yourself then, >>But what is social media do you mean? What is our untapped desire that why we're all participating in social media, where we've missed the opportunity for all these years to be human in everything that we're doing? Yeah. I mean, the idea that you can be, you know, wherever you are and be able to reach the people who have answers to be able to help you make better decisions is something that we've had that desire for a very long time. We've just been, not able to do that for so long that it's now it's time we get on >>With that. I would do the cube to Dave and I talk all the time. We want to broadcast out the data because I think people want to be part of something. And I think at the end of the day, it's human psychology is that being part of something makes psychology of the soul work better. It's like, okay, I want to be part of a group. I want to belong. It's a yearning, it's a tribe. Whatever that kind of collective group is, whether you know, the clown or the, or the guru or whatever, I think that's a people are yearning for that collectiveness of Griff groups. And I think the data gap is gravity. Like how do you a joke? It could be a serious conversation. It could be something provocative. I think content is a nice piece of gravity to kind of bring people together versus, you know, tweeting, Hey, look, how big I am. I got a zillion followers. >>Okay. So let's back up though. So content, so we can talk about the, the, the, the, the concept that has content. That's a lovely thing to do at a data conference, talking about the content it's about things we care about. That's what content is. So if we take that a step further and we actually extrapolate and say, how does this impact me? It's not because it's content it's because we're talking about topics that matter to each of us. And so the more we get back to that sort of conversation, the more we get back to that sort of point, I think we have a bigger opportunity to have conversations that matter and not be able to be. We are wasting our time doing the silly stuff. >>Okay. I'm getting the hook here, Marcia conversations that matter. That's really what it's all about. Changing the world. Thanks for calling the cube. Great to see you again. And, uh, we'll be right back after this short break live in Las Vegas date, you continues wall-to-wall coverage here, inside the cube, inside the digital experience in psycho with IBM social lounge. We right back after this short break,

okay we're back here live at the IBM iod information on demand conference hashtag IBM iod this is the cube so looking the anglo Mookie bonds flagship program we go out for the events extracting from the noise i'm john furrier might join my co-host Davey lonte and we'd love to have analysts in here and in this case former analyst James Cole Beatles welcome to back to the cube thank you very much John thank you Dave pleasure see you again finger of being at IOD you're a thought leader you are an influencer you work at IBM so you you're out there the front lines doing some great work so thank you very much tell us explains the folks out there not about the show because we've had some people coming in last year you were private in but what does this fit what is this vector in context to what's relevant the market obviously big data and analytics is the hottest thing on the planet right now and you got social business now emerging categorically here but it has a couple different flavors to it right within IBM's context yeah but the messaging is simple right you got analytics that drives value outcomes social business is the preferred way of people going to operate their businesses engagement and all that is great stuff new channels marketing eccentric cetera explain to them how I OD is fitting into these megatrends into mega trends I think the hottest trends why our customers caring about what's going on here is a lot of a lot of activity around customers what is what does IOD fit into that a bigger picture yeah well you know the world has changed the world culture has changed radically and really in the last decade or so none is everywhere in the world everything is now online and digital increasingly it's streaming in terms of culture look what's happening to Hollywood is being deconstructed by the netflixs of the world you know movies and TV and music and everything is delivered online now all engagement more more engagements with your employer with your you know with merchants with your family everywhere is online things like streaming media so if you look at how the world culture has changed I yesterday I spoke here on a topic that's near and dear to my heart called big media it's the support of the ascendance of streaming media and not just the area as I laid out but in education like MOOCs distance learning we use it internally at IBM for our think fridays and Ginni Rometty and the executive team you know every Friday its cloud or its big data or whatever you know we need all need to get up to speed on the world culture has changed now analytics is fundamental to that whole proposition in terms of world culture analytics driving gagement analytics in terms of you know in a business context analytics a 360-degree view and you have data warehouses and the master data and you have predictive models to drive segmentation and target marketing and all that good stuff you know that's been in business for a long time that those set of practices they have become prevalent in most industries now not just in say retailing you know the Amazons of the world they're pervasive across all industries big data is fundamental to that you know engagement model its social social in the sense that social is one of many channels through which business is engaged with through which many people engage the social is assumed assuming a degree of importance in the fabric of modern life that goes beyond simple you know engagement with you know brands and whatnot social is how people create is how they declare who they are it's their identity and so social in your personal life we all know about Facebook and Twitter and everything else and YouTube but social has revolutionized enterprise cultures everywhere you know we use social internally of course we use our own Lotus connections most large and even many mid-sized firms now use social for interactions among employees or throughout their Val you chain so social business is about all of that it's the b2c it's the b2b it's the e2e and employ to employ all these different models of engagement they all demand a number of things obviously the social platform they demand the data of various sorts structured unstructured in shared repositories or cubes or Mars or whatnot they it demands the the big data platforms not only at respite in motion the streaming media to make it all happen in real time so at IOD if you see what the themes are this year and really it's been a building for several years cloud everything social is running in the cloud now more and more not just public Claus but Federation's of public and private clouds it's it's all about cognitive computing which is a relatively new term in the Sun sets achieved a certain amount of vogue in the last year or so which is really fundamentally as an evolutionary trend it's basically a I for the 21st century but leveraging unstructured data and and machine learning and so forth and predictive analytics and you know well the whole world learn what metadata was with the whole NSA yeah comments no it's like me and then just to wrap it up in memory real-time blu acceleration you know you need real-time you need streaming you need collaboration and social you know peer-to-peer user-generated content all of that to make this new world culture really take off and IBM provides all that we recognize that that's where the world's going we've been orienting reorienting all of our solutions around these models cloud social increasingly going forward and you know we provide solutions that enable our customers in all industries to go there and big data is fundamental to all of that as we say we're computer science meets social science that's always been Silicon angles kind of masthead view but to unpack what you just said from the market relevance you mentioned Netflix we saw Amazon coming out their own movie they're going to go direct with their own programming so so but that speaks to the direct business model of the web was originally pioneered as hey direct business model cut the middleman out but now that dimension has been explored so that kind of what you're saying there so that's cool the end user pieces interesting image is social so what's your take on the end user orientation what's the expectation because you got social you got a trash you got in motion you got learning machines providing great recommendations got the Watson kind of yeah reasoning for people so personalization recommendation engines the sea change attention time currency big days of all those buzzwords all right what is the expectation for users in the future right now we're moving into this new world where I can self serve myself monologue based the information from the web now it's all coming at everyone real time the alarms are going off as Jeff Jonas says what is that prefer user experience the direct business model people get that I think the business to see that but now the end users are now at the center of the value proposition how do what's the role of the user now they're participating in the media there are also consumers of the media yeah and they now have different devices so what's the sources of data so fundamentally yeah the role of the consumers expectations now is always everything is always on everything is always online everything is all digital everything is all real time and streaming everything is all self-service everything is all available in the palm of my hand and then the back-end infrastructure the cross-channel infrastructure users don't care about individual socials they really don't they don't really fundamentally care about Facebook or Twitter or whatever you have they just care that what their experience is seamless as they move from one channel to another they're not perceived as channels anymore they're simply perceived as places or communities that overlap too in a dizzying array of socials thus social is where we all live and thus social increasingly is mobile increasingly mobile is you know the user expects that the handoff from my smartphone to my tablet to my laptop to my digital TV sentence and so forth that it all happens through the magic of infrastructure that it's being taken care of and they don't have to worry about that handoff it all it's all part of one seamless experience yeah they always just say the search business it's the it's the it's the intersection of contextual and behavioral yeah and now you take that online behaviors community contextual is context to what people are interested at any given time yeah it's so many longtail distributions at any given time so do you see the the new media companies that the new brands that might emerge mean there's all the talk about Marissa Mayer kind of turning over yahoo and yeah she some say putting lipstick on a pig but but but is that they're just an old older branch trying to be cool but is that what users want just like media but just user experience me like we're small media but we got big ideas but the thing is the outcomes right small frying big blues go figure are the outcomes still the same company still want to drive sales for their business sell a product provide great value you just want to find great content and find people I mean the same concept of the old web search find out and run sumit give any vision on how that environment will evolve for a user like is it going to be pushed at me do you see it a new portal developing is mmm Facebook's kind of a walled garden humble don't care about that what's your take on that the future vision of a user experience online user experience online future vision in many ways I think let's talk about Internet of Things because that keeps coming more and more into the discussion it's it's not so much that the user wants a seamless experience across channel cross device all that but a big part of that experience is the user knows that increasingly they'll have some confidence that whatever environments physical environments there in our being obviously there's privacy implications that surveillance here are being monitored and tracked and optimized to meet their requirements to some degree in other words environmental monitoring internet of things in your smart home you want to configure so you smart home so that every room that you walk into is as you as you're moving there even before you get there has already been optimized to your needs that ideally there should prediction Oh Jim's walking into the bathroom so turn the light on and also start to heat up the water because it's ten o'clock at night Jim's usually takes his bath around this time you sort of want that experience to be handled by the internet of things like nest these new tools like nest oh yeah yeah so essentially then it's my user experience is not just me interacting with devices but me simply moving through environments that are continuously optimized to my knees and needs of my family you know the whole notion of autonomous vehicles your vehicle if it's your personal vehicle then you want to always autumn optimize the experience in terms of like you know the heat setting and and the entertainment justement saan the you know the media center and they're always to be tailored to your specific needs at any point in time but also let's say you take a zipcar you rent a zipcar and you've got an ID with that company or any of the other companies that provide those on-demand rental car services ideally in this scenario that whatever vehicle you you rent through them for a few hours or so when you enter it it becomes your vehicle is completely customized to your needs because you're a loyal customer of that firm and they've got your profile information this is just a hypothetical I'm not speaking to anything that I actually know about what they're doing but fundamentally you know ideally any on-demand vehicle or conveyance or other item that you you lease in this new economy is personalized to your needs while you're using it and then as it were depersonalized when you check it back in so the next person can have it personalized to their use as long as they need it that's the vision of a big part of the vision of customer experience management personalization not just of your personal devices but personalization of almost any device or environment in which you are operating so that's one kanodia wants this question no I would ask one more question on that on the user experience came on Twitter from a big data alex says while you're on the subject which a my Alex I don't great great friend of the cube but thanks for the tweet today we don't have our crowd shado-pan we can get the chat going there but why not talk about AR and I've been in reality I mean honestly Internet of Things is now not the palm of your hand it could be on your wrist or on your clothing the wearables on the glasses and just gave out three invites to google glass so this is again another edition augmented reality is software paradigm as well what is that what is it what does that fit into that what's your take on augmented reality augmented reality ok so augmented reality is that which I don't use myself I've just simply seen it demonstrated and plenty of places so augmented reality is all about layers of additional information overlaid on whatever visual video view or image view that you happen to be carrying with you or have available to you while you're walking around in your normal life so right now conceivably if this is an AR a setting that I would environment or enabled device I would be able to see for example that ok who's in this room in the sense that who is declared that they are in this area of Mandalay Bay right now and why specifically are they doing to the extent that they allow that information to be seen and o of these people here which of these people if any might be the person I'm going to be speaking with it for 30 so that if they happen to be in this environment i can see that i can see that they're to some degree they may have indicated status waiting for james could be a list to get done with the Wikibon people oh that's kind of cool so I'd see that overlay and I walk to other parts of the Convention Center I might also see overlays as I walk around like oh there's a course down as several rooms down that I actually put in my schedule it's going to start in about five minutes I'll just duck you into there because it reminds me through the overlay that's the whole notion of personalization of the environment in which you're walking around in real time dynamically and contextual in alignment with your needs or with your requirements are in alignment also with these whatever data those environment managers wish to share to anybody who's subscribing in that contact so that's a context-aware that theme have been talking about here on textual essentially it's a public space that's personalized to your needs in the sense that you have a personalized view in a dynamically update okay that sounds like crowd chat Oh are we running a trip crouched at right now crouch at San overlay so just as lovely overlay so look to the minute social network yeah tailored to the needs of the group yep that adds value on top of that data yeah so James I gotta get your take on something so we had Merv on yesterday great Adrian with my great Buy analyst day and he was on last week at Big Data NYC you know we did our own little vent there Don coincident with hadoop world so Murph said well we're just entering the trough of disillusionment for big data yeah you love those Gartner you know I love medications tools I mean they are genius and I get him but he said that's a good thing because it goes left to right so we're making progress here ok right but I'm getting nervous the internet of things I love the concept we don't we don't work on industrial internet and you know a smarter planet it's in there so I love it but I'm getting nervous here's why I look back at a lot of the promises that were made in the BI days 360-degree other business predictive analytics a lot of things that are now talking about in the hood sort of Hadoop big data movement that we're actually fulfilling with this new wave that the old wave really wasn't able to fill because the cousin sort of distracted doing sarbanes-oxley and reporting in and balanced scorecards so so I'm nervous he's old school now it when he when he referenced is something that was hot in the mid part of the two thousand decade okay go ahead okay we had a guy on today talking about balance core would you know we're just talking about crowd chat that's the hottest day in 2013 like five years or hurt anybody mentions sarbanes-oxley so what kind of saved that whole business Roy thank you and Ron but so heavy right so what I'm nervous about as we as I've seen a number of waves over the years where the the vendor community promises a vision great vision great marketing and then all of a sudden something hotter comes along like Internet of Things and says don't know this is really it so my question to you is will help us it'll help me in my mind you know close that dissonance gap is are these two initiatives the sort of big data analytics for everybody putting analytics in the hands of business users yeah or is that sort of complementary to the internet of thing his internet of things just the new big trillion dollar market that everybody's going to go after and forget about all those promises about analytics everywhere help me sure Jay through that my job is to clarify confusion hey um you know if you look at the convergence of various call them paradigms there's a lot of big data analytics is one of them right now clearly there's cloud clearly their social there's big data analytics in mobile and there's something called Internet of Things so some some talk about smack smac social mobile analytic a que a big data cloud if you add IOT of there it's smack yet I don't think it works or smash yet but fundamentally if you think about Internet of Things it's it's all about machines or automated devices of various sorts probes and you know your smartphone and whatever I know servers or even you know the autonomous vehicles those are things that do things and you know they might be sources of data they would are they might be consumers of data they might conceivably even be intermediaries or brokers or routers or data what I'm getting at is that if you look at big data analytics I always think of it as a pipeline all data it's like data sources and data consumers and then there's all these databases and other functions that operate between them to move data and analytics and insight from one end to the other of the pipe in a conceptual way think of the internet of things as well a new category of sources of data these devices whether they be probes or monitors or your smart phones and new consumers and they all those same things are probably going to be many of them consumers of data and there's message passing among them and then the data that they passed might be passed in real time through streaming like InfoSphere streams it might be cached or stored and various intermediate databases and various analytics performed on them so think of you know I like to think of the internet of persons places and things persons that's human endpoints consumers and and sources of data that's all of us that's social places that's geospatial you know you think about it the Internet of geospatial you know geo spatial coordinates of of data and analytics and then there's things there's you know automated endpoints or you know hardware even Nana from macro to nano devices so it's just a new range of sources and and consumers of data and new types of analytics that are performed in new functions that can be performed and outcomes enable when you as it were stack in and out of things with social with claw with mobile new possibilities in terms of optimization in real time it throughout the you know the smarter planet if you think about the smarter planet vision it's all about interconnected instrumented and intelligent instrumented you know instrumentation that traditionally it suggests hardware instrumentation that's what probes our sensors and actuators that's the Internet of Things it's a fundamental infrastructure within smarter planet I'd love that thank you for clarifying i could write a blog post out of that and i think i'm very well made so um now i want to follow up and bring it back to the users I know snack and I thought you were going to say a story no smack MapReduce analytics and query or sell smack on the cube so so I want bring it back to the users so we had a great conversation yesterday actually last week I'll be met it was on off you know ah be met and he said look why are there any any you know where all the big data apps he said you need three things to for big data apps you need domain expertise you need algorithms which are free and you need data scientists like oh we'll never get there all right oh so rules really free while there are that was this argument yeah it means a source if people charge him for algorithms big trouble was this point I think okay sure so and then we had a discussion yesterday about how in the early days of the automobile industry you know the forecast was this is problematic the gap to adoption is just aren't enough chauffeurs know the premise that we were putting forth in the discussion yesterday I don't know who that was with was that with Judith it was good was that look we've got to figure out a way to get analytics in the hands of the business user we can't have to go through a data scientist or some business analyst no that's not going to work and we'll never get adoption so what what's going to bridge that gap is it is it the things you talked about before all these you know cool solutions that you guys are developing the project neo that you announce today visualization yeah there's another piece of that what puts it in the hands of guys like me that I can actually use the data in new and productive ways yeah well self-service business intelligence and visualization tools that are embedded in the very experience of using apps for example on your smartphone democratization of data science down to all of us you need the right tools you need you need the tools that the new generation of people like my children's generation just adopt and they work in there just a tune from from the cradle to working with data and visualizations and creating visual you know analytics of various sorts though they may not perceive it as being analytics they miss may perceive it as working with shapes and patterns and stuff yeah you would stop yeah so playing around you know in a sandbox i love that terminology data scientists working you know sandboxes which is data that's martes that they build to do regression analysis and segmentation and decision trees and all you know all that good stuff you know the fact is your sandbox can conceivably be completely on your handheld device with all the visualizations built-in you're simply doing searches and queries you know you're asking natural language questions you're looking at the responses you're changing your queries you're changing your visualizations and so forth to see if anything pops out at you as being significant playing around it you know it's as simple a matter that that these kinds of tools such as IBM you know cognos and so forth enable everybody to become as it worried a data scientist without having to you know become a maquette their profession it's just a part of the fabric of living in modern society where data surrounds us people are going to start playing with data and they're going to start teaching themselves all these capabilities in the same way that when they invented automobiles and you know wasn't Henry 42 invented them it was in like the late 1800s by engineers in Europe and America you know it's like we didn't all become auto mechanics you know there are trained auto mechanics but I think most human beings in the modern world know that there's a thing called an automobile that has an engine that needs gasoline and oil and occasionally needs to be brought to a professional mechanic for a repair and so forth we have many of us have a rough idea of something called a carburetor blah blah blah you know in the same way that when computers came up after world war two and then gradually invaded our lives through PCs and everything we all didn't become computer scientist but most of us have an idea of what a hard disk is most of it no most of us know something about something called software and things are called operating systems in the same way now in this new world most of us will become big data analytics geeks practical into the extent that will learn enough of the basic terms of art and the relationships among the various components to live our lives and when the stuff breaks down we call the likes of IBM to come and fix it or better yet they just buy our products and they just work magically all the time without fail conversing and comfortable with the concepts to the point which you can leverage them and what about visualization where does that fit visualization visualization is where the rubber meets the road of analytics is it's where human beings how human beings extract meaning insight fundamentally maybe that's like yeah you extracted inside a lots of different ways you do searches and so forth but to play around it to actually see you know a heat map or a geospatial map or or or you know a pie chart or whatever you see things with your eyes that you may not have realized we're there and if you can play around and play with different visualizations against the same data set things will pop out that you know the statistical model just seek the raw output of a data mining our predictive model or statistical analysis those patterns may not suggest themselves and rows of numbers that would pop out to an average human being or to a data scientist they need the visualizations to see things that you know because in other words when you think about analytics it's all about the algorithms that are drilling through the data to find those patterns but it's also about the visualizations the algorithms and you need the visualizations and of course you need the data to really enable human beings of all levels of expertise to find meaning and fundamentally visualizations are a lingua franca between non-expert human beings and expert eamon beings between data scientists visualizations are a lingua franca Hey look what I saw what do you think you know that's the whole promise of tools like concert for example we demonstrated this this morning it's a collaborative environment as sharing of visualizations and data sets and so forth among business analysts and the normal knowledge worker you know it with it you know like what do you see here's what I see what do you think I don't see that here's another visualization what do you see there oh yeah I think I see what you mean and here's my annotation about what I have broader context I've you know here's what I oh this is great that's the whole notion of humans deriving insight we derive it in socials we derive it in teams of that some Dave might be adept at seeing things that Jim is just absolutely blind to or you know Nancy might see things that both of us are applying to but we're all looking at the same pictures and we're all working with the same data part art yeah it's all so let's talk about some plumbing conversations you know one of the things that we noticed we were at the splunk conference this year's blown came out of nowhere taking log files making them manageable saving time for people so the thing that comes out of the splunk conversation is that it's just so easy to use that their customer testimonials are overwhelmingly positive around the area hey I just dumped my data into this the splunk box and it grid good stuffs happening I can search it it can give me insight save me time so that's the kind of ease of use so so how does IBM getting to that scenario because you guys have some good products we've got on the platform side but you also have some older products legacy Lotus other environments collaborative software that's all coming together in converging so how do we get to that environment where it's just that he just dumped your data in and let it do its magic well Odin go that's the very proposition that we provide with our puresystems puredata systems portfolio tree data system and big insights right for Hadoop so forth big in size you know we have an appliance now yeah we have pdh so that's the whole create load and go scenario that because Bob pidgeotto unless wretched and others demonstrated on the main stage yesterday and today so we did we do that and we are simple and straight being easy to use and so forth that's our value prop that's the whole value prop of an appliance you know simple you don't need a ton of expertise we pre build all the expert in a expertise patterns that you can use to derive quick value from this deployment we provide industry solution accelerates from machine data analytics on top of big insights to do the kinds of things you're talking about with splunk offerings so fundamentally you know that's scenario we all we and we're you know we have many fine competitors we offer that capability now in terms of the broader context you're describing we're a well-established provider of solutions we go back more than a hundred years we have many different product portfolios we have lots and lots of customers who would invested in IBM for a long time they might have our older products our newer products in various combinations we support the older generations we strive to migrate our customers to the newer releases when they're ready we don't force them to migrate so we make very we're very careful in our row maps to provide them with a migration path and to make it worth their while to upgrade when the time comes to the newer feature ok so I got it don't change gears to the to the shiny new toy conversation which is you know you know we love that in Silicon Valley what's a shiny new toy there's always an emerging markets when you have see changes like this where there's a whole the new whole new wave comes in creates new wealth old gets destructed new tags over whatever the conversation goes but I got to ask you okay well Elsa to the IBM landscape that you that you're over overlooking with big data and under the under the hood with cloud etc there's always that one thing that kind of breaks out as the leader the leading toy a shiny object that that people gravitate to as as I'm honest I won't say lost later because you got you know it's not not about giving away free it's it's the product that goes well we this is the lead horse you know and in this game right yeah so what is that what is the IBM thing right now that you're doubling down on is it blu acceleration is it incites is it point2 with a few highlights right now that's really cutting through the new the new the new soil of yeah we're developing our own rip off version of google glass thank you know I'm saying it's always I mean I'm gonna say shiny too but there's always that sexy product well I want that I want L customers name I want that product which leads more you know how she lifts for other products is there one is there a few you can talk about that you've noticed anecdotally is going to be specific data but just observational a shiny toy for the consumer market or for the business business business mark okay yeah yeah is it Watson is Watson the draw is it what's the headline looking for the lead lead dog here what's the attack there's always one an emerging market well you can put your the spot here well you could say that the funny thing is the whole notion of a shiny new toy implies something tangible when the world is gone more and more intangible in the cloud so we are moving our entire portfolio beginning links the big data analytics solutions into the cloud cloud first development going forward our other core principles for the pure data systems portfolio and the light for the shiny the shiny new thing the new cons could be shiny new concept or new paradigm yeah but the shiny new thing is the cloud the cloud is something pervasive and the cloud is something that it really multi form factors that's not very sexy but customers want flexibility you know they want to acquire the same functionality either as a licensed software package and running on commodity hardware we offer that for our big data analytics offerings or as an appliance and one sort or another that specialized particular occurrence or as a SAS cloud offering or as a capability that they can deploy in a virtualization layer on top of IBM or non-ibm hardware or they want the abilities you can mix and match those various deployment form factors so in many ways the whole notion of multi form factor flexibility is the shiny new thing it's the hybrid model for deployment of these capabilities on Prem in the cloud combination thereof that's not terribly sexy because it's totally it's totally abstract but it's totally real I mean demand wise people can see them that drives my business because when you go to the cloud I mean that's where you can really begin to scale seriously beyond the petabytes the whole notion of big media it will exist entirely in the cloud big media I like to think is the next sexy thing because streaming is coming into every aspect of human existence where stream computing a lot of people who focus on Big Data think of volume as being like big headline oh god we'd go to petabytes and exabytes and all that yeah it's important some really fixate on variety all these disparate sources of data and now we have all the sensor data and that's very important we have all the social media and everything all those new sources that's extremely important but look at the velocity everybody is expecting real-time instantaneous continuous streaming you know everything we do all of our entertainment all of our education surveillance you know everything is completely streaming I think ubiquitous streaming to every device and everybody themselves continue to continuing to stream their very lives everywhere all the time is the sexy new thing Dave and I talk about running data we coined that term running data what four years ago so I got to get you got to get kind of a thought leader they're watching us and we're watching streaming data right now from these said these are your guys are streaming this is big media give us some wanna get your thought leader perspective here some thought leader mojo around um the hashtag data economy you know you need now you're moving into a conversation with c-level folks and they said James tell me what the hell is this data economy thing right so what is the data economy in your words kind of like I mean I'll say it's a mindset I'll everything else what's your take on that we've been discussing that internally and externally at IBM we're trying to get our heads around what that means here's my take as one IBM are one thought Leigh right by the way the trick of being a thought leader is just to let your own thoughts lead you where they will turn around where all my followers yeah hopefully they want to lead you to far astray where you're out in the wilderness too long that's an important type of people are talking about because people are trying to put the definition around at economy can you actually have a business construct around yeah data here is my taken on the layers of the meaning of data economy it's monetizing your data the whole notion of monetization of your data data becomes a product that you generate internally or that you source from externally but you repackage it up and then resell with value add the whole notion of data monetization and you know implies a marketplace for data based products you know when I say data I'm using it in the broader context of it could be streaming media as the kind of one is a very valuable category of you know data like you know whatever kollywood provides so there's a whole notion of monetizing your data or providing a marketplace for others to monetize their data and you take a transaction fee from that or it also means in more of a traditional big data or data warehousing bi sense it means that you drive superior outcomes for your your own business from your own data you know through the usual method of better decision if better decisions on trustworthy data and the like so if you look at data monetization in terms of those layers including the marketplace including you know data-driven okay in many ways the whole notion of a data economy hinges on everybody's realization now that the chief resource for betterment of humanity one of the chief resources going forward for us to get smarter as a species on this planet is to continue to harness the data that we ourselves generate you know people stop what data is being the new oil what oil was there before we ever evolved but data wasn't there before we we landed on earth or before we evolved we generate that so it's our own exhaust your own exhaust that's actually a renewable resource data exhaust from data from exhausted gold that's what we say data is the data exhaust it's good if you can harness it and put it together as Jeff Jones says the puzzle piece is the picture the big picture at the smarter picture the smarter planet so on the final question I want to wrap up here to our next guest but what's going on with you these days talk about what's up with you you know you're very active on Facebook will you give a good following I'll be coming up what's happening you know I'll make sure I said big birthday for you on your Facebook page what's going on in your life I'll see you're working at IBM one of the things are interesting what's on your mind these days when you're at leisure are you hanging out you think what are you thinking about the most what are you doing with your you know things with your family's cherith let's see what's going on well I hang out at home with my wife and drink beer and listen to music and tweet about it everybody knows that stuff kind of beer do you drink whatever is on sale I'm not going to say where we buy it but it's a very nice place that whose initials are TJ but fundamentally you know my my mind is an open book because I evangelize I put my thoughts and my work thoughts and love my personal thoughts out there on socials I lived completely ons but I completely unsocial I self-edit but fundamentally the thought leadership I produce that the blogs and whatnot I produce all the time I put them out there for general discussion and I get a lot of good sort of feedback the world and including from inside of IBM I just try to stretch people's minds what's going on with me I'm just enjoying what I'm doing for a living now people save Jim you're with IBM why aren't you an analyst I'm still doing very analyst style work in in a vendor context I'm a thought leader I was a thought leader as I try to be being a thought leader is like being a humorist it's like it's a statement of your ambition not your outcome or your results yeah you can write jokes too you're blue in the face but if nobody laughs then you're not a successful comedian likewise i can write thought leadership pieces till I'm blue in the face but if nobody responds that I'm not leaving anybody anywhere i'm just going around in circles so my my ambition and every single day is to say at least one thing that might stretch somebody's box a little bit wider yeah yeah I think I think IBM smart they've been in social for a while the content markings about you know marketing to individuals yeah with credibility so I love analysts I love all my buds like like Merv and everybody else and I'm you know sort of a similar cat but you know there's a role for X analysts inside of solution providers and we have any number John Hegarty we have we have Brian Hill another X forest to write you know it's it's a you know it's a big industry but it's a small industry we have smart people on both sides of the equation solution provider and influencer my line um under people 99 seats and you know I I suck up to my superiors at IBM i suck up to any analyst who says nice things about me and hosts be on their show and i was going out of my life i'm just a big suck up well we like we like to have been looking forward to doing some crowd chats with you our new crouch an application with you guys lock you into that immediately it's a thought leader haven that the Crouch as as it turns out Dave what's your take on the analyst role at IBM just do a little analysis of the analyst at IBM which you're taken well I think it's under situation I think that the role that they that IBM's put James in is precisely the way in which corporations vendors should use former analysts they should give you a wide latitude a platform and and not try to filter you you know and you're good like that and so guess what I do the usual marketing stuff to the traditional but I do the new generation of thought leadership marketing and there's a role for both of those to me marketing have said this is if I said it was I said a hundred times marketing should be a source of value to people and it's so easy to make marketing a source of value by writing great content or producing great content so yeah that's my take on a jonathan your your marketing is a great explainer you explain the value to the market and thereby hopefully for your company generate demand hopefully in the direction of your cut your customers buying your things but that's what analysts the influencers should be explainers it's you know probably Dave I mean has influenced as influences that we are with with a qu here's my take on it when you have social media of direct full transparency there's no you can't head fake anyone anymore that all those days are gone so analyst bloggers people who are head faking a journalist's head faking the house the audiences will find out everything so to me it's like it's the metaphor of when someone knocks on your door your house and you open it up and they want to sell you something you shut the door in their face when you come in there and they say hey I want to hang out I got you know I got some free beer and a big-screen TV you want to watch some football maybe you invite him in the living room so the idea of communities and direct marketing's about when if you let them into your living room yeah you're not selling right you are creating value see what i do i drop smart i try to drop smart ideas into every conversational contacts throughout socials and also at events like i od so you know a big part of what I do is I thought leadership marketer is not just right you know you're clever blogs and all that but I simply participate in all the relevant conversations where I want I want ideas to be introduced and oh by they want way I definitely want people to be aware that I am an IBM employee and my company's provides really good products and services and support you know that's really a chief role of an evangelist in a high-tech slider that's one of the reasons why we started crouched at because the hashtag get so difficult to go deep into so creates crowd chatter let's go deeper and have a conversation and add some value to it you know it's you thinking about earned media as parents been kicked around but in communities the endorsement of trust earning a position whether you work at IBM people don't care a he works at IBM or whatever if you're creating value and you maybe have some free beer you get an entry but you win on your own merits you know I'm saying at the end of the day the content is the own merits and I think that's the open source paradigm that is hitting the content business which is community marketing if your pain-in-the-ass think you're going to get bounced out right out of the community or if you're selling something you're on so you guys do a great job really am i awesome you thank you James I really love what you add to the iod experience here with this corner and all the interviews is great great material well thanks for having us here really appreciate it I learned a lot it's been great you guys are great to work with very professional the products got great great-looking luqman portfolio hidden all hitting all the buttons there so hitting all the Gulf box so this is the cube we'll be right back with our last interview coming up shortly with Jeff Jonas he's got some surprises for us so we'll we'll see what he brings brings to his a game apparently he told me last night is bring his a-game to the cube so I'm a huge Jeff Jonas fan he's a rock star we love them on the cube iza teka athlete like yourself we write back with our next guest after this short break

okay we're back live here inside the cube rounding out day one of exclusive coverage of IBM information on demand I'm John further the founder SiliconANGLE enjoy my co-host Davey lonte we're here in heat you saw who's the vice president I said that speaks that you know I think you always get promoted you've been on the cube so many times you doing so well it's all your reason tatian was so amazing I always liked SVP the cute good things happen that's exactly why i be MVP is a big deal unlike some of the starters where everyone gets EVP all these other titles but welcome back thank you so the storytelling has been phenomenal here although murs a little bit critical some of the presentations earlier from gardner but the stories higher your IBM just from last year take us through what's changed from iod last year to this year the story has gotten tighter yes comprehensive give us the quick okay quick view um okay here's the point of view here's the point of view first you got to invest in a platform which we've all talked about and i will tell you it's not just us saying it i would say other vendors are now copying what we're saying cuz if you went to strata yes which you were there we were there probably heard some of the messages that's right why everybody wants to be a platform okay one two elevated risk uncertainty governance I think privacy privacy security risk this is what people are talking about they want to invest in a more why because you know what the decisions matter they want to make bigger beds they want to do more things around customer experience they want to improve products they want to improve pricing the third area is really a cultural statement like applying analytics in the organization because the people and the skills I would say the culture conversation is happening a lot more this year than it was a year ago not just at IOD but in the industry so I think what you're seeing here at IOD is actually a reflection of what the conversations are happening so our organizations culturally ready for this I mean you guys are going to say yes and everybody comes on says oh yes we're seeing it all over the place but are they really ready it depends I think some are some are absolutely ready some are not and probably the best examples are and it really depends on the industry so I'll give you a few examples so in the government area I think people see the power of applying things like real-time contextual insight leveraging stream computing why because national security matters a lot of fraudulent activity because that's measurable you can drive revenue or savings healthcare people know that a lot of decision-making is being made without a comprehensive view of the analytics and the data now the other area that's interesting is most people like to talk about text analytics unstructured data a lot of social media data but the bulk of the data that's actually being used currently in terms of big data analytics is really transactional data why because that's what's maintained in most operational systems where health systems so you're going to see a lot more data warehouse augmentation use cases leverage you can do on the front end or the back end you're going to see kind of more in terms of comprehensive view of the customer right augmenting like an existing customer loyalty or segmentation data with additional let's say activity data that they're interacting with and that was the usta kind of demo showing social data cell phone metadata is that considered transactional you know it is well call me to record right CDR call detail records well the real time is important to you mentioned the US open just for folks out there was a demo on stage when you guys open data yeah at all the trend sentiment data the social data but that's people's thoughts right so you can see what people are doing now that's big yeah you know what's amazing about that just one second which is what we were doing was we were predicting it based on the past but then we were modifying it based on real time activity and conversation so let's say something hot happened and all of a sudden it was interesting when Brian told me this he was like oh yeah Serena's average Twitter score was like 2,200 twit tweets a day and then if some activity were to happen let's say I don't know she didn't he wrote she had got into a romance or let's say she decided to launch a new product then all of a sudden you'd see an accused spike rate in activity social activity that would then predict how they wanted to operate that environment that's amazing and you know we you know we love daily seen our our crowd spots be finder we have the new crowd chat one and this idea of connecting consumers is loose data it's ephemeral data it's transient data but it's now capture will so people can have a have fun into tennis tournament and then it's over they go back home to work you still have that metadata we do that's very kind of its transient and ephemeral that's value so you know Merv was saying also that your groups doing a lot of value creation let's talk about that for a second business outcomes what do you what's the top conversation when you walk into a customer that says hey you know here's point a point B B's my outcome mm-hmm one of those conversations like I mean what are they what are some of the outcomes you just talked to use case you tell customers but like what did some of the exact you know what I'll tell you one use case so and this was actually in the healthcare hotel you won healthcare use case in one financial services use case both conversations happened actually in the last two weeks so in the healthcare use case there's already let's say a model that's happening for this particular hospital now they have a workflow process typically in a workflow process you you're applying capabilities where you've modeled out your steps right you do a before be before see and you automate this leveraging BPM type capabilities in a data context you don't actually start necessarily with knowing what the workflow is you kind of let the data determine what the workflow should be so in the this was in an ICU arena historically if you wanted to decide who was the healthiest of the patients in the ICU because you had another trauma coming in there was a workflow that said you had to go check the nurses the patient's profile and say who gets kicked out of what bed or moved because they're most likely to be in a healthy state that's a predefined workflow but if you're applying streams for example all the sudden you could have real-time visibility without necessarily a nurse calling a doctor who that calls the local staff who then calls the cleaning crew rate you could actually have a dashboard that says with eighty percent confidence beds2 and ate those patients because of the following conditions could be the ones that you are proactive in and saying oh you know what not only can they be released but we have this degree of confidence around them being because of the days that it's coming obvious information that changes then potentially you know the way your kind of setting your rules and policies around your workflow another example which was really a government use case was think about in government security so in security scenarios and national security state there is you never quite know exactly what people are intended to do other than you know they're intending something bad right and they're intentionally trying not to be found so human trafficking it's an ugly topic but I want to bring it up for a second here what you're doing is you're actually looking at data compositions and and different patterns and resolving entities and based on that that will dictate kind of potentially a whole new flow or a treatment or remediation or activity or savior which is not the predefined workflow it's you're letting the data actually all of a sudden connect to other data points that then you're arriving at the insight to take the action where is completely different I wanna go back to sleep RFI course not healthcare examples yeah so where are we today is that something that's actually being implemented is that something they sort of a proof of concept well that's actually being done at it's being done in a couple different hospitals one of which is actually in hospital in Canada and then we're also leveraging streams in the emory university intensive Timothy Buckman on you did earlier oh yeah the ICU of the future right absolutely brilliant trafficking example brings up you know Ashley that's the underbelly of the world in society but like data condition to Jeff Jonas been on the queue as you know many times and he talks with his puzzle pieces in a way that the data is traveling on a network a network that's distributed essentially that's network computing I mean estate management so look at network management you can look at patterns right so so that's an interesting example so that begs the next question what is the craziest most interesting use case you seen oh my gosh okay now i got i think about oh yes and you can talk about and i can talk about that creates business value or society value oh you know I okay um for you are putting me on the spot the craziest one so 3 we could be great could be g-rated don't you know they go to 2k yeah you know what I participated three weeks ago tiaa-cref actually hosted a fraud summit where it was all investigators like they were doing crime investigation so more than sixty percent of the guys in the room carried weapons because they were Security Intelligence they were pleased they were DA's they repented I was not packing anyway and there was about so 60-plus percent were those right and then only about thirty percent in the room were what i would consider the data scientists in the room like these are the guys are trying to decide which claims are not true or false so forth there were at least like three or four use cases in that discussion that came out they were unbelievable so one is in the fraud area in particular and in crime they're luring the data there what does luring the data they're taking location-based data for geographic region they're putting crime data on top of that right historical like drug rings and even like datasets in miami-dade county the DA told me they were doing things where rather than looking at people that are doing the drugs they they realize people that had possession of a drug typically purchased within a certain location and they had these abandoned properties and were able to identify entire rings based on that another one this is also semi drug-related is in the energy utility space there was in the middle part of the United States houses in Nice urban areas where they were completely torn apart on the interior and build into marijuana houses and so of course they're utilizing high levels of gas and electricity in order to maintain the water fertilization everything else well what happens is it drives peaks in the way that the energy utility looks on a given day pattern so based on that they're able to detect how inappropriate activities are happening and whether it's a single opportunistic type activity whether it's saying this was doing laundry or irrigating the Erie hey we well you know what's interesting about electricity to is especially someone's using electricity but no one's like using any of the gas you're like home but no one's cooking you know something's a little long but it was fascinating i mean really fascinating there were like several other crime scenarios in terms of speed i actually did not know the US Postal Service is like the longest running federal institution that actually tracked like mail fraud and one of the use cases i'm sure jeff has talked about here on the cube is probably a moneygram use case but we talked about that we talked I mean it the stories were unreal because I was spending time with forensic scientists as well as forensic investigators and that's a completely do we're getting we're getting the few minutes need for a platform to handle all this diversity so that's the security risk the governance everything you gotta go cuz your star for the analyst me I can't watch this conversation one final question one of the best yet as we get drugs in there we got other things packing guns guns and drugs you in traffic you know tobacco if you go / news / tobacco well write the knowledge worker all right final question for I know you gotta go this big data applications were you know the guys in the mailroom the guys work for the post office are now unable to actually do this kind of high-level kind of date basically data science yeah if you will or being an analyst so that what I want you to share the folks your vision of the definition of the knowledge worker overused word that's been kicked around for the PC generates but now with handheld with analytical real-time with streaming all this stuff happening at the edge how is it going to change that the knowledge work or the person in the trenches it could be person the cubicle the person on the go the mobile sales person or anyone you know I some people feel threatened when they hear that you're going to apply data and analytics everywhere because you're it implies that you're automating things but that's actually not the value the real value is the insight so that you can double down on the decisions you want to make so if you're more confident you're going to take bigger bets right and decision-making historically has been I think reserved for a very elite few and what we're talking about now is a democratization of that insight and with that comes a lot of empowerment a lot empowerment for everyone and you don't have to be a data scientist be able to be able to make decisions and inform decisions if anything you know actually Tim Buckman I had a good conversation about them as a professional you know what I if I was a physician I'd want to work at the hospital that has the advanced capabilities why because it allows me as a professional physician to then be able to do what I was trained to do not to detect and have to pay attention to all these alarms going off you know I want to work at the institutions and organizations that are investing appropriately because it pushes the caliber of the work I get to do so I think it just changes the dynamics for everyone tim was like a high-priced logistics manager you want to work with people want to work with leaders and now we're in a modern era this new wave is upon us who care and they want to improve and this is about continuing to improve Dave and I always talk about the open source world that those principles are going mainstream to every aspect of business collaboration openness transparency not controlled absolutely absolutely Indy thanks so much for coming in the queue and know you're busy think of your time we are here live in the cube getting all the signal from the noise and some good commentary at the end a one we have one more guest ray way right up next stay tuned right back the queue

okay welcome back to IBM's information on demand live in Las Vegas this is the cube SiliconANGLE movie bonds flagship program we go out to the events it's check the student from the noise talk to the thought leaders get all the data share that with you and you go to SiliconANGLE com or Wikibon or to get all the footage and we're if you want to participate with us we're rolling out our new innovative crowd activated innovation application called crowd chat go to crouch at net / IBM iod just login with your twitter handle or your linkedin and participate and share your voice is going to be on the record transcript of the cube conversations I'm John furrier with silicon items with my co-host hi buddy I'm Dave vellante Wikibon dork thanks for watching aren't you Oh bhambri is here she's the vice president of big data and analytics at IBM many time cube guests as you welcome back good to see you again thank you so we were both down at New York City last week for the hadoop world really amazing to see how that industry has evolved I mean you guys I've said the number of times today and I said this to you before you superglued your your big data or your analytics business to the Big Data meme and really created a new category I don't know if that was by design or you know or not but it certainly happened suddenly by design well congratulations then because because I think that you know again even a year a year and a half ago those two terms big data and analytics were sort of separate now it's really considered as one right yeah yeah I think because initially as people our businesses started getting really flooded with big data right dealing with the large volumes dealing with structured semi-structured or unstructured data they were looking at that you know how do you store and manage this data in a cost-effective manner but you know if you're just only storing this data that's useless and now obviously it's people realize that they need and there is insights from this data that has to be gleaned and there's technology that is available to do that so so customers are moving very quickly to that it's not just about cost savings in terms of handling this data but getting insights from it so so big data and analytics you know is becoming it's it's becoming synonymous heroes interesting to me on Jules is you know just following this business it's all it's like there's a zillion different nails out there and and and everybody has a hammer and they're hitting the nail with their unique camera but I've it's like IBM as a lot of different hammers so we could talk about that a little bit you've got a very diverse portfolio you don't try to force one particular solution on the client you it sort of an it's the Pens sort of answer we could talk about that a little bit yeah sure so in the context of big data when we look at just let's start with transactional data right that continues to be the number one source where there is very valuable insights to be gleaned from it so the volumes are growing that you know we have retailers that are handling now 2.5 million transactions per hour a telco industry handling 10 billion call data detailed records every day so when you look at that level that volume of transactions obviously you need to be you need engines that can handle that that can process analyze and gain insights from this that you can get you can do ad hoc analytics on this run queries and get information out of this at the same speed at which this data is getting generated so you know we we announced the blu acceleration rate witches are in memory columnstore which gives you the power to handle these kinds of volumes and be able to really query and get value out of this very quickly so but now when you look at you know you go beyond the structured data or beyond transactional data there is semi structured unstructured data that's where which is still data at rest is where you know we have big insights which leverages Apache Hadoop open source but we've built lots of capabilities on top of that where we get we give the customers the best of open source plus at the same time the ability to analyze this data so you know we have text analytics capabilities we provide machine learning algorithms we have provided integration with that that customers can do predictive modeling on this data using SPSS using open source languages like our and in terms of visualization they can visualize this data using cognos they can visualize this data using MicroStrategy so we are giving customers like you said it's not just you know there's one hammer and they have to use that for every nail the other aspect has been around real time and we heard that a lot at strada right in the like I've been going to start us since the beginning and those that time even though we were talking about real time but nobody else true nobody was talking nobody was back in the hadoop world days ago one big bats job yeah so in real time is now the hotbed of the conversation a journalist storm he's new technologies coming out with him with yarn has done it's been interesting yeah you seen the same thing yeah so so and and of course you know we have a very mature technology in that space you know InfoSphere streams for a real-time analytics has been around for a long time it was you know developed initially for the US government and so we've been you know in the space for more than anybody else and we have deployments in the telco space where you know these tens of billions of call detail records are being processed analyzed in real time and you know these telcos are using it to predict customer churn to prevent customer churn gaining all kinds of insights and extremely high you know very low latency so so it's good to see that you know other companies are recognizing the need for it and are you know bringing other offerings out in this space yes every time before somebody says oh I want to go you know low latency and I want to use spark you say okay no problem we could do that and streets is interesting because if I understand it you're basically acting on the data producing analytics prior to persisting the data on in memory it's all in memory and but yet at the same time is it of my question is is it evolving where you now can blend that sort of real-time yeah activity with maybe some some batch data and and talk about how that's evolving yeah absolutely so so streams is for for you know where as data is coming in it can be processed filtered patterns can be seen in streams of data by correlating connecting different streams of data and based on a certain events occurring actions can be taken now it is possible that you know all of this data doesn't need to be persisted but there may be some aspects or some attributes of this data that need to be persisted you could persist this data in a database that is use it as a way to populate your warehouse you could persist it in a Hadoop based offering like BigInsights where you can you know bring in other kinds of data and enrich the data it's it's like data loans from data and a different picture emerges Jeff Jonas's puzzle right so that's that that's very valid and so so when we look at the real time it is about taking action in real time but there is data that can be persisted from that in both the warehouse as well as on something like the insides are too I want to throw a term at you and see what what what this means to you we actually doing some crowd chats with with IBM on this topic data economy was going to SS you have no date economy what does the data economy mean to you what our customers you know doing with the data economy yes okay so so my take on this is that there are there are two aspects of this one is that the cost of storing the data and analyzing the data processing the data has gone down substantially the but the value in this data because you can now process analyze petabytes of this data you can bring in not just structured but semi-structured and unstructured data you can glean information from different types of data and a different picture emerges so the value that is in this data has gone up substantially I previously a lot of this data was probably discarded people without people knowing that there is useful information in this so to the business the value in the data has gone up what they can do with this data in terms of making business decisions in terms of you know making their customers and consumers more satisfied giving them the right products and services and how they can monetize that data has gone up but the cost of storing and analyzing and processing has gone down rich which i think is fantastic right so it's a huge win win for businesses it's a huge win win for the consumers because they are getting now products and services from you know the businesses which they were not before so that that to me is the economy of data so this is why I John I think IBM is really going to kill it in this in this business because they've got such a huge portfolio they've got if you look at where I OD has evolved data management information management data governance all the stuff on privacy these were all cost items before people looked at him on I gotta deal with all this data and now it's there's been a bit flip uh-huh IBM is just in this wonderful position to take advantage of it of course Ginny's trying to turn that you know the the battleship and try to get everybody aligned but the moons and stars are aligning and really there's a there's a tailwind yeah we have a question on domains where we have a question on Twitter from Jim Lundy analyst former Gartner analyst says own firm now shout out to Jim Jim thanks for for watching as always I know you're a cube cube alum and also avid watcher and now now a loyal member of the crowd chat community the question is blu acceleration is helps drive more data into actionable analytics and dashboards mm-hmm can I BM drive new more new deals with it I've sued so can you expound it answers yes yes yes and can you elaborate on that for Jim yeah I you know with blu acceleration you know we have had customers that have evaluated blue and against sa bihana and have found that what blue can provide is is they ahead of what SI p hana can provide so we have a number of accounts where you know people are going with the performance the throughput you know what blue provides is is very unique and it's very head of what anybody else has in the market in solving SI p including SI p and and you know it's ultimately its value to the business right and that's what we are trying to do that how do we let our customers the right technology so that they can deal with all of this data get their arms around it get value from this data quickly that's that's really of a sense here wonderful part of Jim's question is yes the driving new deals for sure a new product new deals me to drive new footprints is that maybe what he's asking right in other words you traditional IBM accounts are doing doing deals are you able to drive new footprints yeah yeah we you know there are there are customers that you know I'm not gonna take any names here but which have come to us which are new to IBM right so it's a it's that to us and that's happening that new business that's Nate new business and that's happening with us for all our big data offerings because you know the richness that is there in the portfolio it's not that we have like you were saying Dave it's not that we have one hammer and we are going to use it for every nail that is out there you know as people are looking at blue big insights for her to streams for real time and with all this comes the whole lifecycle management and governance right so security privacy all those things don't don't go away so all the stuff that was relevant for the relational data now we are able to bring that to big data very quickly and which is I think of huge value to customers and as people are moving very quickly in this big data space there's nobody else who can just bring all of these assets together from and and you know provide an integrated platform what use cases to Jim's point I don't you know I know you don't want to name names but can you name you how about some use cases that that these customers are using with blue like but use cases and they solving so you know I from from a use case a standpoint it is really like you know people are seeing performance which is you know 30 32 times faster than what they had seen when they were not using and in-memory columnstore you know so eight to twenty five thirty two times per men's gains is is you know something that is huge and is getting more and more people attracted to this so let's take an industry take financial services for example so the big the big ones in financial services are a risk people want to know you know are they credit risk yeah there's obviously marketing serving up serving up ads a fraud detection you would think is another one that in more real time are these these you know these will be the segments and of course you know retail where again you know there is like i was saying right that the number of transactions that are being handled is is growing phenomenally i gave one example which was around 2.5 million transactions per hour which was unheard of before and the information that has to be gleaned from it which is you know to leverage this for demand forecasting to leverage this for gaining insights in terms of giving the customers the right kind of coupons to make sure that those coupons are getting you know are being used so it was you know before the world used to be you get the coupons in your email in your mail then the world changed to that you get coupons after you've done the transaction now where we are seeing customers is that when a customer walks in the store that's where they get the coupons based on which i layer in so it's a combination of the transactional data the location data right and we are able to bring all of this together so so it's blue combined with you know what things like streams and big insights can do that makes the use cases even more powerful and unique so I like this new format of the crowd chatting emily is a one hour crowd chat where it's kind of like thought leaders just going to pounding away but this is more like reddit AMA but much better question coming in from grant case is one of the themes to you is one of the themes we've heard about in Makino was the lack of analytical talent what is going on to contribute more value for an organization skilling up the work for or implementing better software tools for knowledge workers so in terms so skills is definitely an issue that has been a been a challenge in the in the industry with and it got pretty compound with big data and the new technology is coming in from the standpoint of you know what we are doing for the data scientists which is you know the people who are leveraging data to to gain new insights to explore and and and discover what other attributes they should be adding to their predictive models to improve the accuracy of those models so there is there's a very rich set of tools which are used for exploration and discovery so we have which is both from you know Cognos has such such such capabilities we have such capabilities with our data Explorer absolutely basically tooling for the predictive on the modeling sister right now the efforts them on the modeling and for the predictive and descriptive analytics right I mean there's a lot of when you look at that Windows petabytes of data before people even get to predictive there's a lot of value to be gleaned from descriptive analytics and being able to do it at scale at petabytes of data was difficult before and and now that's possible with extra excellent visualization right so that it's it's taking things too that it the analytics is becoming interactive it's not just that you know you you you are able to do this in real time ask the questions get the right answers because the the models running on petabytes of data and the results coming from that is now possible so so interactive analytics is where this is going so another question is Jim was asking i was one of ibm's going around doing blue accelerator upgrades with all its existing clients loan origination is a no brainer upgrade I don't even know that was the kind of follow-up that I had asked is that new accounts is a new footprint or is it just sort of you it is spending existing it's it's boat it's boat what is the characteristic of a company that is successfully or characteristics of a company that is successfully leveraging data yeah so companies are thinking about now that you know their existing edw which is that enterprise data warehouse needs to be expanded so you know before if they were only dealing with warehouses which one handling just structure data they are augmenting that so this is from a technology standpoint right there augmenting that and building their logical data warehouse which takes care of not just the structure data but also semi-structured and unstructured data are bringing augmenting the warehouses with Hadoop based offerings like big insights with real-time offerings like streams so that from an IT standpoint they are ready to deal with all kinds of data and be able to analyze and gain information from all kinds of data now from the standpoint of you know how do you start the Big Data journey it the platform that at least you know we provide is a plug-and-play so there are different starting points for for businesses they may have started with warehouses they bring in a poly structured store with big inside / Hadoop they are building social profiles from social and public data which was not being done before matching that with the enterprise data which may be in CRM systems master data management systems inside the enterprise and which creates quadrants of comparisons and they are gaining more insights about the customer based on master data management based on social profiles that they are building so so this is one big trend that we are seeing you know to take this journey they have to you know take smaller smaller bites digests that get value out of it and you know eat it in chunks rather than try to you know eat the whole pie in one chunk so a lot of companies starting with exploration proof of concepts implementing certain use cases in four to six weeks getting value and then continuing to add more and more data sources and more and more applications so there are those who would say those existing edw so many people man some people would say they should be retired you would disagree with that no no I yeah I I think we very much need that experience and expertise businesses need that experience and expertise because it's not an either/or it's not that that goes away and there comes a different kind of a warehouse it's an evolution right but there's a tension there though wouldn't you say there's an organizational tension between the sort of newbies and the existing you know edw crowd i would say that maybe you know three years ago that was there was a little bit of that but there is i mean i talked to a lot of customers and there is i don't see that anymore so people are people are you know they they understand they know what's happening they are moving with the times and they know that this evolution is where the market is going where the business is going and where the technology you know they're going to be made obsolete if they don't embrace it right yeah yeah so so as we get on time I want to ask you a personal question what's going on with you these days with within IBM asli you're in a hot area you are at just in New York last week tell us what's going on in your life these days I mean things going well I mean what things you're looking at what are you paying attention to what's on your radar when you wake up and get to work before you get to work what's what are you thinking about what's the big picture so so obviously you know big data has been really fascinating right lots of lots of different kinds of applications in different industries so working with the customers in telco and healthcare banking financial sector has been very educational right so a lot of learning and that's very exciting and what's on my radar is we are obviously now seeing that we've done a lot of work in terms of helping customers develop and their Big Data Platform on-premise now we are seeing more and more a trend where people want to put this on the cloud so that's something that we have now a lot of I mean it's not like we haven't paid attention to the cloud but you know in the in the coming months you are going to see more from us are where you know how do we build cus how do we help customers build both private and and and public cloud offerings are and and you know where they can provide analytics as a service two different lines of business by setting up the clouds soso cloud is certainly on my mind software acquisition that was a hole in the portfolio and that filled it you guys got to drive that so so both software and then of course OpenStack right from an infrastructure standpoint for what's happening in the open source so we are you know leveraging both of those and like I said you'll hear more about that OpenStack is key as I say for you guys because you have you have street cred when it comes to open source I mean what you did in Linux and made a you know great business out of that so everybody will point it you know whether it's Oracle or IBM and HP say oh they just want to sell us our stack you've got to demonstrate and that you're open and OpenStack it's great way to do that and other initiatives as well so like I say that's a V excited about that yeah yeah okay I sure well thanks very much for coming on the cube it's always a pleasure to thank you see you yeah same here great having you back thank you very much okay we'll be right back live here inside the cube here and IV IBM information on demand hashtag IBM iod go to crouch at net / IBM iod and join the conversation where we're going to have a on the record crowd chat conversation with the folks out the who aren't here on-site or on-site Worth's we're here alive in Las Vegas I'm Java with Dave on to write back the q